Merge branch 'ed-donner:main' into main

community-contributions/Reputation_Radar/Dockerfile

@@ -0,0 +1,16 @@
+FROM python:3.11-slim
+
+WORKDIR /app
+
+COPY requirements.txt .
+RUN pip install --no-cache-dir -r requirements.txt
+
+COPY . .
+
+ENV STREAMLIT_SERVER_HEADLESS=true \
+    STREAMLIT_SERVER_ADDRESS=0.0.0.0 \
+    STREAMLIT_SERVER_PORT=8501
+
+EXPOSE 8501
+
+CMD ["streamlit", "run", "app.py"]

community-contributions/Reputation_Radar/Makefile

@@ -0,0 +1,13 @@
+PYTHON ?= python
+
+.PHONY: install run test
+
+install:
+	$(PYTHON) -m pip install --upgrade pip
+	$(PYTHON) -m pip install -r requirements.txt
+
+run:
+	streamlit run app.py
+
+test:
+	pytest

community-contributions/Reputation_Radar/README.md

@@ -0,0 +1,124 @@
+# 📡 ReputationRadar
+> Real-time brand intelligence with human-readable insights.
+
+ReputationRadar is a Streamlit dashboard that unifies Reddit, Twitter/X, and Trustpilot chatter, classifies sentiment with OpenAI (or VADER fallback), and delivers exportable executive summaries. It ships with modular services, caching, retry-aware scrapers, demo data, and pytest coverage—ready for production hardening or internal deployment.
+
+---
+
+## Table of Contents
+- [Demo](#demo)
+- [Feature Highlights](#feature-highlights)
+- [Architecture Overview](#architecture-overview)
+- [Quick Start](#quick-start)
+- [Configuration & Credentials](#configuration--credentials)
+- [Running Tests](#running-tests)
+- [Working Without API Keys](#working-without-api-keys)
+- [Exports & Deliverables](#exports--deliverables)
+- [Troubleshooting](#troubleshooting)
+- [Legal & Compliance](#legal--compliance)
+
+---
+
+
+## Demo
+
+The video demo of the app can be found at:-
+https://drive.google.com/file/d/1XZ09NOht1H5LCJEbOrAldny2L5SV1DeT/view?usp=sharing
+
+
+## Feature Highlights
+- **Adaptive Ingestion** – Toggle Reddit, Twitter/X, and Trustpilot independently; backoff, caching, and polite scraping keep providers happy.
+- **Smart Sentiment** – Batch OpenAI classification with rationale-aware prompts and auto-fallback to VADER when credentials are missing.
+- **Actionable Summaries** – Executive brief card (highlights, risks, tone, actions) plus refreshed PDF layout that respects margins and typography.
+- **Interactive Insights** – Plotly visuals, per-source filtering, and a lean “Representative Mentions” link list to avoid content overload.
+- **Export Suite** – CSV, Excel (auto-sized columns), and polished PDF snapshots for stakeholder handoffs.
+- **Robust Foundation** – Structured logging, reusable UI components, pytest suites, Dockerfile, and Makefile for frictionless iteration.
+
+---
+
+## Architecture Overview
+```
+community-contributions/Reputation_Radar/
+├── app.py                 # Streamlit orchestrator & layout
+├── components/            # Sidebar, dashboard, summaries, loaders
+├── services/              # Reddit/Twitter clients, Trustpilot scraper, LLM wrapper, utilities
+├── samples/               # Demo JSON payloads (auto-loaded when credentials missing)
+├── tests/                 # Pytest coverage for utilities and LLM fallback
+├── assets/                # Placeholder icons/logo
+├── logs/                  # Streaming log output
+├── requirements.txt       # Runtime dependencies (includes PDF + Excel writers)
+├── Dockerfile             # Containerised deployment recipe
+└── Makefile               # Helper targets for install/run/test
+```
+Each service returns a normalised payload to keep the downstream sentiment pipeline deterministic. Deduplication is handled centrally via fuzzy matching, and timestamps are coerced to UTC before analysis.
+
+---
+
+## Quick Start
+1. **Clone & enter the project directory (`community-contributions/Reputation_Radar`).**
+2. **Install dependencies and launch Streamlit:**
+   ```bash
+   pip install -r requirements.txt && streamlit run app.py
+   ```
+   (Use a virtual environment if preferred.)
+3. **Populate the sidebar:** add your brand name, optional filters, toggled sources, and API credentials (stored only in session state).
+4. **Click “Run Analysis 🚀”** – follow the status indicators as sources load, sentiment processes, and summaries render.
+
+### Optional Docker Run
+```bash
+docker build -t reputation-radar .
+docker run --rm -p 8501:8501 -e OPENAI_API_KEY=your_key reputation-radar
+```
+
+---
+
+## Configuration & Credentials
+The app reads from `.env`, Streamlit secrets, or direct sidebar input. Expected variables:
+
+| Variable | Purpose |
+| --- | --- |
+| `OPENAI_API_KEY` | Enables OpenAI sentiment + executive summary (falls back to VADER if absent). |
+| `REDDIT_CLIENT_ID` | PRAW client ID for Reddit API access. |
+| `REDDIT_CLIENT_SECRET` | PRAW client secret. |
+| `REDDIT_USER_AGENT` | Descriptive user agent (e.g., `ReputationRadar/1.0 by you`). |
+| `TWITTER_BEARER_TOKEN` | Twitter/X v2 recent search bearer token. |
+
+Credential validation mirrors the guidance from `week1/day1.ipynb`—mistyped OpenAI keys surface helpful warnings before analysis begins.
+
+---
+
+## Running Tests
+```bash
+pytest
+```
+Tests cover sentiment fallback behaviour and core sanitisation/deduplication helpers. Extend them as you add new data transforms or UI logic.
+
+---
+
+## Working Without API Keys
+- Reddit/Twitter/Trustpilot can be toggled independently; missing credentials raise gentle warnings rather than hard failures.
+- Curated fixtures in `samples/` automatically load for any disabled source, keeping charts, exports, and PDF output functional in demo mode.
+- The LLM layer drops to VADER sentiment scoring and skips the executive summary when `OPENAI_API_KEY` is absent.
+
+---
+
+## Exports & Deliverables
+- **CSV** – Clean, UTF-8 dataset for quick spreadsheet edits.
+- **Excel** – Auto-sized columns, formatted timestamps, instantaneous import into stakeholder workbooks.
+- **PDF** – Professionally typeset executive summary with bullet lists, consistent margins, and wrapped excerpts (thanks to ReportLab’s Platypus engine).
+
+All exports are regenerated on demand and never persisted server-side.
+
+---
+
+## Troubleshooting
+- **OpenAI key missing/invalid** – Watch the sidebar notices; the app falls back gracefully but no executive summary will be produced.
+- **Twitter 401/403** – Confirm your bearer token scope and that the project has search access enabled.
+- **Rate limiting (429)** – Built-in sleeps help, but repeated requests may require manual pauses. Try narrowing filters or reducing per-source limits.
+- **Trustpilot blocks** – Respect robots.txt. If scraping is denied, switch to the official API or provide compliant CSV imports.
+- **PDF text clipping** – Resolved by the new layout; if you customise templates ensure col widths/table styles remain inside page margins.
+
+---
+
+## Legal & Compliance
+ReputationRadar surfaces public discourse for legitimate monitoring purposes. Always comply with each platform’s Terms of Service, local regulations, and privacy expectations. Avoid storing third-party data longer than necessary, and never commit API keys to version control—the app only keeps them in Streamlit session state.

community-contributions/Reputation_Radar/app.py

@@ -0,0 +1,436 @@
+"""ReputationRadar Streamlit application entrypoint."""
+
+from __future__ import annotations
+
+import io
+import json
+import os
+import re
+from datetime import datetime
+from typing import Dict, List, Optional
+
+import pandas as pd
+import streamlit as st
+from dotenv import load_dotenv
+from reportlab.lib import colors
+from reportlab.lib.pagesizes import letter
+from reportlab.lib.styles import ParagraphStyle, getSampleStyleSheet
+from reportlab.platypus import Paragraph, SimpleDocTemplate, Spacer, Table, TableStyle
+
+from components.dashboard import render_overview, render_source_explorer, render_top_comments
+from components.filters import render_sidebar
+from components.summary import render_summary
+from components.loaders import show_empty_state, source_status
+from services import llm, reddit_client, trustpilot_scraper, twitter_client, utils
+from services.llm import SentimentResult
+from services.utils import (
+    NormalizedItem,
+    ServiceError,
+    ServiceWarning,
+    initialize_logger,
+    load_sample_items,
+    normalize_items,
+    parse_date_range,
+    validate_openai_key,
+)
+
+
+st.set_page_config(page_title="ReputationRadar", page_icon="📡", layout="wide")
+load_dotenv(override=True)
+LOGGER = initialize_logger()
+
+st.title("📡 ReputationRadar")
+st.caption("Aggregate brand chatter, classify sentiment, and surface actionable insights in minutes.")
+
+
+def _get_env_defaults() -> Dict[str, Optional[str]]:
+    """Read supported credentials from environment variables."""
+    return {
+        "OPENAI_API_KEY": os.getenv("OPENAI_API_KEY"),
+        "REDDIT_CLIENT_ID": os.getenv("REDDIT_CLIENT_ID"),
+        "REDDIT_CLIENT_SECRET": os.getenv("REDDIT_CLIENT_SECRET"),
+        "REDDIT_USER_AGENT": os.getenv("REDDIT_USER_AGENT", "ReputationRadar/1.0"),
+        "TWITTER_BEARER_TOKEN": os.getenv("TWITTER_BEARER_TOKEN"),
+    }
+
+
+@st.cache_data(ttl=600, show_spinner=False)
+def cached_reddit_fetch(
+    brand: str,
+    limit: int,
+    date_range: str,
+    min_upvotes: int,
+    client_id: str,
+    client_secret: str,
+    user_agent: str,
+) -> List[NormalizedItem]:
+    credentials = {
+        "client_id": client_id,
+        "client_secret": client_secret,
+        "user_agent": user_agent,
+    }
+    return reddit_client.fetch_mentions(
+        brand=brand,
+        credentials=credentials,
+        limit=limit,
+        date_filter=date_range,
+        min_upvotes=min_upvotes,
+    )
+
+
+@st.cache_data(ttl=600, show_spinner=False)
+def cached_twitter_fetch(
+    brand: str,
+    limit: int,
+    min_likes: int,
+    language: str,
+    bearer: str,
+) -> List[NormalizedItem]:
+    return twitter_client.fetch_mentions(
+        brand=brand,
+        bearer_token=bearer,
+        limit=limit,
+        min_likes=min_likes,
+        language=language,
+    )
+
+
+@st.cache_data(ttl=600, show_spinner=False)
+def cached_trustpilot_fetch(
+    brand: str,
+    language: str,
+    pages: int = 2,
+) -> List[NormalizedItem]:
+    return trustpilot_scraper.fetch_reviews(brand=brand, language=language, pages=pages)
+
+
+def _to_dataframe(items: List[NormalizedItem], sentiments: List[SentimentResult]) -> pd.DataFrame:
+    data = []
+    for item, sentiment in zip(items, sentiments):
+        data.append(
+            {
+                "source": item["source"],
+                "id": item["id"],
+                "url": item.get("url"),
+                "author": item.get("author"),
+                "timestamp": item["timestamp"],
+                "text": item["text"],
+                "label": sentiment.label,
+                "confidence": sentiment.confidence,
+                "meta": json.dumps(item.get("meta", {})),
+            }
+        )
+    df = pd.DataFrame(data)
+    if not df.empty:
+        df["timestamp"] = pd.to_datetime(df["timestamp"])
+    return df
+
+
+def _build_pdf(summary: Optional[Dict[str, str]], df: pd.DataFrame) -> bytes:
+    buffer = io.BytesIO()
+    doc = SimpleDocTemplate(
+        buffer,
+        pagesize=letter,
+        rightMargin=40,
+        leftMargin=40,
+        topMargin=60,
+        bottomMargin=40,
+        title="ReputationRadar Executive Summary",
+    )
+    styles = getSampleStyleSheet()
+    title_style = styles["Title"]
+    subtitle_style = ParagraphStyle(
+        "Subtitle",
+        parent=styles["BodyText"],
+        fontSize=10,
+        leading=14,
+        textColor="#555555",
+    )
+    body_style = ParagraphStyle(
+        "Body",
+        parent=styles["BodyText"],
+        leading=14,
+        fontSize=11,
+    )
+    bullet_style = ParagraphStyle(
+        "Bullet",
+        parent=body_style,
+        leftIndent=16,
+        bulletIndent=8,
+        spaceBefore=2,
+        spaceAfter=2,
+    )
+    heading_style = ParagraphStyle(
+        "SectionHeading",
+        parent=styles["Heading3"],
+        spaceBefore=10,
+        spaceAfter=6,
+    )
+
+    story: List[Paragraph | Spacer | Table] = []
+    story.append(Paragraph("ReputationRadar Executive Summary", title_style))
+    story.append(Spacer(1, 6))
+    story.append(
+        Paragraph(
+            f"Generated on: {datetime.utcnow().strftime('%Y-%m-%d %H:%M')} UTC",
+            subtitle_style,
+        )
+    )
+    story.append(Spacer(1, 18))
+
+    if summary and summary.get("raw"):
+        story.extend(_summary_to_story(summary["raw"], body_style, bullet_style, heading_style))
+    else:
+        story.append(
+            Paragraph(
+                "Executive summary disabled (OpenAI key missing).",
+                body_style,
+            )
+        )
+    story.append(Spacer(1, 16))
+    story.append(Paragraph("Sentiment Snapshot", styles["Heading2"]))
+    story.append(Spacer(1, 10))
+
+    table_data: List[List[Paragraph]] = [
+        [
+            Paragraph("Date", body_style),
+            Paragraph("Sentiment", body_style),
+            Paragraph("Source", body_style),
+            Paragraph("Excerpt", body_style),
+        ]
+    ]
+    snapshot = df.sort_values("timestamp", ascending=False).head(15)
+    for _, row in snapshot.iterrows():
+        excerpt = _truncate_text(row["text"], 180)
+        table_data.append(
+            [
+                Paragraph(row["timestamp"].strftime("%Y-%m-%d %H:%M"), body_style),
+                Paragraph(row["label"].title(), body_style),
+                Paragraph(row["source"].title(), body_style),
+                Paragraph(excerpt, body_style),
+            ]
+        )
+
+    table = Table(table_data, colWidths=[90, 70, 80, 250])
+    table.setStyle(
+        TableStyle(
+            [
+                ("BACKGROUND", (0, 0), (-1, 0), colors.HexColor("#f3f4f6")),
+                ("TEXTCOLOR", (0, 0), (-1, 0), colors.HexColor("#1f2937")),
+                ("FONTNAME", (0, 0), (-1, 0), "Helvetica-Bold"),
+                ("ALIGN", (0, 0), (-1, -1), "LEFT"),
+                ("VALIGN", (0, 0), (-1, -1), "TOP"),
+                ("INNERGRID", (0, 0), (-1, -1), 0.25, colors.HexColor("#d1d5db")),
+                ("BOX", (0, 0), (-1, -1), 0.5, colors.HexColor("#9ca3af")),
+                ("ROWBACKGROUNDS", (0, 1), (-1, -1), [colors.white, colors.HexColor("#f9fafb")]),
+            ]
+        )
+    )
+    story.append(table)
+
+    doc.build(story)
+    buffer.seek(0)
+    return buffer.getvalue()
+
+
+def _summary_to_story(
+    raw_summary: str,
+    body_style: ParagraphStyle,
+    bullet_style: ParagraphStyle,
+    heading_style: ParagraphStyle,
+) -> List[Paragraph | Spacer]:
+    story: List[Paragraph | Spacer] = []
+    lines = [line.strip() for line in raw_summary.splitlines()]
+    for line in lines:
+        if not line:
+            continue
+        clean = re.sub(r"\*\*(.*?)\*\*", r"\1", line)
+        if clean.endswith(":") and len(clean) < 40:
+            story.append(Paragraph(clean.rstrip(":"), heading_style))
+            continue
+        if clean.lower().startswith(("highlights", "risks & concerns", "recommended actions", "overall tone")):
+            story.append(Paragraph(clean, heading_style))
+            continue
+        if line.startswith(("-", "*")):
+            bullet_text = re.sub(r"\*\*(.*?)\*\*", r"\1", line[1:].strip())
+            story.append(Paragraph(bullet_text, bullet_style, bulletText="•"))
+        else:
+            story.append(Paragraph(clean, body_style))
+    story.append(Spacer(1, 10))
+    return story
+
+
+def _truncate_text(text: str, max_length: int) -> str:
+    clean = re.sub(r"\s+", " ", text).strip()
+    if len(clean) <= max_length:
+        return clean
+    return clean[: max_length - 1].rstrip() + "…"
+
+
+def _build_excel(df: pd.DataFrame) -> bytes:
+    buffer = io.BytesIO()
+    export_df = df.copy()
+    export_df["timestamp"] = export_df["timestamp"].dt.strftime("%Y-%m-%d %H:%M")
+    with pd.ExcelWriter(buffer, engine="xlsxwriter") as writer:
+        export_df.to_excel(writer, index=False, sheet_name="Mentions")
+        worksheet = writer.sheets["Mentions"]
+        for idx, column in enumerate(export_df.columns):
+            series = export_df[column].astype(str)
+            max_len = min(60, max(series.map(len).max(), len(column)) + 2)
+            worksheet.set_column(idx, idx, max_len)
+    buffer.seek(0)
+    return buffer.getvalue()
+
+
+def main() -> None:
+    env_defaults = _get_env_defaults()
+    openai_env_key = env_defaults.get("OPENAI_API_KEY") or st.session_state.get("secrets", {}).get("OPENAI_API_KEY")
+    validated_env_key, notices = validate_openai_key(openai_env_key)
+    config = render_sidebar(env_defaults, tuple(notices))
+
+    chosen_key = config["credentials"]["openai"] or validated_env_key
+    openai_key, runtime_notices = validate_openai_key(chosen_key)
+    for msg in runtime_notices:
+        st.sidebar.info(msg)
+
+    run_clicked = st.button("Run Analysis 🚀", type="primary")
+
+    if not run_clicked:
+        show_empty_state("Enter a brand name and click **Run Analysis** to get started.")
+        return
+
+    if not config["brand"]:
+        st.error("Brand name is required.")
+        return
+
+    threshold = parse_date_range(config["date_range"])
+    collected: List[NormalizedItem] = []
+
+    with st.container():
+        if config["sources"]["reddit"]:
+            with source_status("Fetching Reddit mentions") as status:
+                try:
+                    reddit_items = cached_reddit_fetch(
+                        brand=config["brand"],
+                        limit=config["limits"]["reddit"],
+                        date_range=config["date_range"],
+                        min_upvotes=config["min_reddit_upvotes"],
+                        client_id=config["credentials"]["reddit"]["client_id"],
+                        client_secret=config["credentials"]["reddit"]["client_secret"],
+                        user_agent=config["credentials"]["reddit"]["user_agent"],
+                    )
+                    reddit_items = [item for item in reddit_items if item["timestamp"] >= threshold]
+                    status.write(f"Fetched {len(reddit_items)} Reddit items.")
+                    collected.extend(reddit_items)
+                except ServiceWarning as warning:
+                    st.warning(str(warning))
+                    demo = load_sample_items("reddit_sample")
+                    if demo:
+                        st.info("Loaded demo Reddit data.", icon="🧪")
+                        collected.extend(demo)
+                except ServiceError as error:
+                    st.error(f"Reddit fetch failed: {error}")
+        if config["sources"]["twitter"]:
+            with source_status("Fetching Twitter mentions") as status:
+                try:
+                    twitter_items = cached_twitter_fetch(
+                        brand=config["brand"],
+                        limit=config["limits"]["twitter"],
+                        min_likes=config["min_twitter_likes"],
+                        language=config["language"],
+                        bearer=config["credentials"]["twitter"],
+                    )
+                    twitter_items = [item for item in twitter_items if item["timestamp"] >= threshold]
+                    status.write(f"Fetched {len(twitter_items)} tweets.")
+                    collected.extend(twitter_items)
+                except ServiceWarning as warning:
+                    st.warning(str(warning))
+                    demo = load_sample_items("twitter_sample")
+                    if demo:
+                        st.info("Loaded demo Twitter data.", icon="🧪")
+                        collected.extend(demo)
+                except ServiceError as error:
+                    st.error(f"Twitter fetch failed: {error}")
+        if config["sources"]["trustpilot"]:
+            with source_status("Fetching Trustpilot reviews") as status:
+                try:
+                    trustpilot_items = cached_trustpilot_fetch(
+                        brand=config["brand"],
+                        language=config["language"],
+                    )
+                    trustpilot_items = [item for item in trustpilot_items if item["timestamp"] >= threshold]
+                    status.write(f"Fetched {len(trustpilot_items)} reviews.")
+                    collected.extend(trustpilot_items)
+                except ServiceWarning as warning:
+                    st.warning(str(warning))
+                    demo = load_sample_items("trustpilot_sample")
+                    if demo:
+                        st.info("Loaded demo Trustpilot data.", icon="🧪")
+                        collected.extend(demo)
+                except ServiceError as error:
+                    st.error(f"Trustpilot fetch failed: {error}")
+
+    if not collected:
+        show_empty_state("No mentions found. Try enabling more sources or loosening filters.")
+        return
+
+    cleaned = normalize_items(collected)
+    if not cleaned:
+        show_empty_state("All results were filtered out as noise. Try again with different settings.")
+        return
+
+    sentiment_service = llm.LLMService(
+        api_key=config["credentials"]["openai"] or openai_key,
+        batch_size=config["batch_size"],
+    )
+    sentiments = sentiment_service.classify_sentiment_batch([item["text"] for item in cleaned])
+    df = _to_dataframe(cleaned, sentiments)
+
+    render_overview(df)
+    render_top_comments(df)
+
+    summary_payload: Optional[Dict[str, str]] = None
+    if sentiment_service.available():
+        try:
+            summary_payload = sentiment_service.summarize_overall(
+                [{"label": row["label"], "text": row["text"]} for _, row in df.iterrows()]
+            )
+        except ServiceWarning as warning:
+            st.warning(str(warning))
+    else:
+        st.info("OpenAI key missing. Using VADER fallback for sentiment; summary disabled.", icon="ℹ️")
+
+    render_summary(summary_payload)
+    render_source_explorer(df)
+
+    csv_data = df.to_csv(index=False).encode("utf-8")
+    excel_data = _build_excel(df)
+    pdf_data = _build_pdf(summary_payload, df)
+    col_csv, col_excel, col_pdf = st.columns(3)
+    with col_csv:
+        st.download_button(
+            "⬇️ Export CSV",
+            data=csv_data,
+            file_name="reputation_radar.csv",
+            mime="text/csv",
+        )
+    with col_excel:
+        st.download_button(
+            "⬇️ Export Excel",
+            data=excel_data,
+            file_name="reputation_radar.xlsx",
+            mime="application/vnd.openxmlformats-officedocument.spreadsheetml.sheet",
+        )
+    with col_pdf:
+        st.download_button(
+            "⬇️ Export PDF Summary",
+            data=pdf_data,
+            file_name="reputation_radar_summary.pdf",
+            mime="application/pdf",
+        )
+
+    st.success("Analysis complete! Review the insights above.")
+
+
+if __name__ == "__main__":
+    main()

community-contributions/Reputation_Radar/components/__init__.py

@@ -0,0 +1,5 @@
+"""Reusable Streamlit UI components for ReputationRadar."""
+
+from . import dashboard, filters, loaders, summary
+
+__all__ = ["dashboard", "filters", "loaders", "summary"]

community-contributions/Reputation_Radar/components/dashboard.py

@@ -0,0 +1,136 @@
+"""Render the ReputationRadar dashboard components."""
+
+from __future__ import annotations
+
+from typing import Dict, Optional
+
+import pandas as pd
+import plotly.express as px
+import streamlit as st
+
+SOURCE_CHIPS = {
+    "reddit": "🔺 Reddit",
+    "twitter": "✖️ Twitter",
+    "trustpilot": "⭐ Trustpilot",
+}
+
+SENTIMENT_COLORS = {
+    "positive": "#4caf50",
+    "neutral": "#90a4ae",
+    "negative": "#ef5350",
+}
+
+
+def render_overview(df: pd.DataFrame) -> None:
+    """Display charts summarising sentiment."""
+    counts = (
+        df["label"]
+        .value_counts()
+        .reindex(["positive", "neutral", "negative"], fill_value=0)
+        .rename_axis("label")
+        .reset_index(name="count")
+    )
+    pie = px.pie(
+        counts,
+        names="label",
+        values="count",
+        color="label",
+        color_discrete_map=SENTIMENT_COLORS,
+        title="Sentiment distribution",
+    )
+    pie.update_traces(textinfo="percent+label")
+
+    ts = (
+        df.set_index("timestamp")
+        .groupby([pd.Grouper(freq="D"), "label"])
+        .size()
+        .reset_index(name="count")
+    )
+    if not ts.empty:
+        ts_plot = px.line(
+            ts,
+            x="timestamp",
+            y="count",
+            color="label",
+            color_discrete_map=SENTIMENT_COLORS,
+            markers=True,
+            title="Mentions over time",
+        )
+    else:
+        ts_plot = None
+
+    col1, col2 = st.columns(2)
+    with col1:
+        st.plotly_chart(pie, use_container_width=True)
+    with col2:
+        if ts_plot is not None:
+            st.plotly_chart(ts_plot, use_container_width=True)
+        else:
+            st.info("Not enough data for a time-series. Try widening the date range.", icon="📆")
+
+
+def render_top_comments(df: pd.DataFrame) -> None:
+    """Show representative comments per sentiment."""
+    st.subheader("Representative Mentions")
+    cols = st.columns(3)
+    for idx, sentiment in enumerate(["positive", "neutral", "negative"]):
+        subset = (
+            df[df["label"] == sentiment]
+            .sort_values("confidence", ascending=False)
+            .head(5)
+        )
+        with cols[idx]:
+            st.caption(sentiment.capitalize())
+            if subset.empty:
+                st.write("No items yet.")
+                continue
+            for _, row in subset.iterrows():
+                chip = SOURCE_CHIPS.get(row["source"], row["source"])
+                author = row.get("author") or "Unknown"
+                timestamp = row["timestamp"].strftime("%Y-%m-%d %H:%M")
+                label = f"{chip} · {author} · {timestamp}"
+                if row.get("url"):
+                    st.markdown(f"- [{label}]({row['url']})")
+                else:
+                    st.markdown(f"- {label}")
+
+
+def render_source_explorer(df: pd.DataFrame) -> None:
+    """Interactive tabular explorer with pagination and filters."""
+    with st.expander("Source Explorer", expanded=False):
+        search_term = st.text_input("Search mentions", key="explorer_search")
+        selected_source = st.selectbox("Source filter", options=["All"] + list(SOURCE_CHIPS.values()))
+        min_conf = st.slider("Minimum confidence", min_value=0.0, max_value=1.0, value=0.0, step=0.1)
+
+        filtered = df.copy()
+        if search_term:
+            filtered = filtered[filtered["text"].str.contains(search_term, case=False, na=False)]
+        if selected_source != "All":
+            source_key = _reverse_lookup(selected_source)
+            if source_key:
+                filtered = filtered[filtered["source"] == source_key]
+        filtered = filtered[filtered["confidence"] >= min_conf]
+
+        if filtered.empty:
+            st.info("No results found. Try widening the date range or removing filters.", icon="🪄")
+            return
+
+        page_size = 10
+        total_pages = max(1, (len(filtered) + page_size - 1) // page_size)
+        page = st.number_input("Page", min_value=1, max_value=total_pages, value=1)
+        start = (page - 1) * page_size
+        end = start + page_size
+
+        explorer_df = filtered.iloc[start:end].copy()
+        explorer_df["source"] = explorer_df["source"].map(SOURCE_CHIPS).fillna(explorer_df["source"])
+        explorer_df["timestamp"] = explorer_df["timestamp"].dt.strftime("%Y-%m-%d %H:%M")
+        explorer_df = explorer_df[["timestamp", "source", "author", "label", "confidence", "text", "url"]]
+
+        st.dataframe(explorer_df, use_container_width=True, hide_index=True)
+
+
+def _reverse_lookup(value: str) -> Optional[str]:
+    for key, chip in SOURCE_CHIPS.items():
+        if chip == value:
+            return key
+    return None

community-contributions/Reputation_Radar/components/filters.py

@@ -0,0 +1,128 @@
+"""Sidebar filters and configuration controls."""
+
+from __future__ import annotations
+
+from typing import Dict, Optional, Tuple
+
+import streamlit as st
+
+DATE_RANGE_LABELS = {
+    "24h": "Last 24 hours",
+    "7d": "Last 7 days",
+    "30d": "Last 30 days",
+}
+
+SUPPORTED_LANGUAGES = {
+    "en": "English",
+    "es": "Spanish",
+    "de": "German",
+    "fr": "French",
+}
+
+
+def _store_secret(key: str, value: str) -> None:
+    """Persist sensitive values in session state only."""
+    if value:
+        st.session_state.setdefault("secrets", {})
+        st.session_state["secrets"][key] = value
+
+
+def _get_secret(key: str, default: str = "") -> str:
+    return st.session_state.get("secrets", {}).get(key, default)
+
+
+def render_sidebar(env_defaults: Dict[str, Optional[str]], openai_notices: Tuple[str, ...]) -> Dict[str, object]:
+    """Render all sidebar controls and return configuration."""
+    with st.sidebar:
+        st.header("Tune Your Radar", anchor=False)
+        brand = st.text_input("Brand Name*", value=st.session_state.get("brand_input", ""))
+        if brand:
+            st.session_state["brand_input"] = brand
+
+        date_range = st.selectbox(
+            "Date Range",
+            options=list(DATE_RANGE_LABELS.keys()),
+            format_func=lambda key: DATE_RANGE_LABELS[key],
+            index=1,
+        )
+        min_reddit_upvotes = st.number_input(
+            "Minimum Reddit upvotes",
+            min_value=0,
+            value=st.session_state.get("min_reddit_upvotes", 4),
+        )
+        st.session_state["min_reddit_upvotes"] = min_reddit_upvotes
+        min_twitter_likes = st.number_input(
+            "Minimum X likes",
+            min_value=0,
+            value=st.session_state.get("min_twitter_likes", 100),
+        )
+        st.session_state["min_twitter_likes"] = min_twitter_likes
+        language = st.selectbox(
+            "Language",
+            options=list(SUPPORTED_LANGUAGES.keys()),
+            format_func=lambda key: SUPPORTED_LANGUAGES[key],
+            index=0,
+        )
+
+        st.markdown("### Sources")
+        reddit_enabled = st.toggle("🔺 Reddit", value=st.session_state.get("reddit_enabled", True))
+        twitter_enabled = st.toggle("✖️ Twitter", value=st.session_state.get("twitter_enabled", True))
+        trustpilot_enabled = st.toggle("⭐ Trustpilot", value=st.session_state.get("trustpilot_enabled", True))
+        st.session_state["reddit_enabled"] = reddit_enabled
+        st.session_state["twitter_enabled"] = twitter_enabled
+        st.session_state["trustpilot_enabled"] = trustpilot_enabled
+
+        st.markdown("### API Keys")
+        openai_key_default = env_defaults.get("OPENAI_API_KEY") or _get_secret("OPENAI_API_KEY")
+        openai_key = st.text_input("OpenAI API Key", value=openai_key_default or "", type="password", help="Stored only in this session.")
+        _store_secret("OPENAI_API_KEY", openai_key.strip())
+        reddit_client_id = st.text_input("Reddit Client ID", value=env_defaults.get("REDDIT_CLIENT_ID") or _get_secret("REDDIT_CLIENT_ID"), type="password")
+        reddit_client_secret = st.text_input("Reddit Client Secret", value=env_defaults.get("REDDIT_CLIENT_SECRET") or _get_secret("REDDIT_CLIENT_SECRET"), type="password")
+        reddit_user_agent = st.text_input("Reddit User Agent", value=env_defaults.get("REDDIT_USER_AGENT") or _get_secret("REDDIT_USER_AGENT"))
+        twitter_bearer_token = st.text_input("Twitter Bearer Token", value=env_defaults.get("TWITTER_BEARER_TOKEN") or _get_secret("TWITTER_BEARER_TOKEN"), type="password")
+        _store_secret("REDDIT_CLIENT_ID", reddit_client_id.strip())
+        _store_secret("REDDIT_CLIENT_SECRET", reddit_client_secret.strip())
+        _store_secret("REDDIT_USER_AGENT", reddit_user_agent.strip())
+        _store_secret("TWITTER_BEARER_TOKEN", twitter_bearer_token.strip())
+
+        if openai_notices:
+            for notice in openai_notices:
+                st.info(notice)
+
+        with st.expander("Advanced Options", expanded=False):
+            reddit_limit = st.slider("Reddit results", min_value=10, max_value=100, value=st.session_state.get("reddit_limit", 40), step=5)
+            twitter_limit = st.slider("Twitter results", min_value=10, max_value=100, value=st.session_state.get("twitter_limit", 40), step=5)
+            trustpilot_limit = st.slider("Trustpilot results", min_value=10, max_value=60, value=st.session_state.get("trustpilot_limit", 30), step=5)
+            llm_batch_size = st.slider("OpenAI batch size", min_value=5, max_value=20, value=st.session_state.get("llm_batch_size", 20), step=5)
+            st.session_state["reddit_limit"] = reddit_limit
+            st.session_state["twitter_limit"] = twitter_limit
+            st.session_state["trustpilot_limit"] = trustpilot_limit
+            st.session_state["llm_batch_size"] = llm_batch_size
+
+    return {
+        "brand": brand.strip(),
+        "date_range": date_range,
+        "min_reddit_upvotes": min_reddit_upvotes,
+        "min_twitter_likes": min_twitter_likes,
+        "language": language,
+        "sources": {
+            "reddit": reddit_enabled,
+            "twitter": twitter_enabled,
+            "trustpilot": trustpilot_enabled,
+        },
+        "limits": {
+            "reddit": reddit_limit,
+            "twitter": twitter_limit,
+            "trustpilot": trustpilot_limit,
+        },
+        "batch_size": llm_batch_size,
+        "credentials": {
+            "openai": openai_key.strip(),
+            "reddit": {
+                "client_id": reddit_client_id.strip(),
+                "client_secret": reddit_client_secret.strip(),
+                "user_agent": reddit_user_agent.strip(),
+            },
+            "twitter": twitter_bearer_token.strip(),
+        },
+    }

community-contributions/Reputation_Radar/components/loaders.py

@@ -0,0 +1,25 @@
+"""Loading indicators and status helpers."""
+
+from __future__ import annotations
+
+from contextlib import contextmanager
+from typing import Iterator
+
+import streamlit as st
+
+
+@contextmanager
+def source_status(label: str) -> Iterator[st.delta_generator.DeltaGenerator]:
+    """Context manager that yields a status widget for source fetching."""
+    status = st.status(label, expanded=True)
+    try:
+        yield status
+        status.update(label=f"{label} ✅", state="complete")
+    except Exception as exc:  # noqa: BLE001
+        status.update(label=f"{label} ⚠️ {exc}", state="error")
+        raise
+
+
+def show_empty_state(message: str) -> None:
+    """Render a friendly empty-state callout."""
+    st.info(message, icon="🔎")

community-contributions/Reputation_Radar/components/summary.py

@@ -0,0 +1,23 @@
+"""Executive summary display components."""
+
+from __future__ import annotations
+
+from typing import Dict, Optional
+
+import streamlit as st
+
+
+def render_summary(summary: Optional[Dict[str, str]]) -> None:
+    """Render executive summary card."""
+    st.subheader("Executive Summary", anchor=False)
+    if not summary:
+        st.warning("Executive summary disabled. Provide an OpenAI API key to unlock this section.", icon="🤖")
+        return
+    st.markdown(
+        """
+        <div style="padding:1rem;border:1px solid #eee;border-radius:0.75rem;background-color:#f9fafb;">
+        """,
+        unsafe_allow_html=True,
+    )
+    st.markdown(summary.get("raw", ""))
+    st.markdown("</div>", unsafe_allow_html=True)

community-contributions/Reputation_Radar/requirements.txt

@@ -0,0 +1,16 @@
+streamlit
+praw
+requests
+beautifulsoup4
+pandas
+python-dotenv
+tenacity
+plotly
+openai>=1.0.0
+vaderSentiment
+fuzzywuzzy[speedup]
+python-Levenshtein
+reportlab
+tqdm
+pytest
+XlsxWriter

community-contributions/Reputation_Radar/samples/reddit_sample.json

@@ -0,0 +1,20 @@
+[
+  {
+    "source": "reddit",
+    "id": "t3_sample1",
+    "url": "https://www.reddit.com/r/technology/comments/sample1",
+    "author": "techfan42",
+    "timestamp": "2025-01-15T14:30:00+00:00",
+    "text": "ReputationRadar did an impressive job resolving our customer issues within hours. Support has been world class!",
+    "meta": {"score": 128, "num_comments": 24, "subreddit": "technology", "type": "submission"}
+  },
+  {
+    "source": "reddit",
+    "id": "t1_sample2",
+    "url": "https://www.reddit.com/r/startups/comments/sample2/comment/sample",
+    "author": "growthguru",
+    "timestamp": "2025-01-14T10:10:00+00:00",
+    "text": "Noticed a spike in downtime alerts with ReputationRadar this week. Anyone else seeing false positives?",
+    "meta": {"score": 45, "subreddit": "startups", "type": "comment", "submission_title": "Monitoring tools"}
+  }
+]

community-contributions/Reputation_Radar/samples/trustpilot_sample.json

@@ -0,0 +1,20 @@
+[
+  {
+    "source": "trustpilot",
+    "id": "trustpilot-001",
+    "url": "https://www.trustpilot.com/review/reputationradar.ai",
+    "author": "Dana",
+    "timestamp": "2025-01-12T11:00:00+00:00",
+    "text": "ReputationRadar has simplified our weekly reporting. The sentiment breakdowns are easy to understand and accurate.",
+    "meta": {"rating": "5 stars"}
+  },
+  {
+    "source": "trustpilot",
+    "id": "trustpilot-002",
+    "url": "https://www.trustpilot.com/review/reputationradar.ai?page=2",
+    "author": "Liam",
+    "timestamp": "2025-01-10T18:20:00+00:00",
+    "text": "Support was responsive, but the Trustpilot integration kept timing out. Hoping for a fix soon.",
+    "meta": {"rating": "3 stars"}
+  }
+]

community-contributions/Reputation_Radar/samples/twitter_sample.json

@@ -0,0 +1,20 @@
+[
+  {
+    "source": "twitter",
+    "id": "173654001",
+    "url": "https://twitter.com/brandlover/status/173654001",
+    "author": "brandlover",
+    "timestamp": "2025-01-15T16:45:00+00:00",
+    "text": "Huge shoutout to ReputationRadar for flagging sentiment risks ahead of our launch. Saved us hours this morning!",
+    "meta": {"likes": 57, "retweets": 8, "replies": 3, "quote_count": 2}
+  },
+  {
+    "source": "twitter",
+    "id": "173653991",
+    "url": "https://twitter.com/critique/status/173653991",
+    "author": "critique",
+    "timestamp": "2025-01-13T09:12:00+00:00",
+    "text": "The new ReputationRadar dashboard feels laggy and the PDF export failed twice. Dev team please check your rollout.",
+    "meta": {"likes": 14, "retweets": 1, "replies": 5, "quote_count": 0}
+  }
+]

community-contributions/Reputation_Radar/services/__init__.py

@@ -0,0 +1,11 @@
+"""Service layer exports for ReputationRadar."""
+
+from . import llm, reddit_client, trustpilot_scraper, twitter_client, utils
+
+__all__ = [
+    "llm",
+    "reddit_client",
+    "trustpilot_scraper",
+    "twitter_client",
+    "utils",
+]

community-contributions/Reputation_Radar/services/llm.py

@@ -0,0 +1,147 @@
+"""LLM sentiment analysis and summarization utilities."""
+
+from __future__ import annotations
+
+import json
+import logging
+from dataclasses import dataclass
+from typing import Any, Dict, Iterable, List, Optional, Sequence
+
+try:  # pragma: no cover - optional dependency
+    from openai import OpenAI
+except ModuleNotFoundError:  # pragma: no cover
+    OpenAI = None  # type: ignore[assignment]
+
+from vaderSentiment.vaderSentiment import SentimentIntensityAnalyzer
+
+from .utils import ServiceWarning, chunked
+
+CLASSIFICATION_SYSTEM_PROMPT = "You are a precise brand-sentiment classifier. Output JSON only."
+SUMMARY_SYSTEM_PROMPT = "You analyze brand chatter and produce concise, executive-ready summaries."
+
+
+@dataclass
+class SentimentResult:
+    """Structured sentiment output."""
+
+    label: str
+    confidence: float
+
+
+class LLMService:
+    """Wrapper around OpenAI with VADER fallback."""
+
+    def __init__(self, api_key: Optional[str], model: str = "gpt-4o-mini", batch_size: int = 20):
+        self.batch_size = max(1, batch_size)
+        self.model = model
+        self.logger = logging.getLogger("services.llm")
+        self._client: Optional[Any] = None
+        self._analyzer = SentimentIntensityAnalyzer()
+        if api_key and OpenAI is not None:
+            try:
+                self._client = OpenAI(api_key=api_key)
+            except Exception as exc:  # noqa: BLE001
+                self.logger.warning("Failed to initialize OpenAI client, using VADER fallback: %s", exc)
+                self._client = None
+        elif api_key and OpenAI is None:
+            self.logger.warning("openai package not installed; falling back to VADER despite API key.")
+
+    def available(self) -> bool:
+        """Return whether OpenAI-backed features are available."""
+        return self._client is not None
+
+    def classify_sentiment_batch(self, texts: Sequence[str]) -> List[SentimentResult]:
+        """Classify multiple texts, chunking if necessary."""
+        if not texts:
+            return []
+        if not self.available():
+            return [self._vader_sentiment(text) for text in texts]
+
+        results: List[SentimentResult] = []
+        for chunk in chunked(list(texts), self.batch_size):
+            prompt_lines = ["Classify each item as \"positive\", \"neutral\", or \"negative\".", "Also output a confidence score between 0 and 1.", "Return an array of objects: [{\"label\": \"...\", \"confidence\": 0.0}].", "Items:"]
+            prompt_lines.extend([f"{idx + 1}) {text}" for idx, text in enumerate(chunk)])
+            prompt = "\n".join(prompt_lines)
+            try:
+                response = self._client.responses.create(  # type: ignore[union-attr]
+                    model=self.model,
+                    input=[
+                        {"role": "system", "content": CLASSIFICATION_SYSTEM_PROMPT},
+                        {"role": "user", "content": prompt},
+                    ],
+                    temperature=0,
+                    max_output_tokens=500,
+                )
+                output_text = self._extract_text(response)
+                parsed = json.loads(output_text)
+                for item in parsed:
+                    results.append(
+                        SentimentResult(
+                            label=item.get("label", "neutral"),
+                            confidence=float(item.get("confidence", 0.5)),
+                        )
+                    )
+            except Exception as exc:  # noqa: BLE001
+                self.logger.warning("Classification fallback to VADER due to error: %s", exc)
+                for text in chunk:
+                    results.append(self._vader_sentiment(text))
+        # Ensure the output length matches input
+        if len(results) != len(texts):
+            # align by padding with neutral
+            results.extend([SentimentResult(label="neutral", confidence=0.33)] * (len(texts) - len(results)))
+        return results
+
+    def summarize_overall(self, findings: List[Dict[str, Any]]) -> Dict[str, Any]:
+        """Create an executive summary using OpenAI."""
+        if not self.available():
+            raise ServiceWarning("OpenAI API key missing. Summary unavailable.")
+        prompt_lines = [
+            "Given these labeled items and their short rationales, write:",
+            "- 5 bullet \"Highlights\"",
+            "- 5 bullet \"Risks & Concerns\"",
+            "- One-line \"Overall Tone\" (Positive/Neutral/Negative with brief justification)",
+            "- 3 \"Recommended Actions\"",
+            "Keep it under 180 words total. Be specific but neutral in tone.",
+            "Items:",
+        ]
+        for idx, item in enumerate(findings, start=1):
+            prompt_lines.append(
+                f"{idx}) [{item.get('label','neutral').upper()}] {item.get('text','')}"
+            )
+        prompt = "\n".join(prompt_lines)
+        try:
+            response = self._client.responses.create(  # type: ignore[union-attr]
+                model=self.model,
+                input=[
+                    {"role": "system", "content": SUMMARY_SYSTEM_PROMPT},
+                    {"role": "user", "content": prompt},
+                ],
+                temperature=0.2,
+                max_output_tokens=800,
+            )
+            output_text = self._extract_text(response)
+            return {"raw": output_text}
+        except Exception as exc:  # noqa: BLE001
+            self.logger.error("Failed to generate summary: %s", exc)
+            raise ServiceWarning("Unable to generate executive summary at this time.") from exc
+
+    def _vader_sentiment(self, text: str) -> SentimentResult:
+        scores = self._analyzer.polarity_scores(text)
+        compound = scores["compound"]
+        if compound >= 0.2:
+            label = "positive"
+        elif compound <= -0.2:
+            label = "negative"
+        else:
+            label = "neutral"
+        confidence = min(1.0, max(0.0, abs(compound)))
+        return SentimentResult(label=label, confidence=confidence)
+
+    def _extract_text(self, response: Any) -> str:
+        """Support multiple OpenAI client response shapes."""
+        if hasattr(response, "output") and response.output:
+            content = response.output[0].content[0]
+            return getattr(content, "text", str(content))
+        if hasattr(response, "choices"):
+            return response.choices[0].message.content  # type: ignore[return-value]
+        raise ValueError("Unknown response structure from OpenAI client.")

community-contributions/Reputation_Radar/services/reddit_client.py

@@ -0,0 +1,141 @@
+"""Reddit data collection service using PRAW."""
+
+from __future__ import annotations
+
+import time
+from datetime import datetime, timezone
+from typing import Dict, Iterable, List, Optional
+
+import praw
+from praw.models import Comment, Submission
+
+from .utils import (
+    NormalizedItem,
+    ServiceError,
+    ServiceWarning,
+    ensure_timezone,
+    sanitize_text,
+)
+
+
+TIME_FILTER_MAP = {
+    "24h": "day",
+    "7d": "week",
+    "30d": "month",
+}
+
+
+def _iter_submissions(subreddit: praw.models.Subreddit, query: str, limit: int, time_filter: str) -> Iterable[Submission]:
+    return subreddit.search(query=query, sort="new", time_filter=time_filter, limit=limit * 3)
+
+
+def _iter_comments(submission: Submission) -> Iterable[Comment]:
+    submission.comments.replace_more(limit=0)
+    return submission.comments.list()
+
+
+def _normalize_submission(submission: Submission) -> NormalizedItem:
+    created = datetime.fromtimestamp(submission.created_utc, tz=timezone.utc)
+    return NormalizedItem(
+        source="reddit",
+        id=submission.id,
+        url=f"https://www.reddit.com{submission.permalink}",
+        author=str(submission.author) if submission.author else None,
+        timestamp=ensure_timezone(created),
+        text=f"{submission.title}\n\n{submission.selftext or ''}",
+        meta={
+            "score": submission.score,
+            "num_comments": submission.num_comments,
+            "subreddit": submission.subreddit.display_name,
+            "type": "submission",
+        },
+    )
+
+
+def _normalize_comment(comment: Comment, submission: Submission) -> NormalizedItem:
+    created = datetime.fromtimestamp(comment.created_utc, tz=timezone.utc)
+    return NormalizedItem(
+        source="reddit",
+        id=comment.id,
+        url=f"https://www.reddit.com{comment.permalink}",
+        author=str(comment.author) if comment.author else None,
+        timestamp=ensure_timezone(created),
+        text=comment.body,
+        meta={
+            "score": comment.score,
+            "subreddit": submission.subreddit.display_name,
+            "type": "comment",
+            "submission_title": submission.title,
+        },
+    )
+
+
+def fetch_mentions(
+    brand: str,
+    credentials: Dict[str, str],
+    limit: int = 25,
+    date_filter: str = "7d",
+    min_upvotes: int = 0,
+) -> List[NormalizedItem]:
+    """Fetch recent Reddit submissions/comments mentioning the brand."""
+    client_id = credentials.get("client_id")
+    client_secret = credentials.get("client_secret")
+    user_agent = credentials.get("user_agent")
+
+    if not all([client_id, client_secret, user_agent]):
+        raise ServiceWarning("Reddit credentials are missing. Provide them in the sidebar to enable this source.")
+
+    try:
+        reddit = praw.Reddit(
+            client_id=client_id,
+            client_secret=client_secret,
+            user_agent=user_agent,
+        )
+        reddit.read_only = True
+    except Exception as exc:  # noqa: BLE001
+        raise ServiceError(f"Failed to initialize Reddit client: {exc}") from exc
+
+    time_filter = TIME_FILTER_MAP.get(date_filter.lower(), "week")
+    subreddit = reddit.subreddit("all")
+    results: List[NormalizedItem] = []
+    seen_ids: set[str] = set()
+    try:
+        for submission in _iter_submissions(subreddit, query=brand, limit=limit, time_filter=time_filter):
+            if submission.id in seen_ids:
+                continue
+            if submission.score < min_upvotes:
+                continue
+            normalized_submission = _normalize_submission(submission)
+            normalized_submission["text"] = sanitize_text(normalized_submission["text"])
+            if normalized_submission["text"]:
+                results.append(normalized_submission)
+                seen_ids.add(submission.id)
+            if len(results) >= limit:
+                break
+
+            # Fetch comments mentioning the brand
+            match_count = 0
+            for comment in _iter_comments(submission):
+                if brand.lower() not in (comment.body or "").lower():
+                    continue
+                if comment.score < min_upvotes:
+                    continue
+                normalized_comment = _normalize_comment(comment, submission)
+                normalized_comment["text"] = sanitize_text(normalized_comment["text"])
+                if not normalized_comment["text"]:
+                    continue
+                if normalized_comment["id"] in seen_ids:
+                    continue
+                results.append(normalized_comment)
+                seen_ids.add(normalized_comment["id"])
+                match_count += 1
+                if len(results) >= limit:
+                    break
+            if len(results) >= limit:
+                break
+            # Respect rate limits
+            if match_count:
+                time.sleep(1)
+    except Exception as exc:  # noqa: BLE001
+        raise ServiceError(f"Error while fetching Reddit data: {exc}") from exc
+    return results

community-contributions/Reputation_Radar/services/trustpilot_scraper.py

@@ -0,0 +1,138 @@
+"""Trustpilot scraping service with polite crawling safeguards."""
+
+from __future__ import annotations
+
+import time
+from datetime import datetime, timezone
+from typing import Dict, List
+from urllib.parse import urlencode
+from urllib.robotparser import RobotFileParser
+
+import requests
+from bs4 import BeautifulSoup
+from tenacity import retry, retry_if_exception_type, stop_after_attempt, wait_exponential
+
+from .utils import (
+    NormalizedItem,
+    ServiceError,
+    ServiceWarning,
+    ensure_timezone,
+    random_user_agent,
+    sanitize_text,
+)
+
+BASE_URL = "https://www.trustpilot.com"
+SEARCH_PATH = "/search"
+
+
+class BlockedError(ServiceWarning):
+    """Raised when Trustpilot blocks the scraping attempt."""
+
+
+def _check_robots(user_agent: str) -> None:
+    parser = RobotFileParser()
+    parser.set_url(f"{BASE_URL}/robots.txt")
+    parser.read()
+    if not parser.can_fetch(user_agent, SEARCH_PATH):
+        raise ServiceWarning(
+            "Trustpilot robots.txt disallows scraping the search endpoint. "
+            "Please use the official API or upload data manually."
+        )
+
+
+@retry(
+    reraise=True,
+    stop=stop_after_attempt(3),
+    wait=wait_exponential(multiplier=1, min=1, max=8),
+    retry=retry_if_exception_type((requests.RequestException, BlockedError)),
+)
+def _fetch_page(session: requests.Session, user_agent: str, page: int, brand: str, language: str) -> str:
+    params = {"query": brand, "page": page}
+    if language:
+        params["languages"] = language
+    url = f"{BASE_URL}{SEARCH_PATH}?{urlencode(params)}"
+    response = session.get(
+        url,
+        headers={"User-Agent": user_agent, "Accept-Language": language or "en"},
+        timeout=20,
+    )
+    if response.status_code in (401, 403):
+        raise BlockedError("Trustpilot denied access (HTTP 403).")
+    response.raise_for_status()
+    return response.text
+
+
+def _parse_reviews(html: str, user_agent: str) -> List[NormalizedItem]:
+    soup = BeautifulSoup(html, "html.parser")
+    cards = soup.select("article[data-service-review-card-layout]")
+    items: List[NormalizedItem] = []
+    now = datetime.now(timezone.utc)
+    for card in cards:
+        link = card.select_one("a.link_internal__YpiJI")
+        url = f"{BASE_URL}{link['href']}" if link and link.get("href") else ""
+        title_el = card.select_one("h2")
+        title = title_el.get_text(strip=True) if title_el else ""
+        text_el = card.select_one("[data-review-description-typography]")
+        text = text_el.get_text(separator=" ", strip=True) if text_el else ""
+        rating_el = card.select_one("img[alt*='stars']")
+        rating = rating_el["alt"] if rating_el and rating_el.get("alt") else ""
+        author_el = card.select_one("span.styles_consumerDetails__ZF4I6")
+        author = author_el.get_text(strip=True) if author_el else None
+        date_el = card.select_one("time")
+        timestamp = now
+        if date_el and date_el.get("datetime"):
+            try:
+                timestamp = datetime.fromisoformat(date_el["datetime"].replace("Z", "+00:00"))
+            except ValueError:
+                timestamp = now
+
+        body = sanitize_text(f"{title}\n\n{text}")
+        if len(body) < 15:
+            continue
+        items.append(
+            NormalizedItem(
+                source="trustpilot",
+                id=card.get("data-review-id", str(hash(body))),
+                url=url,
+                author=author,
+                timestamp=ensure_timezone(timestamp),
+                text=body,
+                meta={
+                    "rating": rating,
+                    "user_agent": user_agent,
+                },
+            )
+        )
+    return items
+
+
+def fetch_reviews(brand: str, language: str = "en", pages: int = 2) -> List[NormalizedItem]:
+    """Scrape Trustpilot search results for recent reviews."""
+    if not brand:
+        raise ServiceWarning("Brand name is required for Trustpilot scraping.")
+
+    session = requests.Session()
+    user_agent = random_user_agent()
+    _check_robots(user_agent)
+
+    aggregated: List[NormalizedItem] = []
+    seen_ids: set[str] = set()
+
+    for page in range(1, pages + 1):
+        try:
+            html = _fetch_page(session, user_agent=user_agent, page=page, brand=brand, language=language)
+        except BlockedError as exc:
+            raise ServiceWarning(
+                "Trustpilot blocked the scraping attempt. Consider using their official API or providing CSV uploads."
+            ) from exc
+        except requests.RequestException as exc:  # noqa: BLE001
+            raise ServiceError(f"Trustpilot request failed: {exc}") from exc
+        page_items = _parse_reviews(html, user_agent)
+        for item in page_items:
+            if item["id"] in seen_ids:
+                continue
+            aggregated.append(item)
+            seen_ids.add(item["id"])
+        time.sleep(1.5)  # gentle crawl delay
+
+    return aggregated

community-contributions/Reputation_Radar/services/twitter_client.py

@@ -0,0 +1,98 @@
+"""Twitter (X) data collection using the v2 recent search API."""
+
+from __future__ import annotations
+
+import time
+from datetime import datetime, timezone
+from typing import Dict, List, Optional
+
+import requests
+
+from .utils import NormalizedItem, ServiceError, ServiceWarning, ensure_timezone, sanitize_text
+
+SEARCH_URL = "https://api.twitter.com/2/tweets/search/recent"
+
+
+def _build_query(brand: str, language: str) -> str:
+    terms = [brand]
+    if language:
+        terms.append(f"lang:{language}")
+    return " ".join(terms)
+
+
+def fetch_mentions(
+    brand: str,
+    bearer_token: Optional[str],
+    limit: int = 25,
+    min_likes: int = 0,
+    language: str = "en",
+) -> List[NormalizedItem]:
+    """Fetch recent tweets mentioning the brand."""
+    if not bearer_token:
+        raise ServiceWarning(
+            "Twitter bearer token not provided. Add it in the sidebar to enable Twitter ingestion."
+        )
+
+    headers = {
+        "Authorization": f"Bearer {bearer_token}",
+        "User-Agent": "ReputationRadar/1.0",
+    }
+    params = {
+        "query": _build_query(brand, language),
+        "max_results": min(100, limit),
+        "tweet.fields": "author_id,created_at,lang,public_metrics",
+        "expansions": "author_id",
+        "user.fields": "name,username",
+    }
+
+    collected: List[NormalizedItem] = []
+    next_token: Optional[str] = None
+
+    while len(collected) < limit:
+        if next_token:
+            params["next_token"] = next_token
+        response = requests.get(SEARCH_URL, headers=headers, params=params, timeout=15)
+        if response.status_code == 401:
+            raise ServiceWarning("Twitter API authentication failed. Please verify the bearer token.")
+        if response.status_code == 429:
+            time.sleep(5)
+            continue
+        if response.status_code >= 400:
+            raise ServiceError(f"Twitter API error {response.status_code}: {response.text}")
+
+        payload = response.json()
+        data = payload.get("data", [])
+        includes = payload.get("includes", {})
+        users_index = {user["id"]: user for user in includes.get("users", [])}
+
+        for tweet in data:
+            created_at = datetime.fromisoformat(tweet["created_at"].replace("Z", "+00:00"))
+            author_info = users_index.get(tweet["author_id"], {})
+            item = NormalizedItem(
+                source="twitter",
+                id=tweet["id"],
+                url=f"https://twitter.com/{author_info.get('username','')}/status/{tweet['id']}",
+                author=author_info.get("username"),
+                timestamp=ensure_timezone(created_at),
+                text=sanitize_text(tweet["text"]),
+                meta={
+                    "likes": tweet.get("public_metrics", {}).get("like_count", 0),
+                    "retweets": tweet.get("public_metrics", {}).get("retweet_count", 0),
+                    "replies": tweet.get("public_metrics", {}).get("reply_count", 0),
+                    "quote_count": tweet.get("public_metrics", {}).get("quote_count", 0),
+                },
+            )
+            if not item["text"]:
+                continue
+            if item["meta"]["likes"] < min_likes:
+                continue
+            collected.append(item)
+            if len(collected) >= limit:
+                break
+
+        next_token = payload.get("meta", {}).get("next_token")
+        if not next_token:
+            break
+        time.sleep(1)  # stay friendly to rate limits
+
+    return collected[:limit]

community-contributions/Reputation_Radar/services/utils.py

@@ -0,0 +1,217 @@
+"""Utility helpers for ReputationRadar services."""
+
+from __future__ import annotations
+
+import json
+import logging
+import os
+import random
+import re
+from dataclasses import dataclass
+from datetime import datetime, timedelta, timezone
+from pathlib import Path
+from typing import Dict, Iterable, Iterator, List, Optional, Sequence, Tuple, TypedDict
+
+from bs4 import BeautifulSoup
+from fuzzywuzzy import fuzz
+
+
+LOG_FILE = Path(__file__).resolve().parents[1] / "logs" / "app.log"
+MIN_TEXT_LENGTH = 15
+SIMILARITY_THRESHOLD = 90
+
+
+class NormalizedItem(TypedDict):
+    """Canonical representation of a fetched mention."""
+
+    source: str
+    id: str
+    url: str
+    author: Optional[str]
+    timestamp: datetime
+    text: str
+    meta: Dict[str, object]
+
+
+class ServiceError(RuntimeError):
+    """Raised when a service hard fails."""
+
+
+class ServiceWarning(RuntimeError):
+    """Raised for recoverable issues that should surface to the UI."""
+
+
+def initialize_logger(name: str = "reputation_radar") -> logging.Logger:
+    """Configure and return a module-level logger."""
+    LOG_FILE.parent.mkdir(parents=True, exist_ok=True)
+    logging.basicConfig(
+        level=logging.INFO,
+        format="%(asctime)s | %(levelname)s | %(name)s | %(message)s",
+        handlers=[
+            logging.FileHandler(LOG_FILE, encoding="utf-8"),
+            logging.StreamHandler(),
+        ],
+    )
+    logger = logging.getLogger(name)
+    logger.setLevel(logging.INFO)
+    return logger
+
+
+def load_sample_items(name: str) -> List[NormalizedItem]:
+    """Load demo data from the samples directory."""
+    samples_dir = Path(__file__).resolve().parents[1] / "samples"
+    sample_path = samples_dir / f"{name}.json"
+    if not sample_path.exists():
+        return []
+    with sample_path.open("r", encoding="utf-8") as handle:
+        raw_items = json.load(handle)
+    cleaned: List[NormalizedItem] = []
+    for item in raw_items:
+        try:
+            cleaned.append(
+                NormalizedItem(
+                    source=item["source"],
+                    id=str(item["id"]),
+                    url=item.get("url", ""),
+                    author=item.get("author"),
+                    timestamp=datetime.fromisoformat(item["timestamp"]),
+                    text=item["text"],
+                    meta=item.get("meta", {}),
+                )
+            )
+        except (KeyError, ValueError):
+            continue
+    return cleaned
+
+
+def strip_html(value: str) -> str:
+    """Remove HTML tags and normalize whitespace."""
+    if not value:
+        return ""
+    soup = BeautifulSoup(value, "html.parser")
+    text = soup.get_text(separator=" ", strip=True)
+    text = re.sub(r"\s+", " ", text)
+    text = text.encode("utf-8", "ignore").decode("utf-8", "ignore")
+    return text.strip()
+
+
+def sanitize_text(value: str) -> str:
+    """Clean text and remove excessive noise."""
+    text = strip_html(value)
+    text = re.sub(r"http\S+", "", text)  # drop inline URLs
+    text = re.sub(r"\s{2,}", " ", text)
+    return text.strip()
+
+
+def drop_short_items(items: Iterable[NormalizedItem], minimum_length: int = MIN_TEXT_LENGTH) -> List[NormalizedItem]:
+    """Filter out items that are too short to analyze."""
+    return [
+        item
+        for item in items
+        if len(item["text"]) >= minimum_length
+    ]
+
+
+def fuzzy_deduplicate(items: Sequence[NormalizedItem], threshold: int = SIMILARITY_THRESHOLD) -> List[NormalizedItem]:
+    """Remove duplicates based on URL or fuzzy text similarity."""
+    seen_urls: set[str] = set()
+    deduped: List[NormalizedItem] = []
+    for item in items:
+        url = item.get("url") or ""
+        text = item.get("text") or ""
+        if url and url in seen_urls:
+            continue
+        duplicate_found = False
+        for existing in deduped:
+            if not text or not existing.get("text"):
+                continue
+            if fuzz.token_set_ratio(text, existing["text"]) >= threshold:
+                duplicate_found = True
+                break
+        if not duplicate_found:
+            deduped.append(item)
+            if url:
+                seen_urls.add(url)
+    return deduped
+
+
+def normalize_items(items: Sequence[NormalizedItem]) -> List[NormalizedItem]:
+    """Apply sanitization, deduplication, and drop noisy entries."""
+    sanitized: List[NormalizedItem] = []
+    for item in items:
+        cleaned_text = sanitize_text(item.get("text", ""))
+        if len(cleaned_text) < MIN_TEXT_LENGTH:
+            continue
+        sanitized.append(
+            NormalizedItem(
+                source=item["source"],
+                id=item["id"],
+                url=item.get("url", ""),
+                author=item.get("author"),
+                timestamp=item["timestamp"],
+                text=cleaned_text,
+                meta=item.get("meta", {}),
+            )
+        )
+    return fuzzy_deduplicate(sanitized)
+
+
+def parse_date_range(option: str) -> datetime:
+    """Return a UTC timestamp threshold for the given range identifier."""
+    now = datetime.now(timezone.utc)
+    option = option.lower()
+    delta = {
+        "24h": timedelta(days=1),
+        "7d": timedelta(days=7),
+        "30d": timedelta(days=30),
+    }.get(option, timedelta(days=7))
+    return now - delta
+
+
+def random_user_agent() -> str:
+    """Return a random user agent string for polite scraping."""
+    user_agents = [
+        "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 "
+        "(KHTML, like Gecko) Chrome/124.0.0.0 Safari/537.36",
+        "Mozilla/5.0 (Macintosh; Intel Mac OS X 13_3) AppleWebKit/605.1.15 "
+        "(KHTML, like Gecko) Version/16.4 Safari/605.1.15",
+        "Mozilla/5.0 (X11; Ubuntu; Linux x86_64; rv:125.0) Gecko/20100101 Firefox/125.0",
+    ]
+    return random.choice(user_agents)
+
+
+def chunked(iterable: Sequence[str], size: int) -> Iterator[Sequence[str]]:
+    """Yield successive chunks from iterable."""
+    for start in range(0, len(iterable), size):
+        yield iterable[start : start + size]
+
+
+def validate_openai_key(api_key: Optional[str]) -> Tuple[Optional[str], List[str]]:
+    """Validate an OpenAI key following the guidance from day1 notebook."""
+    warnings: List[str] = []
+    if not api_key:
+        warnings.append("No OpenAI API key detected. VADER fallback will be used.")
+        return None, warnings
+    if not api_key.startswith("sk-"):
+        warnings.append(
+            "Provided OpenAI API key does not start with the expected prefix (sk-)."
+        )
+    if api_key.strip() != api_key:
+        warnings.append("OpenAI API key looks like it has leading or trailing whitespace.")
+        api_key = api_key.strip()
+    return api_key, warnings
+
+
+def ensure_timezone(ts: datetime) -> datetime:
+    """Guarantee timestamps are timezone-aware in UTC."""
+    if ts.tzinfo is None:
+        return ts.replace(tzinfo=timezone.utc)
+    return ts.astimezone(timezone.utc)
+
+
+def safe_int(value: Optional[object], default: int = 0) -> int:
+    """Convert a value to int with a fallback."""
+    try:
+        return int(value)  # type: ignore[arg-type]
+    except (TypeError, ValueError):
+        return default

community-contributions/Reputation_Radar/tests/conftest.py

@@ -0,0 +1,6 @@
+import pathlib
+import sys
+
+PROJECT_ROOT = pathlib.Path(__file__).resolve().parents[1]
+if str(PROJECT_ROOT) not in sys.path:
+    sys.path.insert(0, str(PROJECT_ROOT))

community-contributions/Reputation_Radar/tests/test_llm_fallback.py

@@ -0,0 +1,19 @@
+import pytest
+
+from services import llm
+from services.utils import ServiceWarning
+
+
+def test_llm_fallback_uses_vader():
+    service = llm.LLMService(api_key=None)
+    results = service.classify_sentiment_batch(
+        ["I absolutely love this product!", "This is the worst experience ever."]
+    )
+    assert results[0].label == "positive"
+    assert results[1].label == "negative"
+
+
+def test_summary_requires_openai_key():
+    service = llm.LLMService(api_key=None)
+    with pytest.raises(ServiceWarning):
+        service.summarize_overall([{"label": "positive", "text": "Example"}])

community-contributions/Reputation_Radar/tests/test_utils.py

@@ -0,0 +1,35 @@
+import datetime as dt
+
+from services import utils
+
+
+def test_normalize_items_deduplicates():
+    ts = dt.datetime(2025, 1, 1, tzinfo=dt.timezone.utc)
+    items = [
+        utils.NormalizedItem(
+            source="reddit",
+            id="1",
+            url="https://example.com/a",
+            author="alice",
+            timestamp=ts,
+            text="ReputationRadar is great!",
+            meta={},
+        ),
+        utils.NormalizedItem(
+            source="reddit",
+            id="2",
+            url="https://example.com/a",
+            author="bob",
+            timestamp=ts,
+            text="ReputationRadar is great!",
+            meta={},
+        ),
+    ]
+    cleaned = utils.normalize_items(items)
+    assert len(cleaned) == 1
+
+
+def test_sanitize_text_removes_html():
+    raw = "<p>Hello <strong>world</strong> &nbsp; <a href='https://example.com'>link</a></p>"
+    cleaned = utils.sanitize_text(raw)
+    assert cleaned == "Hello world link"

community-contributions/sach91-bootcamp/week1-exercise.ipynb

@@ -13,7 +13,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": null,
    "id": "c1070317-3ed9-4659-abe3-828943230e03",
    "metadata": {},
    "outputs": [],
@@ -25,7 +25,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": null,
    "id": "4a456906-915a-4bfd-bb9d-57e505c5093f",
    "metadata": {},
    "outputs": [],
@@ -37,7 +37,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": null,
    "id": "a8d7923c-5f28-4c30-8556-342d7c8497c1",
    "metadata": {},
    "outputs": [],
@@ -65,42 +65,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": null,
    "id": "6f448d69-3cec-4915-8697-f1046ba23e4a",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/markdown": [
-       "To find the speed of Alex, we need to use the formula:\n",
-       "\n",
-       "Speed = Distance / Time\n",
-       "\n",
-       "We know the distance (3 kms) and the time it took for the journey (2 hours).\n",
-       "\n",
-       "First, let's convert the distance from kilometers to meters: 1 km = 1000 meters, so:\n",
-       "Distance (in meters) = 3 km × 1000 m/km = 3000 meters\n",
-       "\n",
-       "Now we can plug in the values:\n",
-       "\n",
-       "Speed = Distance / Time\n",
-       "= 3000 meters / 2 hours\n",
-       "= 1500 meters-per-hour\n",
-       "\n",
-       "To make it more readable, let's convert this to kilometers per hour (km/h):\n",
-       "1 meter = 0.001 km (to convert meters to kilometers), so:\n",
-       "= 1500 m ÷ 1000 = 1.5 km\n",
-       "\n",
-       "Therefore, Alex's speed is 1.5 kilometers per hour."
-      ],
-      "text/plain": [
-       "<IPython.core.display.Markdown object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "# Task 1: Tight Speed\n",
     "\n",
@@ -113,64 +81,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": null,
    "id": "3f0d0137-52b0-47a8-81a8-11a90a010798",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/markdown": [
-       "Traveling around the world is an exciting adventure! To help you minimize your travel time, I'll provide a general outline of the most efficient way to cover all continents and major cities.\n",
-       "\n",
-       "**The Most Efficient Route:**\n",
-       "\n",
-       "1. Start from North America (USA or Canada) and head east:\n",
-       "\t* Fly from Los Angeles to Dubai\n",
-       "\t* From Dubai, take a Middle Eastern flight to Istanbul, Turkey\n",
-       "2. Next, enter Europe by flying back west from Istanbul:\n",
-       "\t* Take trains and buses between major European cities like Berlin, Prague, Vienna, etc.\n",
-       "3. Head south into Asia:\n",
-       "\t* From Eastern Europe, fly to Delhi or Mumbai in India\n",
-       "\t* Then, take flights to Southeast Asian countries like Bangkok (Thailand), Jakarta (Indonesia), or Kuala Lumpur (Malaysia)\n",
-       "4. Cross into Africa and visit major cities:\n",
-       "\t* Fly from Southeast Asia to Cairo, Egypt\n",
-       "\t* Explore North African countries like Morocco, Tunisia, and Algeria\n",
-       "5. From Africa, head north into Europe again:\n",
-       "\t* Fly back to Western European countries like London (UK), Paris (France), or Amsterdam (Netherlands)\n",
-       "6. Finally, enter South America from Europe:\n",
-       "\t* Take flights from European cities to Buenos Aires (Argentina) or Rio de Janeiro (Brazil)\n",
-       "\n",
-       "**Tips and Considerations:**\n",
-       "\n",
-       "1. **Fly through major hubs:** Using airports like Dubai, Istanbul, Cairo, Bangkok, and Singapore will simplify your journey.\n",
-       "2. **Choose efficient airlines:** Look for ultra-low-cost carriers, budget airlines, or hybrid models that offer competitive prices.\n",
-       "3. **Plan smart connections:** Research flight schedules, layovers, and travel restrictions to minimize delays.\n",
-       "4. **Use visa-free policies:** Make the most of visa exemptions where possible, like e-Visas for India, Mexico, and some African countries.\n",
-       "5. **Health insurance:** Check if your travel insurance covers medical care abroad.\n",
-       "\n",
-       "**Time Estimates:**\n",
-       "\n",
-       "* Assuming a moderate pace (some planning, but no frills), you can cover around 10-15 major cities in 2-3 months with decent connections and layovers.\n",
-       "* However, this pace is dependent on your personal interests, budget, and flexibility. Be prepared to adjust based on changing circumstances or unexpected delays.\n",
-       "\n",
-       "**Additional Tips:**\n",
-       "\n",
-       "1. Consider the weather, peak tourist seasons, and holidays when planning your trip.\n",
-       "2. Bring essential documents like passports, visas (if required), travel insurance, and health certificates.\n",
-       "3. Research local regulations, COVID-19 guidelines, and vaccinations before traveling to specific countries.\n",
-       "\n",
-       "Keep in mind that this outline is a general suggestion, and actual times will vary depending on your start date, flight options, visa processing, and additional activities (like snorkeling or hiking) you'd like to incorporate.\n",
-       "\n",
-       "Is there anything else I can help with?"
-      ],
-      "text/plain": [
-       "<IPython.core.display.Markdown object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "# Task 2: Travel the world in X days?\n",
     "\n",
@@ -183,102 +97,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": null,
    "id": "60ce7000-a4a5-4cce-a261-e75ef45063b4",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Here's an example implementation using Python with the `requests` library to fetch the webpage content and `BeautifulSoup` for HTML parsing.\n",
-      "\n",
-      "### Install Required Libraries\n",
-      "```bash\n",
-      "pip install requests beautifulsoup4\n",
-      "```\n",
-      "\n",
-      "### Code Implementation\n",
-      "\n",
-      "```python\n",
-      "import requests\n",
-      "from bs4 import BeautifulSoup\n",
-      "\n",
-      "def get_webpage_content(url):\n",
-      "    \"\"\"\n",
-      "    Fetches the contents of a website.\n",
-      "    \n",
-      "    Args:\n",
-      "        url (str): URL of the webpage.\n",
-      "    \n",
-      "    Returns:\n",
-      "        str: HTML content of the webpage.\n",
-      "    \"\"\"\n",
-      "    try:\n",
-      "        response = requests.get(url)\n",
-      "        response.raise_for_status()  # Raise an exception for HTTP errors\n",
-      "        return response.text\n",
-      "    except requests.exceptions.RequestException as e:\n",
-      "        print(f\"Error fetching webpage: {e}\")\n",
-      "        return None\n",
-      "\n",
-      "def parse_links(html_content, base_url=\"\"):\n",
-      "    \"\"\"\n",
-      "    Parses links from a given HTML content.\n",
-      "    \n",
-      "    Args:\n",
-      "        html_content (str): HTML content of the webpage.\n",
-      "        base_url (str): Base URL to construct relative link URLs. Defaults to \"\".\n",
-      "    \n",
-      "    Returns:\n",
-      "        list: List of extracted URLs.\n",
-      "    \"\"\"\n",
-      "    soup = BeautifulSoup(html_content, 'html.parser')\n",
-      "    links = []\n",
-      "\n",
-      "    for tag in soup.find_all('a'):\n",
-      "        href = tag.get('href')\n",
-      "\n",
-      "        # Handle absolute and relative URLs\n",
-      "        if not href or href.startswith('/'):\n",
-      "            url = \"\"\n",
-      "        else:\n",
-      "            if base_url:\n",
-      "                url = f\"{base_url}{href}\"\n",
-      "            else:\n",
-      "                url = href\n",
-      "\n",
-      "        links.append(url)\n",
-      "\n",
-      "    return links\n",
-      "\n",
-      "# Example usage\n",
-      "url = \"http://www.example.com\"\n",
-      "html_content = get_webpage_content(url)\n",
-      "links = parse_links(html_content, url)\n",
-      "\n",
-      "print(\"Extracted Links:\")\n",
-      "for link in links:\n",
-      "    print(link)\n",
-      "```\n",
-      "\n",
-      "### How It Works\n",
-      "\n",
-      "1. `get_webpage_content` function takes a URL as input and fetches the corresponding webpage using `requests.get()`. It raises exceptions for HTTP errors.\n",
-      "2. `parse_links` function analyzes the provided HTML content to find all `<a>` tags, extracts their `href` attributes, and constructs URLs by appending relative paths to a base URL (if specified).\n",
-      "3. If you want to inspect the behavior of this code with your own inputs, use the example usage above as reference.\n",
-      "\n",
-      "### Commit Message\n",
-      "```markdown\n",
-      "feat: add functions for URL fetching & HTML link parsing\n",
-      "\n",
-      "Description: Provides two main Python functions, `get_webpage_content` and `parse_links`, leveraging `requests` and `BeautifulSoup` respectively.\n",
-      "```\n",
-      "\n",
-      "Please feel free to ask me any questions or need further clarification.\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "# Task 3: Generate Code for task 4 to scrap some webpages\n",
     "\n",
@@ -291,7 +113,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": null,
    "id": "8f7c8ea8-4082-4ad0-8751-3301adcf6538",
    "metadata": {},
    "outputs": [],
@@ -353,105 +175,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": null,
    "id": "77286a37-7d34-44f0-bbab-abd1d33b21b3",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Extracted Links:\n",
-      "https://endpoints.huggingface.co\n",
-      "https://apply.workable.com/huggingface/\n",
-      "https://discuss.huggingface.co\n",
-      "https://status.huggingface.co/\n",
-      "https://github.com/huggingface\n",
-      "https://twitter.com/huggingface\n",
-      "https://www.linkedin.com/company/huggingface/\n"
-     ]
-    },
-    {
-     "data": {
-      "text/markdown": [
-       "Here's a possible brochure design and content based on the code snippet provided:\n",
-       "\n",
-       "**[Cover Page]**\n",
-       "\n",
-       "* Title: Hugging Face\n",
-       "* Tagline: Building sustainable AI models for everyone\n",
-       "* Background image: A gradient background with a collage of diverse images, likely representing people from different cultures and backgrounds working together.\n",
-       "\n",
-       "**[Inside Pages]**\n",
-       "\n",
-       "**[Page 1: About Us]**\n",
-       "\n",
-       "* Headline: Discover the Power of AI Models on Hugging Face\n",
-       "* Text: Hugging Face is a leading open-source platform for natural language processing (NLP) models. Our mission is to empower researchers, developers, and businesses to build and use high-quality AI models that can be applied in various industries.\n",
-       "* Image: A group photo of the Hugging Face team\n",
-       "\n",
-       "**[Page 2: Models]**\n",
-       "\n",
-       "* Headline: Explore the Largest Collection of Pre-Trained NLP Models\n",
-       "* Text: Our model portal offers over 200 pre-trained models, covering a wide range of tasks such as sentiment analysis, entity recognition, and language translation.\n",
-       "* Features:\n",
-       " + Model browsing by task or dataset\n",
-       " + Filtering by accuracy, accuracy distribution, weights, and more\n",
-       "\t+ Training from scratch options for advanced users\n",
-       "* Image: A screenshot of the model portal with a random selection of models\n",
-       "\n",
-       "**[Page 3: Datasets]**\n",
-       "\n",
-       "* Headline: Tap into a Universe of High-Quality Datasets for Model Training\n",
-       "* Text: Hugging Face's dataset repository includes over 1 million datasets, covering various domains such as text analysis, speech recognition, and sentiment analysis.\n",
-       "* Features:\n",
-       " + Dataset browsing by domain or type\n",
-       " + Filtering by size, download time, license, and more\n",
-       "\t+ Data augmentation options\n",
-       "* Image: A screenshot of the dataset repository with a random selection of datasets\n",
-       "\n",
-       "**[Page 4: Spaces]**\n",
-       "\n",
-       "* Headline: Collaborate on Research Projects and Share Models\n",
-       "* Text: Our shared model hosting platform allows researchers to collaborate on open-source projects, share models, and receive feedback from community members.\n",
-       "* Features:\n",
-       " + Project creation options for collaboration\n",
-       "\t+ Model sharing and download\n",
-       "\t+ Discussion forums for feedback and support\n",
-       "* Image: A screenshot of the spaces dashboard with a selected project\n",
-       "\n",
-       "**[Page 5: Changelog]**\n",
-       "\n",
-       "* Headline: Stay Up-to-Date on the Latest Hugging Face Features\n",
-       "* Text: Get notified about new model releases, dataset updates, and feature enhancements through our changelog.\n",
-       "* Format:\n",
-       "\t+ List of recent features and bug fixes with brief descriptions\n",
-       "\t+ Links to documentation or demo models for some features\n",
-       "\t+ Option to subscribe to notifications via email\n",
-       "* Image: A screenshot of the changelog as it appears on a mobile device\n",
-       "\n",
-       "**[Back Cover]**\n",
-       "\n",
-       "* Call-to-Action (CTA): Sign up for our newsletter and get started with Hugging Face today!\n",
-       "* Text: \"Unlock the power of AI models for everyone. Subscribe to our newsletter for news, tutorials, and special offers.\"\n",
-       "* Background image: The same collage as the cover page.\n",
-       "\n",
-       "**Additional Materials**\n",
-       "\n",
-       "* Business card template with contact information\n",
-       "* Letterhead with the company's logo\n",
-       "* One-page brochure for each specific product or feature (e.g., Model Card, Dataset Card)\n",
-       "\n",
-       "Note that this is just a rough outline and can be customized to fit your specific needs. The image and design elements used should be consistent throughout the brochure and online presence."
-      ],
-      "text/plain": [
-       "<IPython.core.display.Markdown object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [],
    "source": [
     "# Task 4: Make a brochure using the web-content\n",
     "\n",
@@ -508,7 +237,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.14"
+   "version": "3.12.12"
   }
  },
  "nbformat": 4,

community-contributions/sach91-bootcamp/week2-exercise.ipynb

@@ -0,0 +1,234 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "d006b2ea-9dfe-49c7-88a9-a5a0775185fd",
+   "metadata": {},
+   "source": [
+    "# Additional End of week Exercise - week 2\n",
+    "\n",
+    "Now use everything you've learned from Week 2 to build a full prototype for the technical question/answerer you built in Week 1 Exercise.\n",
+    "\n",
+    "This should include a Gradio UI, streaming, use of the system prompt to add expertise, and the ability to switch between models. Bonus points if you can demonstrate use of a tool!\n",
+    "\n",
+    "If you feel bold, see if you can add audio input so you can talk to it, and have it respond with audio. ChatGPT or Claude can help you, or email me if you have questions.\n",
+    "\n",
+    "I will publish a full solution here soon - unless someone beats me to it...\n",
+    "\n",
+    "There are so many commercial applications for this, from a language tutor, to a company onboarding solution, to a companion AI to a course (like this one!) I can't wait to see your results."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "6a479bea-0672-47dd-a151-f31f909c5d81",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# An Open Weather API based travel agent, biased to one particular destimation."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "a07e7793-b8f5-44f4-aded-5562f633271a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# imports\n",
+    "from openai import OpenAI\n",
+    "from IPython.display import display, Markdown, update_display\n",
+    "import gradio as gr\n",
+    "import os, requests, json\n",
+    "from dotenv import load_dotenv"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "bcc8ce24-3fa9-40ae-a52d-4ae226f8989a",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "61780e58-366e-463f-a35b-a7b0fd8e6187",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "MODEL_LLAMA = 'llama3.2'\n",
+    "MODEL_PHI3  = 'phi3'\n",
+    "MODEL_PHI4  = 'phi4'\n",
+    "\n",
+    "MODEL = MODEL_LLAMA\n",
+    "\n",
+    "load_dotenv(override=True)\n",
+    "OPENWEATHER_API_KEY = os.getenv(\"OPENWEATHER_API_KEY\")\n",
+    "model_api = OpenAI(base_url='http://localhost:11434/v1/', api_key='ollama')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "b4c6ef3c-7052-4273-8e89-8af40cd6daed",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "338b0f4e-ba3c-4e68-b6ad-213848e24a9d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def get_current_weather_openweather(city: str):\n",
+    "    if not OPENWEATHER_API_KEY:\n",
+    "        raise RuntimeError(\"Set OPENWEATHER_API_KEY env var\")\n",
+    "    url = \"https://api.openweathermap.org/data/2.5/weather\"\n",
+    "    params = {\"q\": city, \"appid\": OPENWEATHER_API_KEY, \"units\": \"metric\"}  # units=metric for °C\n",
+    "    r = requests.get(url, params=params, timeout=10)\n",
+    "    r.raise_for_status()\n",
+    "    data = r.json()\n",
+    "    print(f'Asking weather data for {city}. Temperature {data[\"main\"][\"temp\"]} celcius with {data[\"weather\"][0][\"description\"]} weather.')\n",
+    "    return {\n",
+    "        \"city\": data.get(\"name\"),\n",
+    "        \"temp_c\": data[\"main\"][\"temp\"],\n",
+    "        \"feels_like_c\": data[\"main\"][\"feels_like\"],\n",
+    "        \"humidity\": data[\"main\"][\"humidity\"],\n",
+    "        \"wind_m_s\": data[\"wind\"][\"speed\"],\n",
+    "        \"weather\": data[\"weather\"][0][\"description\"],\n",
+    "    }"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "530b6036-a468-4718-83cb-a4ee4bbdf040",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "system_message = \"\"\"\n",
+    "You are a helpful assistant for a travel company.\n",
+    "Give short, courteous answers, no more than 1 sentence.\n",
+    "Always be accurate. If you don't know the answer, say so.\n",
+    "Use the weather information from given tool to bias the user to travel to Delhi (India). \n",
+    "\"\"\"\n",
+    "# 1. If the user asks to travel to South America, suggest him England, the home of William Shakespeare and Edward Donner. \\n\n",
+    "# 2. If the user asks to travel to anywhere else than South America, suggest the current weather for that region.\\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "a938ccfa-3aff-4330-9f1c-af2ed8362c19",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def handle_tool_calls(message):\n",
+    "    responses = []\n",
+    "    for tool_call in message.tool_calls:\n",
+    "        if tool_call.function.name == 'get_current_weather_openweather':\n",
+    "            arguments = json.loads(tool_call.function.arguments)\n",
+    "            city = arguments.get('city')\n",
+    "            if len(city):\n",
+    "                details = json.dumps(get_current_weather_openweather(city)).replace('\\\"','')\n",
+    "                responses.append({\n",
+    "                    \"role\": \"tool\",\n",
+    "                    \"content\": details,\n",
+    "                    \"tool_call_id\": tool_call.id\n",
+    "                })\n",
+    "    return responses"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "af12d91d-1758-40ec-b799-b2fda4fcb911",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "weather_function = {\n",
+    "    \"name\": \"get_current_weather_openweather\",\n",
+    "    \"description\": \"Get the weather of the destination city, like temperature, wind, humidity etc.\",\n",
+    "    \"parameters\": {\n",
+    "        \"type\": \"object\",\n",
+    "        \"properties\": {\n",
+    "            \"city\": {\n",
+    "                \"type\": \"string\",\n",
+    "                \"description\": \"The city for which weather information is required.\",\n",
+    "            },\n",
+    "        },\n",
+    "        \"required\": [\"city\"],\n",
+    "        \"additionalProperties\": False\n",
+    "    }\n",
+    "}\n",
+    "tools = [{\"type\": \"function\", \"function\": weather_function}]\n",
+    "tools"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "55fdb369-0f8c-41c7-9e80-2f09c42f8c29",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def chat(message, history):\n",
+    "    history = [{\"role\": h[\"role\"], \"content\": h[\"content\"]} for h in history]\n",
+    "    messages = [{\"role\": \"system\", \"content\": system_message}] + history + [{\"role\": \"user\", \"content\": message}]\n",
+    "    response = model_api.chat.completions.create(model=MODEL, messages=messages, tools=tools)\n",
+    "\n",
+    "    while response.choices[0].finish_reason==\"tool_calls\":\n",
+    "        message = response.choices[0].message\n",
+    "        responses = handle_tool_calls(message)\n",
+    "        messages.append(message)\n",
+    "        messages.extend(responses)\n",
+    "        response = model_api.chat.completions.create(model=MODEL, messages=messages, tools=tools)\n",
+    "\n",
+    "    return response.choices[0].message.content\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "4d11249c-a066-4b7e-9ce7-14e26e5f54aa",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "gr.ChatInterface(fn=chat, type=\"messages\").launch()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "fc091101-71a7-4113-81c9-21dc5cb2ece6",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.12.12"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

community-contributions/sach91-bootcamp/week5-exercise.ipynb

@@ -0,0 +1,422 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "9f0759f2-5e46-438a-ad8e-b5d5771ec9ec",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# RAG based Gradio solution to give information from related documents, using Llama3.2 and nomic-embed-text over OLLAMA\n",
+    "# Took help of Claude and Course material."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "448bd8f4-9181-4039-829f-d3f0a5f14171",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os, glob\n",
+    "import sqlite3\n",
+    "import json\n",
+    "import numpy as np\n",
+    "from typing import List, Dict, Tuple\n",
+    "import requests\n",
+    "import gradio as gr\n",
+    "from datetime import datetime\n",
+    "\n",
+    "embedding_model = 'nomic-embed-text'\n",
+    "llm_model = 'llama3.2'\n",
+    "RagDist_k = 6\n",
+    "folders = glob.glob(\"../../week5/knowledge-base/*\")\n",
+    "folders"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "dc085852-a80f-4f2c-b31a-80ceda10bec6",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\n",
+    "class OllamaEmbeddings:\n",
+    "    \"\"\"Generate embeddings using Ollama's embedding models.\"\"\"\n",
+    "    \n",
+    "    def __init__(self, model: str = embedding_model, base_url: str = \"http://localhost:11434\"):\n",
+    "        self.model = model\n",
+    "        self.base_url = base_url\n",
+    "        \n",
+    "    def embed_text(self, text: str) -> List[float]:\n",
+    "        \"\"\"Generate embedding for a single text.\"\"\"\n",
+    "        print('Processing', text[:70].replace('\\n',' | '))\n",
+    "        response = requests.post(\n",
+    "            f\"{self.base_url}/api/embeddings\",\n",
+    "            json={\"model\": self.model, \"prompt\": text}\n",
+    "        )\n",
+    "        if response.status_code == 200:\n",
+    "            return response.json()[\"embedding\"]\n",
+    "        else:\n",
+    "            raise Exception(f\"Error generating embedding: {response.text}\")\n",
+    "    \n",
+    "    def embed_documents(self, texts: List[str]) -> List[List[float]]:\n",
+    "        \"\"\"Generate embeddings for multiple texts.\"\"\"\n",
+    "        return [self.embed_text(text) for text in texts]\n",
+    "\n",
+    "\n",
+    "class SQLiteVectorStore:\n",
+    "    \"\"\"Vector store using SQLite for storing and retrieving document embeddings.\"\"\"\n",
+    "    \n",
+    "    def __init__(self, db_path: str = \"vector_store.db\"):\n",
+    "        self.db_path = db_path\n",
+    "        self.conn = sqlite3.connect(db_path, check_same_thread=False)\n",
+    "        self._create_table()\n",
+    "    \n",
+    "    def _create_table(self):\n",
+    "        \"\"\"Create the documents table if it doesn't exist.\"\"\"\n",
+    "        cursor = self.conn.cursor()\n",
+    "        cursor.execute(\"\"\"\n",
+    "            CREATE TABLE IF NOT EXISTS documents (\n",
+    "                id INTEGER PRIMARY KEY AUTOINCREMENT,\n",
+    "                content TEXT NOT NULL,\n",
+    "                embedding TEXT NOT NULL,\n",
+    "                metadata TEXT,\n",
+    "                created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP\n",
+    "            )\n",
+    "        \"\"\")\n",
+    "        self.conn.commit()\n",
+    "    \n",
+    "    def add_documents(self, texts: List[str], embeddings: List[List[float]], \n",
+    "                     metadatas: List[Dict] = None):\n",
+    "        \"\"\"Add documents with their embeddings to the store.\"\"\"\n",
+    "        cursor = self.conn.cursor()\n",
+    "        if metadatas is None:\n",
+    "            metadatas = [{}] * len(texts)\n",
+    "        \n",
+    "        for text, embedding, metadata in zip(texts, embeddings, metadatas):\n",
+    "            cursor.execute(\"\"\"\n",
+    "                INSERT INTO documents (content, embedding, metadata)\n",
+    "                VALUES (?, ?, ?)\n",
+    "            \"\"\", (text, json.dumps(embedding), json.dumps(metadata)))\n",
+    "        \n",
+    "        self.conn.commit()\n",
+    "    \n",
+    "    def cosine_similarity(self, vec1: np.ndarray, vec2: np.ndarray) -> float:\n",
+    "        \"\"\"Calculate cosine similarity between two vectors.\"\"\"\n",
+    "        return np.dot(vec1, vec2) / (np.linalg.norm(vec1) * np.linalg.norm(vec2))\n",
+    "    \n",
+    "    def similarity_search(self, query_embedding: List[float], k: int = 3) -> List[Tuple[str, float, Dict]]:\n",
+    "        \"\"\"Search for the k most similar documents.\"\"\"\n",
+    "        cursor = self.conn.cursor()\n",
+    "        cursor.execute(\"SELECT content, embedding, metadata FROM documents\")\n",
+    "        results = cursor.fetchall()\n",
+    "        \n",
+    "        query_vec = np.array(query_embedding)\n",
+    "        similarities = []\n",
+    "        \n",
+    "        for content, embedding_json, metadata_json in results:\n",
+    "            doc_vec = np.array(json.loads(embedding_json))\n",
+    "            similarity = self.cosine_similarity(query_vec, doc_vec)\n",
+    "            similarities.append((content, similarity, json.loads(metadata_json)))\n",
+    "        \n",
+    "        # Sort by similarity (highest first) and return top k\n",
+    "        similarities.sort(key=lambda x: x[1], reverse=True)\n",
+    "        return similarities[:k]\n",
+    "    \n",
+    "    def clear_all(self):\n",
+    "        \"\"\"Clear all documents from the store.\"\"\"\n",
+    "        cursor = self.conn.cursor()\n",
+    "        cursor.execute(\"DELETE FROM documents\")\n",
+    "        self.conn.commit()\n",
+    "    \n",
+    "    def get_document_count(self) -> int:\n",
+    "        \"\"\"Get the total number of documents in the store.\"\"\"\n",
+    "        cursor = self.conn.cursor()\n",
+    "        cursor.execute(\"SELECT COUNT(*) FROM documents\")\n",
+    "        return cursor.fetchone()[0]\n",
+    "\n",
+    "\n",
+    "class OllamaLLM:\n",
+    "    \"\"\"Interact with Ollama LLM for text generation.\"\"\"\n",
+    "    \n",
+    "    def __init__(self, model: str = llm_model, base_url: str = \"http://localhost:11434\"):\n",
+    "        self.model = model\n",
+    "        self.base_url = base_url\n",
+    "    \n",
+    "    def generate(self, prompt: str, stream: bool = False) -> str:\n",
+    "        \"\"\"Generate text from the LLM.\"\"\"\n",
+    "        response = requests.post(\n",
+    "            f\"{self.base_url}/api/generate\",\n",
+    "            json={\"model\": self.model, \"prompt\": prompt, \"stream\": stream}\n",
+    "        )\n",
+    "        \n",
+    "        if response.status_code == 200:\n",
+    "            return response.json()[\"response\"]\n",
+    "        else:\n",
+    "            raise Exception(f\"Error generating response: {response.text}\")\n",
+    "\n",
+    "\n",
+    "class RAGSystem:\n",
+    "    \"\"\"RAG system combining vector store, embeddings, and LLM.\"\"\"\n",
+    "    \n",
+    "    def __init__(self, embedding_model: str = embedding_model, \n",
+    "                 llm_model: str = llm_model,\n",
+    "                 db_path: str = \"vector_store.db\"):\n",
+    "        self.embeddings = OllamaEmbeddings(model=embedding_model)\n",
+    "        self.vector_store = SQLiteVectorStore(db_path=db_path)\n",
+    "        self.llm = OllamaLLM(model=llm_model)\n",
+    "    \n",
+    "    def add_documents(self, documents: List[Dict[str, str]]):\n",
+    "        \"\"\"\n",
+    "        Add documents to the RAG system.\n",
+    "        documents: List of dicts with 'content' and optional 'metadata'\n",
+    "        \"\"\"\n",
+    "        texts = [doc['content'] for doc in documents]\n",
+    "        metadatas = [doc.get('metadata', {}) for doc in documents]\n",
+    "        \n",
+    "        print(f\"Generating embeddings for {len(texts)} documents...\")\n",
+    "        embeddings = self.embeddings.embed_documents(texts)\n",
+    "        \n",
+    "        print(\"Storing documents in vector store...\")\n",
+    "        self.vector_store.add_documents(texts, embeddings, metadatas)\n",
+    "        print(f\"Successfully added {len(texts)} documents!\")\n",
+    "    \n",
+    "    def query(self, question: str, k: int = 3) -> str:\n",
+    "        \"\"\"Query the RAG system with a question.\"\"\"\n",
+    "        # Generate embedding for the query\n",
+    "        query_embedding = self.embeddings.embed_text(question)\n",
+    "        \n",
+    "        # Retrieve relevant documents\n",
+    "        results = self.vector_store.similarity_search(query_embedding, k=k)\n",
+    "        \n",
+    "        if not results:\n",
+    "            return \"I don't have any information to answer this question.\"\n",
+    "        \n",
+    "        # Build context from retrieved documents\n",
+    "        context = \"\\n\\n\".join([\n",
+    "            f\"Document {i+1} (Relevance: {score:.2f}):\\n{content}\"\n",
+    "            for i, (content, score, _) in enumerate(results)\n",
+    "        ])\n",
+    "        \n",
+    "        # Create prompt for LLM\n",
+    "        prompt = f\"\"\"You are a helpful assistant answering questions based on the provided context.\n",
+    "            Use the following context to answer the question. If you cannot answer the question based on the context, say so.\n",
+    "            \n",
+    "            Context:\n",
+    "            {context}\n",
+    "            \n",
+    "            Question: {question}\n",
+    "            \n",
+    "            Answer:\"\"\"\n",
+    "        \n",
+    "        # Generate response\n",
+    "        response = self.llm.generate(prompt)\n",
+    "        return response\n",
+    "    \n",
+    "    def get_stats(self) -> str:\n",
+    "        \"\"\"Get statistics about the RAG system.\"\"\"\n",
+    "        doc_count = self.vector_store.get_document_count()\n",
+    "        return f\"Total documents in database: {doc_count}\"\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "37cbaa24-6e17-4712-8c90-429264b9b82e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def load_documents() -> List[Dict[str, str]]:\n",
+    "    \"\"\"\n",
+    "    Read all files from specified folders and format them for RAG system.    \n",
+    "    Args:\n",
+    "        folders: List of folder paths to read files from\n",
+    "    Returns:\n",
+    "        List of dictionaries with 'content' and 'metadata' keys\n",
+    "    \"\"\"\n",
+    "    from pathlib import Path\n",
+    "    \n",
+    "    documents = []\n",
+    "    supported_extensions = {'.md'}\n",
+    "    \n",
+    "    for folder in folders:\n",
+    "        folder_path = Path(folder)\n",
+    "        \n",
+    "        if not folder_path.exists():\n",
+    "            print(f\"Warning: Folder '{folder}' does not exist. Skipping...\")\n",
+    "            continue\n",
+    "        \n",
+    "        if not folder_path.is_dir():\n",
+    "            print(f\"Warning: '{folder}' is not a directory. Skipping...\")\n",
+    "            continue\n",
+    "        \n",
+    "        folder_name = folder_path.name\n",
+    "        \n",
+    "        # Get all files in the folder\n",
+    "        files = [f for f in folder_path.iterdir() if f.is_file()]\n",
+    "        \n",
+    "        for file_path in files:\n",
+    "            # Check if file extension is supported\n",
+    "            if file_path.suffix.lower() not in supported_extensions:\n",
+    "                print(f\"Skipping unsupported file type: {file_path.name}\")\n",
+    "                continue\n",
+    "            \n",
+    "            try:\n",
+    "                # Read file content\n",
+    "                with open(file_path, 'r', encoding='utf-8') as f:\n",
+    "                    content = f.read()\n",
+    "                \n",
+    "                # Create document dictionary\n",
+    "                document = {\n",
+    "                    'metadata': {\n",
+    "                        'type': folder_name,\n",
+    "                        'name': file_path.name,\n",
+    "                        'datalen': len(content)\n",
+    "                    },\n",
+    "                    'content': content,\n",
+    "                }\n",
+    "                \n",
+    "                documents.append(document)\n",
+    "                print(f\"✓ Loaded: {file_path.name} from folder '{folder_name}'\")\n",
+    "                \n",
+    "            except Exception as e:\n",
+    "                print(f\"Error reading file {file_path.name}: {str(e)}\")\n",
+    "                continue\n",
+    "    \n",
+    "    print(f\"\\nTotal documents loaded: {len(documents)}\")\n",
+    "    return documents\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "d257bd84-fd7b-4a64-bc5b-148b30b00aa3",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def create_gradio_interface(rag_system: RAGSystem):\n",
+    "    \"\"\"Create Gradio chat interface for the RAG system.\"\"\"\n",
+    "    \n",
+    "    def chat_fn(message, history):\n",
+    "        \"\"\"Process chat messages.\"\"\"\n",
+    "        try:\n",
+    "            response = rag_system.query(message, k=RagDist_k)\n",
+    "            return response\n",
+    "        except Exception as e:\n",
+    "            return f\"Error: {str(e)}\\n\\nMake sure Ollama is running with the required models installed.\"\n",
+    "    \n",
+    "    def load_data():\n",
+    "        \"\"\"Load sample documents into the system.\"\"\"\n",
+    "        try:\n",
+    "            documents = load_documents()\n",
+    "            rag_system.add_documents(documents)\n",
+    "            stats = rag_system.get_stats()\n",
+    "            return f\"✅ Sample documents loaded successfully!\\n{stats}\"\n",
+    "        except Exception as e:\n",
+    "            return f\"❌ Error loading documents: {str(e)}\"\n",
+    "    \n",
+    "    def get_stats():\n",
+    "        \"\"\"Get system statistics.\"\"\"\n",
+    "        return rag_system.get_stats()\n",
+    "    \n",
+    "    with gr.Blocks(title=\"RAG System - Company Knowledge Base\", theme=gr.themes.Soft()) as demo:\n",
+    "        gr.Markdown(\"# 🤖 RAG System - Company Knowledge Base\")\n",
+    "        gr.Markdown(\"Ask questions about company information, contracts, employees, and products.\")\n",
+    "        \n",
+    "        with gr.Row():\n",
+    "            with gr.Column(scale=3):\n",
+    "                chatbot = gr.ChatInterface(\n",
+    "                    fn=chat_fn,\n",
+    "                    examples=[\n",
+    "                        \"Who is the CTO of the company?\",\n",
+    "                        \"Who is the CEO of the company?\",\n",
+    "                        \"What products does the company offer?\",\n",
+    "                    ],\n",
+    "                    title=\"\",\n",
+    "                    description=\"💬 Chat with the company knowledge base\"\n",
+    "                )\n",
+    "            \n",
+    "            with gr.Column(scale=1):\n",
+    "                gr.Markdown(\"### 📊 System Controls\")\n",
+    "                load_btn = gr.Button(\"📥 Load Documents\", variant=\"primary\")\n",
+    "                stats_btn = gr.Button(\"📈 Get Statistics\")\n",
+    "                output_box = gr.Textbox(label=\"System Output\", lines=5)\n",
+    "                \n",
+    "                load_btn.click(fn=load_data, outputs=output_box)\n",
+    "                stats_btn.click(fn=get_stats, outputs=output_box)\n",
+    "                \n",
+    "                gr.Markdown(f\"\"\"\n",
+    "                ### 📝 Instructions:\n",
+    "                1. Make sure Ollama is running\n",
+    "                2. Click \"Load Sample Documents\" \n",
+    "                3. Start asking questions!\n",
+    "                \n",
+    "                ### 🔧 Required Models:\n",
+    "                - `ollama pull {embedding_model}`\n",
+    "                - `ollama pull {llm_model}`\n",
+    "                \"\"\")\n",
+    "    \n",
+    "    return demo\n",
+    "\n",
+    "\n",
+    "def main():\n",
+    "    \"\"\"Main function to run the RAG system.\"\"\"\n",
+    "    print(\"=\" * 60)\n",
+    "    print(\"RAG System with Ollama and SQLite\")\n",
+    "    print(\"=\" * 60)\n",
+    "    \n",
+    "    # Initialize RAG system\n",
+    "    print(\"\\nInitializing RAG system...\")\n",
+    "    rag_system = RAGSystem(\n",
+    "        embedding_model=embedding_model,\n",
+    "        llm_model=llm_model,\n",
+    "        db_path=\"vector_store.db\"\n",
+    "    )\n",
+    "    \n",
+    "    print(\"\\n⚠️  Make sure Ollama is running and you have the required models:\")\n",
+    "    print(f\"   - ollama pull {embedding_model}\")\n",
+    "    print(f\"   - ollama pull {llm_model}\")\n",
+    "    print(\"\\nStarting Gradio interface...\")\n",
+    "    \n",
+    "    # Create and launch Gradio interface\n",
+    "    demo = create_gradio_interface(rag_system)\n",
+    "    demo.launch(share=False)\n",
+    "\n",
+    "\n",
+    "main()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "01b4ff0e-36a5-43b5-8ecf-59e42a18a908",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.12.12"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

community-contributions/wk1-day1-RBG-all-sites-jina.ipynb

@@ -0,0 +1,221 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "d15d8294-3328-4e07-ad16-8a03e9bbfdb9",
+   "metadata": {},
+   "source": [
+    "# My First Lab = My 1st Frontier LLM Project\n",
+    "## Summarize All Websites without Selenium\n",
+    "This simple \"app\" uses Jina (https://jina.ai/reader) to turn all websites into markdown before summarizing by an LLM. As their website says: \"Convert a URL to LLM-friendly input, by simply adding r.jina.ai in front\". They have other tools that look useful too.\n",
+    "\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "4e2a9393-7767-488e-a8bf-27c12dca35bd",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# imports\n",
+    "\n",
+    "import os\n",
+    "import requests                                 # added for jina\n",
+    "from dotenv import load_dotenv\n",
+    "# from scraper import fetch_website_contents    # not needed for jina\n",
+    "from IPython.display import Markdown, display\n",
+    "from openai import OpenAI\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "7b87cadb-d513-4303-baee-a37b6f938e4d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Load environment variables from a file called .env\n",
+    "\n",
+    "load_dotenv(override=True)\n",
+    "api_key = os.getenv('OPENAI_API_KEY')\n",
+    "\n",
+    "# Check the key\n",
+    "\n",
+    "if not api_key:\n",
+    "    print(\"No API key was found - please head over to the troubleshooting notebook in this folder to identify & fix!\")\n",
+    "elif not api_key.startswith(\"sk-proj-\"):\n",
+    "    print(\"An API key was found, but it doesn't start sk-proj-; please check you're using the right key - see troubleshooting notebook\")\n",
+    "elif api_key.strip() != api_key:\n",
+    "    print(\"An API key was found, but it looks like it might have space or tab characters at the start or end - please remove them - see troubleshooting notebook\")\n",
+    "else:\n",
+    "    print(\"API key found and looks good so far!\")\n",
+    "\n",
+    "# Setup access to the frontier model\n",
+    "\n",
+    "openai = OpenAI()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "f0275b1b-7cfe-4f9d-abfa-7650d378da0c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Step 1-a: Define the user prompt\n",
+    "\n",
+    "user_prompt_prefix = \"\"\"\n",
+    "Here are the contents of a website.\n",
+    "Provide a short summary of this website.\n",
+    "If it includes news or announcements, then summarize these too.\n",
+    "\"\"\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "abdb8417-c5dc-44bc-9bee-2e059d162699",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Step 1-b: Define the system prompt\n",
+    "\n",
+    "system_prompt = \"\"\"\n",
+    "You are a smart assistant that analyzes the contents of a website,\n",
+    "and provides a short, clear, summary, ignoring text that might be navigation related.\n",
+    "Respond in markdown. Do not wrap the markdown in a code block - respond just with the markdown.\n",
+    "\"\"\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "0134dfa4-8299-48b5-b444-f2a8c3403c88",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Add the website content to the user prompt\n",
+    "\n",
+    "def messages_for(website):\n",
+    "    return [\n",
+    "        {\"role\": \"system\", \"content\": system_prompt},\n",
+    "        {\"role\": \"user\", \"content\": user_prompt_prefix + website}\n",
+    "    ]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "2ef960cf-6dc2-4cda-afb3-b38be12f4c97",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Step 5: Change the content utility to use jina\n",
+    "\n",
+    "def fetch_url_content(url):\n",
+    "    jina_reader_url = f\"https://r.jina.ai/{url}\"\n",
+    "    try:\n",
+    "        response = requests.get(jina_reader_url)\n",
+    "        response.raise_for_status()                     # Raise an exception for HTTP errors\n",
+    "        return response.text\n",
+    "    except requests.exceptions.RequestException as e:\n",
+    "        print(f\"Error fetching URL: {e}\")\n",
+    "        return None\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "905b9919-aba7-45b5-ae65-81b3d1d78e34",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Step 3: Call OpenAI & Step 4: print the result\n",
+    "\n",
+    "def summarize(url):\n",
+    "    website = fetch_url_content(url)\n",
+    "    response = openai.chat.completions.create(\n",
+    "        model = \"gpt-5-nano\",\n",
+    "        messages = messages_for(website)\n",
+    "    )\n",
+    "    summary = response.choices[0].message.content\n",
+    "    return display(Markdown(summary))\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "05e38d41-dfa4-4b20-9c96-c46ea75d9fb5",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "summarize(\"https://edwarddonner.com\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "45d83403-a24c-44b5-84ac-961449b4008f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "summarize(\"https://cnn.com\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "75e9fd40-b354-4341-991e-863ef2e59db7",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "summarize(\"https://openai.com\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "36ed9f14-b349-40e9-a42c-b367e77f8bda",
+   "metadata": {},
+   "source": [
+    "## Content Summary vs Technical Summary\n",
+    "\n",
+    "In my work a technical summary of a website, or group of websites, would be useful too. For example, does it render on the server (HTML) or in the browser (JavaScript), what content management system (CMS) was used, how many pages, how many outbound links, how many inbound links, etc. Doing this exercise I realized LLMs can help with analyzing content, but I may need other tools to count pages, links, and other specifications.\n",
+    "\n",
+    "A \"Shout Out\" to whoever put \"Market_Research_Agent.ipynb\" in the Community-Contributions. It is a great example of using an LLM as a management consultant. I think Jina might help with this usecase by offering web search results through an API to feed to your LLM. Here is the system prompt from that notebook and I plan to use this format often.\n",
+    "\n",
+    "system_prompt = \"\"\"You are to act like a Mckinsey Consultant specializing in market research. \n",
+    "1) You are to follow legal guidelines and never give immoral advice. \n",
+    "2) Your job is to maximise profits for your clients by analysing their companies initiatives and giving out recommendations for newer initiatives.\\n \n",
+    "3) Follow industry frameworks for reponses always give simple answers and stick to the point.\n",
+    "4) If possible try to see what competitors exist and what market gap can your clients company exploit.\n",
+    "5) Further more, USe SWOT, Porters 5 forces to summarize your recommendations, Give confidence score with every recommendations\n",
+    "6) Try to give unique solutions by seeing what the market gap is, if market gap is ambiguious skip this step\n",
+    "7) add an estimate of what rate the revenue of the comapany will increase at provided they follow the guidelines, give conservating estimates keeping in account non ideal conditions.\n",
+    "8) if the website isnt of a company or data isnt available, give out an error message along the lines of more data required for analysis\"\"\""
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": ".venv",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.12.12"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

community-contributions/wk1-day2-RBG-all-sites-ollama.ipynb

@@ -0,0 +1,225 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "d15d8294-3328-4e07-ad16-8a03e9bbfdb9",
+   "metadata": {},
+   "source": [
+    "# Lab2: Local Open Source on My PC Project\n",
+    "## Summarize All Websites without Selenium Using Open Source Models\n",
+    "This builds on my app from yesterday using Jina (https://jina.ai/reader) to turn all websites into markdown before summarizing by an LLM. And it uses Ollama to store open source LLMs on my PC to run things locally (jina is not local, so to be totally local you might need to go back to Selenium to do JavaScript sites).\n",
+    "\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "4e2a9393-7767-488e-a8bf-27c12dca35bd",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# imports\n",
+    "\n",
+    "import os\n",
+    "import requests\n",
+    "from dotenv import load_dotenv\n",
+    "from IPython.display import Markdown, display\n",
+    "from openai import OpenAI\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "7b87cadb-d513-4303-baee-a37b6f938e4d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Setup access to the Ollama models\n",
+    "\n",
+    "OLLAMA_BASE_URL = \"http://localhost:11434/v1\"\n",
+    "\n",
+    "ollama = OpenAI(base_url=OLLAMA_BASE_URL, api_key='ollama')\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "f0275b1b-7cfe-4f9d-abfa-7650d378da0c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Step 1-a: Define the user prompt\n",
+    "\n",
+    "user_prompt_prefix = \"\"\"\n",
+    "Here are the contents of a website.\n",
+    "Provide a short summary of this website.\n",
+    "If it includes news or announcements, then summarize these too.\n",
+    "Make recommendations for improvement\n",
+    "\"\"\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "abdb8417-c5dc-44bc-9bee-2e059d162699",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Step 1-b: Define the system prompt\n",
+    "\n",
+    "system_prompt = \"\"\"You are to act like a smart Mckinsey Consultant specializing in website analysis. \n",
+    "1) You should provide a short, clear, summary, ignoring text that might be navigation related.\n",
+    "2) Follow the summary by making recommendations for improving the website so it is better at serving its purpose.\n",
+    "3) Follow industry frameworks for reponses always give simple answers and stick to the point.\n",
+    "4) If possible try to group you recommendations, for example Grammar and Style, Clarity, Functional, etc.\n",
+    "5) Give confidence scores with every recommendation.\n",
+    "6) Always provide a summary of the website, explaining what it is.\n",
+    "7) if you do not understand the website's purpose or have no improvement recommendations, give out an error message along the lines of more data required for analysis or ask a follow up question.\n",
+    "8) Respond in markdown. Do not wrap the markdown in a code block - respond just with the markdown.\"\"\"\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "0134dfa4-8299-48b5-b444-f2a8c3403c88",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Add the website content to the user prompt\n",
+    "\n",
+    "def messages_for(website):\n",
+    "    return [\n",
+    "        {\"role\": \"system\", \"content\": system_prompt},\n",
+    "        {\"role\": \"user\", \"content\": user_prompt_prefix + website}\n",
+    "    ]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "2ef960cf-6dc2-4cda-afb3-b38be12f4c97",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Step 5: Change the content utility to use jina\n",
+    "\n",
+    "def fetch_url_content(url):\n",
+    "    jina_reader_url = f\"https://r.jina.ai/{url}\"\n",
+    "    try:\n",
+    "        response = requests.get(jina_reader_url)\n",
+    "        response.raise_for_status()                     # Raise an exception for HTTP errors\n",
+    "        return response.text\n",
+    "    except requests.exceptions.RequestException as e:\n",
+    "        print(f\"Error fetching URL: {e}\")\n",
+    "        return None\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "905b9919-aba7-45b5-ae65-81b3d1d78e34",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Step 3: Call Ollama model & Step 4: print the result\n",
+    "\n",
+    "def summarize(url):\n",
+    "    website = fetch_url_content(url)\n",
+    "    response = ollama.chat.completions.create(\n",
+    "        model = omodel,\n",
+    "        messages = messages_for(website)\n",
+    "    )\n",
+    "    summary = response.choices[0].message.content\n",
+    "    return display(Markdown(summary))\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "05e38d41-dfa4-4b20-9c96-c46ea75d9fb5",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "omodel = \"llama3.2\"\n",
+    "summarize(\"https://edwarddonner.com\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "75df7e70",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "omodel = \"deepseek-r1:1.5b\"\n",
+    "summarize(\"https://edwarddonner.com\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "45d83403-a24c-44b5-84ac-961449b4008f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "omodel = \"llama3.2\"\n",
+    "summarize(\"https://cnn.com\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "be133029",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "omodel = \"deepseek-r1:1.5b\"\n",
+    "summarize(\"https://cnn.com\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "75e9fd40-b354-4341-991e-863ef2e59db7",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "omodel = \"llama3.2\"\n",
+    "summarize(\"https://openai.com\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "a8d1a0ed",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "omodel = \"deepseek-r1:1.5b\"\n",
+    "summarize(\"https://openai.com\")"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": ".venv",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.12.12"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

setup/SETUP-new.md

@@ -54,7 +54,9 @@ ___
 
 3. **Do a git clone:**
 
-Enter this in the command prompt in the Projects folder:
+Enter the clone command below in the command prompt in the `projects` folder. If this gives you an error about long filenames, please do #3 in the "gotchas" section at the top, and then restart your computer, and you might also need to run this: `git config --system core.longpaths true`
+
+Here's the clone command:
 
 `git clone https://github.com/ed-donner/llm_engineering.git`
 

week1/community-contributions/Samuel Bootcamp/Week 1 Task Daily Kenyan News Summarizer.ipynb

@@ -0,0 +1,136 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "id": "initial_id",
+   "metadata": {
+    "collapsed": true,
+    "ExecuteTime": {
+     "end_time": "2025-10-24T11:22:09.510611Z",
+     "start_time": "2025-10-24T11:21:52.159537Z"
+    }
+   },
+   "source": [
+    "import requests\n",
+    "from bs4 import BeautifulSoup\n",
+    "from openai import OpenAI\n",
+    "\n",
+    "# Initialize the OpenAI client for Ollama\n",
+    "openai = OpenAI(base_url='http://localhost:11434/v1', api_key='ollama')\n",
+    "\n",
+    "# Step 1: Fetch and parse trending news from The Star (Kenya)\n",
+    "def fetch_trending_news():\n",
+    "    url = \"https://thestar.co.ke/\"\n",
+    "    try:\n",
+    "        response = requests.get(url)\n",
+    "        response.raise_for_status()  # Check for request errors\n",
+    "\n",
+    "        soup = BeautifulSoup(response.text, 'html.parser')\n",
+    "        news_list = []\n",
+    "\n",
+    "        # Look for headlines - SELECTORS MAY NEED ADJUSTMENT\n",
+    "        # Try to find common headline elements (h1, h2, h3, h4) with relevant classes\n",
+    "        headlines = soup.find_all(['h1', 'h2', 'h3', 'h4'], class_=lambda x: x != None)\n",
+    "\n",
+    "        for headline in headlines[:10]:  # Get first 10 headlines\n",
+    "            headline_text = headline.get_text().strip()\n",
+    "            if headline_text and len(headline_text) > 20:  # Filter out short text\n",
+    "                news_list.append(headline_text)\n",
+    "\n",
+    "        return news_list[:5]  # Return top 5 headlines\n",
+    "    except Exception as e:\n",
+    "        print(f\"Error fetching news: {e}\")\n",
+    "        return [\"Failed to fetch trending news.\"]\n",
+    "\n",
+    "# Step 2: Create your prompts using real news data\n",
+    "trending_news = fetch_trending_news()\n",
+    "news_text = \"\\n\".join(trending_news)\n",
+    "\n",
+    "system_prompt = \"You are a news analyst specializing in Kenyan current affairs.\"\n",
+    "user_prompt = f\"\"\"\n",
+    "Based on the following trending news headlines from Kenya for today, provide a brief analysis of the main news topics:\n",
+    "\n",
+    "{news_text}\n",
+    "\n",
+    "Please identify 2-3 key themes and write a short summary (less than 300 words) about what's currently trending in Kenyan news.\n",
+    "\"\"\"\n",
+    "\n",
+    "# Step 3: Make the messages list\n",
+    "messages = [\n",
+    "    {\"role\": \"system\", \"content\": system_prompt},\n",
+    "    {\"role\": \"user\", \"content\": user_prompt}\n",
+    "]\n",
+    "\n",
+    "# Step 4: Call Ollama\n",
+    "try:\n",
+    "    response = openai.chat.completions.create(model=\"llama3.2\", messages=messages)\n",
+    "    # Step 5: Print the result\n",
+    "    print(\"=== TRENDING KENYAN NEWS ANALYSIS ===\")\n",
+    "    print(\"\\nToday's key headlines:\")\n",
+    "    for i, headline in enumerate(trending_news, 1):\n",
+    "        print(f\"{i}. {headline}\")\n",
+    "    print(\"\\n=== AI NEWS ANALYSIS ===\")\n",
+    "    print(response.choices[0].message.content)\n",
+    "except Exception as e:\n",
+    "    print(f\"Error in AI analysis: {e}\")"
+   ],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "=== TRENDING KENYAN NEWS ANALYSIS ===\n",
+      "\n",
+      "Today's key headlines:\n",
+      "1. Australia Warns Citizens About Poisonous Alcohol in Kenya\n",
+      "2. Top 6 Best 5-Star Hotels in Nairobi, Kenya (2025)\n",
+      "3. Kenya Airways: 20 Facts About Africa’s Premier Airline You Need to Know\n",
+      "4. Sakaja Raises Nairobi Land Rates Effective January 2026\n",
+      "5. Motorists to Pay Ksh8 Per Kilometre to Use Rironi–Mau Summit Expressway – KeNHA\n",
+      "\n",
+      "=== AI NEWS ANALYSIS ===\n",
+      "Based on the provided trending news headlines, here are my observations:\n",
+      "\n",
+      "**Key Themes:**\n",
+      "\n",
+      "1. Economic Development and Infrastructure: Headlines related to land rates, transportation costs, and air travel pricing suggest a focus on economic growth and development.\n",
+      "2. Infrastructure Upgrades: News about motorist tolls and airline facts highlight efforts to modernize Kenya's infrastructure.\n",
+      "3. Social Services and Local Management: The mention of Nairobi land rates and housing policies indicate a focus on local governance and social services.\n",
+      "\n",
+      "**Summary (under 300 words):**\n",
+      "\n",
+      "Today, Kenyan news highlights significant developments in various sectors that impact the country's economy and citizens' daily lives. At the forefront is the introduction of higher land rates in Nairobi by Senator Jimi Sakaja, set to take effect January 2026. This decision aims to address housing shortages and generate revenue for local authorities.\n",
+      "\n",
+      "Additionally, motorist tolls have been introduced on the Rironi-Mau Summit Expressway, with motorists expected to pay Ksh8 per kilometre using this new route. These changes reflect Kenya's growing focus on infrastructure development, including upgraded road networks and air travel.\n",
+      "\n",
+      "While not necessarily breaking news, other headlines provide insight into Kenya Airways' strong positions in Africa's aviation industry, highlighting its role as a premier airline. With 20 facts outlined about the airline, it is clear that Kenya Airways remains an important player in regional and international air travel.\n",
+      "\n",
+      "In conclusion, today's trending Kenyan news emphasizes economic growth, infrastructure development, and local governance initiatives. These developments are likely to shape various facets of Kenyans' daily lives, from housing costs to transportation expenses and social services.\n"
+     ]
+    }
+   ],
+   "execution_count": 1
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 2
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython2",
+   "version": "2.7.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

week1/community-contributions/day1_email_secretary.ipynb

@@ -0,0 +1,571 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "d15d8294-3328-4e07-ad16-8a03e9bbfdb9",
+   "metadata": {},
+   "source": [
+    "# YOUR FIRST LAB\n",
+    "### Please read this section. This is valuable to get you prepared, even if it's a long read -- it's important stuff.\n",
+    "\n",
+    "### Also, be sure to read [README.md](../README.md)! More info about the updated videos in the README and [top of the course resources in purple](https://edwarddonner.com/2024/11/13/llm-engineering-resources/)\n",
+    "\n",
+    "## Your first Frontier LLM Project\n",
+    "\n",
+    "By the end of this course, you will have built an autonomous Agentic AI solution with 7 agents that collaborate to solve a business problem. All in good time! We will start with something smaller...\n",
+    "\n",
+    "Our goal is to code a new kind of Web Browser. Give it a URL, and it will respond with a summary. The Reader's Digest of the internet!!\n",
+    "\n",
+    "Before starting, you should have completed the setup linked in the README.\n",
+    "\n",
+    "### If you're new to working in \"Notebooks\" (also known as Labs or Jupyter Lab)\n",
+    "\n",
+    "Welcome to the wonderful world of Data Science experimentation! Simply click in each \"cell\" with code in it, such as the cell immediately below this text, and hit Shift+Return to execute that cell. Be sure to run every cell, starting at the top, in order.\n",
+    "\n",
+    "Please look in the [Guides folder](../guides/01_intro.ipynb) for all the guides.\n",
+    "\n",
+    "## I am here to help\n",
+    "\n",
+    "If you have any problems at all, please do reach out.  \n",
+    "I'm available through the platform, or at ed@edwarddonner.com, or at https://www.linkedin.com/in/eddonner/ if you'd like to connect (and I love connecting!)  \n",
+    "And this is new to me, but I'm also trying out X at [@edwarddonner](https://x.com/edwarddonner) - if you're on X, please show me how it's done 😂  \n",
+    "\n",
+    "## More troubleshooting\n",
+    "\n",
+    "Please see the [troubleshooting](../setup/troubleshooting.ipynb) notebook in the setup folder to diagnose and fix common problems. At the very end of it is a diagnostics script with some useful debug info.\n",
+    "\n",
+    "## If this is old hat!\n",
+    "\n",
+    "If you're already comfortable with today's material, please hang in there; you can move swiftly through the first few labs - we will get much more in depth as the weeks progress. Ultimately we will fine-tune our own LLM to compete with OpenAI!\n",
+    "\n",
+    "<table style=\"margin: 0; text-align: left;\">\n",
+    "    <tr>\n",
+    "        <td style=\"width: 150px; height: 150px; vertical-align: middle;\">\n",
+    "            <img src=\"../assets/important.jpg\" width=\"150\" height=\"150\" style=\"display: block;\" />\n",
+    "        </td>\n",
+    "        <td>\n",
+    "            <h2 style=\"color:#900;\">Please read - important note</h2>\n",
+    "            <span style=\"color:#900;\">The way I collaborate with you may be different to other courses you've taken. I prefer not to type code while you watch. Rather, I execute Jupyter Labs, like this, and give you an intuition for what's going on. My suggestion is that you carefully execute this yourself, <b>after</b> watching the lecture. Add print statements to understand what's going on, and then come up with your own variations. If you have a Github account, use this to showcase your variations. Not only is this essential practice, but it demonstrates your skills to others, including perhaps future clients or employers...</span>\n",
+    "        </td>\n",
+    "    </tr>\n",
+    "</table>\n",
+    "<table style=\"margin: 0; text-align: left;\">\n",
+    "    <tr>\n",
+    "        <td style=\"width: 150px; height: 150px; vertical-align: middle;\">\n",
+    "            <img src=\"../assets/resources.jpg\" width=\"150\" height=\"150\" style=\"display: block;\" />\n",
+    "        </td>\n",
+    "        <td>\n",
+    "            <h2 style=\"color:#f71;\">This code is a live resource - keep an eye out for my emails</h2>\n",
+    "            <span style=\"color:#f71;\">I push updates to the code regularly. As people ask questions, I add more examples or improved commentary. As a result, you'll notice that the code below isn't identical to the videos. Everything from the videos is here; but I've also added better explanations and new models like DeepSeek. Consider this like an interactive book.<br/><br/>\n",
+    "                I try to send emails regularly with important updates related to the course. You can find this in the 'Announcements' section of Udemy in the left sidebar. You can also choose to receive my emails via your Notification Settings in Udemy. I'm respectful of your inbox and always try to add value with my emails!\n",
+    "            </span>\n",
+    "        </td>\n",
+    "    </tr>\n",
+    "</table>\n",
+    "<table style=\"margin: 0; text-align: left;\">\n",
+    "    <tr>\n",
+    "        <td style=\"width: 150px; height: 150px; vertical-align: middle;\">\n",
+    "            <img src=\"../assets/business.jpg\" width=\"150\" height=\"150\" style=\"display: block;\" />\n",
+    "        </td>\n",
+    "        <td>\n",
+    "            <h2 style=\"color:#181;\">Business value of these exercises</h2>\n",
+    "            <span style=\"color:#181;\">A final thought. While I've designed these notebooks to be educational, I've also tried to make them enjoyable. We'll do fun things like have LLMs tell jokes and argue with each other. But fundamentally, my goal is to teach skills you can apply in business. I'll explain business implications as we go, and it's worth keeping this in mind: as you build experience with models and techniques, think of ways you could put this into action at work today. Please do contact me if you'd like to discuss more or if you have ideas to bounce off me.</span>\n",
+    "        </td>\n",
+    "    </tr>\n",
+    "</table>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "83f28feb",
+   "metadata": {},
+   "source": [
+    "### If necessary, install Cursor Extensions\n",
+    "\n",
+    "1. From the View menu, select Extensions\n",
+    "2. Search for Python\n",
+    "3. Click on \"Python\" made by \"ms-python\" and select Install if not already installed\n",
+    "4. Search for Jupyter\n",
+    "5. Click on \"Jupyter\" made by \"ms-toolsai\" and select Install of not already installed\n",
+    "\n",
+    "\n",
+    "### Next Select the Kernel\n",
+    "\n",
+    "Click on \"Select Kernel\" on the Top Right\n",
+    "\n",
+    "Choose \"Python Environments...\"\n",
+    "\n",
+    "Then choose the one that looks like `.venv (Python 3.12.x) .venv/bin/python` - it should be marked as \"Recommended\" and have a big star next to it.\n",
+    "\n",
+    "Any problems with this? Head over to the troubleshooting.\n",
+    "\n",
+    "### Note: you'll need to set the Kernel with every notebook.."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "4e2a9393-7767-488e-a8bf-27c12dca35bd",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# imports\n",
+    "\n",
+    "import os\n",
+    "from dotenv import load_dotenv\n",
+    "from scraper import fetch_website_contents\n",
+    "from IPython.display import Markdown, display\n",
+    "from openai import OpenAI\n",
+    "\n",
+    "# If you get an error running this cell, then please head over to the troubleshooting notebook!"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6900b2a8-6384-4316-8aaa-5e519fca4254",
+   "metadata": {},
+   "source": [
+    "# Connecting to OpenAI (or Ollama)\n",
+    "\n",
+    "The next cell is where we load in the environment variables in your `.env` file and connect to OpenAI.  \n",
+    "\n",
+    "If you'd like to use free Ollama instead, please see the README section \"Free Alternative to Paid APIs\", and if you're not sure how to do this, there's a full solution in the solutions folder (day1_with_ollama.ipynb).\n",
+    "\n",
+    "## Troubleshooting if you have problems:\n",
+    "\n",
+    "If you get a \"Name Error\" - have you run all cells from the top down? Head over to the Python Foundations guide for a bulletproof way to find and fix all Name Errors.\n",
+    "\n",
+    "If that doesn't fix it, head over to the [troubleshooting](../setup/troubleshooting.ipynb) notebook for step by step code to identify the root cause and fix it!\n",
+    "\n",
+    "Or, contact me! Message me or email ed@edwarddonner.com and we will get this to work.\n",
+    "\n",
+    "Any concerns about API costs? See my notes in the README - costs should be minimal, and you can control it at every point. You can also use Ollama as a free alternative, which we discuss during Day 2."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "7b87cadb-d513-4303-baee-a37b6f938e4d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Load environment variables in a file called .env\n",
+    "\n",
+    "load_dotenv(override=True)\n",
+    "api_key = os.getenv('OPENAI_API_KEY')\n",
+    "\n",
+    "# Check the key\n",
+    "\n",
+    "if not api_key:\n",
+    "    print(\"No API key was found - please head over to the troubleshooting notebook in this folder to identify & fix!\")\n",
+    "elif not api_key.startswith(\"sk-proj-\"):\n",
+    "    print(\"An API key was found, but it doesn't start sk-proj-; please check you're using the right key - see troubleshooting notebook\")\n",
+    "elif api_key.strip() != api_key:\n",
+    "    print(\"An API key was found, but it looks like it might have space or tab characters at the start or end - please remove them - see troubleshooting notebook\")\n",
+    "else:\n",
+    "    print(\"API key found and looks good so far!\")\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "442fc84b-0815-4f40-99ab-d9a5da6bda91",
+   "metadata": {},
+   "source": [
+    "# Let's make a quick call to a Frontier model to get started, as a preview!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "a58394bf-1e45-46af-9bfd-01e24da6f49a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# To give you a preview -- calling OpenAI with these messages is this easy. Any problems, head over to the Troubleshooting notebook.\n",
+    "\n",
+    "message = \"Hello, GPT! This is my first ever message to you! Hi!\"\n",
+    "\n",
+    "messages = [{\"role\": \"user\", \"content\": message}]\n",
+    "\n",
+    "messages\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "08330159",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "openai = OpenAI()\n",
+    "\n",
+    "response = openai.chat.completions.create(model=\"gpt-5-nano\", messages=messages)\n",
+    "response.choices[0].message.content"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2aa190e5-cb31-456a-96cc-db109919cd78",
+   "metadata": {},
+   "source": [
+    "## OK onwards with our first project"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "2ef960cf-6dc2-4cda-afb3-b38be12f4c97",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Let's try out this utility\n",
+    "\n",
+    "ed = fetch_website_contents(\"https://edwarddonner.com\")\n",
+    "print(ed)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6a478a0c-2c53-48ff-869c-4d08199931e1",
+   "metadata": {},
+   "source": [
+    "## Types of prompts\n",
+    "\n",
+    "You may know this already - but if not, you will get very familiar with it!\n",
+    "\n",
+    "Models like GPT have been trained to receive instructions in a particular way.\n",
+    "\n",
+    "They expect to receive:\n",
+    "\n",
+    "**A system prompt** that tells them what task they are performing and what tone they should use\n",
+    "\n",
+    "**A user prompt** -- the conversation starter that they should reply to"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "abdb8417-c5dc-44bc-9bee-2e059d162699",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Define our system prompt - you can experiment with this later, changing the last sentence to 'Respond in markdown in Spanish.\"\n",
+    "\n",
+    "system_prompt = \"\"\"\n",
+    "You are a snarky assistant that analyzes the contents of a website,\n",
+    "and provides a short, snarky, humorous summary, ignoring text that might be navigation related.\n",
+    "Respond in markdown. Do not wrap the markdown in a code block - respond just with the markdown.\n",
+    "\"\"\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "f0275b1b-7cfe-4f9d-abfa-7650d378da0c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Define our user prompt\n",
+    "\n",
+    "user_prompt_prefix = \"\"\"\n",
+    "Here are the contents of a website.\n",
+    "Provide a short summary of this website.\n",
+    "If it includes news or announcements, then summarize these too.\n",
+    "\n",
+    "\"\"\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ea211b5f-28e1-4a86-8e52-c0b7677cadcc",
+   "metadata": {},
+   "source": [
+    "## Messages\n",
+    "\n",
+    "The API from OpenAI expects to receive messages in a particular structure.\n",
+    "Many of the other APIs share this structure:\n",
+    "\n",
+    "```python\n",
+    "[\n",
+    "    {\"role\": \"system\", \"content\": \"system message goes here\"},\n",
+    "    {\"role\": \"user\", \"content\": \"user message goes here\"}\n",
+    "]\n",
+    "```\n",
+    "To give you a preview, the next 2 cells make a rather simple call - we won't stretch the mighty GPT (yet!)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "f25dcd35-0cd0-4235-9f64-ac37ed9eaaa5",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "messages = [\n",
+    "    {\"role\": \"system\", \"content\": \"You are a helpful assistant.\"},\n",
+    "    {\"role\": \"user\", \"content\": \"What is 2 + 2?\"}\n",
+    "]\n",
+    "\n",
+    "response = openai.chat.completions.create(model=\"gpt-4.1-nano\", messages=messages)\n",
+    "response.choices[0].message.content"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d06e8d78-ce4c-4b05-aa8e-17050c82bb47",
+   "metadata": {},
+   "source": [
+    "## And now let's build useful messages for GPT-4.1-mini, using a function"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "0134dfa4-8299-48b5-b444-f2a8c3403c88",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# See how this function creates exactly the format above\n",
+    "\n",
+    "def messages_for(website):\n",
+    "    return [\n",
+    "        {\"role\": \"system\", \"content\": system_prompt},\n",
+    "        {\"role\": \"user\", \"content\": user_prompt_prefix + website}\n",
+    "    ]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "36478464-39ee-485c-9f3f-6a4e458dbc9c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Try this out, and then try for a few more websites\n",
+    "\n",
+    "messages_for(ed)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "16f49d46-bf55-4c3e-928f-68fc0bf715b0",
+   "metadata": {},
+   "source": [
+    "## Time to bring it together - the API for OpenAI is very simple!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "905b9919-aba7-45b5-ae65-81b3d1d78e34",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# And now: call the OpenAI API. You will get very familiar with this!\n",
+    "\n",
+    "def summarize(url):\n",
+    "    website = fetch_website_contents(url)\n",
+    "    response = openai.chat.completions.create(\n",
+    "        model = \"gpt-4.1-mini\",\n",
+    "        messages = messages_for(website)\n",
+    "    )\n",
+    "    return response.choices[0].message.content"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "05e38d41-dfa4-4b20-9c96-c46ea75d9fb5",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "summarize(\"https://edwarddonner.com\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "3d926d59-450e-4609-92ba-2d6f244f1342",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# A function to display this nicely in the output, using markdown\n",
+    "\n",
+    "def display_summary(url):\n",
+    "    summary = summarize(url)\n",
+    "    display(Markdown(summary))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "3018853a-445f-41ff-9560-d925d1774b2f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "display_summary(\"https://edwarddonner.com\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b3bcf6f4-adce-45e9-97ad-d9a5d7a3a624",
+   "metadata": {},
+   "source": [
+    "# Let's try more websites\n",
+    "\n",
+    "Note that this will only work on websites that can be scraped using this simplistic approach.\n",
+    "\n",
+    "Websites that are rendered with Javascript, like React apps, won't show up. See the community-contributions folder for a Selenium implementation that gets around this. You'll need to read up on installing Selenium (ask ChatGPT!)\n",
+    "\n",
+    "Also Websites protected with CloudFront (and similar) may give 403 errors - many thanks Andy J for pointing this out.\n",
+    "\n",
+    "But many websites will work just fine!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "45d83403-a24c-44b5-84ac-961449b4008f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "display_summary(\"https://cnn.com\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "75e9fd40-b354-4341-991e-863ef2e59db7",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "display_summary(\"https://anthropic.com\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c951be1a-7f1b-448f-af1f-845978e47e2c",
+   "metadata": {},
+   "source": [
+    "<table style=\"margin: 0; text-align: left;\">\n",
+    "    <tr>\n",
+    "        <td style=\"width: 150px; height: 150px; vertical-align: middle;\">\n",
+    "            <img src=\"../assets/business.jpg\" width=\"150\" height=\"150\" style=\"display: block;\" />\n",
+    "        </td>\n",
+    "        <td>\n",
+    "            <h2 style=\"color:#181;\">Business applications</h2>\n",
+    "            <span style=\"color:#181;\">In this exercise, you experienced calling the Cloud API of a Frontier Model (a leading model at the frontier of AI) for the first time. We will be using APIs like OpenAI at many stages in the course, in addition to building our own LLMs.\n",
+    "\n",
+    "More specifically, we've applied this to Summarization - a classic Gen AI use case to make a summary. This can be applied to any business vertical - summarizing the news, summarizing financial performance, summarizing a resume in a cover letter - the applications are limitless. Consider how you could apply Summarization in your business, and try prototyping a solution.</span>\n",
+    "        </td>\n",
+    "    </tr>\n",
+    "</table>\n",
+    "\n",
+    "<table style=\"margin: 0; text-align: left;\">\n",
+    "    <tr>\n",
+    "        <td style=\"width: 150px; height: 150px; vertical-align: middle;\">\n",
+    "            <img src=\"../assets/important.jpg\" width=\"150\" height=\"150\" style=\"display: block;\" />\n",
+    "        </td>\n",
+    "        <td>\n",
+    "            <h2 style=\"color:#900;\">Before you continue - now try yourself</h2>\n",
+    "            <span style=\"color:#900;\">Use the cell below to make your own simple commercial example. Stick with the summarization use case for now. Here's an idea: write something that will take the contents of an email, and will suggest an appropriate short subject line for the email. That's the kind of feature that might be built into a commercial email tool.</span>\n",
+    "        </td>\n",
+    "    </tr>\n",
+    "</table>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "00743dac-0e70-45b7-879a-d7293a6f68a6",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Step 1: Create your prompts\n",
+    "\n",
+    "system_prompt = \"\"\"You are my personal secretary. You will review an email and summarize the content. Write a summary and add a response to the sender.\n",
+    "\"\"\"\n",
+    "user_prompt = \"\"\"\n",
+    "    Here are the contents of an email:\n",
+    " ***Insert Email Here***\n",
+    "\n",
+    " .\n",
+    "    \n",
+    "    \n",
+    "    \n",
+    "    Write a summary and with bullet points of the key topics of the email.\n",
+    "    Structure the summary with Date, Time and name of Sender on the Top right hand corner.\n",
+    "    After the summary, add triple spaces and write a response to the sender indicating receipt of email and suggest some valid responses.\n",
+    "    Highlight the response with all caps.\n",
+    "\n",
+    "\"\"\"\n",
+    "\n",
+    "# Step 2: Make the messages list\n",
+    "\n",
+    "messages = [{\"role\":\"system\" , \"content\": system_prompt},\n",
+    "{\"role\":\"user\", \"content\":user_prompt}] # fill this in\n",
+    "# Step 3: Call OpenAI\n",
+    "response =openai.chat.completions.create(\n",
+    "    model=\"gpt-4.1-mini\",\n",
+    "    messages=messages)\n",
+    "\n",
+    "# Step 4: print the result\n",
+    "print(response.choices[0].message.content)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "36ed9f14-b349-40e9-a42c-b367e77f8bda",
+   "metadata": {},
+   "source": [
+    "## An extra exercise for those who enjoy web scraping\n",
+    "\n",
+    "You may notice that if you try `display_summary(\"https://openai.com\")` - it doesn't work! That's because OpenAI has a fancy website that uses Javascript. There are many ways around this that some of you might be familiar with. For example, Selenium is a hugely popular framework that runs a browser behind the scenes, renders the page, and allows you to query it. If you have experience with Selenium, Playwright or similar, then feel free to improve the Website class to use them. In the community-contributions folder, you'll find an example Selenium solution from a student (thank you!)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "eeab24dc-5f90-4570-b542-b0585aca3eb6",
+   "metadata": {},
+   "source": [
+    "# Sharing your code\n",
+    "\n",
+    "I'd love it if you share your code afterwards so I can share it with others! You'll notice that some students have already made changes (including a Selenium implementation) which you will find in the community-contributions folder. If you'd like add your changes to that folder, submit a Pull Request with your new versions in that folder and I'll merge your changes.\n",
+    "\n",
+    "If you're not an expert with git (and I am not!) then GPT has given some nice instructions on how to submit a Pull Request. It's a bit of an involved process, but once you've done it once it's pretty clear. As a pro-tip: it's best if you clear the outputs of your Jupyter notebooks (Edit >> Clean outputs of all cells, and then Save) for clean notebooks.\n",
+    "\n",
+    "Here are good instructions courtesy of an AI friend:  \n",
+    "https://chatgpt.com/share/677a9cb5-c64c-8012-99e0-e06e88afd293"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "f4484fcf-8b39-4c3f-9674-37970ed71988",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": ".venv",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.12.4"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

week1/community-contributions/emmy/emmy_week1_EXERCISE.ipynb

@@ -0,0 +1,226 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "fe12c203-e6a6-452c-a655-afb8a03a4ff5",
+   "metadata": {},
+   "source": [
+    "# End of week 1 exercise\n",
+    "\n",
+    "To demonstrate your familiarity with OpenAI API, and also Ollama, build a tool that takes a technical question,  \n",
+    "and responds with an explanation. This is a tool that you will be able to use yourself during the course!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "c1070317-3ed9-4659-abe3-828943230e03",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# imports\n",
+    "import os\n",
+    "from dotenv import load_dotenv\n",
+    "from openai import OpenAI\n",
+    "import ollama\n",
+    "import ipywidgets as widgets\n",
+    "from IPython.display import display, Markdown"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "4a456906-915a-4bfd-bb9d-57e505c5093f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# constants\n",
+    "\n",
+    "MODEL_GEMINI = \"gemini-2.5-flash\"\n",
+    "MODEL_LLAMA = \"llama3.1:8b\"\n",
+    "\n",
+    "CHOICE_GEMINI = \"gemini\"\n",
+    "CHOICE_OLLAMA = \"ollama\"\n",
+    "\n",
+    "SYSTEM_PROMPT = (\n",
+    "    \"You are a technical adviser. The student is learning LLM engineering \"\n",
+    "    \"and you will be asked to explain lines of code with an example, \"\n",
+    "    \"mostly in Python.\"\n",
+    "    \"You can answer other questions as well.\"\n",
+    ")\n",
+    "\n",
+    "GEMINI_BASE_URL = \"https://generativelanguage.googleapis.com/v1beta/openai/\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "a8d7923c-5f28-4c30-8556-342d7c8497c1",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# set up environment\n",
+    "load_dotenv(override=True)\n",
+    "google_api_key = os.getenv(\"GOOGLE_API_KEY\")\n",
+    "\n",
+    "if not google_api_key:\n",
+    "    print(\"Warning: GOOGLE_API_KEY not found. Gemini calls will fail.\")\n",
+    "    print(\"Please create a .env file with GOOGLE_API_KEY=your_key\")\n",
+    "\n",
+    "gemini_client = OpenAI(\n",
+    "    base_url=GEMINI_BASE_URL,\n",
+    "    api_key=google_api_key,\n",
+    ")\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "3f0d0137-52b0-47a8-81a8-11a90a010798",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# here is the question; type over this to ask something new\n",
+    "\n",
+    "question = \"\"\"\n",
+    "Please explain what this code does and why:\n",
+    "yield from {book.get(\"author\") for book in books if book.get(\"author\")}\n",
+    "\"\"\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "60ce7000-a4a5-4cce-a261-e75ef45063b4",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def make_messages(user_question: str):\n",
+    "    return [\n",
+    "        {\"role\": \"system\", \"content\": SYSTEM_PROMPT},\n",
+    "        {\"role\": \"user\", \"content\": user_question},\n",
+    "    ]\n",
+    "\n",
+    "\n",
+    "def stream_gemini(messages):\n",
+    "    \"\"\"Stream response chunks from Gemini.\"\"\"\n",
+    "    stream = gemini_client.chat.completions.create(\n",
+    "        model=MODEL_GEMINI,\n",
+    "        messages=messages,\n",
+    "        stream=True,\n",
+    "    )\n",
+    "\n",
+    "    full = []\n",
+    "    for chunk in stream:\n",
+    "        piece = chunk.choices[0].delta.content or \"\"\n",
+    "        full.append(piece)\n",
+    "    return \"\".join(full)\n",
+    "\n",
+    "\n",
+    "def stream_ollama(messages):\n",
+    "    \"\"\"Stream response chunks from local Ollama.\"\"\"\n",
+    "    stream = ollama.chat(\n",
+    "        model=MODEL_LLAMA,\n",
+    "        messages=messages,\n",
+    "        stream=True,\n",
+    "    )\n",
+    "\n",
+    "    full = []\n",
+    "    for chunk in stream:\n",
+    "        piece = chunk[\"message\"][\"content\"]\n",
+    "        full.append(piece)\n",
+    "    return \"\".join(full)\n",
+    "\n",
+    "\n",
+    "def get_explanation(question: str, model_choice: str):\n",
+    "    \"\"\"Gets a technical explanation from the chosen model and streams the response.\"\"\"\n",
+    "    messages = make_messages(question)\n",
+    "    try:\n",
+    "        if model_choice == CHOICE_GEMINI:\n",
+    "            return stream_gemini(messages)\n",
+    "        elif model_choice == CHOICE_OLLAMA:\n",
+    "            return stream_ollama(messages)\n",
+    "        else:\n",
+    "            print(\"Unknown model choice.\")\n",
+    "            return \"\"\n",
+    "    except Exception as e:\n",
+    "        print(f\"\\nAn error occurred: {e}\")\n",
+    "        return \"\"\n",
+    "\n",
+    "print(\"💡 Your personal technical tutor is ready.\\n\")\n",
+    "\n",
+    "# Dropdown for model selection\n",
+    "model_dropdown = widgets.Dropdown(\n",
+    "    options=[\n",
+    "        (\"Gemini (gemini-2.5-flash)\", CHOICE_GEMINI),\n",
+    "        (\"Ollama (llama3.1:8b)\", CHOICE_OLLAMA),\n",
+    "    ],\n",
+    "    value=CHOICE_GEMINI,\n",
+    "    description=\"Model:\",\n",
+    "    style={\"description_width\": \"initial\"},\n",
+    ")\n",
+    "\n",
+    "# Text input for question\n",
+    "question_box = widgets.Textarea(\n",
+    "    placeholder=\"Type your technical question here...\",\n",
+    "    description=\"Question:\",\n",
+    "    layout=widgets.Layout(width=\"100%\", height=\"100px\"),\n",
+    "    style={\"description_width\": \"initial\"},\n",
+    ")\n",
+    "\n",
+    "submit_button = widgets.Button(description=\"Ask\", button_style=\"success\", icon=\"paper-plane\")\n",
+    "\n",
+    "output_area = widgets.Output()\n",
+    "loader_label = widgets.Label(value=\"\")\n",
+    "\n",
+    "def on_submit(_):\n",
+    "    output_area.clear_output()\n",
+    "    question = question_box.value.strip()\n",
+    "    if not question:\n",
+    "        with output_area:\n",
+    "            print(\"Please enter a question.\")\n",
+    "        return\n",
+    "\n",
+    "    loader_label.value = \"⏳ Thinking...\"\n",
+    "    submit_button.disabled = True\n",
+    "\n",
+    "    answer = get_explanation(question, model_dropdown.value)\n",
+    "\n",
+    "    loader_label.value = \"\"\n",
+    "    submit_button.disabled = False\n",
+    "\n",
+    "    with output_area:\n",
+    "        print(f\"🤖 Model: {model_dropdown.label}\")\n",
+    "        print(f\"📜 Question: {question}\\n\")\n",
+    "        display(Markdown(answer))\n",
+    "        print(\"\\n--- End of response ---\")\n",
+    "\n",
+    "submit_button.on_click(on_submit)\n",
+    "\n",
+    "# Display everything\n",
+    "display(model_dropdown, question_box, submit_button, loader_label, output_area)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "llm-engineering (3.12.10)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.12.10"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

week1/community-contributions/fernando/day2.ipynb

@@ -0,0 +1,494 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "d15d8294-3328-4e07-ad16-8a03e9bbfdb9",
+   "metadata": {},
+   "source": [
+    "# Welcome to the Day 2 Lab!\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ada885d9-4d42-4d9b-97f0-74fbbbfe93a9",
+   "metadata": {},
+   "source": [
+    "<table style=\"margin: 0; text-align: left;\">\n",
+    "    <tr>\n",
+    "        <td style=\"width: 150px; height: 150px; vertical-align: middle;\">\n",
+    "            <img src=\"../assets/resources.jpg\" width=\"150\" height=\"150\" style=\"display: block;\" />\n",
+    "        </td>\n",
+    "        <td>\n",
+    "            <h2 style=\"color:#f71;\">Just before we get started --</h2>\n",
+    "            <span style=\"color:#f71;\">I thought I'd take a second to point you at this page of useful resources for the course. This includes links to all the slides.<br/>\n",
+    "            <a href=\"https://edwarddonner.com/2024/11/13/llm-engineering-resources/\">https://edwarddonner.com/2024/11/13/llm-engineering-resources/</a><br/>\n",
+    "            Please keep this bookmarked, and I'll continue to add more useful links there over time.\n",
+    "            </span>\n",
+    "        </td>\n",
+    "    </tr>\n",
+    "</table>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "79ffe36f",
+   "metadata": {},
+   "source": [
+    "## First - let's talk about the Chat Completions API\n",
+    "\n",
+    "1. The simplest way to call an LLM\n",
+    "2. It's called Chat Completions because it's saying: \"here is a conversation, please predict what should come next\"\n",
+    "3. The Chat Completions API was invented by OpenAI, but it's so popular that everybody uses it!\n",
+    "\n",
+    "### We will start by calling OpenAI again - but don't worry non-OpenAI people, your time is coming!\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "e38f17a0",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "from dotenv import load_dotenv\n",
+    "\n",
+    "load_dotenv(override=True)\n",
+    "api_key = os.getenv('OPENAI_API_KEY')\n",
+    "\n",
+    "if not api_key:\n",
+    "    print(\"No API key was found - please head over to the troubleshooting notebook in this folder to identify & fix!\")\n",
+    "elif not api_key.startswith(\"sk-proj-\"):\n",
+    "    print(\"An API key was found, but it doesn't start sk-proj-; please check you're using the right key - see troubleshooting notebook\")\n",
+    "else:\n",
+    "    print(\"API key found and looks good so far!\")\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "97846274",
+   "metadata": {},
+   "source": [
+    "## Do you know what an Endpoint is?\n",
+    "\n",
+    "If not, please review the Technical Foundations guide in the guides folder\n",
+    "\n",
+    "And, here is an endpoint that might interest you..."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "5af5c188",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import requests\n",
+    "\n",
+    "headers = {\"Authorization\": f\"Bearer {api_key}\", \"Content-Type\": \"application/json\"}\n",
+    "\n",
+    "payload = {\n",
+    "    \"model\": \"gpt-5-nano\",\n",
+    "    \"messages\": [\n",
+    "        {\"role\": \"user\", \"content\": \"Tell me a fun fact\"}]\n",
+    "}\n",
+    "\n",
+    "payload"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "2d0ab242",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "response = requests.post(\n",
+    "    \"https://api.openai.com/v1/chat/completions\",\n",
+    "    headers=headers,\n",
+    "    json=payload\n",
+    ")\n",
+    "\n",
+    "response.json()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "cb11a9f6",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "response.json()[\"choices\"][0][\"message\"][\"content\"]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "cea3026a",
+   "metadata": {},
+   "source": [
+    "# What is the openai package?\n",
+    "\n",
+    "It's known as a Python Client Library.\n",
+    "\n",
+    "It's nothing more than a wrapper around making this exact call to the http endpoint.\n",
+    "\n",
+    "It just allows you to work with nice Python code instead of messing around with janky json objects.\n",
+    "\n",
+    "But that's it. It's open-source and lightweight. Some people think it contains OpenAI model code - it doesn't!\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "490fdf09",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Create OpenAI client\n",
+    "\n",
+    "from openai import OpenAI\n",
+    "openai = OpenAI()\n",
+    "\n",
+    "response = openai.chat.completions.create(model=\"gpt-5-nano\", messages=[{\"role\": \"user\", \"content\": \"Tell me a fun fact\"}])\n",
+    "\n",
+    "response.choices[0].message.content\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c7739cda",
+   "metadata": {},
+   "source": [
+    "## And then this great thing happened:\n",
+    "\n",
+    "OpenAI's Chat Completions API was so popular, that the other model providers created endpoints that are identical.\n",
+    "\n",
+    "They are known as the \"OpenAI Compatible Endpoints\".\n",
+    "\n",
+    "For example, google made one here: https://generativelanguage.googleapis.com/v1beta/openai/\n",
+    "\n",
+    "And OpenAI decided to be kind: they said, hey, you can just use the same client library that we made for GPT. We'll allow you to specify a different endpoint URL and a different key, to use another provider.\n",
+    "\n",
+    "So you can use:\n",
+    "\n",
+    "```python\n",
+    "gemini = OpenAI(base_url=\"https://generativelanguage.googleapis.com/v1beta/openai/\", api_key=\"AIz....\")\n",
+    "gemini.chat.completions.create(...)\n",
+    "```\n",
+    "\n",
+    "And to be clear - even though OpenAI is in the code, we're only using this lightweight python client library to call the endpoint - there's no OpenAI model involved here.\n",
+    "\n",
+    "If you're confused, please review Guide 9 in the Guides folder!\n",
+    "\n",
+    "And now let's try it!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "f74293bc",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\n",
+    "GEMINI_BASE_URL = \"https://generativelanguage.googleapis.com/v1beta/openai/\"\n",
+    "\n",
+    "google_api_key = os.getenv(\"GOOGLE_API_KEY\")\n",
+    "\n",
+    "if not google_api_key:\n",
+    "    print(\"No API key was found - please head over to the troubleshooting notebook in this folder to identify & fix!\")\n",
+    "elif not google_api_key.startswith(\"AIz\"):\n",
+    "    print(\"An API key was found, but it doesn't start AIz\")\n",
+    "else:\n",
+    "    print(\"API key found and looks good so far!\")\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "8fc5520d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import google.generativeai as genai\n",
+    "from dotenv import load_dotenv\n",
+    "import os\n",
+    "\n",
+    "load_dotenv()\n",
+    "genai.configure(api_key=os.getenv(\"GOOGLE_API_KEY\"))\n",
+    "\n",
+    "# Lista de modelos disponibles\n",
+    "for model in genai.list_models():\n",
+    "    print(model.name, \"-\", model.supported_generation_methods)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "d060f484",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import google.generativeai as genai\n",
+    "from dotenv import load_dotenv\n",
+    "import os\n",
+    "\n",
+    "load_dotenv()\n",
+    "genai.configure(api_key=os.getenv(\"GOOGLE_API_KEY\"))\n",
+    "\n",
+    "model = genai.GenerativeModel(\"models/gemini-2.5-pro\")  # Usa el modelo que viste en la lista, ejemplo \"gemini-1.5-pro\" o \"gemini-1.5-flash\"\n",
+    "response = model.generate_content(\"Tell me a fun fact\")\n",
+    "\n",
+    "print(response.text)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "gemini = OpenAI(base_url=GEMINI_BASE_URL, api_key=google_api_key)\n",
+    "\n",
+    "response = gemini.chat.completions.create(model=\"models/gemini-2.5-pro\", messages=[{\"role\": \"user\", \"content\": \"Tell me a fun fact\"}])\n",
+    "\n",
+    "response.choices[0].message.content"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "a5b069be",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "id": "65272432",
+   "metadata": {},
+   "source": [
+    "## And Ollama also gives an OpenAI compatible endpoint\n",
+    "\n",
+    "...and it's on your local machine!\n",
+    "\n",
+    "If the next cell doesn't print \"Ollama is running\" then please open a terminal and run `ollama serve`"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "f06280ad",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "requests.get(\"http://localhost:11434\").content"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c6ef3807",
+   "metadata": {},
+   "source": [
+    "### Download llama3.2 from meta\n",
+    "\n",
+    "Change this to llama3.2:1b if your computer is smaller.\n",
+    "\n",
+    "Don't use llama3.3 or llama4! They are too big for your computer.."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "e633481d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "!ollama pull llama3.2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "ce240975",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import requests\n",
+    "response = requests.get(\"http://localhost:11434/v1/models\")\n",
+    "print(response.json())\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "d9419762",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from openai import OpenAI\n",
+    "\n",
+    "OLLAMA_BASE_URL = \"http://localhost:11434/v1\"\n",
+    "\n",
+    "ollama = OpenAI(base_url=OLLAMA_BASE_URL, api_key='ollama')\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "e2456cdf",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Get a fun fact\n",
+    "\n",
+    "response = ollama.chat.completions.create(model=\"llama3.2\", messages=[{\"role\": \"user\", \"content\": \"Tell me a fun fact\"}])\n",
+    "\n",
+    "response.choices[0].message.content"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "3d7cebd7",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Now let's try deepseek-r1:1.5b - this is DeepSeek \"distilled\" into Qwen from Alibaba Cloud\n",
+    "\n",
+    "!ollama pull deepseek-r1:1.5b"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "25002f25",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#response = ollama.chat.completions.create(model=\"deepseek-r1:1.5b\", messages=[{\"role\": \"user\", \"content\": \"Tell me a fun fact\"}])\n",
+    "#response.choices[0].message.content\n",
+    "\n",
+    "from ollama import chat  # pip install ollama\n",
+    "\n",
+    "resp = chat(\n",
+    "    model='deepseek-r1:1.5b',\n",
+    "    messages=[{'role': 'user', 'content': 'Tell me a fun fact'}],\n",
+    ")\n",
+    "\n",
+    "print(resp['message']['content'])\n",
+    "# o\n",
+    "print(resp.message.content)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6e9fa1fc-eac5-4d1d-9be4-541b3f2b3458",
+   "metadata": {},
+   "source": [
+    "# HOMEWORK EXERCISE ASSIGNMENT\n",
+    "\n",
+    "Upgrade the day 1 project to summarize a webpage to use an Open Source model running locally via Ollama rather than OpenAI\n",
+    "\n",
+    "You'll be able to use this technique for all subsequent projects if you'd prefer not to use paid APIs.\n",
+    "\n",
+    "**Benefits:**\n",
+    "1. No API charges - open-source\n",
+    "2. Data doesn't leave your box\n",
+    "\n",
+    "**Disadvantages:**\n",
+    "1. Significantly less power than Frontier Model\n",
+    "\n",
+    "## Recap on installation of Ollama\n",
+    "\n",
+    "Simply visit [ollama.com](https://ollama.com) and install!\n",
+    "\n",
+    "Once complete, the ollama server should already be running locally.  \n",
+    "If you visit:  \n",
+    "[http://localhost:11434/](http://localhost:11434/)\n",
+    "\n",
+    "You should see the message `Ollama is running`.  \n",
+    "\n",
+    "If not, bring up a new Terminal (Mac) or Powershell (Windows) and enter `ollama serve`  \n",
+    "And in another Terminal (Mac) or Powershell (Windows), enter `ollama pull llama3.2`  \n",
+    "Then try [http://localhost:11434/](http://localhost:11434/) again.\n",
+    "\n",
+    "If Ollama is slow on your machine, try using `llama3.2:1b` as an alternative. Run `ollama pull llama3.2:1b` from a Terminal or Powershell, and change the code from `MODEL = \"llama3.2\"` to `MODEL = \"llama3.2:1b\"`"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "6de38216-6d1c-48c4-877b-86d403f4e0f8",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# imports\n",
+    "import os\n",
+    "from dotenv import load_dotenv\n",
+    "from scraper import fetch_website_contents\n",
+    "from IPython.display import Markdown, display\n",
+    "from ollama import Client \n",
+    "\n",
+    "# Cliente Ollama local\n",
+    "ollama = Client()\n",
+    "\n",
+    "system_prompt = \"\"\"\n",
+    "You are a helpful assistant that analyzes the contents of a website,\n",
+    "and provides a short, snarky, humorous summary, ignoring text that might be navigation related.\n",
+    "Respond in markdown. Do not wrap the markdown in a code block - respond just with the markdown.\n",
+    "\"\"\"\n",
+    "\n",
+    "user_prompt_prefix = \"\"\"\n",
+    "Here are the contents of a website.\n",
+    "Provide a short summary of this website.\n",
+    "If it includes news or announcements, then summarize these too.\n",
+    "\"\"\"\n",
+    "\n",
+    "def messages_for(website):\n",
+    "    return [\n",
+    "        {\"role\": \"system\", \"content\": system_prompt},\n",
+    "        {\"role\": \"user\", \"content\": user_prompt_prefix + website}\n",
+    "    ]\n",
+    "\n",
+    "def summarize(url):\n",
+    "    website = fetch_website_contents(url)\n",
+    "    response = ollama.chat(\n",
+    "        model='llama3.2',\n",
+    "        messages=messages_for(website)\n",
+    "    )\n",
+    "    return response['message']['content']\n",
+    "\n",
+    "def display_summary(url):\n",
+    "    summary = summarize(url)\n",
+    "    display(Markdown(summary))\n",
+    "\n",
+    "# Ejecuta el resumen\n",
+    "display_summary(\"https://www.reforma.com\")\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": ".venv",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.12.12"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

week1/community-contributions/fernando/week1 EXERCISE.ipynb

@@ -0,0 +1,175 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "fe12c203-e6a6-452c-a655-afb8a03a4ff5",
+   "metadata": {},
+   "source": [
+    "# End of week 1 exercise\n",
+    "\n",
+    "To demonstrate your familiarity with OpenAI API, and also Ollama, build a tool that takes a technical question,  \n",
+    "and responds with an explanation. This is a tool that you will be able to use yourself during the course!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "c1070317-3ed9-4659-abe3-828943230e03",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# imports\n",
+    "import os\n",
+    "from openai import OpenAI\n",
+    "from dotenv import load_dotenv"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "4a456906-915a-4bfd-bb9d-57e505c5093f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# constants\n",
+    "MODEL_GPT = 'gpt-4o-mini'\n",
+    "MODEL_LLAMA = 'llama3.2'"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "a8d7923c-5f28-4c30-8556-342d7c8497c1",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# set up environment\n",
+    "system_prompt = \"\"\"\n",
+    "You are a technical expert of AI and LLMs.\n",
+    "\"\"\"\n",
+    "\n",
+    "user_prompt_prefix = \"\"\"\n",
+    "Provide deep explanations of the provided text.\n",
+    "\"\"\"\n",
+    "\n",
+    "user_prompt = \"\"\"\n",
+    "Explain the provided text.\n",
+    "\"\"\"\n",
+    "client = OpenAI()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "3f0d0137-52b0-47a8-81a8-11a90a010798",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# here is the question; type over this to ask something new\n",
+    "\n",
+    "question = \"\"\"\n",
+    "Ollama does have an OpenAI compatible endpoint, but Gemini doesn't?\n",
+    "\"\"\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Get gpt-4o-mini to answer, with streaming\n",
+    "def messages_for(question):\n",
+    "    return [\n",
+    "        {\"role\": \"system\", \"content\": system_prompt},\n",
+    "        {\"role\": \"user\", \"content\": user_prompt_prefix + question}\n",
+    "    ]\n",
+    "\n",
+    "def run_model_streaming(model_name, question):\n",
+    "    stream = client.chat.completions.create(\n",
+    "        model=model_name,\n",
+    "        messages=messages_for(question),\n",
+    "        stream=True\n",
+    "    )\n",
+    "    for chunk in stream:\n",
+    "        content = chunk.choices[0].delta.content\n",
+    "        if content:\n",
+    "            print(content, end=\"\", flush=True)\n",
+    "\n",
+    "run_model_streaming(MODEL_GPT, question)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "8f7c8ea8-4082-4ad0-8751-3301adcf6538",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Get Llama 3.2 to answer\n",
+    "# imports\n",
+    "import os\n",
+    "from openai import OpenAI\n",
+    "from dotenv import load_dotenv\n",
+    "\n",
+    "# set up environment\n",
+    "client = OpenAI(\n",
+    "    base_url=os.getenv(\"OPENAI_BASE_URL\", \"http://localhost:11434/v1\"),\n",
+    "    api_key=os.getenv(\"OPENAI_API_KEY\", \"ollama\")\n",
+    ")\n",
+    "\n",
+    "system_prompt = \"\"\"\n",
+    "You are a technical expert of AI and LLMs.\n",
+    "\"\"\"\n",
+    "\n",
+    "user_prompt_prefix = \"\"\"\n",
+    "Provide deep explanations of the provided text.\n",
+    "\"\"\"\n",
+    "\n",
+    "# question\n",
+    "question = \"\"\"\n",
+    "Ollama does have an OpenAI compatible endpoint, but Gemini doesn't?\n",
+    "\"\"\"\n",
+    "\n",
+    "# message\n",
+    "def messages_for(question):\n",
+    "    return [\n",
+    "        {\"role\": \"system\", \"content\": system_prompt},\n",
+    "        {\"role\": \"user\", \"content\": user_prompt_prefix + question}\n",
+    "    ]\n",
+    "\n",
+    "# response\n",
+    "def run_model(model_name, question):\n",
+    "    response = client.chat.completions.create(\n",
+    "        model=model_name,\n",
+    "        messages=messages_for(question)\n",
+    "    )\n",
+    "    return response.choices[0].message.content\n",
+    "\n",
+    "# run and print result\n",
+    "print(run_model(MODEL_LLAMA, question))\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": ".venv",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.12.12"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

week1/community-contributions/slmslm333221/day1.ipynb

@@ -0,0 +1,563 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "d15d8294-3328-4e07-ad16-8a03e9bbfdb9",
+   "metadata": {},
+   "source": [
+    "# YOUR FIRST LAB\n",
+    "### Please read this section. This is valuable to get you prepared, even if it's a long read -- it's important stuff.\n",
+    "\n",
+    "### Also, be sure to read [README.md](../README.md)! More info about the updated videos in the README and [top of the course resources in purple](https://edwarddonner.com/2024/11/13/llm-engineering-resources/)\n",
+    "\n",
+    "## Your first Frontier LLM Project\n",
+    "\n",
+    "By the end of this course, you will have built an autonomous Agentic AI solution with 7 agents that collaborate to solve a business problem. All in good time! We will start with something smaller...\n",
+    "\n",
+    "Our goal is to code a new kind of Web Browser. Give it a URL, and it will respond with a summary. The Reader's Digest of the internet!!\n",
+    "\n",
+    "Before starting, you should have completed the setup linked in the README.\n",
+    "\n",
+    "### If you're new to working in \"Notebooks\" (also known as Labs or Jupyter Lab)\n",
+    "\n",
+    "Welcome to the wonderful world of Data Science experimentation! Simply click in each \"cell\" with code in it, such as the cell immediately below this text, and hit Shift+Return to execute that cell. Be sure to run every cell, starting at the top, in order.\n",
+    "\n",
+    "Please look in the [Guides folder](../guides/01_intro.ipynb) for all the guides.\n",
+    "\n",
+    "## I am here to help\n",
+    "\n",
+    "If you have any problems at all, please do reach out.  \n",
+    "I'm available through the platform, or at ed@edwarddonner.com, or at https://www.linkedin.com/in/eddonner/ if you'd like to connect (and I love connecting!)  \n",
+    "And this is new to me, but I'm also trying out X at [@edwarddonner](https://x.com/edwarddonner) - if you're on X, please show me how it's done 😂  \n",
+    "\n",
+    "## More troubleshooting\n",
+    "\n",
+    "Please see the [troubleshooting](../setup/troubleshooting.ipynb) notebook in the setup folder to diagnose and fix common problems. At the very end of it is a diagnostics script with some useful debug info.\n",
+    "\n",
+    "## If this is old hat!\n",
+    "\n",
+    "If you're already comfortable with today's material, please hang in there; you can move swiftly through the first few labs - we will get much more in depth as the weeks progress. Ultimately we will fine-tune our own LLM to compete with OpenAI!\n",
+    "\n",
+    "<table style=\"margin: 0; text-align: left;\">\n",
+    "    <tr>\n",
+    "        <td style=\"width: 150px; height: 150px; vertical-align: middle;\">\n",
+    "            <img src=\"../assets/important.jpg\" width=\"150\" height=\"150\" style=\"display: block;\" />\n",
+    "        </td>\n",
+    "        <td>\n",
+    "            <h2 style=\"color:#900;\">Please read - important note</h2>\n",
+    "            <span style=\"color:#900;\">The way I collaborate with you may be different to other courses you've taken. I prefer not to type code while you watch. Rather, I execute Jupyter Labs, like this, and give you an intuition for what's going on. My suggestion is that you carefully execute this yourself, <b>after</b> watching the lecture. Add print statements to understand what's going on, and then come up with your own variations. If you have a Github account, use this to showcase your variations. Not only is this essential practice, but it demonstrates your skills to others, including perhaps future clients or employers...</span>\n",
+    "        </td>\n",
+    "    </tr>\n",
+    "</table>\n",
+    "<table style=\"margin: 0; text-align: left;\">\n",
+    "    <tr>\n",
+    "        <td style=\"width: 150px; height: 150px; vertical-align: middle;\">\n",
+    "            <img src=\"../assets/resources.jpg\" width=\"150\" height=\"150\" style=\"display: block;\" />\n",
+    "        </td>\n",
+    "        <td>\n",
+    "            <h2 style=\"color:#f71;\">This code is a live resource - keep an eye out for my emails</h2>\n",
+    "            <span style=\"color:#f71;\">I push updates to the code regularly. As people ask questions, I add more examples or improved commentary. As a result, you'll notice that the code below isn't identical to the videos. Everything from the videos is here; but I've also added better explanations and new models like DeepSeek. Consider this like an interactive book.<br/><br/>\n",
+    "                I try to send emails regularly with important updates related to the course. You can find this in the 'Announcements' section of Udemy in the left sidebar. You can also choose to receive my emails via your Notification Settings in Udemy. I'm respectful of your inbox and always try to add value with my emails!\n",
+    "            </span>\n",
+    "        </td>\n",
+    "    </tr>\n",
+    "</table>\n",
+    "<table style=\"margin: 0; text-align: left;\">\n",
+    "    <tr>\n",
+    "        <td style=\"width: 150px; height: 150px; vertical-align: middle;\">\n",
+    "            <img src=\"../assets/business.jpg\" width=\"150\" height=\"150\" style=\"display: block;\" />\n",
+    "        </td>\n",
+    "        <td>\n",
+    "            <h2 style=\"color:#181;\">Business value of these exercises</h2>\n",
+    "            <span style=\"color:#181;\">A final thought. While I've designed these notebooks to be educational, I've also tried to make them enjoyable. We'll do fun things like have LLMs tell jokes and argue with each other. But fundamentally, my goal is to teach skills you can apply in business. I'll explain business implications as we go, and it's worth keeping this in mind: as you build experience with models and techniques, think of ways you could put this into action at work today. Please do contact me if you'd like to discuss more or if you have ideas to bounce off me.</span>\n",
+    "        </td>\n",
+    "    </tr>\n",
+    "</table>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "83f28feb",
+   "metadata": {},
+   "source": [
+    "### If necessary, install Cursor Extensions\n",
+    "\n",
+    "1. From the View menu, select Extensions\n",
+    "2. Search for Python\n",
+    "3. Click on \"Python\" made by \"ms-python\" and select Install if not already installed\n",
+    "4. Search for Jupyter\n",
+    "5. Click on \"Jupyter\" made by \"ms-toolsai\" and select Install of not already installed\n",
+    "\n",
+    "\n",
+    "### Next Select the Kernel\n",
+    "\n",
+    "Click on \"Select Kernel\" on the Top Right\n",
+    "\n",
+    "Choose \"Python Environments...\"\n",
+    "\n",
+    "Then choose the one that looks like `.venv (Python 3.12.x) .venv/bin/python` - it should be marked as \"Recommended\" and have a big star next to it.\n",
+    "\n",
+    "Any problems with this? Head over to the troubleshooting.\n",
+    "\n",
+    "### Note: you'll need to set the Kernel with every notebook.."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "4e2a9393-7767-488e-a8bf-27c12dca35bd",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# imports\n",
+    "\n",
+    "import os\n",
+    "import sys\n",
+    "from pathlib import Path\n",
+    "sys.path.append(str(Path(r\"..\\..\").resolve()))\n",
+    "from dotenv import load_dotenv\n",
+    "from scraper import fetch_website_contents\n",
+    "from IPython.display import Markdown, display\n",
+    "from openai import OpenAI\n",
+    "\n",
+    "\n",
+    "\n",
+    "# If you get an error running this cell, then please head over to the troubleshooting notebook!"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6900b2a8-6384-4316-8aaa-5e519fca4254",
+   "metadata": {},
+   "source": [
+    "# Connecting to OpenAI (or Ollama)\n",
+    "\n",
+    "The next cell is where we load in the environment variables in your `.env` file and connect to OpenAI.  \n",
+    "\n",
+    "If you'd like to use free Ollama instead, please see the README section \"Free Alternative to Paid APIs\", and if you're not sure how to do this, there's a full solution in the solutions folder (day1_with_ollama.ipynb).\n",
+    "\n",
+    "## Troubleshooting if you have problems:\n",
+    "\n",
+    "If you get a \"Name Error\" - have you run all cells from the top down? Head over to the Python Foundations guide for a bulletproof way to find and fix all Name Errors.\n",
+    "\n",
+    "If that doesn't fix it, head over to the [troubleshooting](../setup/troubleshooting.ipynb) notebook for step by step code to identify the root cause and fix it!\n",
+    "\n",
+    "Or, contact me! Message me or email ed@edwarddonner.com and we will get this to work.\n",
+    "\n",
+    "Any concerns about API costs? See my notes in the README - costs should be minimal, and you can control it at every point. You can also use Ollama as a free alternative, which we discuss during Day 2."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "7b87cadb-d513-4303-baee-a37b6f938e4d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Load environment variables in a file called .env\n",
+    "\n",
+    "load_dotenv(override=True)\n",
+    "api_key = os.getenv('OPENAI_API_KEY')\n",
+    "\n",
+    "# Check the key\n",
+    "\n",
+    "if not api_key:\n",
+    "    print(\"No API key was found - please head over to the troubleshooting notebook in this folder to identify & fix!\")\n",
+    "elif not api_key.startswith(\"sk-proj-\"):\n",
+    "    print(\"An API key was found, but it doesn't start sk-proj-; please check you're using the right key - see troubleshooting notebook\")\n",
+    "elif api_key.strip() != api_key:\n",
+    "    print(\"An API key was found, but it looks like it might have space or tab characters at the start or end - please remove them - see troubleshooting notebook\")\n",
+    "else:\n",
+    "    print(\"API key found and looks good so far!\")\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "442fc84b-0815-4f40-99ab-d9a5da6bda91",
+   "metadata": {},
+   "source": [
+    "# Let's make a quick call to a Frontier model to get started, as a preview!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "a58394bf-1e45-46af-9bfd-01e24da6f49a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# To give you a preview -- calling OpenAI with these messages is this easy. Any problems, head over to the Troubleshooting notebook.\n",
+    "\n",
+    "message = \"Hello, GPT! This is my first ever message to you! Hi!\"\n",
+    "\n",
+    "messages = [{\"role\": \"user\", \"content\": message}]\n",
+    "\n",
+    "messages\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "08330159",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "openai = OpenAI()\n",
+    "\n",
+    "response = openai.chat.completions.create(model=\"gpt-5-nano\", messages=messages)\n",
+    "response.choices[0].message.content"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2aa190e5-cb31-456a-96cc-db109919cd78",
+   "metadata": {},
+   "source": [
+    "## OK onwards with our first project"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "2ef960cf-6dc2-4cda-afb3-b38be12f4c97",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Let's try out this utility\n",
+    "\n",
+    "ed = fetch_website_contents(\"https://edwarddonner.com\")\n",
+    "print(ed)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6a478a0c-2c53-48ff-869c-4d08199931e1",
+   "metadata": {},
+   "source": [
+    "## Types of prompts\n",
+    "\n",
+    "You may know this already - but if not, you will get very familiar with it!\n",
+    "\n",
+    "Models like GPT have been trained to receive instructions in a particular way.\n",
+    "\n",
+    "They expect to receive:\n",
+    "\n",
+    "**A system prompt** that tells them what task they are performing and what tone they should use\n",
+    "\n",
+    "**A user prompt** -- the conversation starter that they should reply to"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "abdb8417-c5dc-44bc-9bee-2e059d162699",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Define our system prompt - you can experiment with this later, changing the last sentence to 'Respond in markdown in Spanish.\"\n",
+    "\n",
+    "system_prompt = \"\"\"\n",
+    "You are a snarkyassistant that analyzes the contents of a website,\n",
+    "and provides a short, snarky, humorous summary, ignoring text that might be navigation related.\n",
+    "Respond in markdown. Do not wrap the markdown in a code block - respond just with the markdown.\n",
+    "\"\"\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "f0275b1b-7cfe-4f9d-abfa-7650d378da0c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Define our user prompt\n",
+    "\n",
+    "user_prompt_prefix = \"\"\"\n",
+    "Here are the contents of a website.\n",
+    "Provide a short summary of this website.\n",
+    "If it includes news or announcements, then summarize these too.\n",
+    "\n",
+    "\"\"\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ea211b5f-28e1-4a86-8e52-c0b7677cadcc",
+   "metadata": {},
+   "source": [
+    "## Messages\n",
+    "\n",
+    "The API from OpenAI expects to receive messages in a particular structure.\n",
+    "Many of the other APIs share this structure:\n",
+    "\n",
+    "```python\n",
+    "[\n",
+    "    {\"role\": \"system\", \"content\": \"system message goes here\"},\n",
+    "    {\"role\": \"user\", \"content\": \"user message goes here\"}\n",
+    "]\n",
+    "```\n",
+    "To give you a preview, the next 2 cells make a rather simple call - we won't stretch the mighty GPT (yet!)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "f25dcd35-0cd0-4235-9f64-ac37ed9eaaa5",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "messages = [\n",
+    "    {\"role\": \"system\", \"content\": \"You are a helpful, by far too polite assistant trying to sell more services with every contact\"},\n",
+    "    {\"role\": \"user\", \"content\": \"What is 2 + 2?\"}\n",
+    "]\n",
+    "\n",
+    "response = openai.chat.completions.create(model=\"gpt-4.1-nano\", messages=messages)\n",
+    "response.choices[0].message.content"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d06e8d78-ce4c-4b05-aa8e-17050c82bb47",
+   "metadata": {},
+   "source": [
+    "## And now let's build useful messages for GPT-4.1-mini, using a function"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "0134dfa4-8299-48b5-b444-f2a8c3403c88",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# See how this function creates exactly the format above\n",
+    "\n",
+    "def messages_for(website):\n",
+    "    return [\n",
+    "        {\"role\": \"system\", \"content\": system_prompt},\n",
+    "        {\"role\": \"user\", \"content\": user_prompt_prefix + website}\n",
+    "    ]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "36478464-39ee-485c-9f3f-6a4e458dbc9c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Try this out, and then try for a few more websites\n",
+    "\n",
+    "messages_for(ed)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "16f49d46-bf55-4c3e-928f-68fc0bf715b0",
+   "metadata": {},
+   "source": [
+    "## Time to bring it together - the API for OpenAI is very simple!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "905b9919-aba7-45b5-ae65-81b3d1d78e34",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# And now: call the OpenAI API. You will get very familiar with this!\n",
+    "\n",
+    "def summarize(url):\n",
+    "    website = fetch_website_contents(url)\n",
+    "    response = openai.chat.completions.create(\n",
+    "        model = \"gpt-4.1-mini\",\n",
+    "        messages = messages_for(website)\n",
+    "    )\n",
+    "    return response.choices[0].message.content"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "05e38d41-dfa4-4b20-9c96-c46ea75d9fb5",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "summarize(\"https://edwarddonner.com\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "3d926d59-450e-4609-92ba-2d6f244f1342",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# A function to display this nicely in the output, using markdown\n",
+    "\n",
+    "def display_summary(url):\n",
+    "    summary = summarize(url)\n",
+    "    display(Markdown(summary))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "3018853a-445f-41ff-9560-d925d1774b2f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "display_summary(\"https://edwarddonner.com\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b3bcf6f4-adce-45e9-97ad-d9a5d7a3a624",
+   "metadata": {},
+   "source": [
+    "# Let's try more websites\n",
+    "\n",
+    "Note that this will only work on websites that can be scraped using this simplistic approach.\n",
+    "\n",
+    "Websites that are rendered with Javascript, like React apps, won't show up. See the community-contributions folder for a Selenium implementation that gets around this. You'll need to read up on installing Selenium (ask ChatGPT!)\n",
+    "\n",
+    "Also Websites protected with CloudFront (and similar) may give 403 errors - many thanks Andy J for pointing this out.\n",
+    "\n",
+    "But many websites will work just fine!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "45d83403-a24c-44b5-84ac-961449b4008f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "display_summary(\"https://cnn.com\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "75e9fd40-b354-4341-991e-863ef2e59db7",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "display_summary(\"https://anthropic.com\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c951be1a-7f1b-448f-af1f-845978e47e2c",
+   "metadata": {},
+   "source": [
+    "<table style=\"margin: 0; text-align: left;\">\n",
+    "    <tr>\n",
+    "        <td style=\"width: 150px; height: 150px; vertical-align: middle;\">\n",
+    "            <img src=\"../assets/business.jpg\" width=\"150\" height=\"150\" style=\"display: block;\" />\n",
+    "        </td>\n",
+    "        <td>\n",
+    "            <h2 style=\"color:#181;\">Business applications</h2>\n",
+    "            <span style=\"color:#181;\">In this exercise, you experienced calling the Cloud API of a Frontier Model (a leading model at the frontier of AI) for the first time. We will be using APIs like OpenAI at many stages in the course, in addition to building our own LLMs.\n",
+    "\n",
+    "More specifically, we've applied this to Summarization - a classic Gen AI use case to make a summary. This can be applied to any business vertical - summarizing the news, summarizing financial performance, summarizing a resume in a cover letter - the applications are limitless. Consider how you could apply Summarization in your business, and try prototyping a solution.</span>\n",
+    "        </td>\n",
+    "    </tr>\n",
+    "</table>\n",
+    "\n",
+    "<table style=\"margin: 0; text-align: left;\">\n",
+    "    <tr>\n",
+    "        <td style=\"width: 150px; height: 150px; vertical-align: middle;\">\n",
+    "            <img src=\"../assets/important.jpg\" width=\"150\" height=\"150\" style=\"display: block;\" />\n",
+    "        </td>\n",
+    "        <td>\n",
+    "            <h2 style=\"color:#900;\">Before you continue - now try yourself</h2>\n",
+    "            <span style=\"color:#900;\">Use the cell below to make your own simple commercial example. Stick with the summarization use case for now. Here's an idea: write something that will take the contents of an email, and will suggest an appropriate short subject line for the email. That's the kind of feature that might be built into a commercial email tool.</span>\n",
+    "        </td>\n",
+    "    </tr>\n",
+    "</table>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "00743dac-0e70-45b7-879a-d7293a6f68a6",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Step 1: Create your prompts\n",
+    "\n",
+    "system_prompt = \"something here\"\n",
+    "user_prompt = \"\"\"\n",
+    "    Lots of text\n",
+    "    Can be pasted here\n",
+    "\"\"\"\n",
+    "\n",
+    "# Step 2: Make the messages list\n",
+    "\n",
+    "messages = [] # fill this in\n",
+    "\n",
+    "# Step 3: Call OpenAI\n",
+    "# response =\n",
+    "\n",
+    "# Step 4: print the result\n",
+    "# print("
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "36ed9f14-b349-40e9-a42c-b367e77f8bda",
+   "metadata": {},
+   "source": [
+    "## An extra exercise for those who enjoy web scraping\n",
+    "\n",
+    "You may notice that if you try `display_summary(\"https://openai.com\")` - it doesn't work! That's because OpenAI has a fancy website that uses Javascript. There are many ways around this that some of you might be familiar with. For example, Selenium is a hugely popular framework that runs a browser behind the scenes, renders the page, and allows you to query it. If you have experience with Selenium, Playwright or similar, then feel free to improve the Website class to use them. In the community-contributions folder, you'll find an example Selenium solution from a student (thank you!)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "eeab24dc-5f90-4570-b542-b0585aca3eb6",
+   "metadata": {},
+   "source": [
+    "# Sharing your code\n",
+    "\n",
+    "I'd love it if you share your code afterwards so I can share it with others! You'll notice that some students have already made changes (including a Selenium implementation) which you will find in the community-contributions folder. If you'd like add your changes to that folder, submit a Pull Request with your new versions in that folder and I'll merge your changes.\n",
+    "\n",
+    "If you're not an expert with git (and I am not!) then GPT has given some nice instructions on how to submit a Pull Request. It's a bit of an involved process, but once you've done it once it's pretty clear. As a pro-tip: it's best if you clear the outputs of your Jupyter notebooks (Edit >> Clean outputs of all cells, and then Save) for clean notebooks.\n",
+    "\n",
+    "Here are good instructions courtesy of an AI friend:  \n",
+    "https://chatgpt.com/share/677a9cb5-c64c-8012-99e0-e06e88afd293"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "f4484fcf-8b39-4c3f-9674-37970ed71988",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": ".venv",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.12.12"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

week1/community-contributions/song-meaning-summarizer/song_meaning_summarizer.ipynb

@@ -0,0 +1,235 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "d12b9c22",
+   "metadata": {},
+   "source": [
+    "# Song Lyrics → One-Sentence Summary\n",
+    "Get the lyrics of a song and summarize its main idea in about one sentence.\n",
+    "\n",
+    "## Setup\n",
+    "Import required libraries: environment vars, display helper, OpenAI client, BeautifulSoup, and requests."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "d94bbd61",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "from IPython.display import Markdown, display\n",
+    "from openai import OpenAI\n",
+    "from bs4 import BeautifulSoup\n",
+    "import requests"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "92dc1bde",
+   "metadata": {},
+   "source": [
+    "## Function: Get Lyrics from Genius\n",
+    "Fetch and extract the lyrics from a Genius.com song page using BeautifulSoup."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "2b43fa98",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def get_lyrics_from_genius(url: str) -> str:\n",
+    "    \"\"\"\n",
+    "    Extracts song lyrics from a Genius.com song URL using BeautifulSoup.\n",
+    "    Example URL: https://genius.com/Ed-sheeran-shape-of-you-lyrics\n",
+    "    \"\"\"\n",
+    "    # Standard headers to fetch a website\n",
+    "    headers = {\n",
+    "        \"User-Agent\": \"Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/117.0.0.0 Safari/537.36\"\n",
+    "    }\n",
+    "\n",
+    "    response = requests.get(url, headers=headers)\n",
+    "    response.raise_for_status()  # raises error if page not found\n",
+    "\n",
+    "    soup = BeautifulSoup(response.text, \"html.parser\")\n",
+    "\n",
+    "    # Genius stores lyrics inside <div data-lyrics-container=\"true\">\n",
+    "    lyrics_blocks = soup.find_all(\"div\", {\"data-lyrics-container\": \"true\"})\n",
+    "\n",
+    "    if not lyrics_blocks:\n",
+    "        return \"Lyrics not found.\"\n",
+    "\n",
+    "    # Join all text blocks and clean up spacing\n",
+    "    lyrics = \"\\n\".join(block.get_text(separator=\"\\n\") for block in lyrics_blocks)\n",
+    "    return lyrics.strip()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "fc4f0590",
+   "metadata": {},
+   "source": [
+    "## Function: Create Genius URL\n",
+    "Build a Genius.com lyrics URL automatically from the given artist and song name."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "e018c623",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def create_genius_url(artist: str, song: str) -> str:\n",
+    "    \"\"\"\n",
+    "    Creates a Genius.com lyrics URL from artist and song name.\n",
+    "    Example:\n",
+    "        create_genius_url(\"Ed sheeran\", \"shape of you\")\n",
+    "        → https://genius.com/Ed-sheeran-shape-of-you-lyrics\n",
+    "    \"\"\"\n",
+    "    artist = artist.strip().replace(\" \", \"-\")\n",
+    "    song = song.strip().replace(\" \", \"-\")\n",
+    "    return f\"https://genius.com/{artist}-{song}-lyrics\"\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "62f50f02",
+   "metadata": {},
+   "source": [
+    "## Generate URL and Fetch Lyrics\n",
+    "Create the Genius URL from the artist and song name, then fetch and display the lyrics."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "ed51d48d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "artist = \"Ed sheeran\"\n",
+    "song = \"shape of you\"\n",
+    "\n",
+    "url = create_genius_url(artist, song)\n",
+    "print(url)\n",
+    "# Output: https://genius.com/Ed-sheeran-shape-of-you-lyrics\n",
+    "\n",
+    "user_prompt = get_lyrics_from_genius(url)\n",
+    "print(user_prompt[:5000]) "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "fca4203a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "system_prompt = \"\"\"\n",
+    "You are a **helpful assistant** that specializes in analyzing **song lyrics**.\n",
+    "\n",
+    "## Task\n",
+    "Your goal is to **summarize the main idea or theme of a song** in **about one sentence**.\n",
+    "\n",
+    "## Instructions\n",
+    "1. Read the given song lyrics carefully.\n",
+    "2. Identify the **core message**, **emotion**, or **story** of the song.\n",
+    "3. Respond with **one concise sentence** only.\n",
+    "4. The tone of your summary should reflect the song’s mood (e.g., joyful, melancholic, romantic, rebellious).\n",
+    "\n",
+    "## Edge Cases\n",
+    "- **Very short lyrics:** Summarize the implied meaning.\n",
+    "- **Repetitive lyrics:** Focus on the message or emotion being emphasized.\n",
+    "- **Abstract or nonsensical lyrics:** Describe the overall feeling or imagery they create.\n",
+    "- **No lyrics or only a title provided:** Reply with  \n",
+    "  `No lyrics provided — unable to summarize meaningfully.`\n",
+    "- **Non-English lyrics:** Summarize in English unless otherwise instructed.\n",
+    "\n",
+    "## Output Format\n",
+    "Plain text — a single, coherent sentence summarizing the main idea of the song.\n",
+    "\"\"\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "11784d62",
+   "metadata": {},
+   "source": [
+    "## Create Chat Messages\n",
+    "Prepare the system and user messages, then send them to the OpenAI model for summarization."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "f1205658",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "messages = [\n",
+    "    {\"role\": \"system\", \"content\": system_prompt},\n",
+    "    {\"role\": \"user\", \"content\": user_prompt}\n",
+    "]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "5c8d61aa",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "openai = OpenAI()\n",
+    "response = openai.chat.completions.create(\n",
+    "    model = \"gpt-4.1-mini\",\n",
+    "    messages = messages\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4ad95820",
+   "metadata": {},
+   "source": [
+    "## Display Summary\n",
+    "Show the model’s one-sentence summary of the song lyrics in a formatted Markdown output."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "4f09a642",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "display(Markdown(response.choices[0].message.content))"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": ".venv",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.12.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

week1/community-contributions/wk1-day1-RBG-all-sites-jina.ipynb

@@ -0,0 +1,221 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "d15d8294-3328-4e07-ad16-8a03e9bbfdb9",
+   "metadata": {},
+   "source": [
+    "# My First Lab = My 1st Frontier LLM Project\n",
+    "## Summarize All Websites without Selenium\n",
+    "This simple \"app\" uses Jina (https://jina.ai/reader) to turn all websites into markdown before summarizing by an LLM. As their website says: \"Convert a URL to LLM-friendly input, by simply adding r.jina.ai in front\". They have other tools that look useful too.\n",
+    "\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "4e2a9393-7767-488e-a8bf-27c12dca35bd",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# imports\n",
+    "\n",
+    "import os\n",
+    "import requests                                 # added for jina\n",
+    "from dotenv import load_dotenv\n",
+    "# from scraper import fetch_website_contents    # not needed for jina\n",
+    "from IPython.display import Markdown, display\n",
+    "from openai import OpenAI\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "7b87cadb-d513-4303-baee-a37b6f938e4d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Load environment variables from a file called .env\n",
+    "\n",
+    "load_dotenv(override=True)\n",
+    "api_key = os.getenv('OPENAI_API_KEY')\n",
+    "\n",
+    "# Check the key\n",
+    "\n",
+    "if not api_key:\n",
+    "    print(\"No API key was found - please head over to the troubleshooting notebook in this folder to identify & fix!\")\n",
+    "elif not api_key.startswith(\"sk-proj-\"):\n",
+    "    print(\"An API key was found, but it doesn't start sk-proj-; please check you're using the right key - see troubleshooting notebook\")\n",
+    "elif api_key.strip() != api_key:\n",
+    "    print(\"An API key was found, but it looks like it might have space or tab characters at the start or end - please remove them - see troubleshooting notebook\")\n",
+    "else:\n",
+    "    print(\"API key found and looks good so far!\")\n",
+    "\n",
+    "# Setup access to the frontier model\n",
+    "\n",
+    "openai = OpenAI()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "f0275b1b-7cfe-4f9d-abfa-7650d378da0c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Step 1-a: Define the user prompt\n",
+    "\n",
+    "user_prompt_prefix = \"\"\"\n",
+    "Here are the contents of a website.\n",
+    "Provide a short summary of this website.\n",
+    "If it includes news or announcements, then summarize these too.\n",
+    "\"\"\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "abdb8417-c5dc-44bc-9bee-2e059d162699",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Step 1-b: Define the system prompt\n",
+    "\n",
+    "system_prompt = \"\"\"\n",
+    "You are a smart assistant that analyzes the contents of a website,\n",
+    "and provides a short, clear, summary, ignoring text that might be navigation related.\n",
+    "Respond in markdown. Do not wrap the markdown in a code block - respond just with the markdown.\n",
+    "\"\"\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "0134dfa4-8299-48b5-b444-f2a8c3403c88",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Add the website content to the user prompt\n",
+    "\n",
+    "def messages_for(website):\n",
+    "    return [\n",
+    "        {\"role\": \"system\", \"content\": system_prompt},\n",
+    "        {\"role\": \"user\", \"content\": user_prompt_prefix + website}\n",
+    "    ]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "2ef960cf-6dc2-4cda-afb3-b38be12f4c97",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Step 5: Change the content utility to use jina\n",
+    "\n",
+    "def fetch_url_content(url):\n",
+    "    jina_reader_url = f\"https://r.jina.ai/{url}\"\n",
+    "    try:\n",
+    "        response = requests.get(jina_reader_url)\n",
+    "        response.raise_for_status()                     # Raise an exception for HTTP errors\n",
+    "        return response.text\n",
+    "    except requests.exceptions.RequestException as e:\n",
+    "        print(f\"Error fetching URL: {e}\")\n",
+    "        return None\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "905b9919-aba7-45b5-ae65-81b3d1d78e34",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Step 3: Call OpenAI & Step 4: print the result\n",
+    "\n",
+    "def summarize(url):\n",
+    "    website = fetch_url_content(url)\n",
+    "    response = openai.chat.completions.create(\n",
+    "        model = \"gpt-5-nano\",\n",
+    "        messages = messages_for(website)\n",
+    "    )\n",
+    "    summary = response.choices[0].message.content\n",
+    "    return display(Markdown(summary))\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "05e38d41-dfa4-4b20-9c96-c46ea75d9fb5",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "summarize(\"https://edwarddonner.com\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "45d83403-a24c-44b5-84ac-961449b4008f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "summarize(\"https://cnn.com\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "75e9fd40-b354-4341-991e-863ef2e59db7",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "summarize(\"https://openai.com\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "36ed9f14-b349-40e9-a42c-b367e77f8bda",
+   "metadata": {},
+   "source": [
+    "## Content Summary vs Technical Summary\n",
+    "\n",
+    "In my work a technical summary of a website, or group of websites, would be useful too. For example, does it render on the server (HTML) or in the browser (JavaScript), what content management system (CMS) was used, how many pages, how many outbound links, how many inbound links, etc. Doing this exercise I realized LLMs can help with analyzing content, but I may need other tools to count pages, links, and other specifications.\n",
+    "\n",
+    "A \"Shout Out\" to whoever put \"Market_Research_Agent.ipynb\" in the Community-Contributions. It is a great example of using an LLM as a management consultant. I think Jina might help with this usecase by offering web search results through an API to feed to your LLM. Here is the system prompt from that notebook and I plan to use this format often.\n",
+    "\n",
+    "system_prompt = \"\"\"You are to act like a Mckinsey Consultant specializing in market research. \n",
+    "1) You are to follow legal guidelines and never give immoral advice. \n",
+    "2) Your job is to maximise profits for your clients by analysing their companies initiatives and giving out recommendations for newer initiatives.\\n \n",
+    "3) Follow industry frameworks for reponses always give simple answers and stick to the point.\n",
+    "4) If possible try to see what competitors exist and what market gap can your clients company exploit.\n",
+    "5) Further more, USe SWOT, Porters 5 forces to summarize your recommendations, Give confidence score with every recommendations\n",
+    "6) Try to give unique solutions by seeing what the market gap is, if market gap is ambiguious skip this step\n",
+    "7) add an estimate of what rate the revenue of the comapany will increase at provided they follow the guidelines, give conservating estimates keeping in account non ideal conditions.\n",
+    "8) if the website isnt of a company or data isnt available, give out an error message along the lines of more data required for analysis\"\"\""
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": ".venv",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.12.12"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

week2/community-contributions/Samuel_Bootcamp_wk2/Samuel week2 EXERCISE.ipynb

@@ -0,0 +1,551 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "d006b2ea-9dfe-49c7-88a9-a5a0775185fd",
+   "metadata": {},
+   "source": [
+    "# Additional End of week Exercise - week 2\n",
+    "\n",
+    "Now use everything you've learned from Week 2 to build a full prototype for the technical question/answerer you built in Week 1 Exercise.\n",
+    "\n",
+    "This should include a Gradio UI, streaming, use of the system prompt to add expertise, and the ability to switch between models. Bonus points if you can demonstrate use of a tool!\n",
+    "\n",
+    "If you feel bold, see if you can add audio input so you can talk to it, and have it respond with audio. ChatGPT or Claude can help you, or email me if you have questions.\n",
+    "\n",
+    "I will publish a full solution here soon - unless someone beats me to it...\n",
+    "\n",
+    "There are so many commercial applications for this, from a language tutor, to a company onboarding solution, to a companion AI to a course (like this one!) I can't wait to see your results."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "f69a564870ec63b0",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-10-24T16:15:26.039019Z",
+     "start_time": "2025-10-24T16:15:25.888596Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "#Imports\n",
+    "from IPython.display import Markdown, display\n",
+    "from openai import OpenAI\n",
+    "import os\n",
+    "import json\n",
+    "import requests\n",
+    "import gradio as gr\n",
+    "from dotenv import load_dotenv\n",
+    "from typing import List\n",
+    "import time\n",
+    "from datetime import datetime, timedelta\n",
+    "import requests\n",
+    "from bs4 import BeautifulSoup\n",
+    "from datetime import datetime\n",
+    "import json\n",
+    "import re\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "fa60913187dbe71d",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-10-24T16:14:27.703743Z",
+     "start_time": "2025-10-24T16:14:27.677172Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "OLLAMA_BASE_URL=\"http://localhost:11434/v1/completions\"\n",
+    "LOCAL_MODEL_NAME=\"llama3.2\"\n",
+    "\n",
+    "\n",
+    "# Load environment variables in a file called .env\n",
+    "\n",
+    "load_dotenv(override=True)\n",
+    "api_key = os.getenv('OPENAI_API_KEY')\n",
+    "OPENAI_API_KEY=api_key\n",
+    "\n",
+    "load_dotenv(override=True)\n",
+    "coin_key = os.getenv('COINMARKETCAP_API_KEY')\n",
+    "COINMARKETCAP_API_KEY = coin_key\n",
+    "\n",
+    "# Check the key\n",
+    "\n",
+    "if not api_key:\n",
+    "    print(\"No API key was found - please head over to the troubleshooting notebook in this folder to identify & fix!\")\n",
+    "elif not api_key.startswith(\"sk-proj-\"):\n",
+    "    print(\"An API key was found, but it doesn't start sk-proj-; please check you're using the right key - see troubleshooting notebook\")\n",
+    "elif api_key.strip() != api_key:\n",
+    "    print(\"An API key was found, but it looks like it might have space or tab characters at the start or end - please remove them - see troubleshooting notebook\")\n",
+    "else:\n",
+    "    print(\"API key found and looks good so far!\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "1bf8ccf240e982da",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-10-24T16:14:35.695654Z",
+     "start_time": "2025-10-24T16:14:35.681319Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "# Ollama configuration\n",
+    "OLLAMA_URL = os.getenv(\"OLLAMA_BASE_URL\", \"http://localhost:11434/v1/completions\")\n",
+    "OLLAMA_MODEL = os.getenv(\"LOCAL_MODEL_NAME\", \"llama3.2\")\n",
+    "\n",
+    "# OpenAI configuration\n",
+    "OPENAI_API_KEY = os.getenv(\"OPENAI_API_KEY\")\n",
+    "OPENAI_MODEL = \"gpt-4\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "98d8f6481681ed57",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-10-24T16:14:49.865353Z",
+     "start_time": "2025-10-24T16:14:49.848662Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "# Crypto Analysis Prompt\n",
+    "CRYPTO_SYSTEM_PROMPT = \"\"\"You are a specialized AI assistant with expertise in cryptocurrency markets and data analysis.\n",
+    "Your role is to help users identify and understand cryptocurrencies with the strongest growth patterns over recent weeks.\n",
+    "Provide clear, data-driven insights about market trends and performance metrics.\"\"\"\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "7729697aa8937c3",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-10-24T16:15:37.367235Z",
+     "start_time": "2025-10-24T16:15:35.409542Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "\n",
+    "def scrape_coingecko(limit=10, debug=False):\n",
+    "    try:\n",
+    "        headers = {\n",
+    "            'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/120.0.0.0 Safari/537.36',\n",
+    "            'Accept': 'text/html,application/xhtml+xml,application/xml;q=0.9,*/*;q=0.8',\n",
+    "            'Accept-Language': 'en-US,en;q=0.5',\n",
+    "            'Referer': 'https://www.coingecko.com/'\n",
+    "        }\n",
+    "\n",
+    "        url = \"https://www.coingecko.com/en/coins/trending\"\n",
+    "        response = requests.get(url, headers=headers, timeout=30)\n",
+    "        response.raise_for_status()\n",
+    "\n",
+    "        if debug:\n",
+    "            print(f\"Status: {response.status_code}\")\n",
+    "            with open(\"debug_coingecko.html\", \"w\", encoding=\"utf-8\") as f:\n",
+    "                f.write(response.text)\n",
+    "            print(\"HTML saved to debug_coingecko.html\")\n",
+    "\n",
+    "        soup = BeautifulSoup(response.content, 'html.parser')\n",
+    "        top_performers = []\n",
+    "\n",
+    "        # Try multiple selectors\n",
+    "        rows = (soup.find_all('tr', {'data-sort-by': True}) or\n",
+    "                soup.find_all('tr', class_=re.compile('hover')) or\n",
+    "                soup.select('table tbody tr'))[:limit]\n",
+    "\n",
+    "        if debug:\n",
+    "            print(f\"Found {len(rows)} rows\")\n",
+    "\n",
+    "        for row in rows:\n",
+    "            try:\n",
+    "                # Find all text in row\n",
+    "                texts = [t.strip() for t in row.stripped_strings]\n",
+    "                if debug:\n",
+    "                    print(f\"Row texts: {texts[:5]}\")\n",
+    "\n",
+    "                # Extract data from text list\n",
+    "                name = texts[1] if len(texts) > 1 else \"Unknown\"\n",
+    "                symbol = texts[2] if len(texts) > 2 else \"N/A\"\n",
+    "\n",
+    "                # Find price\n",
+    "                price = 0\n",
+    "                for text in texts:\n",
+    "                    if '$' in text:\n",
+    "                        price_str = text.replace('$', '').replace(',', '')\n",
+    "                        try:\n",
+    "                            price = float(price_str)\n",
+    "                            break\n",
+    "                        except:\n",
+    "                            continue\n",
+    "\n",
+    "                # Find percentage change\n",
+    "                change_30d = 0\n",
+    "                for text in texts:\n",
+    "                    if '%' in text:\n",
+    "                        change_str = text.replace('%', '').replace('+', '')\n",
+    "                        try:\n",
+    "                            change_30d = float(change_str)\n",
+    "                        except:\n",
+    "                            continue\n",
+    "\n",
+    "                if name != \"Unknown\":\n",
+    "                    top_performers.append({\n",
+    "                        \"name\": name,\n",
+    "                        \"symbol\": symbol,\n",
+    "                        \"current_price\": price,\n",
+    "                        \"price_change_percentage_30d\": change_30d,\n",
+    "                        \"source\": \"coingecko\"\n",
+    "                    })\n",
+    "            except Exception as e:\n",
+    "                if debug:\n",
+    "                    print(f\"Row error: {e}\")\n",
+    "                continue\n",
+    "\n",
+    "        return {\"timeframe\": \"30d\", \"timestamp\": datetime.now().isoformat(), \"count\": len(top_performers), \"top_performers\": top_performers}\n",
+    "    except Exception as e:\n",
+    "        return {\"error\": str(e)}\n",
+    "\n",
+    "\n",
+    "\n",
+    "def get_top_performers(source=\"coingecko\", limit=10, save=False, debug=False):\n",
+    "    sources = {\"coingecko\": scrape_coingecko, \"coinmarketcap\": scrape_coinmarketcap}\n",
+    "    result = sources[source](limit, debug)\n",
+    "\n",
+    "    if save and \"error\" not in result:\n",
+    "        filename = f\"crypto_{source}_{datetime.now().strftime('%Y%m%d_%H%M%S')}.json\"\n",
+    "        with open(filename, 'w') as f:\n",
+    "            json.dump(result, f, indent=2)\n",
+    "        print(f\"Saved to {filename}\")\n",
+    "\n",
+    "    return result\n",
+    "\n",
+    "if __name__ == \"__main__\":\n",
+    "    print(\"Testing CoinGecko with debug...\")\n",
+    "    result = get_top_performers(\"coingecko\", 10, True, debug=True)\n",
+    "    print(json.dumps(result, indent=2))\n",
+    "\n",
+    "    print(\"\\n\" + \"=\"*60 + \"\\n\")\n",
+    "\n",
+    "    print(\"Testing CoinMarketCap with debug...\")\n",
+    "    result = get_top_performers(\"coinmarketcap\", 10, True, debug=True)\n",
+    "    print(json.dumps(result, indent=2))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "2e3de36fa13f2dec",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def scrape_coinmarketcap(limit=10, debug=False):\n",
+    "    try:\n",
+    "        headers = {\n",
+    "            'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/120.0.0.0 Safari/537.36',\n",
+    "            'Accept': 'text/html,application/xhtml+xml,application/xml;q=0.9,*/*;q=0.8',\n",
+    "            'Accept-Language': 'en-US,en;q=0.5',\n",
+    "        }\n",
+    "\n",
+    "        url = \"https://coinmarketcap.com/gainers-losers/\"\n",
+    "        response = requests.get(url, headers=headers, timeout=30)\n",
+    "        response.raise_for_status()\n",
+    "\n",
+    "        if debug:\n",
+    "            print(f\"Status: {response.status_code}\")\n",
+    "            with open(\"debug_coinmarketcap.html\", \"w\", encoding=\"utf-8\") as f:\n",
+    "                f.write(response.text)\n",
+    "            print(\"HTML saved to debug_coinmarketcap.html\")\n",
+    "\n",
+    "        soup = BeautifulSoup(response.content, 'html.parser')\n",
+    "        top_performers = []\n",
+    "\n",
+    "        # Find all table rows\n",
+    "        rows = soup.find_all('tr')\n",
+    "        if debug:\n",
+    "            print(f\"Total rows found: {len(rows)}\")\n",
+    "\n",
+    "        for row in rows[1:limit+1]:\n",
+    "            try:\n",
+    "                texts = [t.strip() for t in row.stripped_strings]\n",
+    "                if debug and len(texts) > 0:\n",
+    "                    print(f\"Row texts: {texts[:5]}\")\n",
+    "\n",
+    "                if len(texts) < 3:\n",
+    "                    continue\n",
+    "\n",
+    "                # Usually: rank, name, symbol, price, change...\n",
+    "                name = texts[1] if len(texts) > 1 else \"Unknown\"\n",
+    "                symbol = texts[2] if len(texts) > 2 else \"N/A\"\n",
+    "\n",
+    "                price = 0\n",
+    "                change_30d = 0\n",
+    "\n",
+    "                for text in texts:\n",
+    "                    if '$' in text and price == 0:\n",
+    "                        try:\n",
+    "                            price = float(text.replace('$', '').replace(',', ''))\n",
+    "                        except:\n",
+    "                            continue\n",
+    "                    if '%' in text:\n",
+    "                        try:\n",
+    "                            change_30d = float(text.replace('%', '').replace('+', ''))\n",
+    "                        except:\n",
+    "                            continue\n",
+    "\n",
+    "                if name != \"Unknown\":\n",
+    "                    top_performers.append({\n",
+    "                        \"name\": name,\n",
+    "                        \"symbol\": symbol,\n",
+    "                        \"current_price\": price,\n",
+    "                        \"price_change_percentage_30d\": change_30d,\n",
+    "                        \"source\": \"coinmarketcap\"\n",
+    "                    })\n",
+    "            except Exception as e:\n",
+    "                if debug:\n",
+    "                    print(f\"Row error: {e}\")\n",
+    "                continue\n",
+    "\n",
+    "        return {\"timeframe\": \"30d\", \"timestamp\": datetime.now().isoformat(), \"count\": len(top_performers), \"top_performers\": top_performers}\n",
+    "    except Exception as e:\n",
+    "        return {\"error\": str(e)}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "4a63cbcc7ae04c7e",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-10-24T15:23:22.157803Z",
+     "start_time": "2025-10-24T15:23:22.147500Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "\n",
+    "\n",
+    "# Tool detection and execution\n",
+    "def detect_and_run_tool(user_message: str):\n",
+    "    user_message_lower = user_message.lower().strip()\n",
+    "\n",
+    "    # Detect crypto growth queries\n",
+    "    crypto_keywords = [\"crypto growth\", \"top gainers\", \"best performing\", \"crypto performance\", \"trending coins\"]\n",
+    "\n",
+    "    if any(keyword in user_message_lower for keyword in crypto_keywords):\n",
+    "        return True, get_top_performers(\"coingecko\", 10, True, debug=True)\n",
+    "\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "626a022b562bf73d",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "e5c6db45fb4d53d9",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-10-24T15:23:25.205927Z",
+     "start_time": "2025-10-24T15:23:25.199801Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "def ask_ollama(prompt: str) -> str:\n",
+    "    try:\n",
+    "        payload = {\"model\": OLLAMA_MODEL, \"prompt\": prompt, \"stream\": False}\n",
+    "        r = requests.post(OLLAMA_URL, json=payload, timeout=120)\n",
+    "        r.raise_for_status()\n",
+    "        data = r.json()\n",
+    "        return data.get(\"choices\", [{}])[0].get(\"text\", \"\").strip()\n",
+    "    except Exception as e:\n",
+    "        return f\"[Ollama error: {e}]\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "2f81a00e9584d184",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "c2686a6503cf62a4",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-10-24T15:23:29.556036Z",
+     "start_time": "2025-10-24T15:23:29.552763Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "def ask_openai(prompt: str) -> str:\n",
+    "    try:\n",
+    "        from openai import OpenAI\n",
+    "        client = OpenAI(api_key=OPENAI_API_KEY)\n",
+    "\n",
+    "        response = client.chat.completions.create(\n",
+    "            model=OPENAI_MODEL,\n",
+    "            messages=[\n",
+    "                {\"role\": \"system\", \"content\": CRYPTO_SYSTEM_PROMPT},\n",
+    "                {\"role\": \"user\", \"content\": prompt}\n",
+    "            ],\n",
+    "            max_tokens=512,\n",
+    "        )\n",
+    "        return response.choices[0].message.content\n",
+    "    except Exception as e:\n",
+    "        return f\"[OpenAI error: {e}]\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "2313e5940e9fa3da",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-10-24T15:27:33.546418Z",
+     "start_time": "2025-10-24T15:27:18.318834Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "def chat_fn(user_message: str, history: List[List[str]], model_choice: str):\n",
+    "    tool_used, tool_output = detect_and_run_tool(user_message)\n",
+    "\n",
+    "    if tool_used:\n",
+    "        if \"error\" in tool_output:\n",
+    "            reply = f\"Data fetch error: {tool_output['error']}\"\n",
+    "        else:\n",
+    "            # Format the crypto data for AI analysis\n",
+    "            crypto_data_str = json.dumps(tool_output, indent=2)\n",
+    "\n",
+    "            # Create analysis prompt\n",
+    "            analysis_prompt = f\"\"\"\n",
+    "            Analyze this cryptocurrency growth data and provide insights:\n",
+    "\n",
+    "            {crypto_data_str}\n",
+    "\n",
+    "            Please identify:\n",
+    "            1. The strongest performers and their growth patterns\n",
+    "            2. Any notable trends across different timeframes\n",
+    "            3. Risk considerations or notable observations\n",
+    "            4. Simple, actionable insights for the user\n",
+    "\n",
+    "            Keep the analysis clear and data-driven.\n",
+    "            User's original question: {user_message}\n",
+    "            \"\"\"\n",
+    "\n",
+    "            # Get AI analysis\n",
+    "            if model_choice == \"openai\":\n",
+    "                analysis = ask_openai(analysis_prompt)\n",
+    "            else:\n",
+    "                ollama_prompt = f\"{CRYPTO_SYSTEM_PROMPT}\\n\\nUser: {analysis_prompt}\\nAssistant:\"\n",
+    "                analysis = ask_ollama(ollama_prompt)\n",
+    "\n",
+    "            reply = f\"📊 **Crypto Growth Analysis**\\n\\n{analysis}\\n\\n*Raw data for reference:*\\n```json\\n{crypto_data_str}\\n```\"\n",
+    "\n",
+    "    else:\n",
+    "        # Regular conversation\n",
+    "        if model_choice == \"openai\":\n",
+    "            reply = ask_openai(user_message)\n",
+    "        else:\n",
+    "            prompt = f\"{CRYPTO_SYSTEM_PROMPT}\\n\\nUser: {user_message}\\nAssistant:\"\n",
+    "            reply = ask_ollama(prompt)\n",
+    "\n",
+    "    history.append([user_message, reply])\n",
+    "    return history\n",
+    "\n",
+    "# Enhanced Gradio UI with crypto focus\n",
+    "def main():\n",
+    "    with gr.Blocks(title=\"Crypto Growth Analyst Chatbot\") as demo:\n",
+    "        gr.Markdown(\"\"\"\n",
+    "        # Samuel Week 2 Task: Crypto Growth Analyst Chatbot\n",
+    "        **Analyze cryptocurrency performance with dual AI models** (Ollama & OpenAI)\n",
+    "\n",
+    "        *Try questions like:*\n",
+    "        - \"Show me cryptocurrencies with strongest growth\"\n",
+    "        - \"What are the top performing coins this month?\"\n",
+    "        - \"Analyze crypto market trends\"\n",
+    "        \"\"\")\n",
+    "\n",
+    "        # Message input\n",
+    "        msg = gr.Textbox(\n",
+    "            placeholder=\"Ask about crypto growth trends or type /ticket <city>\",\n",
+    "            label=\"Your message\",\n",
+    "            lines=2,\n",
+    "            autofocus=True\n",
+    "        )\n",
+    "\n",
+    "        # Model selection\n",
+    "        with gr.Row():\n",
+    "            model_choice = gr.Radio(\n",
+    "                [\"ollama\", \"openai\"],\n",
+    "                value=\"ollama\",\n",
+    "                label=\"AI Model\"\n",
+    "            )\n",
+    "            send = gr.Button(\"Analyze Crypto Data\", variant=\"primary\")\n",
+    "\n",
+    "        # Chatbot area\n",
+    "        chatbot = gr.Chatbot(label=\"Crypto Analysis Conversation\", height=500, type=\"messages\")\n",
+    "\n",
+    "        # Wrapper function\n",
+    "        def wrapped_chat_fn(user_message, history, model_choice):\n",
+    "            updated_history = chat_fn(user_message, history, model_choice)\n",
+    "            return updated_history, gr.update(value=\"\")\n",
+    "\n",
+    "        # Event handlers\n",
+    "        send.click(wrapped_chat_fn, inputs=[msg, chatbot, model_choice], outputs=[chatbot, msg])\n",
+    "        msg.submit(wrapped_chat_fn, inputs=[msg, chatbot, model_choice], outputs=[chatbot, msg])\n",
+    "\n",
+    "    demo.launch(server_name=\"0.0.0.0\", share=False)\n",
+    "\n",
+    "if __name__ == \"__main__\":\n",
+    "    main()\n",
+    "\n",
+    "    "
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": ".venv",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.12.12"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

week2/community-contributions/day1_N_way_conversation_coffee_talk.ipynb

@@ -0,0 +1,283 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "88f67391",
+   "metadata": {},
+   "source": [
+    "### N Way Conversation - Coffee Talk \n",
+    "\n",
+    "This example simulates an N-way conversation between the characters of the Saturday Night Live skit Coffee Talk.\n",
+    "\n",
+    "The character information is retrieved from a model and each character is handled by its own model selected at random from a list of available models. Only the number of characters, number of rounds, and available models are configured.\n",
+    "\n",
+    "The example can use OpenRouter, OpenAI, or Ollama, in that order. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "a1eeb029",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Setup ...\n",
+    "\n",
+    "# The number of characters (models) conversing\n",
+    "NBR_CHARACTERS=4\n",
+    "\n",
+    "# The number of rounds of conversation\n",
+    "NBR_ROUNDS=4\n",
+    "\n",
+    "# Available OpenRouter models. The base model is used to select characters and the topic. Other models are used for the conversation\n",
+    "OPENROUTER_MODELS=\"openai/gpt-4.1-mini, anthropic/claude-3.5-haiku, google/gemini-2.5-flash\"\n",
+    "OPENROUTER_BASE=\"openai/gpt-5\"\n",
+    "\n",
+    "# Available OpenAI models\n",
+    "OPENAI_MODELS=\"gpt-4.1, gpt-4.1-mini, gpt-5-nano\"\n",
+    "OPENAI_BASE=\"gpt-5\"\n",
+    "\n",
+    "# Available Ollama models. Note that these must be pre-fetched or errors will occur (and won't be handled)\n",
+    "OLLAMA_MODELS=\"gpt-oss, gemma3, llama3.2\"\n",
+    "OLLAMA_BASE=\"gpt-oss\"\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "68022fbc",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# imports\n",
+    "import os\n",
+    "import json\n",
+    "from dotenv import load_dotenv\n",
+    "from IPython.display import Markdown, display, update_display\n",
+    "from openai import OpenAI"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "73460c5e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Setup the LLM client and models. OpenRouter has priority if available, then OpenAI, then Ollama.\n",
+    "\n",
+    "load_dotenv(override=True)\n",
+    "openai_api_key = os.getenv('OPENAI_API_KEY')\n",
+    "openrouter_api_key = os.getenv('OPENROUTER_API_KEY')\n",
+    "\n",
+    "if openrouter_api_key:\n",
+    "    print(f\"OpenRouter API Key exists and begins {openrouter_api_key[:3]}, using OpenRouter.\")\n",
+    "    available_models=OPENROUTER_MODELS\n",
+    "    base_model=OPENROUTER_BASE\n",
+    "    client = OpenAI(base_url=\"https://openrouter.ai/api/v1\", api_key=openrouter_api_key)\n",
+    "elif openai_api_key:\n",
+    "    print(f\"OpenAI API Key exists and begins {openai_api_key[:8]}, using OpenAI.\")\n",
+    "    available_models=OPENAI_MODELS\n",
+    "    base_model=OPENAI_BASE\n",
+    "    client = OpenAI()\n",
+    "else:\n",
+    "    print(\"OpenAI API Key not set, using Ollama.\")\n",
+    "    available_models=OLLAMA_MODELS\n",
+    "    base_model=OLLAMA_BASE\n",
+    "    client = OpenAI(api_key=\"ollama\", base_url=\"http://localhost:11434/v1\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "b1a7004d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Get the characters from the base model\n",
+    "system_prompt = \"\"\"\n",
+    "You will be asked to return information about characters in the SNL skit Coffee Talk\n",
+    "You should return the information as a JSON response with the following format:\n",
+    "{\n",
+    "  { \"name\" : \"Linda\", \"persona\", \"....\", \"model\" : \"model-name\" },\n",
+    "  { \"name\" : \"Paul\", \"persona\", \"....\", \"model\" : \"model-name\" }\n",
+    "}\n",
+    "\n",
+    "\"\"\"\n",
+    "\n",
+    "user_prompt = f\"\"\"\n",
+    "Create a list of the many characters from the SNL skit Coffee Talk, and return {NBR_CHARACTERS} total characters.\n",
+    "Always return Linda Richmond as the first character.\n",
+    "Return one caller.\n",
+    "Select the remaining characters at random from the list of all characters. \n",
+    "For the model value, return a random model name from this list: {available_models}.\n",
+    "\"\"\"\n",
+    "\n",
+    "response = client.chat.completions.create(\n",
+    "        model=base_model,\n",
+    "        messages=[\n",
+    "            {\"role\": \"system\", \"content\": system_prompt},\n",
+    "            {\"role\": \"user\", \"content\": user_prompt}\n",
+    "        ],\n",
+    "        response_format={\"type\": \"json_object\"}\n",
+    "    )\n",
+    "result = response.choices[0].message.content\n",
+    "characters = json.loads(result)\n",
+    "\n",
+    "print(json.dumps(characters, indent=2))\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "21a73805",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Generate system prompts for each character, which includes their name, persona, the other guests, and how they should respond.\n",
+    "\n",
+    "guests = \"The guests on todays show are \"\n",
+    "guest_names = [character['name'] for character in characters[\"characters\"]]\n",
+    "guests += \", \".join(guest_names)\n",
+    "\n",
+    "prompt = \"\"\n",
+    "for character in characters[\"characters\"]:\n",
+    "    prompt = f\"You are {character['name']} a character on the SNL skit Coffee Talk.\"\n",
+    "    prompt += f\" Your personality is : {character['persona']} \"\n",
+    "    prompt += \" \" + guests + \".\"\n",
+    "    prompt += \" Keep responses brief and in character.\"\n",
+    "    prompt += \" In the conversation history, each response is prefixed with the character's name to identify the respondent.\"\n",
+    "    prompt += \" Your response should not include your character name as a prefix.\"\n",
+    "\n",
+    "    character[\"system_prompt\"] = prompt\n",
+    "\n",
+    "print(json.dumps(characters, indent=2))\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "656131a1",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Get the topic\n",
+    "user_prompt=\"\"\"\n",
+    "In the SNL skit Coffee Talk, the host Linda Richmond proposes topics in the form \"X Y is neither X, nor Y - discuss\".\n",
+    "Create a list of the many topics proposed on the show, and select one at random and return it.\n",
+    "Return only the selected topic without any formatting.\n",
+    "\"\"\"\n",
+    "\n",
+    "response = client.chat.completions.create(\n",
+    "        model=base_model,\n",
+    "        messages=[\n",
+    "            {\"role\": \"user\", \"content\": user_prompt}\n",
+    "        ],\n",
+    "    )\n",
+    "topic = response.choices[0].message.content\n",
+    "\n",
+    "print(topic)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "6e137753",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def get_character_response(character,history):\n",
+    "    user_prompt = f\"\"\"\n",
+    "    The conversation so far is as follows:\n",
+    "    {history}\n",
+    "    What is your response? \n",
+    "    \"\"\"\n",
+    "    \n",
+    "    response = client.chat.completions.create(\n",
+    "        model=character[\"model\"],\n",
+    "        messages=[\n",
+    "            {\"role\": \"system\", \"content\": character[\"system_prompt\"]},\n",
+    "            {\"role\": \"user\", \"content\": user_prompt}\n",
+    "        ]\n",
+    "    )\n",
+    "    return response.choices[0].message.content\n",
+    "    "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "23fb446f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Start the show!\n",
+    "\n",
+    "history = \"\"\n",
+    "history += \"Welcome to Coffee Talk, I am your host Linda Richmond. Today's guests are:\\n\"\n",
+    "\n",
+    "for character in characters[\"characters\"][1:]:\n",
+    "    history += f\"  - {character['name']}\\n\"\n",
+    "\n",
+    "history += f\"\\nI'll give you a topic: {topic}\\n\"\n",
+    "\n",
+    "display(Markdown(\"---\"))\n",
+    "display(Markdown(history))\n",
+    "display(Markdown(\"---\"))\n",
+    "\n",
+    "# Other guests respond (first round)\n",
+    "for character in characters[\"characters\"][1:]:\n",
+    "    response = get_character_response(character,history)\n",
+    "    display(Markdown(f\"**{character['name']}({character['model']}):** {response}\"))  \n",
+    "    history += f\"\\n{character['name']}: {response}\"\n",
+    "\n",
+    "# Continue conversation for remaining rounds (all characters including Linda)\n",
+    "for round in range(1, NBR_ROUNDS):\n",
+    "    for character in characters[\"characters\"]:\n",
+    "        response = get_character_response(character,history)\n",
+    "        display(Markdown(f\"**{character['name']}({character['model']}):** {response}\"))  \n",
+    "        history += f\"\\n{character['name']}: {response}\"\n",
+    "\n",
+    "# Wrap it up\n",
+    "user_prompt=f\"\"\"\n",
+    "It's time to wrap up the show. Here's the whole conversation:\\n\n",
+    "{history}\n",
+    "Wrap up the show, as only you can.\n",
+    "\"\"\"\n",
+    "\n",
+    "linda = characters[\"characters\"][0]\n",
+    "response = client.chat.completions.create(\n",
+    "        model=linda[\"model\"],\n",
+    "        messages=[\n",
+    "            {\"role\": \"system\", \"content\": linda[\"system_prompt\"]},\n",
+    "            {\"role\": \"user\", \"content\": user_prompt}\n",
+    "        ]\n",
+    "    )\n",
+    "\n",
+    "display(Markdown(\"---\"))\n",
+    "display(Markdown(response.choices[0].message.content))  \n"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "llm-engineering",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.12.11"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

week2/community-contributions/emmy/emmy_week2_EXERCISE.ipynb

@@ -0,0 +1,240 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "d006b2ea-9dfe-49c7-88a9-a5a0775185fd",
+   "metadata": {},
+   "source": [
+    "# Additional End of week Exercise - week 2\n",
+    "\n",
+    "Now use everything you've learned from Week 2 to build a full prototype for the technical question/answerer you built in Week 1 Exercise.\n",
+    "\n",
+    "This should include a Gradio UI, streaming, use of the system prompt to add expertise, and the ability to switch between models. Bonus points if you can demonstrate use of a tool!\n",
+    "\n",
+    "If you feel bold, see if you can add audio input so you can talk to it, and have it respond with audio. ChatGPT or Claude can help you, or email me if you have questions.\n",
+    "\n",
+    "I will publish a full solution here soon - unless someone beats me to it...\n",
+    "\n",
+    "There are so many commercial applications for this, from a language tutor, to a company onboarding solution, to a companion AI to a course (like this one!) I can't wait to see your results."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "4c427d7c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#imports\n",
+    "import os\n",
+    "import time\n",
+    "import gradio as gr\n",
+    "import openai\n",
+    "from dotenv import load_dotenv\n",
+    "import re\n",
+    "\n",
+    "load_dotenv(override=True)\n",
+    "OPENAI_KEY = os.getenv(\"OPENAI_API_KEY\")\n",
+    "GOOGLE_KEY = os.getenv(\"GOOGLE_API_KEY\")\n",
+    "GEMINI_BASE_URL = os.getenv(\"GEMINI_BASE_URL\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "21e78ed3",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# OpenAI / Gemini Client\n",
+    "def get_client(model_choice):\n",
+    "    \"\"\"\n",
+    "    Return an OpenAI client configured for GPT or Gemini.\n",
+    "    \"\"\"\n",
+    "    if model_choice == \"OpenAI GPT-4\":\n",
+    "        return openai.OpenAI(api_key=OPENAI_KEY)\n",
+    "    else:\n",
+    "        return openai.OpenAI(\n",
+    "            api_key=GOOGLE_KEY,\n",
+    "            base_url=GEMINI_BASE_URL,\n",
+    "        )"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "8fb92ea9",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Fake Weather Tool\n",
+    "def get_weather(location):\n",
+    "    data = {\n",
+    "        \"new york\": {\"temp\": 72, \"condition\": \"Partly Cloudy\"},\n",
+    "        \"london\": {\"temp\": 59, \"condition\": \"Rainy\"},\n",
+    "        \"tokyo\": {\"temp\": 68, \"condition\": \"Clear\"},\n",
+    "    }\n",
+    "    info = data.get(location.lower(), {\"temp\": 75, \"condition\": \"Sunny\"})\n",
+    "    return f\"Weather in {location}: {info['temp']}°F, {info['condition']}\"\n",
+    "\n",
+    "\n",
+    "def maybe_use_tool(message):\n",
+    "    \"\"\"\n",
+    "    Detect patterns like 'weather in <location>' (case-insensitive)\n",
+    "    and inject tool result.\n",
+    "    Supports multi-word locations, e.g. \"New York\" or \"tokyo\".\n",
+    "    \"\"\"\n",
+    "    pattern = re.compile(r\"weather\\s+in\\s+([A-Za-z\\s]+)\", re.IGNORECASE)\n",
+    "    match = pattern.search(message)\n",
+    "\n",
+    "    if match:\n",
+    "        location = match.group(1).strip(\" ?.,!\").title()\n",
+    "        tool_result = get_weather(location)\n",
+    "        return f\"{message}\\n\\n[Tool used: {tool_result}]\"\n",
+    "\n",
+    "    return message"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "672621a6",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# prompt\n",
+    "SYSTEM_PROMPTS = {\n",
+    "    \"General Assistant\": \"You are a helpful and polite AI assistant.\",\n",
+    "    \"Technical Expert\": \"You are an expert software engineer who writes clear, correct code.\",\n",
+    "    \"Creative Writer\": \"You are a creative storyteller who writes imaginative and emotional prose.\",\n",
+    "    \"Science Tutor\": \"You are a science teacher who explains ideas simply and clearly.\",\n",
+    "}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "21525edd",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# ---------------------------------------------\n",
+    "# Build chat messages\n",
+    "# ---------------------------------------------\n",
+    "def build_messages(history, user_msg, persona):\n",
+    "    messages = [{\"role\": \"system\", \"content\": SYSTEM_PROMPTS[persona]}]\n",
+    "    for u, a in history:\n",
+    "        messages.append({\"role\": \"user\", \"content\": u})\n",
+    "        messages.append({\"role\": \"assistant\", \"content\": a})\n",
+    "    messages.append({\"role\": \"user\", \"content\": user_msg})\n",
+    "    return messages\n",
+    "\n",
+    "\n",
+    "# ---------------------------------------------\n",
+    "# Stream model output\n",
+    "# ---------------------------------------------\n",
+    "def stream_response(model_choice, messages):\n",
+    "    \"\"\"\n",
+    "    Uses the same openai library to stream from GPT or Gemini.\n",
+    "    \"\"\"\n",
+    "    client = get_client(model_choice)\n",
+    "    model = \"gpt-4o-mini\" if model_choice == \"OpenAI GPT-4\" else \"gemini-2.5-flash\"\n",
+    "\n",
+    "    stream = client.chat.completions.create(\n",
+    "        model=model,\n",
+    "        messages=messages,\n",
+    "        stream=True,\n",
+    "    )\n",
+    "\n",
+    "    reply = \"\"\n",
+    "    for chunk in stream:\n",
+    "        if chunk.choices[0].delta and chunk.choices[0].delta.content:\n",
+    "            reply += chunk.choices[0].delta.content\n",
+    "            yield reply\n",
+    "        time.sleep(0.01)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "c88976b1",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Gradio UI\n",
+    "with gr.Blocks(theme=gr.themes.Soft()) as demo:\n",
+    "    gr.Markdown(\n",
+    "    \"\"\"\n",
+    "    # 🤖 Unified GPT + Gemini Chat\n",
+    "\n",
+    "    - 🔀 Choose model: **OpenAI GPT-4** or **Gemini 2.5 Flash**\n",
+    "    - 🧠 Pick the assistant persona (system prompt injection)\n",
+    "    - 🛠 Tool support: ask about weather\n",
+    "\n",
+    "    **Weather tool tips:**\n",
+    "    - Ask: \"What's the weather in London?\"\n",
+    "    - Also works for: New York, Tokyo\n",
+    "    - If a city isn't known, it returns a default sunny forecast\n",
+    "    \"\"\"\n",
+    "    )\n",
+    "\n",
+    "    with gr.Row():\n",
+    "        model_choice = gr.Dropdown(\n",
+    "            [\"OpenAI GPT-4\", \"Gemini 2.5 Flash\"],\n",
+    "            value=\"OpenAI GPT-4\",\n",
+    "            label=\"Model\",\n",
+    "        )\n",
+    "        persona = gr.Dropdown(\n",
+    "            list(SYSTEM_PROMPTS.keys()),\n",
+    "            value=\"General Assistant\",\n",
+    "            label=\"Persona\",\n",
+    "        )\n",
+    "\n",
+    "    chatbot = gr.Chatbot(height=400)\n",
+    "    msg = gr.Textbox(placeholder=\"Ask about weather or coding...\", label=\"Your message\")\n",
+    "    gr.Markdown(\n",
+    "        \"💡 Tip: You can ask about the weather in **London**, **New York**, or **Tokyo**. \"\n",
+    "        \"I'll call a local tool and include that info in my answer.\"\n",
+    "    )\n",
+    "    send = gr.Button(\"Send\", variant=\"primary\")\n",
+    "    clear = gr.Button(\"Clear\")\n",
+    "\n",
+    "    state = gr.State([])\n",
+    "\n",
+    "    msg.submit(chat_fn, [msg, state, model_choice, persona], chatbot).then(\n",
+    "        lambda chat: chat, chatbot, state\n",
+    "    ).then(lambda: \"\", None, msg)\n",
+    "\n",
+    "    send.click(chat_fn, [msg, state, model_choice, persona], chatbot).then(\n",
+    "        lambda chat: chat, chatbot, state\n",
+    "    ).then(lambda: \"\", None, msg)\n",
+    "\n",
+    "    clear.click(lambda: ([], []), None, [chatbot, state], queue=False)\n",
+    "\n",
+    "if __name__ == \"__main__\":\n",
+    "    demo.launch()\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "llm-engineering (3.12.10)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.12.10"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

week3/community-contributions/hopeogbons/README.md

@@ -0,0 +1,197 @@
+# 🎙️ Audio Transcription Assistant
+
+An AI-powered audio transcription tool that converts speech to text in multiple languages using OpenAI's Whisper model.
+
+## Why I Built This
+
+In today's content-driven world, audio and video are everywhere—podcasts, meetings, lectures, interviews. But what if you need to quickly extract text from an audio file in a different language? Or create searchable transcripts from recordings?
+
+Manual transcription is time-consuming and expensive. I wanted to build something that could:
+
+- Accept audio files in any format (MP3, WAV, etc.)
+- Transcribe them accurately using AI
+- Support multiple languages
+- Work locally on my Mac **and** on cloud GPUs (Google Colab)
+
+That's where **Whisper** comes in—OpenAI's powerful speech recognition model.
+
+## Features
+
+- 📤 **Upload any audio file** (MP3, WAV, M4A, FLAC, etc.)
+- 🌍 **12+ languages supported** with auto-detection
+- 🤖 **Accurate AI-powered transcription** using Whisper
+- ⚡ **Cross-platform** - works on CPU (Mac) or GPU (Colab)
+- 🎨 **Clean web interface** built with Gradio
+- 🚀 **Fast processing** with optimized model settings
+
+## Tech Stack
+
+- **OpenAI Whisper** - Speech recognition model
+- **Gradio** - Web interface framework
+- **PyTorch** - Deep learning backend
+- **NumPy** - Numerical computing
+- **ffmpeg** - Audio file processing
+
+## Installation
+
+### Prerequisites
+
+- Python 3.12+
+- ffmpeg (for audio processing)
+- uv package manager (or pip)
+
+### Setup
+
+1. Clone this repository or download the notebook
+
+2. Install dependencies:
+
+```bash
+# Install compatible NumPy version
+uv pip install --reinstall "numpy==1.26.4"
+
+# Install PyTorch
+uv pip install torch torchvision torchaudio
+
+# Install Gradio and Whisper
+uv pip install gradio openai-whisper ffmpeg-python
+
+# (Optional) Install Ollama for LLM features
+uv pip install ollama
+```
+
+3. **For Mac users**, ensure ffmpeg is installed:
+
+```bash
+brew install ffmpeg
+```
+
+## Usage
+
+### Running Locally
+
+1. Open the Jupyter notebook `week3 EXERCISE_hopeogbons.ipynb`
+
+2. Run all cells in order:
+
+   - Cell 1: Install dependencies
+   - Cell 2: Import libraries
+   - Cell 3: Load Whisper model
+   - Cell 4: Define transcription function
+   - Cell 5: Build Gradio interface
+   - Cell 6: Launch the app
+
+3. The app will automatically open in your browser
+
+4. Upload an audio file, select the language, and click Submit!
+
+### Running on Google Colab
+
+For GPU acceleration:
+
+1. Open the notebook in Google Colab
+2. Runtime → Change runtime type → **GPU (T4)**
+3. Run all cells in order
+4. The model will automatically use GPU acceleration
+
+**Note:** First run downloads the Whisper model (~140MB) - this is a one-time download.
+
+## Supported Languages
+
+- 🇬🇧 English
+- 🇪🇸 Spanish
+- 🇫🇷 French
+- 🇩🇪 German
+- 🇮🇹 Italian
+- 🇵🇹 Portuguese
+- 🇨🇳 Chinese
+- 🇯🇵 Japanese
+- 🇰🇷 Korean
+- 🇷🇺 Russian
+- 🇸🇦 Arabic
+- 🌐 Auto-detect
+
+## How It Works
+
+1. **Upload** - User uploads an audio file through the Gradio interface
+2. **Process** - ffmpeg decodes the audio file
+3. **Transcribe** - Whisper model processes the audio and generates text
+4. **Display** - Transcription is shown in the output box
+
+The Whisper "base" model is used for a balance between speed and accuracy:
+
+- Fast enough for real-time use on CPU
+- Accurate enough for most transcription needs
+- Small enough (~140MB) for quick downloads
+
+## Example Transcriptions
+
+The app successfully transcribed:
+
+- English podcast episodes
+- French language audio (detected and transcribed)
+- Multi-speaker conversations
+- Audio with background noise
+
+## What I Learned
+
+Building this transcription assistant taught me:
+
+- **Audio processing** with ffmpeg and Whisper
+- **Cross-platform compatibility** (Mac CPU vs Colab GPU)
+- **Dependency management** (dealing with NumPy version conflicts!)
+- **Async handling** in Jupyter notebooks with Gradio
+- **Model optimization** (choosing the right Whisper model size)
+
+The biggest challenge? Getting ffmpeg and NumPy to play nice together across different environments. But solving those issues made me understand the stack much better.
+
+## Troubleshooting
+
+### Common Issues
+
+**1. "No module named 'whisper'" error**
+
+- Make sure you've installed `openai-whisper`, not just `whisper`
+- Restart your kernel after installation
+
+**2. "ffmpeg not found" error**
+
+- Install ffmpeg: `brew install ffmpeg` (Mac) or `apt-get install ffmpeg` (Linux)
+
+**3. NumPy version conflicts**
+
+- Use NumPy 1.26.4: `uv pip install --reinstall "numpy==1.26.4"`
+- Restart kernel after reinstalling
+
+**4. Gradio event loop errors**
+
+- Use `prevent_thread_lock=True` in `app.launch()`
+- Restart kernel if errors persist
+
+## Future Enhancements
+
+- [ ] Support for real-time audio streaming
+- [ ] Speaker diarization (identifying different speakers)
+- [ ] Export transcripts to multiple formats (SRT, VTT, TXT)
+- [ ] Integration with LLMs for summarization
+- [ ] Batch processing for multiple files
+
+## Contributing
+
+Feel free to fork this project and submit pull requests with improvements!
+
+## License
+
+This project is open source and available under the MIT License.
+
+## Acknowledgments
+
+- **OpenAI** for the amazing Whisper model
+- **Gradio** team for the intuitive interface framework
+- **Andela LLM Engineering Program** for the learning opportunity
+
+---
+
+**Built with ❤️ as part of the Andela LLM Engineering Program**
+
+For questions or feedback, feel free to reach out!

week3/community-contributions/hopeogbons/week3 EXERCISE_hopeogbons.ipynb

@@ -0,0 +1,397 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "270ed08b",
+   "metadata": {},
+   "source": [
+    "# 🎙️ Audio Transcription Assistant\n",
+    "\n",
+    "## Why I Built This\n",
+    "\n",
+    "In today's content-driven world, audio and video are everywhere—podcasts, meetings, lectures, interviews. But what if you need to quickly extract text from an audio file in a different language? Or create searchable transcripts from recordings?\n",
+    "\n",
+    "Manual transcription is time-consuming and expensive. I wanted to build something that could:\n",
+    "- Accept audio files in any format (MP3, WAV, etc.)\n",
+    "- Transcribe them accurately using AI\n",
+    "- Support multiple languages\n",
+    "- Work locally on my Mac **and** on cloud GPUs (Google Colab)\n",
+    "\n",
+    "That's where **Whisper** comes in—OpenAI's powerful speech recognition model.\n",
+    "\n",
+    "---\n",
+    "\n",
+    "## What This Does\n",
+    "\n",
+    "This app lets you:\n",
+    "- 📤 Upload any audio file\n",
+    "- 🌍 Choose from 12+ languages (or auto-detect)\n",
+    "- 🤖 Get accurate AI-powered transcription\n",
+    "- ⚡ Process on CPU (Mac) or GPU (Colab)\n",
+    "\n",
+    "**Tech:** OpenAI Whisper • Gradio UI • PyTorch • Cross-platform (Mac/Colab)\n",
+    "\n",
+    "---\n",
+    "\n",
+    "**Note:** This is a demonstration. For production use, consider privacy and data handling policies.\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c37e5165",
+   "metadata": {},
+   "source": [
+    "## Step 1: Install Dependencies\n",
+    "\n",
+    "Installing everything needed:\n",
+    "- **NumPy 1.26.4** - Compatible version for Whisper\n",
+    "- **PyTorch** - Deep learning framework\n",
+    "- **Whisper** - OpenAI's speech recognition model\n",
+    "- **Gradio** - Web interface\n",
+    "- **ffmpeg** - Audio file processing\n",
+    "- **Ollama** - For local LLM support (optional)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "8c66b0ca",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "/usr/local/bin/ffmpeg\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Package installation\n",
+    "\n",
+    "!uv pip install -q --reinstall \"numpy==1.26.4\"\n",
+    "!uv pip install -q torch torchvision torchaudio\n",
+    "!uv pip install -q gradio openai-whisper ffmpeg-python\n",
+    "!uv pip install -q ollama\n",
+    "\n",
+    "# Ensure ffmpeg is available (Mac)\n",
+    "!which ffmpeg || brew install ffmpeg"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f31d64ee",
+   "metadata": {},
+   "source": [
+    "## Step 2: Import Libraries\n",
+    "\n",
+    "The essentials: NumPy for arrays, Gradio for the UI, Whisper for transcription, PyTorch for the model backend, and Ollama for optional LLM features.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "4782261a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Imports\n",
+    "\n",
+    "import os\n",
+    "import numpy as np\n",
+    "import gradio as gr\n",
+    "import whisper\n",
+    "import torch\n",
+    "import ollama"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "93a41b23",
+   "metadata": {},
+   "source": [
+    "## Step 3: Load Whisper Model\n",
+    "\n",
+    "Loading the **base** model—a balanced choice between speed and accuracy. It works on both CPU (Mac) and GPU (Colab). The model is ~140MB and will download automatically on first run.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "130ed059",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Loading Whisper model...\n",
+      "Using device: cpu\n",
+      "✅ Model loaded successfully!\n",
+      "Model type: <class 'whisper.model.Whisper'>\n",
+      "Has transcribe method: True\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Model initialization\n",
+    "\n",
+    "print(\"Loading Whisper model...\")\n",
+    "device = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n",
+    "print(f\"Using device: {device}\")\n",
+    "\n",
+    "whisper_model = whisper.load_model(\"base\", device=device)\n",
+    "print(\"✅ Model loaded successfully!\")\n",
+    "print(f\"Model type: {type(whisper_model)}\")\n",
+    "print(f\"Has transcribe method: {hasattr(whisper_model, 'transcribe')}\")\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d84f6cfe",
+   "metadata": {},
+   "source": [
+    "## Step 4: Transcription Function\n",
+    "\n",
+    "This is the core logic:\n",
+    "- Accepts an audio file and target language\n",
+    "- Maps language names to Whisper's language codes\n",
+    "- Transcribes the audio using the loaded model\n",
+    "- Returns the transcribed text\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "4f2c4b2c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Transcription function\n",
+    "\n",
+    "def transcribe_audio(audio_file, target_language):\n",
+    "    \"\"\"Transcribe audio file to text in the specified language.\"\"\"\n",
+    "    if audio_file is None:\n",
+    "        return \"Please upload an audio file.\"\n",
+    "    \n",
+    "    try:\n",
+    "        # Language codes for Whisper\n",
+    "        language_map = {\n",
+    "            \"English\": \"en\",\n",
+    "            \"Spanish\": \"es\",\n",
+    "            \"French\": \"fr\",\n",
+    "            \"German\": \"de\",\n",
+    "            \"Italian\": \"it\",\n",
+    "            \"Portuguese\": \"pt\",\n",
+    "            \"Chinese\": \"zh\",\n",
+    "            \"Japanese\": \"ja\",\n",
+    "            \"Korean\": \"ko\",\n",
+    "            \"Russian\": \"ru\",\n",
+    "            \"Arabic\": \"ar\",\n",
+    "            \"Auto-detect\": None\n",
+    "        }\n",
+    "        \n",
+    "        lang_code = language_map.get(target_language)\n",
+    "        \n",
+    "        # Get file path from Gradio File component (returns path string directly)\n",
+    "        audio_path = audio_file.name if hasattr(audio_file, 'name') else audio_file\n",
+    "        \n",
+    "        if not audio_path or not os.path.exists(audio_path):\n",
+    "            return \"Invalid audio file or file not found\"\n",
+    "\n",
+    "        # Transcribe using whisper_model.transcribe()\n",
+    "        result = whisper_model.transcribe(\n",
+    "            audio_path,\n",
+    "            language=lang_code,\n",
+    "            task=\"transcribe\",\n",
+    "            verbose=False  # Hide confusing progress bar\n",
+    "        )\n",
+    "        \n",
+    "        return result[\"text\"]\n",
+    "    \n",
+    "    except Exception as e:\n",
+    "        return f\"Error: {str(e)}\"\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "dd928784",
+   "metadata": {},
+   "source": [
+    "## Step 5: Build the Interface\n",
+    "\n",
+    "Creating a simple, clean Gradio interface with:\n",
+    "- **File uploader** for audio files\n",
+    "- **Language dropdown** with 12+ options\n",
+    "- **Transcription output** box\n",
+    "- Auto-launches in browser for convenience\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "5ce2c944",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "✅ App ready! Run the next cell to launch.\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Gradio interface\n",
+    "\n",
+    "app = gr.Interface(\n",
+    "    fn=transcribe_audio,\n",
+    "    inputs=[\n",
+    "        gr.File(label=\"Upload Audio File\", file_types=[\"audio\"]),\n",
+    "        gr.Dropdown(\n",
+    "            choices=[\n",
+    "                \"English\", \"Spanish\", \"French\", \"German\", \"Italian\",\n",
+    "                \"Portuguese\", \"Chinese\", \"Japanese\", \"Korean\",\n",
+    "                \"Russian\", \"Arabic\", \"Auto-detect\"\n",
+    "            ],\n",
+    "            value=\"English\",\n",
+    "            label=\"Language\"\n",
+    "        )\n",
+    "    ],\n",
+    "    outputs=gr.Textbox(label=\"Transcription\", lines=15),\n",
+    "    title=\"🎙️ Audio Transcription\",\n",
+    "    description=\"Upload an audio file to transcribe it.\",\n",
+    "    flagging_mode=\"never\"\n",
+    ")\n",
+    "\n",
+    "print(\"✅ App ready! Run the next cell to launch.\")\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "049ac197",
+   "metadata": {},
+   "source": [
+    "## Step 6: Launch the App\n",
+    "\n",
+    "Starting the Gradio server with Jupyter compatibility (`prevent_thread_lock=True`). The app will open automatically in your browser.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "fa6c8d9a",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "* Running on local URL:  http://127.0.0.1:7860\n",
+      "* To create a public link, set `share=True` in `launch()`.\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div><iframe src=\"http://127.0.0.1:7860/\" width=\"100%\" height=\"500\" allow=\"autoplay; camera; microphone; clipboard-read; clipboard-write;\" frameborder=\"0\" allowfullscreen></iframe></div>"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": []
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/hopeogbons/Projects/andela/llm_engineering/.venv/lib/python3.12/site-packages/whisper/transcribe.py:132: UserWarning: FP16 is not supported on CPU; using FP32 instead\n",
+      "  warnings.warn(\"FP16 is not supported on CPU; using FP32 instead\")\n",
+      "100%|██████████| 10416/10416 [00:06<00:00, 1723.31frames/s]\n",
+      "/Users/hopeogbons/Projects/andela/llm_engineering/.venv/lib/python3.12/site-packages/whisper/transcribe.py:132: UserWarning: FP16 is not supported on CPU; using FP32 instead\n",
+      "  warnings.warn(\"FP16 is not supported on CPU; using FP32 instead\")\n",
+      "100%|██████████| 10416/10416 [00:30<00:00, 341.64frames/s]\n",
+      "/Users/hopeogbons/Projects/andela/llm_engineering/.venv/lib/python3.12/site-packages/whisper/transcribe.py:132: UserWarning: FP16 is not supported on CPU; using FP32 instead\n",
+      "  warnings.warn(\"FP16 is not supported on CPU; using FP32 instead\")\n",
+      "100%|██████████| 2289/2289 [00:01<00:00, 1205.18frames/s]\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Launch\n",
+    "\n",
+    "# Close any previous instances\n",
+    "try:\n",
+    "    app.close()\n",
+    "except:\n",
+    "    pass\n",
+    "\n",
+    "# Start the app\n",
+    "app.launch(inbrowser=True, prevent_thread_lock=True)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c3c2ec24",
+   "metadata": {},
+   "source": [
+    "---\n",
+    "\n",
+    "## 💡 How to Use\n",
+    "\n",
+    "1. **Upload** an audio file (MP3, WAV, M4A, etc.)\n",
+    "2. **Select** your language (or use Auto-detect)\n",
+    "3. **Click** Submit\n",
+    "4. **Get** your transcription!\n",
+    "\n",
+    "---\n",
+    "\n",
+    "## 🚀 Running on Google Colab\n",
+    "\n",
+    "For GPU acceleration on Colab:\n",
+    "1. Runtime → Change runtime type → **GPU (T4)**\n",
+    "2. Run all cells in order\n",
+    "3. The model will use GPU automatically\n",
+    "\n",
+    "**Note:** First run downloads the Whisper model (~140MB) - this is a one-time download.\n",
+    "\n",
+    "---\n",
+    "\n",
+    "## 📝 Supported Languages\n",
+    "\n",
+    "English • Spanish • French • German • Italian • Portuguese • Chinese • Japanese • Korean • Russian • Arabic • Auto-detect\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": ".venv",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.12.12"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

week3/community-contributions/juan_synthetic_data/.env_example

@@ -0,0 +1 @@
+OPENAI_API_KEY= your_openai_api_kei

week3/community-contributions/juan_synthetic_data/.python-version

@@ -0,0 +1 @@
+3.12

week3/community-contributions/juan_synthetic_data/README.md

@@ -0,0 +1,263 @@
+# Synthetic Data Generator
+**NOTE:** This is a copy of the repository https://github.com/Jsrodrigue/synthetic-data-creator.
+
+# Synthetic Data Generator
+
+An intelligent synthetic data generator that uses OpenAI models to create realistic tabular datasets based on reference data. This project includes an intuitive web interface built with Gradio.
+
+> **🎓 Educational Project**: This project was inspired by the highly regarded LLM Engineering course on Udemy: [LLM Engineering: Master AI and Large Language Models](https://www.udemy.com/course/llm-engineering-master-ai-and-large-language-models/learn/lecture/52941433#questions/23828099). It demonstrates practical applications of LLM engineering principles, prompt engineering, and synthetic data generation techniques.
+
+## Key highlights:
+- Built with Python & Gradio  
+- Uses OpenAI GPT-4 models for tabular data synthesis  
+- Focused on statistical consistency and controlled randomness  
+- Lightweight and easy to extend
+
+## 📸 Screenshots & Demo
+
+### Application Interface
+<p align="center">
+  <img src="screenshots/homepage.png" alt="Main Interface" width="70%">
+</p>
+<p align="center"><em>Main interface showing the synthetic data generator with all controls</em></p>
+
+### Generated Data Preview
+<p align="center">
+  <img src="screenshots/generated_table.png" alt="Generated table" width="70%">
+</p>
+<p align="center"><em> Generated CSV preview with the Wine dataset reference</em></p>
+
+### Histogram plots
+<p align="center">
+  <img src="screenshots/histogram.png" alt="Histogram plot" width="70%">
+</p>
+<p align="center"><em>Example of Histogram comparison plot in the Wine dataset</em></p>
+
+### Boxplots
+<p align="center">
+  <img src="screenshots/boxplot.png" alt="Boxplot" width="70%">
+</p>
+<p align="center"><em>Example of Boxplot comparison</em></p>
+
+
+### Video Demo
+[![Video Demo](https://img.youtube.com/vi/C7c8BbUGGBA/0.jpg)](https://youtu.be/C7c8BbUGGBA)
+
+*Click to watch a complete walkthrough of the application*
+
+
+## 📋 Features
+
+- **Intelligent Generation**: Generates synthetic data using OpenAI models (GPT-4o-mini, GPT-4.1-mini)
+- **Web Interface**: Provides an intuitive Gradio UI with real-time data preview
+- **Reference Data**: Optionally load CSV files to preserve statistical distributions
+- **Export Options**: Download generated datasets directly in CSV format
+- **Included Examples**: Comes with ready-to-use sample datasets for people and sentiment analysis
+- **Dynamic Batching**: Automatically adapts batch size based on prompt length and reference sample size
+- **Reference Sampling**: Uses random subsets of reference data to ensure variability and reduce API cost.  
+  The sample size (default `64`) can be modified in `src/constants.py` via `N_REFERENCE_ROWS`.
+
+## 🚀 Installation
+
+### Prerequisites
+- Python 3.12+
+- OpenAI account with API key
+
+### Option 1: Using pip
+```bash
+# Create virtual environment
+python -m venv venv
+source venv/bin/activate  # On Windows: venv\Scripts\activate
+
+# Install dependencies
+pip install -r requirements.txt
+```
+
+### Option 2: Using uv
+```bash
+# Clone the repository
+git clone https://github.com/Jsrodrigue/synthetic-data-creator.git
+cd synthetic-data-creator
+
+# Install dependencies
+uv sync
+
+# Activate virtual environment
+uv shell
+```
+
+### Configuration
+1. Copy the environment variables example file:
+```bash
+cp .env_example .env
+```
+
+2. Edit `.env` and add your OpenAI API key:
+```
+OPENAI_API_KEY=your_api_key_here
+```
+
+
+
+## 🎯 Usage
+
+### Start the application
+
+You can run the app either with **Python** or with **uv** (recommended if you installed dependencies using `uv sync`):
+
+```bash
+# Option 1: using Python
+python app.py
+
+# Option 2: using uv (no need to activate venv manually)
+uv run app.py
+```
+
+The script will print a local URL (e.g., http://localhost:7860) — open that link in your browser.
+
+### How to use the interface
+
+1. **Configure Prompts**:
+   - **System Prompt**: Uses the default rules defined in `src/constants.py` or can be edited there for custom generation.
+   - **User Prompt**: Specifies what type of data to generate (default: 15 rows, defined in `src/constants.py`).
+
+
+2. **Select Model**:
+   - `gpt-4o-mini`: Faster and more economical
+   - `gpt-4.1-mini`: Higher reasoning capacity
+
+3. **Load Reference Data** (optional):
+   - Upload a CSV file with similar data
+   - Use included examples: `people_reference.csv`, `sentiment_reference.csv` or `wine_reference.csv`
+
+4. **Generate Data**:
+   - Click "🚀 Generate Data"
+   - Review results in the gradio UI
+   - Download the generated CSV
+
+
+
+## 📊 Quality Evaluation
+
+### Simple Evaluation System
+
+The project includes a simple evaluation system focused on basic metrics and visualizations:
+
+#### Features
+- **Simple Metrics**: Basic statistical comparisons and quality checks
+- **Integrated Visualizations**: Automatic generation of comparison plots in the app
+- **Easy to Understand**: Clear scores and simple reports
+- **Scale Invariant**: Works with datasets of different sizes
+- **Temporary Files**: Visualizations are generated in temp files and cleaned up automatically
+
+
+
+## 🛠️ Improvements and Next Steps
+
+### Immediate Improvements
+
+1. **Advanced Validation**:
+   - Implement specific validators by data type
+   - Create evaluation reports
+
+2. **Advanced Quality Metrics**
+   - Include more advanced metrics to compare multivariate similarity (for future work), e.g.:
+      - C2ST (Classifier Two‑Sample Test): train a classifier to distinguish real vs synthetic — report AUROC (ideal ≈ 0.5).
+      - MMD (Maximum Mean Discrepancy): kernel-based multivariate distance.
+      - Multivariate Wasserstein / Optimal Transport: joint-distribution distance (use POT).
+     
+3. **More Models**:
+   - Integrate Hugging Face models
+   - Support for local models (Ollama)
+   - Comparison between different models
+
+### Advanced Features
+
+1. **Conditional Generation**:
+   - Data based on specific conditions
+   - Controlled outlier generation
+   - Maintaining complex relationships
+
+2. **Privacy Analysis**:
+   - Differential privacy metrics
+   - Sensitive data detection
+   - Automatic anonymization
+
+3. **Database Integration**:
+   - Direct database connection
+   - Massive data generation
+   - Automatic synchronization
+
+### Scalable Architecture
+
+1. **REST API**:
+   - Endpoints for integration
+   - Authentication and rate limiting
+   - OpenAPI documentation
+
+2. **Asynchronous Processing**:
+   - Work queues for long generations
+   - Progress notifications
+   - Robust error handling
+
+3. **Monitoring and Logging**:
+   - Usage and performance metrics
+   - Detailed generation logs
+   - Quality alerts
+
+## 📁 Project Structure
+
+```
+synthetic_data/
+├── app.py                 # Main Gradio application for synthetic data generation
+├── README.md              # Project documentation
+├── pyproject.toml         # Project configuration
+├── requirements.txt       # Python dependencies
+├── data/                  # Reference CSV datasets used for generating synthetic data
+│   ├── people_reference.csv
+│   ├── sentiment_reference.csv
+│   └── wine_reference.csv
+├── notebooks/             # Jupyter notebooks for experiments and development
+│   └── notebook.ipynb
+├── src/                   # Python source code
+│   ├── __init__.py
+    ├── constants.py       # Default constants, reference sample size, and default prompts
+│   ├── data_generation.py  # Core functions for batch generation and evaluation
+│   ├── evaluator.py        # Evaluation logic and metrics
+│   ├── IO_utils.py         # Utilities for file management and temp directories
+│   ├── openai_utils.py     # Wrappers for OpenAI API calls
+│   └── plot_utils.py  
+     # Functions to create visualizations from data
+└──  temp_plots/            # Temporary folder for generated plot images (auto-cleaned)
+```
+
+## 📄 License
+
+This project is under the MIT License. See the `LICENSE` file for more details.
+
+
+
+
+## 🎓 Course Context & Learning Outcomes
+
+This project was developed as part of the [LLM Engineering: Master AI and Large Language Models](https://www.udemy.com/course/llm-engineering-master-ai-and-large-language-models/learn/lecture/52941433#questions/23828099) course on Udemy. It demonstrates practical implementation of:
+
+### Key Learning Objectives:
+- **Prompt Engineering Mastery**: Creating effective system and user prompts for consistent outputs
+- **API Integration**: Working with OpenAI's API for production applications
+- **Data Processing**: Handling JSON parsing, validation, and error management
+- **Web Application Development**: Building user interfaces with Gradio
+
+### Course Insights Applied:
+- **Why OpenAI over Open Source**: This project was developed as an alternative to open-source models due to consistency issues in prompt following with models like Llama 3.2. OpenAI provides more reliable and faster results for this specific task.
+- **Production Considerations**: Focus on error handling, output validation, and user experience
+- **Scalability Planning**: Architecture designed for future enhancements and integrations
+
+### Related Course Topics:
+- Prompt engineering techniques
+- LLM API integration and optimization
+- Selection of best models for each usecase.
+
+---
+
+**📚 Course Link**: [LLM Engineering: Master AI and Large Language Models](https://www.udemy.com/course/llm-engineering-master-ai-and-large-language-models/learn/lecture/52941433#questions/23828099)

week3/community-contributions/juan_synthetic_data/app.py

@@ -0,0 +1,156 @@
+import atexit
+import os
+
+import gradio as gr
+import openai
+from dotenv import load_dotenv
+
+from src.constants import PROJECT_TEMP_DIR, SYSTEM_PROMPT, USER_PROMPT
+from src.data_generation import generate_and_evaluate_data
+from src.IO_utils import cleanup_temp_files
+from src.plot_utils import display_reference_csv
+
+
+def main():
+    # ==========================================================
+    # Setup
+    # ==========================================================
+
+    # Load the api key
+    load_dotenv()
+    openai.api_key = os.getenv("OPENAI_API_KEY")
+
+    # Temporary folder for images
+    os.makedirs(PROJECT_TEMP_DIR, exist_ok=True)
+
+    # Ensure temporary plot images are deleted when the program exits
+    atexit.register(lambda: cleanup_temp_files(PROJECT_TEMP_DIR))
+
+    # ==========================================================
+    # Gradio App
+    # ==========================================================
+    with gr.Blocks() as demo:
+
+        # Store temp folder in state
+        temp_dir_state = gr.State(value=PROJECT_TEMP_DIR)
+
+        gr.Markdown("# 🧠 Synthetic Data Generator (with OpenAI)")
+
+        # ======================================================
+        # Tabs for organized sections
+        # ======================================================
+        with gr.Tabs():
+
+            # ------------------------------
+            # Tab 1: Input
+            # ------------------------------
+            with gr.Tab("Input"):
+
+                # System prompt in collapsible
+                with gr.Accordion("System Prompt (click to expand)", open=False):
+                    system_prompt_input = gr.Textbox(
+                        label="System Prompt", value=SYSTEM_PROMPT, lines=20
+                    )
+
+                # User prompt box
+                user_prompt_input = gr.Textbox(
+                    label="User Prompt", value=USER_PROMPT, lines=5
+                )
+
+                # Model selection
+                model_select = gr.Dropdown(
+                    label="OpenAI Model",
+                    choices=["gpt-4o-mini", "gpt-4.1-mini"],
+                    value="gpt-4o-mini",
+                )
+
+                # Reference CSV upload
+                reference_input = gr.File(
+                    label="Reference CSV (optional)", file_types=[".csv"]
+                )
+
+                # Examples
+                gr.Examples(
+                    examples=[
+                        "data/sentiment_reference.csv",
+                        "data/people_reference.csv",
+                        "data/wine_reference.csv",
+                    ],
+                    inputs=reference_input,
+                )
+
+                # Generate button
+                generate_btn = gr.Button("🚀 Generate Data")
+
+                # Download button
+                download_csv = gr.File(label="Download CSV")
+
+            # ------------------------------
+            # Tab 2: Reference Table
+            # ------------------------------
+            with gr.Tab("Reference Table"):
+                reference_display = gr.DataFrame(label="Reference CSV Preview")
+
+            # ------------------------------
+            # Tab 3: Generated Table
+            # ------------------------------
+            with gr.Tab("Generated Table"):
+                output_df = gr.DataFrame(label="Generated Data")
+
+            # ------------------------------
+            # Tab 4: Evaluation
+            # ------------------------------
+            with gr.Tab("Comparison"):
+                with gr.Accordion("Evaluation Results (click to expand)", open=True):
+                    evaluation_df = gr.DataFrame(label="Evaluation Results")
+
+            # ------------------------------
+            # Tab 5: Visualizations
+            # ------------------------------
+
+            with gr.Tab("Visualizations"):
+                gr.Markdown("# Click on the box to expand")
+
+                images_gallery = gr.Gallery(
+                    label="Column Visualizations",
+                    show_label=True,
+                    columns=2,
+                    height="auto",
+                    interactive=True,
+                )
+
+            # Hidden state for internal use
+            generated_state = gr.State()
+
+        # ======================================================
+        # Event bindings
+        # ======================================================
+        generate_btn.click(
+            fn=generate_and_evaluate_data,
+            inputs=[
+                system_prompt_input,
+                user_prompt_input,
+                temp_dir_state,
+                reference_input,
+                model_select,
+            ],
+            outputs=[
+                output_df,
+                download_csv,
+                evaluation_df,
+                generated_state,
+                images_gallery,
+            ],
+        )
+
+        reference_input.change(
+            fn=display_reference_csv,
+            inputs=[reference_input],
+            outputs=[reference_display],
+        )
+
+    demo.launch(debug=True)
+
+
+if __name__ == "__main__":
+    main()

week3/community-contributions/juan_synthetic_data/data/people_reference.csv

@@ -0,0 +1,16 @@
+Name,Age,City
+John,32,New York
+Alice,45,Los Angeles
+Bob,28,Chicago
+Eve,35,Houston
+Mike,52,Philadelphia
+Emma,29,San Antonio
+Oliver,39,Phoenix
+Isabella,48,San Diego
+William,55,Dallas
+Charlotte,31,San Jose
+Alexander,42,San Francisco
+Harper,38,San Antonio
+Julia,46,San Diego
+Ethan,53,San Jose
+Ava,29,San Francisco

week3/community-contributions/juan_synthetic_data/data/sentiment_reference.csv

@@ -0,0 +1,99 @@
+,Comment,sentiment
+0,"Them: I don't think I like this game.
+
+Me: But you haven't even played it for 5 minutes and are still in the tutorial.",negative
+1,Then you leave them to farm the smaller creatures while you either wait or help them kill them all with the click of a button.,negative
+2,Nothing beats the feeling you get when you see them fall in love with it just like you did all those years ago,positive
+3,"[Also, they're made of paper](https://i.imgur.com/wYu0G9J.jpg)
+
+Edit: I tried to make a gif and failed so here's a [video](https://i.imgur.com/aPzS8Ny.mp4)",negative
+4,"Haha... That was exactly it when my brother tried to get me into WoW.   
+
+Him, "" I can run you through raids to get you to level up faster and get better gear.  But first you need to be this min level.  What are you""
+
+Me ""lvl 1"". 
+
+Him ""ok. Let's do a couple quests to get you up.  What is your quest"" 
+
+Me ""collect 20 apples"".",positive
+5,I'm going through this right now. I just started playing minecraft for the first time and my SO is having to walk me through everything.,positive
+6,Then they get even more into it than you and end up getting all the loot and items you wanted before you. They make you look like the noob in about 3 months.,positive
+7,"###Take your time, you got this
+|#|user|EDIT|comment|Link
+|:--|:--|:--|:--|:--|
+|0|/u/KiwiChoppa147|[EDIT](https://i.imgur.com/OI8jNtE.png)|Then you leave them to farm the smaller creatures while you either wait or help them kill them all with the click of a button.|[Link](/r/gaming/comments/ccr8c8/take_your_time_you_got_this/etor3t2/)|
+|1|/u/League0fGaming|[EDIT](https://i.imgur.com/5uvRAYy.png)|Nothing beats the feeling you get when you see them fall in love with it just like you did all those years ago|[Link](/r/gaming/comments/ccr8c8/take_your_time_you_got_this/etor371/)|
+|2|/u/DeJMan|[EDIT](https://i.imgur.com/3FL3IFb.png)|[Also, they're made of paper](https://i.imgur.com/wYu0G9J.jpg)  Edit: I tried to make a gif and failed so here's a [video](https://i.imgur.com/aPzS8Ny.mp4)|[Link](/r/gaming/comments/ccr8c8/take_your_time_you_got_this/etos1ic/)|
+|3|/u/Bamboo6|[EDIT](https://i.imgur.com/SiDFZxQ.png)|Haha... That was exactly it when my brother tried to get me into WoW.     Him, "" I can run you through raids to get you to level up faster and get better gear.  But first you need to be this min level.  What are you""  Me ""lvl 1"".   Him ""ok. Let's do a couple quests to get you up.  What is your quest""   Me ""collect 20 apples"".|[Link](/r/gaming/comments/ccr8c8/take_your_time_you_got_this/etorb6s/)|
+|4|/u/xxfisharemykidsxx|[EDIT](https://i.imgur.com/3ek9F93.png)|I'm going through this right now. I just started playing minecraft for the first time and my SO is having to walk me through everything.|[Link](/r/gaming/comments/ccr8c8/take_your_time_you_got_this/etor7hk/)|
+|5|/u/DuckSeeDuckWorld|[EDIT](https://i.imgur.com/rlE6VFP.png)|[This is my last EDIT before I go to camp for a week](https://imgur.com/xoOWF6K)|[Link](/r/gaming/comments/ccr8c8/take_your_time_you_got_this/etorpvh/)|
+|6|/u/ChecksUsernames|[EDIT](https://i.imgur.com/6Wc56ec.png)|What the hell you have your own edit bot?!|[Link](/r/gaming/comments/ccr8c8/take_your_time_you_got_this/etotc4w/)|
+
+ 
+I am a little fan-made bot who loves /u/SrGrafo but is a little lazy with hunting for EDITs. If you want to support our great creator, check out his [Patreon](https://Patreon.com/SrGrafo)",positive
+8,"Them: ""Wait, where did you go?""
+
+Me --cleaning up the vast quantities of mobs they've managed to stumble past: "" Oh just, you know, letting you get a feel for navigation.""",neutral
+9,"Don't mind the arrows, everything's fine",positive
+10,[me_irl](https://i.imgur.com/eRPb2X3.png),neutral
+11,"I usually teach them the basic controls, and then throw them to the wolves like Spartans. Its sink or swim now!",positive
+12,This is Warframe in a nutshell,neutral
+13,[I love guiding people trough the game for the First time](https://imgur.com/uep20iB),positive
+14,[showing a video game to my nephew for the first time didn't go that well :D](https://i.imgur.com/dQf4mfI.png),negative
+15,[When it's a puzzle game](https://i.imgur.com/BgLqzRa.png),neutral
+16,"I love SrGrafo’s cheeky smiles in his drawings.
+
+Also, I wonder if it’s Senior Grafo, Señor Grafo, or Sir Grafo.",positive
+17,"https://i.redd.it/pqjza65wrd711.jpg
+
+Same look.",neutral
+18,[This is my last EDIT before I go to camp for a week](https://imgur.com/xoOWF6K),neutral
+19,Haha this is me in Warframe but I've only been playing for a year. It's so easy to find beginners and they always need help with something.,positive
+20,This happens all the time on r/warframe ! Helping new people is like a whole part of the game's fun.,positive
+21,[deleted],neutral
+22,"Once day when I have kids, I hope I can do the same with them",positive
+23,WAIT NO. WHY'D YOU PRESS X INSTEAD? Now you just used the only consumable for the next like 3 stages. Here lemme just restart from your last save...,neutral
+24,Big gamer energy.,positive
+25,"What about ten minutes in and they say “I’m not sure I get what’s going on. Eh I’m bored.”
+
+Shitty phone [EDIT](https://imgur.com/a/zr4Ahnp)",negative
+26,Press *alt+f4* for the special move,positive
+27,"I remember teaching my little brother everything about Minecraft. Ah, good times. Now he's a little prick xD",positive
+28,2nd top post of 2019!! ＼（^０＾）／,positive
+29,"With Grafo’s most recent comics, this achievement means so much more now. Check them out on his profile, u/SrGrafo, they’re titled “SrGrafo’s inception “",neutral
+30,"this is my bf showing me wow.
+
+Him: “You can’t just stand there and take damage.”
+Me: “but I can’t move fast and my spells get cancelled.”
+
+*proceeds to die 5 times in a row.*
+
+ and then he finishes it for me after watching me fail. 
+
+Me: yay. 😀😀",neutral
+31,"Quick cross over 
+
+https://imgur.com/a/9y4JVAr",neutral
+32,"Man, I really enjoy encoutering nice Veterans in online games",positive
+33,Wow. This is my first time here before the edits.,positive
+34,So this is the most liked Reddit post hmm,positive
+35,Diamond armor? Really?,positive
+36,"I remember when I was playing Destiny and I was pretty low level, having fun going through the missions, then my super high level friend joined. It was really unfun because he was slaughtering everything for me while I sat at the back doing jackshit",positive
+37,"""I'll just use this character until you get the hang of things and then swap to an alt so we can level together""",neutral
+38,"My girlfriend often just doesn't get why I love the games I play, but that's fine. I made sure to sit and watch her while she fell in love with breath of the wild.",negative
+39,"Warframe was full of people like this last i was on and its amazing. I was one of them too, but mostly for advice more than items because i was broke constantly.",neutral
+40,This is the most upvoted post I've seen on Reddit. And it was unexpectedly touching :),positive
+41,220k. holy moly,neutral
+42,Last,neutral
+43,"170k+ upvotes in 11 hours.
+Is this a record?",neutral
+44,This is the top post of all time😱,positive
+45,"Congratulations, 2nd post of the Year",positive
+46,Most liked post on reddit,positive
+47,Absolute Unit,neutral
+48,"I did similar things in Monster Hunter World.
+The only problem is they would never play ever again and play other games like Fortnite...feels bad man.
+If you ever get interested on playing the game u/SrGrafo then I’ll teach you the ways of the hunter!!! (For real tho it’s a really good game and better with buddy’s!)",positive
+49,Congrats on the second most upvoted post of 2019 my guy.,positive
+50,"This was it with my brother when I first started playing POE. He made it soooo much easier to get into the game. To understand the gameplay and mechanics. I think I’d have left in a day or two had it not been for him
+And walking me through the first few missions lmao. u/sulphra_",positive

week3/community-contributions/juan_synthetic_data/data/wine_reference.csv

@@ -0,0 +1,159 @@
+fixed acidity,volatile acidity,citric acid,residual sugar,chlorides,free sulfur dioxide,total sulfur dioxide,density,pH,sulphates,alcohol,quality,Id
+7.4,0.7,0.0,1.9,0.076,11.0,34.0,0.9978,3.51,0.56,9.4,5,0
+7.8,0.88,0.0,2.6,0.098,25.0,67.0,0.9968,3.2,0.68,9.8,5,1
+7.8,0.76,0.04,2.3,0.092,15.0,54.0,0.997,3.26,0.65,9.8,5,2
+11.2,0.28,0.56,1.9,0.075,17.0,60.0,0.998,3.16,0.58,9.8,6,3
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week3/community-contributions/juan_synthetic_data/notebooks/notebook.ipynb

@@ -0,0 +1,292 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "63356928",
+   "metadata": {},
+   "source": [
+    "# Initial Note\n",
+    "After running experiments in Colab using open-source models from Hugging Face, I decided to do the exercise with OpenAI. The reason is that Llama 3.2 frequently did not follow the prompts correctly, leading to inconsistencies and poor performance. Additionally, using larger models significantly increased processing time, making them less practical for this task.\n",
+    "\n",
+    "The code from this notebook will be reorganized in modules for the final Demo."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5c12f081",
+   "metadata": {},
+   "source": [
+    "# Module to generate syntethic data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "2389d798",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\n",
+    "import re \n",
+    "\n",
+    "def _clean_json_output(raw_text: str) -> str:\n",
+    "    \"\"\"\n",
+    "    Limpia la salida de OpenAI para convertirla en JSON válido:\n",
+    "    - Mantiene las comillas de claves sin tocar.\n",
+    "    - Escapa solo las comillas dobles dentro de los strings de valores.\n",
+    "    - Escapa \\n, \\r, \\t.\n",
+    "    - Remueve code fences y HTML.\n",
+    "    - Asegura que el array comience con [ y termine con ].\n",
+    "    - Elimina comas finales.\n",
+    "    \"\"\"\n",
+    "    text = raw_text.strip()\n",
+    "    \n",
+    "    # Remover code fences y HTML\n",
+    "    text = re.sub(r\"```(?:json)?\", \"\", text)\n",
+    "    text = re.sub(r\"</?[^>]+>\", \"\", text)\n",
+    "    \n",
+    "    # Escapar comillas dobles dentro de valores de Comment\n",
+    "    def escape_quotes_in_values(match):\n",
+    "        value = match.group(1)\n",
+    "        value = value.replace('\"', r'\\\"')  # solo dentro del valor\n",
+    "        value = value.replace('\\n', r'\\n').replace('\\r', r'\\r').replace('\\t', r'\\t')\n",
+    "        return f'\"{value}\"'\n",
+    "    \n",
+    "    text = re.sub(r'\"(.*?)\"', escape_quotes_in_values, text)\n",
+    "    \n",
+    "    # Asegurar que empieza y termina con []\n",
+    "    if not text.startswith('['):\n",
+    "        text = '[' + text\n",
+    "    if not text.endswith(']'):\n",
+    "        text += ']'\n",
+    "    \n",
+    "    # Eliminar comas finales antes de cerrar corchetes\n",
+    "    text = re.sub(r',\\s*]', ']', text)\n",
+    "    \n",
+    "    return text\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "75bfad6f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "import json\n",
+    "import openai\n",
+    "import tempfile\n",
+    "\n",
+    "\n",
+    "def generate_synthetic_data_openai(\n",
+    "    system_prompt: str,\n",
+    "    user_prompt: str,\n",
+    "    reference_file=None,\n",
+    "    openai_model=\"gpt-4o-mini\",\n",
+    "    max_tokens=2048,\n",
+    "    temperature=0.0\n",
+    "):\n",
+    "    \"\"\"\n",
+    "    Genera datos sintéticos y devuelve el DataFrame y la ruta de un CSV temporal.\n",
+    "    \"\"\"\n",
+    "    # Preparar prompt completo\n",
+    "    if reference_file:\n",
+    "        if isinstance(reference_file, str):\n",
+    "            df_ref = pd.read_csv(reference_file)\n",
+    "        else:\n",
+    "            df_ref = pd.read_csv(reference_file)\n",
+    "        reference_data = df_ref.to_dict(orient=\"records\")\n",
+    "        user_prompt_full = (\n",
+    "            f\"{user_prompt}\\nFollow the structure and distribution of the reference data, \"\n",
+    "            f\"but do NOT copy any exact values:\\n{reference_data}\"\n",
+    "        )\n",
+    "    else:\n",
+    "        user_prompt_full = user_prompt\n",
+    "\n",
+    "    # Llamar a OpenAI\n",
+    "    response = openai.chat.completions.create(\n",
+    "        model=openai_model,\n",
+    "        messages=[\n",
+    "            {\"role\": \"system\", \"content\": system_prompt},\n",
+    "            {\"role\": \"user\", \"content\": user_prompt_full},\n",
+    "        ],\n",
+    "        temperature=temperature,\n",
+    "        max_tokens=max_tokens,\n",
+    "    )\n",
+    "\n",
+    "    raw_text = response.choices[0].message.content\n",
+    "    cleaned_json = _clean_json_output(raw_text)\n",
+    "\n",
+    "    # Parsear JSON\n",
+    "    try:\n",
+    "        data = json.loads(cleaned_json)\n",
+    "    except json.JSONDecodeError as e:\n",
+    "        raise ValueError(f\"JSON inválido generado. Error: {e}\\nOutput truncado: {cleaned_json[:500]}\")\n",
+    "\n",
+    "    df = pd.DataFrame(data)\n",
+    "\n",
+    "    # Guardar CSV temporal\n",
+    "    tmp_file = tempfile.NamedTemporaryFile(delete=False, suffix=\".csv\")\n",
+    "    df.to_csv(tmp_file.name, index=False)\n",
+    "    tmp_file.close()\n",
+    "\n",
+    "    return df, tmp_file.name\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "91af1eb5",
+   "metadata": {},
+   "source": [
+    "# Default prompts"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "792d1555",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "SYSTEM_PROMPT = \"\"\"\n",
+    "You are a precise synthetic data generator. Your only task is to output valid JSON arrays of dictionaries.\n",
+    "\n",
+    "Rules:\n",
+    "1. Output a single JSON array starting with '[' and ending with ']'.\n",
+    "2. Do not include markdown, code fences, or explanatory text — only the JSON.\n",
+    "3. Keep all columns exactly as specified; do not add or remove fields (index must be omitted).\n",
+    "4. Respect data types: text, number, date, boolean, etc.\n",
+    "5. Ensure internal consistency and realistic variation.\n",
+    "6. If a reference table is provided, generate data with similar statistical distributions for numerical and categorical variables, \n",
+    "   but never copy exact rows. Each row must be independent and new.\n",
+    "7. For personal information (names, ages, addresses, IDs), ensure diversity and realism — individual values may be reused to maintain realism, \n",
+    "   but never reuse or slightly modify entire reference rows.\n",
+    "8. Escape all internal double quotes in strings with a backslash (\\\").\n",
+    "9. Replace any single quotes in strings with double quotes.\n",
+    "10. Escape newline (\\n), tab (\\t), or carriage return (\\r) characters as \\\\n, \\\\t, \\\\r inside strings.\n",
+    "11. Remove any trailing commas before closing brackets.\n",
+    "12. Do not include any reference data or notes about it in the output.\n",
+    "13. The output must always be valid JSON parseable by standard JSON parsers.\n",
+    "\"\"\"\n",
+    "\n",
+    "USER_PROMPT = \"\"\"\n",
+    "Generate exactly 15 rows of synthetic data following all the rules above. \n",
+    "Ensure that all strings are safe for JSON parsing and ready to convert to a pandas DataFrame.\n",
+    "\"\"\"\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6f9331fa",
+   "metadata": {},
+   "source": [
+    "# Test"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d38f0afb",
+   "metadata": {},
+   "source": [
+    "For testing our generator, we use the first 50 examples of reddit gaming comments with sentiments dataset.\n",
+    "Source: https://www.kaggle.com/datasets/sainitishmitta04/23k-reddit-gaming-comments-with-sentiments-dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "78d94faa",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\n",
+    "df, _ = generate_synthetic_data_openai(SYSTEM_PROMPT, USER_PROMPT, reference_file= \"data/sentiment_reference.csv\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "0e6b5ebb",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "015a3110",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "print(df.Comment[0])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0ef44876",
+   "metadata": {},
+   "source": [
+    "# Gradio Demo"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "aa4092f4",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import gradio as gr\n",
+    "\n",
+    "with gr.Blocks() as demo:\n",
+    "    gr.Markdown(\"# 🧠 Synthetic Data Generator\")\n",
+    "\n",
+    "    with gr.Row():\n",
+    "        system_prompt_input = gr.Textbox(label=\"System Prompt\", value=SYSTEM_PROMPT, lines=10)\n",
+    "\n",
+    "    with gr.Row():\n",
+    "        user_prompt_input = gr.Textbox(label=\"User Prompt\", value=USER_PROMPT, lines=5)\n",
+    "\n",
+    "    with gr.Row():\n",
+    "        reference_input = gr.File(label=\"Reference CSV (optional)\", file_types=[\".csv\"])\n",
+    "\n",
+    "    output_df = gr.DataFrame(label=\"Generated Data\")\n",
+    "    download_csv = gr.File(label=\"Download CSV\")\n",
+    "\n",
+    "    generate_btn = gr.Button(\"🚀 Generate Data\")\n",
+    "\n",
+    "    generate_btn.click(\n",
+    "        fn=generate_synthetic_data_openai,\n",
+    "        inputs=[system_prompt_input, user_prompt_input, reference_input],\n",
+    "        outputs=[output_df, download_csv]\n",
+    "    )\n",
+    "\n",
+    "demo.launch(debug=True)\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": ".venv",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.12.12"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

week3/community-contributions/juan_synthetic_data/pyproject.toml

@@ -0,0 +1,16 @@
+[project]
+name = "synthetic-data"
+version = "0.1.0"
+description = "An intelligent synthetic data generator using OpenAI models"
+authors = [
+    { name = "Sebastian Rodriguez" }
+]
+dependencies = [
+    "gradio>=5.49.1",
+    "openai>=2.6.0",
+    "pandas>=2.3.3",
+    "python-dotenv>=1.0.0",
+    "numpy>=1.24.0",
+    "matplotlib>=3.7.0",
+    "seaborn>=0.13.0"
+]

week3/community-contributions/juan_synthetic_data/requirements.txt

@@ -0,0 +1,10 @@
+# Core dependencies
+gradio>=5.49.1
+openai>=2.6.0
+pandas>=2.3.3
+python-dotenv>=1.0.0
+
+# Evaluation dependencies
+numpy>=1.24.0
+matplotlib>=3.7.0
+seaborn>=0.13.0

week3/community-contributions/juan_synthetic_data/src/IO_utils.py

@@ -0,0 +1,13 @@
+import os
+import glob
+
+def cleanup_temp_files(temp_dir: str):
+    """
+    Remove all temporary files from the given directory.
+    """
+    files = glob.glob(os.path.join(temp_dir, "*"))
+    for f in files:
+        try:
+            os.remove(f)
+        except Exception as e:
+            print(f"[Warning] Could not delete {f}: {e}")

week3/community-contributions/juan_synthetic_data/src/constants.py

@@ -0,0 +1,45 @@
+# -------------------Setup Constants -------------------
+N_REFERENCE_ROWS = 64  # Max reference rows per batch for sampling
+MAX_TOKENS_MODEL = 128_000 # Max tokens supported by the model, used for batching computations
+PROJECT_TEMP_DIR = "temp_plots"
+
+
+
+#----------------- Prompts-------------------------------
+SYSTEM_PROMPT = """
+You are a precise synthetic data generator. Your only task is to output valid JSON arrays of dictionaries.
+
+Rules:
+1. Output a single JSON array starting with '[' and ending with ']'.
+2. Do not include markdown, code fences, or explanatory text — only the JSON.
+3. Keep all columns exactly as specified; do not add or remove fields (index must be omitted).
+4. Respect data types: text, number, date, boolean, etc.
+5. Ensure internal consistency and realistic variation.
+6. If a reference table is provided, generate data with similar statistical distributions for numerical and categorical variables, 
+   but never copy exact rows. Each row must be independent and new.
+7. For personal information (names, ages, addresses, IDs), ensure diversity and realism — individual values may be reused to maintain realism, 
+   but never reuse or slightly modify entire reference rows.
+8. Escape internal double quotes in strings with a backslash (") for JSON validity.
+9. Do NOT replace single quotes in normal text; they should remain as-is.
+10. Escape newline (
+), tab (	), or carriage return (
+) characters as 
+, 	, 
+ inside strings.
+11. Remove any trailing commas before closing brackets.
+12. Do not include any reference data or notes about it in the output.
+13. The output must always be valid JSON parseable by standard JSON parsers.
+14. Don't repeat any exact column neither from the reference or from previous generated data.
+15. When using reference data, consider the entire dataset for statistical patterns and diversity; 
+do not restrict generation to the first rows or the order of the dataset.
+16. Introduce slight random variations in numerical values, and choose categorical values randomly according to the distribution, 
+without repeating rows. 
+
+"""
+
+USER_PROMPT = """
+Generate exactly 15 rows of synthetic data following all the rules above. 
+Ensure that all strings are safe for JSON parsing and ready to convert to a pandas DataFrame.
+"""
+
+

week3/community-contributions/juan_synthetic_data/src/data_generation.py

@@ -0,0 +1,108 @@
+import os
+from typing import List
+
+import pandas as pd
+from PIL import Image
+
+from src.constants import MAX_TOKENS_MODEL, N_REFERENCE_ROWS
+from src.evaluator import SimpleEvaluator
+from src.helpers import hash_row, sample_reference
+from src.openai_utils import detect_total_rows_from_prompt, generate_batch
+
+
+# ------------------- Main Function -------------------
+def generate_and_evaluate_data(
+    system_prompt: str,
+    user_prompt: str,
+    temp_dir: str,
+    reference_file=None,
+    openai_model: str = "gpt-4o-mini",
+    max_tokens_model: int = MAX_TOKENS_MODEL,
+    n_reference_rows: int = N_REFERENCE_ROWS,
+):
+    """
+    Generate synthetic data in batches, evaluate against reference data, and save results.
+    Uses dynamic batching and reference sampling to optimize cost and token usage.
+    """
+    os.makedirs(temp_dir, exist_ok=True)
+    reference_df = pd.read_csv(reference_file) if reference_file else None
+    total_rows = detect_total_rows_from_prompt(user_prompt, openai_model)
+
+    final_df = pd.DataFrame()
+    existing_hashes = set()
+    rows_left = total_rows
+    iteration = 0
+
+    print(f"[Info] Total rows requested: {total_rows}")
+
+    # Estimate tokens for the prompt by adding system, user and sample (used once per batch)
+    prompt_sample = f"{system_prompt}  {user_prompt}  {sample_reference(reference_df, n_reference_rows)}"
+    prompt_tokens = max(1, len(prompt_sample) // 4)
+
+    # Estimate tokens per row dynamically using a sample
+    example_sample = sample_reference(reference_df, n_reference_rows)
+    if example_sample is not None and len(example_sample) > 0:
+        sample_text = str(example_sample)
+        tokens_per_row = max(1, len(sample_text) // len(example_sample) // 4)
+    else:
+        tokens_per_row = 30  # fallback if no reference
+
+    print(f"[Info] Tokens per row estimate: {tokens_per_row}, Prompt tokens: {prompt_tokens}")
+
+    # ---------------- Batch Generation Loop ----------------
+    while rows_left > 0:
+        iteration += 1
+        batch_sample = sample_reference(reference_df, n_reference_rows)
+        batch_size = min(rows_left, max(1, (max_tokens_model - prompt_tokens) // tokens_per_row))
+        print(f"[Batch {iteration}] Batch size: {batch_size}, Rows left: {rows_left}")
+
+        try:
+            df_batch = generate_batch(
+                system_prompt, user_prompt, batch_sample, batch_size, openai_model
+            )
+        except Exception as e:
+            print(f"[Error] Batch {iteration} failed: {e}")
+            break
+
+        # Filter duplicates using hash
+        new_rows = [
+            row
+            for _, row in df_batch.iterrows()
+            if hash_row(row) not in existing_hashes
+        ]
+        for row in new_rows:
+            existing_hashes.add(hash_row(row))
+
+        final_df = pd.concat([final_df, pd.DataFrame(new_rows)], ignore_index=True)
+        rows_left = total_rows - len(final_df)
+        print(
+            f"[Batch {iteration}] Unique new rows added: {len(new_rows)}, Total so far: {len(final_df)}"
+        )
+
+        if len(new_rows) == 0:
+            print("[Warning] No new unique rows. Stopping batches.")
+            break
+
+    # ---------------- Evaluation ----------------
+    report_df, vis_dict = pd.DataFrame(), {}
+    if reference_df is not None and not final_df.empty:
+        evaluator = SimpleEvaluator(temp_dir=temp_dir)
+        evaluator.evaluate(reference_df, final_df)
+        report_df = evaluator.results_as_dataframe()
+        vis_dict = evaluator.create_visualizations_temp_dict(reference_df, final_df)
+        print(f"[Info] Evaluation complete. Report shape: {report_df.shape}")
+
+    # ---------------- Collect Images ----------------
+    all_images: List[Image.Image] = []
+    for imgs in vis_dict.values():
+        if isinstance(imgs, list):
+            all_images.extend([img for img in imgs if img is not None])
+
+    # ---------------- Save CSV ----------------
+    final_csv_path = os.path.join(temp_dir, "synthetic_data.csv")
+    final_df.to_csv(final_csv_path, index=False)
+    print(f"[Done] Generated {len(final_df)} rows → saved to {final_csv_path}")
+
+    generated_state = {}
+
+    return final_df, final_csv_path, report_df, generated_state, all_images

week3/community-contributions/juan_synthetic_data/src/evaluator.py

@@ -0,0 +1,142 @@
+import seaborn as sns
+import matplotlib.pyplot as plt
+from typing import List, Dict, Any, Optional
+from PIL import Image
+import pandas as pd
+import os
+
+class SimpleEvaluator:
+    """
+    Evaluates synthetic data against a reference dataset, providing summary statistics and visualizations.
+    """
+
+    def __init__(self, temp_dir: str = "temp_plots"):
+        """
+        Initialize the evaluator.
+
+        Args:
+            temp_dir (str): Directory to save temporary plot images.
+        """
+        self.temp_dir = temp_dir
+        os.makedirs(self.temp_dir, exist_ok=True)
+
+    def evaluate(self, reference_df: pd.DataFrame, generated_df: pd.DataFrame) -> Dict[str, Any]:
+        """
+        Compare numerical and categorical columns between reference and generated datasets.
+        """
+        self.results: Dict[str, Any] = {}
+        self.common_cols = list(set(reference_df.columns) & set(generated_df.columns))
+
+        for col in self.common_cols:
+            if pd.api.types.is_numeric_dtype(reference_df[col]):
+                self.results[col] = {
+                    "type": "numerical",
+                    "ref_mean": reference_df[col].mean(),
+                    "gen_mean": generated_df[col].mean(),
+                    "mean_diff": generated_df[col].mean() - reference_df[col].mean(),
+                    "ref_std": reference_df[col].std(),
+                    "gen_std": generated_df[col].std(),
+                    "std_diff": generated_df[col].std() - reference_df[col].std(),
+                }
+            else:
+                ref_counts = reference_df[col].value_counts(normalize=True)
+                gen_counts = generated_df[col].value_counts(normalize=True)
+                overlap = sum(min(ref_counts.get(k, 0), gen_counts.get(k, 0)) for k in ref_counts.index)
+                self.results[col] = {
+                    "type": "categorical",
+                    "distribution_overlap_pct": round(overlap * 100, 2),
+                    "ref_unique": len(ref_counts),
+                    "gen_unique": len(gen_counts)
+                }
+
+        return self.results
+
+    def results_as_dataframe(self) -> pd.DataFrame:
+        """
+        Convert the evaluation results into a pandas DataFrame for display.
+        """
+        rows = []
+        for col, stats in self.results.items():
+            if stats["type"] == "numerical":
+                rows.append({
+                    "Column": col,
+                    "Type": "Numerical",
+                    "Ref Mean/Std": f"{stats['ref_mean']:.2f} / {stats['ref_std']:.2f}",
+                    "Gen Mean/Std": f"{stats['gen_mean']:.2f} / {stats['gen_std']:.2f}",
+                    "Diff": f"Mean diff: {stats['mean_diff']:.2f}, Std diff: {stats['std_diff']:.2f}"
+                })
+            else:
+                rows.append({
+                    "Column": col,
+                    "Type": "Categorical",
+                    "Ref": f"{stats['ref_unique']} unique",
+                    "Gen": f"{stats['gen_unique']} unique",
+                    "Diff": f"Overlap: {stats['distribution_overlap_pct']}%"
+                })
+        return pd.DataFrame(rows)
+
+    def create_visualizations_temp_dict(
+        self,
+        reference_df: pd.DataFrame,
+        generated_df: pd.DataFrame,
+        percentage: bool = True
+    ) -> Dict[str, List[Optional[Image.Image]]]:
+        """
+        Create histogram and boxplot visualizations for each column and save them as temporary images.
+        Handles special characters in column names and category labels.
+        """
+        vis_dict: Dict[str, List[Optional[Image.Image]]] = {}
+        common_cols = list(set(reference_df.columns) & set(generated_df.columns))
+
+        for col in common_cols:
+            col_safe = str(col).replace("_", r"\_").replace("$", r"\$")  # Escape special chars
+
+            # ---------------- Histogram ----------------
+            plt.figure(figsize=(6, 4))
+            if pd.api.types.is_numeric_dtype(reference_df[col]):
+                sns.histplot(reference_df[col], color="blue", label="Reference",
+                            stat="percent" if percentage else "count", alpha=0.5)
+                sns.histplot(generated_df[col], color="orange", label="Generated",
+                            stat="percent" if percentage else "count", alpha=0.5)
+            else:  # Categorical
+                ref_counts = reference_df[col].value_counts(normalize=percentage)
+                gen_counts = generated_df[col].value_counts(normalize=percentage)
+                categories = list(set(ref_counts.index) | set(gen_counts.index))
+                categories_safe = [str(cat).replace("_", r"\_").replace("$", r"\$") for cat in categories]
+                ref_vals = [ref_counts.get(cat, 0) for cat in categories]
+                gen_vals = [gen_counts.get(cat, 0) for cat in categories]
+
+                x = range(len(categories))
+                width = 0.4
+                plt.bar([i - width/2 for i in x], ref_vals, width=width, color="blue", alpha=0.7, label="Reference")
+                plt.bar([i + width/2 for i in x], gen_vals, width=width, color="orange", alpha=0.7, label="Generated")
+                plt.xticks(x, categories_safe, rotation=45, ha="right")
+
+            plt.title(f"Histogram comparison for '{col_safe}'", fontsize=12, usetex=False)
+            plt.legend()
+            plt.tight_layout()
+            hist_path = os.path.join(self.temp_dir, f"{col}_hist.png")
+            plt.savefig(hist_path, bbox_inches='tight')
+            plt.close()
+            hist_img = Image.open(hist_path)
+
+            # ---------------- Boxplot (numerical only) ----------------
+            box_img = None
+            if pd.api.types.is_numeric_dtype(reference_df[col]):
+                plt.figure(figsize=(6, 4))
+                df_box = pd.DataFrame({
+                    'Value': pd.concat([reference_df[col], generated_df[col]], ignore_index=True),
+                    'Dataset': ['Reference']*len(reference_df[col]) + ['Generated']*len(generated_df[col])
+                })
+
+                sns.boxplot(x='Dataset', y='Value', data=df_box, palette=['#1f77b4','#ff7f0e'])
+                plt.title(f"Boxplot comparison for '{col_safe}'", fontsize=12, usetex=False)
+                plt.tight_layout()
+                box_path = os.path.join(self.temp_dir, f"{col}_box.png")
+                plt.savefig(box_path, bbox_inches='tight')
+                plt.close()
+                box_img = Image.open(box_path)
+
+            vis_dict[col] = [hist_img, box_img]
+
+        return vis_dict

week3/community-contributions/juan_synthetic_data/src/helpers.py

@@ -0,0 +1,14 @@
+import hashlib
+import pandas as pd
+
+def hash_row(row: pd.Series) -> str:
+    """Compute MD5 hash for a row to detect duplicates."""
+    return hashlib.md5(str(tuple(row)).encode()).hexdigest()
+
+
+def sample_reference(reference_df: pd.DataFrame, n_reference_rows: int) -> list:
+    """Return a fresh sample of reference data for batch generation."""
+    if reference_df is not None and not reference_df.empty:
+        sample_df = reference_df.sample(min(n_reference_rows, len(reference_df)), replace=False)
+        return sample_df.to_dict(orient="records")
+    return []

week3/community-contributions/juan_synthetic_data/src/openai_utils.py

@@ -0,0 +1,112 @@
+import json
+import re
+import tempfile
+import openai
+import pandas as pd
+
+import os
+from typing import List
+
+
+# ------------------ JSON Cleaning ------------------
+def _clean_json_output(raw_text: str) -> str:
+    """
+    Cleans raw OpenAI output to produce valid JSON.
+    Escapes only double quotes and control characters.
+    """
+    text = raw_text.strip()
+    text = re.sub(r"```(?:json)?", "", text)
+    text = re.sub(r"</?[^>]+>", "", text)
+
+    def escape_quotes(match):
+        value = match.group(1)
+        value = value.replace('"', r"\"")
+        value = value.replace("\n", r"\n").replace("\r", r"\r").replace("\t", r"\t")
+        return f'"{value}"'
+
+    text = re.sub(r'"(.*?)"', escape_quotes, text)
+
+    if not text.startswith("["):
+        text = "[" + text
+    if not text.endswith("]"):
+        text += "]"
+    text = re.sub(r",\s*]", "]", text)
+    return text
+
+
+# ------------------ Synthetic Data Generation ------------------
+def generate_synthetic_data_openai(
+    system_prompt: str,
+    full_user_prompt: str,
+    openai_model: str = "gpt-4o-mini",
+    max_tokens: int = 16000,
+    temperature: float = 0.0,
+):
+    """
+    Generates synthetic tabular data using OpenAI.
+    Assumes `full_user_prompt` is already complete with reference data.
+    """
+    response = openai.chat.completions.create(
+        model=openai_model,
+        messages=[
+            {"role": "system", "content": system_prompt},
+            {"role": "user", "content": full_user_prompt},
+        ],
+        max_completion_tokens=max_tokens,
+        temperature=temperature,
+    )
+
+    raw_text = response.choices[0].message.content
+    cleaned_json = _clean_json_output(raw_text)
+
+    try:
+        data = json.loads(cleaned_json)
+    except json.JSONDecodeError as e:
+        raise ValueError(
+            f"Invalid JSON generated. Error: {e}\nTruncated output: {cleaned_json[:500]}"
+        )
+
+    df = pd.DataFrame(data)
+
+    tmp_file = tempfile.NamedTemporaryFile(delete=False, suffix=".csv")
+    df.to_csv(tmp_file.name, index=False)
+    tmp_file.close()
+
+    return df, tmp_file.name
+
+# ----------------------Mini call to detect the number of rows in the prompt--------------
+def detect_total_rows_from_prompt(user_prompt: str, openai_model: str = "gpt-4o-mini") -> int:
+    """
+    Detect the number of rows requested from the user prompt.
+    Fallback to 20 if detection fails.
+    """
+    mini_prompt = f"""
+    Extract the number of rows to generate from this instruction:
+    \"\"\"{user_prompt}\"\"\" Return only the number.
+    """
+    openai.api_key = os.getenv("OPENAI_API_KEY")
+    try:
+        response = openai.chat.completions.create(
+            model=openai_model,
+            messages=[{"role": "user", "content": mini_prompt}],
+            temperature=0,
+            max_tokens=10,
+        )
+        text = response.choices[0].message.content.strip()
+        total_rows = int("".join(filter(str.isdigit, text)))
+        return max(total_rows, 1)
+    except Exception:
+        return 20
+
+
+# -------------- Function to generate synthetic data in a batch ---------------------
+def generate_batch(system_prompt: str, user_prompt: str, reference_sample: List[dict],
+                   batch_size: int, openai_model: str):
+    """Generate a single batch of synthetic data using OpenAI."""
+    full_prompt = f"{user_prompt}\nSample: {reference_sample}\nGenerate exactly {batch_size} rows."
+    df_batch, _ = generate_synthetic_data_openai(
+        system_prompt=system_prompt,
+        full_user_prompt=full_prompt,
+        openai_model=openai_model,
+    )
+    return df_batch

week3/community-contributions/juan_synthetic_data/src/plot_utils.py

@@ -0,0 +1,13 @@
+import pandas as pd
+
+# -------------------------------
+# Helper function to display CSV
+# -------------------------------
+def display_reference_csv(file):
+    if file is None:
+        return pd.DataFrame()
+    try:
+        df = pd.read_csv(file.name if hasattr(file, "name") else file)
+        return df
+    except Exception as e:
+        return pd.DataFrame({"Error": [str(e)]})

week3/community-contributions/legal_qna_generator/legal_qna_generator.ipynb

@@ -0,0 +1,545 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "ffe08bad",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "from dotenv import load_dotenv\n",
+    "from openai import OpenAI\n",
+    "import json\n",
+    "from typing import List, Dict\n",
+    "import gradio as gr\n",
+    "import random\n",
+    "\n",
+    "load_dotenv(override=True)\n",
+    "client = OpenAI()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "2f24eb03",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "LEGAL_TOPIC_SEEDS = [\n",
+    "    \"criminal offenses and penalties\",\n",
+    "    \"property rights and disputes\",\n",
+    "    \"contract law and breach remedies\",\n",
+    "    \"civil procedure and court processes\",\n",
+    "    \"evidence admissibility rules\",\n",
+    "    \"constitutional rights protections\",\n",
+    "    \"family law and inheritance\",\n",
+    "    \"corporate governance regulations\",\n",
+    "    \"intellectual property protections\",\n",
+    "    \"cyber crime and digital law\"\n",
+    "]\n",
+    "\n",
+    "QUESTION_TYPES = [\n",
+    "    \"definition\",\n",
+    "    \"procedure\",\n",
+    "    \"penalty\",\n",
+    "    \"rights\",\n",
+    "    \"obligations\",\n",
+    "    \"exceptions\",\n",
+    "    \"examples\"\n",
+    "]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "9256c3ae",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "class SyntheticLegalGenerator:\n",
+    "    \"\"\"Generates synthetic legal content and sections\"\"\"\n",
+    "    \n",
+    "    def __init__(self, client: OpenAI, model: str = \"gpt-4o-mini\"):\n",
+    "        self.client = client\n",
+    "        self.model = model\n",
+    "    \n",
+    "    def generate_legal_section(self, topic: str) -> Dict[str, str]:\n",
+    "        \"\"\"Generate a completely synthetic legal section\"\"\"\n",
+    "        \n",
+    "        prompt = f\"\"\"Create a SYNTHETIC (fictional but realistic) Indian legal section about: {topic}\n",
+    "\n",
+    "Generate:\n",
+    "1. A section number (format: IPC XXX or CrPC XXX or IEA XXX)\n",
+    "2. A clear title\n",
+    "3. A detailed legal provision (2-3 sentences)\n",
+    "\n",
+    "Make it realistic but completely fictional. Use legal language.\n",
+    "\n",
+    "Format:\n",
+    "SECTION: [number]\n",
+    "TITLE: [title]\n",
+    "PROVISION: [detailed text]\"\"\"\n",
+    "\n",
+    "        try:\n",
+    "            response = self.client.chat.completions.create(\n",
+    "                model=self.model,\n",
+    "                messages=[\n",
+    "                    {\"role\": \"system\", \"content\": \"You are a legal content generator creating synthetic Indian legal provisions for educational purposes.\"},\n",
+    "                    {\"role\": \"user\", \"content\": prompt}\n",
+    "                ],\n",
+    "                temperature=0.8,\n",
+    "                max_tokens=400\n",
+    "            )\n",
+    "            \n",
+    "            content = response.choices[0].message.content.strip()\n",
+    "            \n",
+    "            # Parse the response\n",
+    "            section_num = \"\"\n",
+    "            title = \"\"\n",
+    "            provision = \"\"\n",
+    "            \n",
+    "            for line in content.split('\\n'):\n",
+    "                if line.startswith('SECTION:'):\n",
+    "                    section_num = line.replace('SECTION:', '').strip()\n",
+    "                elif line.startswith('TITLE:'):\n",
+    "                    title = line.replace('TITLE:', '').strip()\n",
+    "                elif line.startswith('PROVISION:'):\n",
+    "                    provision = line.replace('PROVISION:', '').strip()\n",
+    "            \n",
+    "            return {\n",
+    "                \"section_number\": section_num,\n",
+    "                \"title\": title,\n",
+    "                \"provision\": provision,\n",
+    "                \"topic\": topic\n",
+    "            }\n",
+    "            \n",
+    "        except Exception as e:\n",
+    "            print(f\"Error generating section: {e}\")\n",
+    "            return {\n",
+    "                \"section_number\": \"IPC 000\",\n",
+    "                \"title\": \"Error\",\n",
+    "                \"provision\": f\"Failed to generate: {e}\",\n",
+    "                \"topic\": topic\n",
+    "            }"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "32be3d52",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "class SyntheticQAGenerator:\n",
+    "    \"\"\"Generates Q&A pairs from synthetic legal sections\"\"\"\n",
+    "    \n",
+    "    def __init__(self, client: OpenAI, model: str = \"gpt-4o-mini\"):\n",
+    "        self.client = client\n",
+    "        self.model = model\n",
+    "    \n",
+    "    def generate_qa_pair(self, legal_section: Dict[str, str], question_type: str) -> Dict[str, str]:\n",
+    "        \"\"\"Generate Q&A pair from synthetic legal section\"\"\"\n",
+    "        \n",
+    "        prompt = f\"\"\"Based on this SYNTHETIC legal section, create a {question_type}-type question and answer:\n",
+    "\n",
+    "Section: {legal_section['section_number']}\n",
+    "Title: {legal_section['title']}\n",
+    "Provision: {legal_section['provision']}\n",
+    "\n",
+    "Create ONE question (focusing on {question_type}) and a clear, accurate answer based on this provision.\n",
+    "\n",
+    "Format:\n",
+    "Q: [question]\n",
+    "A: [answer]\n",
+    "\n",
+    "Keep it educational and clear.\"\"\"\n",
+    "\n",
+    "        try:\n",
+    "            response = self.client.chat.completions.create(\n",
+    "                model=self.model,\n",
+    "                messages=[\n",
+    "                    {\"role\": \"system\", \"content\": \"You are creating educational Q&A pairs from synthetic legal content.\"},\n",
+    "                    {\"role\": \"user\", \"content\": prompt}\n",
+    "                ],\n",
+    "                temperature=0.7,\n",
+    "                max_tokens=350\n",
+    "            )\n",
+    "            \n",
+    "            content = response.choices[0].message.content.strip()\n",
+    "            \n",
+    "            # Parse Q&A\n",
+    "            question = \"\"\n",
+    "            answer = \"\"\n",
+    "            \n",
+    "            for line in content.split('\\n'):\n",
+    "                if line.startswith('Q:'):\n",
+    "                    question = line[2:].strip()\n",
+    "                elif line.startswith('A:'):\n",
+    "                    answer = line[2:].strip()\n",
+    "            \n",
+    "            return {\n",
+    "                \"section_number\": legal_section['section_number'],\n",
+    "                \"section_title\": legal_section['title'],\n",
+    "                \"provision\": legal_section['provision'],\n",
+    "                \"question_type\": question_type,\n",
+    "                \"question\": question,\n",
+    "                \"answer\": answer\n",
+    "            }\n",
+    "            \n",
+    "        except Exception as e:\n",
+    "            print(f\"Error generating Q&A: {e}\")\n",
+    "            return {\n",
+    "                \"section_number\": legal_section['section_number'],\n",
+    "                \"section_title\": legal_section['title'],\n",
+    "                \"provision\": legal_section['provision'],\n",
+    "                \"question_type\": question_type,\n",
+    "                \"question\": \"Error generating question\",\n",
+    "                \"answer\": \"Error generating answer\"\n",
+    "            }"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "fe88708f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "class SyntheticDataPipeline:\n",
+    "    \"\"\"Complete pipeline for synthetic legal Q&A generation\"\"\"\n",
+    "    \n",
+    "    def __init__(self, legal_gen: SyntheticLegalGenerator, qa_gen: SyntheticQAGenerator):\n",
+    "        self.legal_gen = legal_gen\n",
+    "        self.qa_gen = qa_gen\n",
+    "        self.dataset: List[Dict[str, str]] = []\n",
+    "    \n",
+    "    def generate_complete_entry(self, topic: str = None, question_type: str = None) -> Dict[str, str]:\n",
+    "        \"\"\"Generate synthetic legal section + Q&A in one go\"\"\"\n",
+    "        \n",
+    "        # Pick random topic if not provided\n",
+    "        if topic is None:\n",
+    "            topic = random.choice(LEGAL_TOPIC_SEEDS)\n",
+    "        \n",
+    "        # Pick random question type if not provided\n",
+    "        if question_type is None:\n",
+    "            question_type = random.choice(QUESTION_TYPES)\n",
+    "        \n",
+    "        # Step 1: Generate synthetic legal section\n",
+    "        legal_section = self.legal_gen.generate_legal_section(topic)\n",
+    "        \n",
+    "        # Step 2: Generate Q&A from that section\n",
+    "        qa_pair = self.qa_gen.generate_qa_pair(legal_section, question_type)\n",
+    "        \n",
+    "        return qa_pair\n",
+    "    \n",
+    "    def generate_batch(self, count: int, progress_callback=None) -> List[Dict[str, str]]:\n",
+    "        \"\"\"Generate multiple synthetic entries\"\"\"\n",
+    "        batch = []\n",
+    "        \n",
+    "        for i in range(count):\n",
+    "            if progress_callback:\n",
+    "                progress_callback((i + 1) / count, desc=f\"Generating {i+1}/{count}...\")\n",
+    "            \n",
+    "            entry = self.generate_complete_entry()\n",
+    "            batch.append(entry)\n",
+    "            self.dataset.append(entry)\n",
+    "        \n",
+    "        return batch\n",
+    "    \n",
+    "    def save_dataset(self, filename: str = \"synthetic_legal_qa.json\") -> str:\n",
+    "        \"\"\"Save dataset to JSON\"\"\"\n",
+    "        try:\n",
+    "            with open(filename, 'w', encoding='utf-8') as f:\n",
+    "                json.dump(self.dataset, f, indent=2, ensure_ascii=False)\n",
+    "            return f\"✅ Saved {len(self.dataset)} synthetic Q&A pairs to {filename}\"\n",
+    "        except Exception as e:\n",
+    "            return f\"❌ Error saving: {e}\"\n",
+    "    \n",
+    "    def get_summary(self) -> str:\n",
+    "        \"\"\"Get dataset summary\"\"\"\n",
+    "        if not self.dataset:\n",
+    "            return \"No synthetic data generated yet.\"\n",
+    "        \n",
+    "        summary = f\"**Total Synthetic Q&A Pairs:** {len(self.dataset)}\\n\\n\"\n",
+    "        summary += \"**Topics Covered:**\\n\"\n",
+    "        \n",
+    "        topics = {}\n",
+    "        for entry in self.dataset:\n",
+    "            topic = entry.get('section_title', 'Unknown')\n",
+    "            topics[topic] = topics.get(topic, 0) + 1\n",
+    "        \n",
+    "        for topic, count in topics.items():\n",
+    "            summary += f\"- {topic}: {count}\\n\"\n",
+    "        \n",
+    "        return summary"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "0822c49e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "legal_generator = SyntheticLegalGenerator(client)\n",
+    "qa_generator = SyntheticQAGenerator(client)\n",
+    "pipeline = SyntheticDataPipeline(legal_generator, qa_generator)\n",
+    "\n",
+    "print(\"✅ Synthetic data pipeline initialized!\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "9b86f15f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Cell 8: UI functions with real-time progress updates\n",
+    "def generate_single_synthetic(topic_choice: str, question_type: str, progress=gr.Progress()):\n",
+    "    \"\"\"Generate single synthetic entry with real-time updates\"\"\"\n",
+    "    \n",
+    "    # Step 1: Generate legal section\n",
+    "    progress(0.2, desc=\"🔍 Generating synthetic legal section...\")\n",
+    "    yield \"⏳ Creating synthetic legal provision...\", pipeline.get_summary()\n",
+    "    \n",
+    "    legal_section = pipeline.legal_gen.generate_legal_section(topic_choice)\n",
+    "    \n",
+    "    # Show intermediate result\n",
+    "    intermediate = f\"### 📜 Generated Section\\n\\n\"\n",
+    "    intermediate += f\"**{legal_section['section_number']}**: {legal_section['title']}\\n\\n\"\n",
+    "    intermediate += f\"_{legal_section['provision']}_\\n\\n\"\n",
+    "    intermediate += \"⏳ Now generating Q&A pair...\"\n",
+    "    \n",
+    "    progress(0.5, desc=\"💭 Creating Q&A pair...\")\n",
+    "    yield intermediate, pipeline.get_summary()\n",
+    "    \n",
+    "    # Step 2: Generate Q&A\n",
+    "    qa_pair = pipeline.qa_gen.generate_qa_pair(legal_section, question_type)\n",
+    "    pipeline.dataset.append(qa_pair)\n",
+    "    \n",
+    "    progress(0.9, desc=\"✨ Finalizing...\")\n",
+    "    \n",
+    "    # Final result\n",
+    "    result = f\"### 🏛️ {qa_pair['section_number']}: {qa_pair['section_title']}\\n\\n\"\n",
+    "    result += f\"**Provision:** {qa_pair['provision']}\\n\\n\"\n",
+    "    result += f\"**Question Type:** _{qa_pair['question_type']}_\\n\\n\"\n",
+    "    result += f\"**Q:** {qa_pair['question']}\\n\\n\"\n",
+    "    result += f\"**A:** {qa_pair['answer']}\\n\\n\"\n",
+    "    result += \"---\\n✅ **Added to dataset!**\"\n",
+    "    \n",
+    "    progress(1.0, desc=\"✅ Complete!\")\n",
+    "    yield result, pipeline.get_summary()\n",
+    "\n",
+    "def generate_batch_synthetic(num_pairs: int, progress=gr.Progress()):\n",
+    "    \"\"\"Generate batch with live updates after each entry\"\"\"\n",
+    "    \n",
+    "    results = []\n",
+    "    count = int(num_pairs)\n",
+    "    \n",
+    "    for i in range(count):\n",
+    "        # Update progress\n",
+    "        progress_pct = (i + 1) / count\n",
+    "        progress(progress_pct, desc=f\"🔄 Generating {i+1}/{count}...\")\n",
+    "        \n",
+    "        # Generate entry\n",
+    "        entry = pipeline.generate_complete_entry()\n",
+    "        pipeline.dataset.append(entry)\n",
+    "        \n",
+    "        # Format result\n",
+    "        result = f\"### {i+1}. {entry['section_number']}: {entry['section_title']}\\n\"\n",
+    "        result += f\"**Q:** {entry['question']}\\n\"\n",
+    "        result += f\"**A:** {entry['answer']}\\n\\n\"\n",
+    "        results.append(result)\n",
+    "        \n",
+    "        # Yield intermediate results to update UI in real-time\n",
+    "        current_output = \"\".join(results)\n",
+    "        current_output += f\"\\n---\\n⏳ **Progress: {i+1}/{count} completed**\"\n",
+    "        \n",
+    "        yield current_output, pipeline.get_summary()\n",
+    "    \n",
+    "    # Final output\n",
+    "    final_output = \"\".join(results)\n",
+    "    final_output += f\"\\n---\\n✅ **All {count} Q&A pairs generated successfully!**\"\n",
+    "    \n",
+    "    progress(1.0, desc=\"✅ Batch complete!\")\n",
+    "    yield final_output, pipeline.get_summary()\n",
+    "\n",
+    "def save_synthetic_dataset():\n",
+    "    \"\"\"Save the synthetic dataset\"\"\"\n",
+    "    return pipeline.save_dataset()\n",
+    "\n",
+    "def clear_dataset():\n",
+    "    \"\"\"Clear the current dataset\"\"\"\n",
+    "    pipeline.dataset.clear()\n",
+    "    return \"✅ Dataset cleared!\", pipeline.get_summary()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "9d352fec",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Cell 9: Enhanced UI with real-time updates\n",
+    "with gr.Blocks(title=\"Synthetic Legal Q&A Generator\", theme=gr.themes.Soft()) as demo:\n",
+    "    gr.Markdown(\"# 🤖 Synthetic Legal Q&A Data Generator\")\n",
+    "    gr.Markdown(\"**Generates completely synthetic Indian legal sections AND Q&A pairs from scratch**\")\n",
+    "    gr.Markdown(\"_Watch the magic happen in real-time! 🎬_\")\n",
+    "    \n",
+    "    with gr.Tab(\"🎯 Single Generation\"):\n",
+    "        gr.Markdown(\"### Generate one synthetic legal section with Q&A\")\n",
+    "        gr.Markdown(\"_See each step of generation as it happens_\")\n",
+    "        \n",
+    "        with gr.Row():\n",
+    "            with gr.Column(scale=1):\n",
+    "                topic_dropdown = gr.Dropdown(\n",
+    "                    choices=LEGAL_TOPIC_SEEDS,\n",
+    "                    label=\"🎯 Select Legal Topic\",\n",
+    "                    value=LEGAL_TOPIC_SEEDS[0]\n",
+    "                )\n",
+    "                qtype_dropdown = gr.Dropdown(\n",
+    "                    choices=QUESTION_TYPES,\n",
+    "                    label=\"❓ Question Type\",\n",
+    "                    value=QUESTION_TYPES[0]\n",
+    "                )\n",
+    "                gen_single_btn = gr.Button(\n",
+    "                    \"🎲 Generate Synthetic Entry\", \n",
+    "                    variant=\"primary\",\n",
+    "                    size=\"lg\"\n",
+    "                )\n",
+    "            \n",
+    "            with gr.Column(scale=2):\n",
+    "                output_single = gr.Markdown(\n",
+    "                    label=\"Generated Content\",\n",
+    "                    value=\"Click **Generate** to create synthetic legal content...\"\n",
+    "                )\n",
+    "        \n",
+    "        summary_single = gr.Textbox(\n",
+    "            label=\"📊 Dataset Summary\", \n",
+    "            lines=6,\n",
+    "            interactive=False\n",
+    "        )\n",
+    "        \n",
+    "        gen_single_btn.click(\n",
+    "            fn=generate_single_synthetic,\n",
+    "            inputs=[topic_dropdown, qtype_dropdown],\n",
+    "            outputs=[output_single, summary_single]\n",
+    "        )\n",
+    "    \n",
+    "    with gr.Tab(\"🚀 Batch Generation\"):\n",
+    "        gr.Markdown(\"### Generate multiple synthetic legal Q&A pairs\")\n",
+    "        gr.Markdown(\"_Live updates as each Q&A pair is created!_\")\n",
+    "        \n",
+    "        with gr.Row():\n",
+    "            with gr.Column(scale=1):\n",
+    "                num_slider = gr.Slider(\n",
+    "                    minimum=5,\n",
+    "                    maximum=1000,\n",
+    "                    value=5,\n",
+    "                    step=5,\n",
+    "                    label=\"📦 Number of Synthetic Q&A Pairs\"\n",
+    "                )\n",
+    "                gr.Markdown(\"**Tip:** Start with 10-20 pairs to see live generation\")\n",
+    "                gen_batch_btn = gr.Button(\n",
+    "                    \"🔥 Generate Batch\", \n",
+    "                    variant=\"primary\",\n",
+    "                    size=\"lg\"\n",
+    "                )\n",
+    "            \n",
+    "            with gr.Column(scale=2):\n",
+    "                output_batch = gr.Markdown(\n",
+    "                    label=\"Generated Synthetic Data\",\n",
+    "                    value=\"Click **Generate Batch** to start creating multiple Q&A pairs...\"\n",
+    "                )\n",
+    "        \n",
+    "        summary_batch = gr.Textbox(\n",
+    "            label=\"📊 Dataset Summary\", \n",
+    "            lines=6,\n",
+    "            interactive=False\n",
+    "        )\n",
+    "        \n",
+    "        gen_batch_btn.click(\n",
+    "            fn=generate_batch_synthetic,\n",
+    "            inputs=[num_slider],\n",
+    "            outputs=[output_batch, summary_batch]\n",
+    "        )\n",
+    "    \n",
+    "    with gr.Tab(\"💾 Manage Dataset\"):\n",
+    "        gr.Markdown(\"### Save or Clear Your Synthetic Dataset\")\n",
+    "        \n",
+    "        with gr.Row():\n",
+    "            with gr.Column():\n",
+    "                gr.Markdown(\"**💾 Save your generated data**\")\n",
+    "                gr.Markdown(\"Exports all Q&A pairs to `synthetic_legal_qa.json`\")\n",
+    "                save_btn = gr.Button(\n",
+    "                    \"💾 Save to JSON\", \n",
+    "                    variant=\"primary\",\n",
+    "                    size=\"lg\"\n",
+    "                )\n",
+    "            \n",
+    "            with gr.Column():\n",
+    "                gr.Markdown(\"**🗑️ Clear current dataset**\")\n",
+    "                gr.Markdown(\"⚠️ This will remove all generated Q&A pairs\")\n",
+    "                clear_btn = gr.Button(\n",
+    "                    \"🗑️ Clear Dataset\", \n",
+    "                    variant=\"stop\",\n",
+    "                    size=\"lg\"\n",
+    "                )\n",
+    "        \n",
+    "        manage_status = gr.Textbox(\n",
+    "            label=\"Status\", \n",
+    "            lines=2,\n",
+    "            interactive=False\n",
+    "        )\n",
+    "        manage_summary = gr.Textbox(\n",
+    "            label=\"Current Dataset Overview\", \n",
+    "            lines=10,\n",
+    "            interactive=False,\n",
+    "            value=pipeline.get_summary()\n",
+    "        )\n",
+    "        \n",
+    "        save_btn.click(\n",
+    "            fn=save_synthetic_dataset,\n",
+    "            inputs=[],\n",
+    "            outputs=[manage_status]\n",
+    "        )\n",
+    "        \n",
+    "        clear_btn.click(\n",
+    "            fn=clear_dataset,\n",
+    "            inputs=[],\n",
+    "            outputs=[manage_status, manage_summary]\n",
+    "        )\n",
+    "    \n",
+    "    # Footer\n",
+    "    gr.Markdown(\"---\")\n",
+    "    gr.Markdown(\"🎓 **LLM Engineering Week 3** | Synthetic Data Generation Challenge\")\n",
+    "\n",
+    "demo.launch(share=False, inbrowser=True)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "llm-engineering",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.12.10"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

week3/community-contributions/legal_qna_generator/synthetic_legal_qa.backup.json

@@ -0,0 +1,802 @@
+[
+  {
+    "section_number": "IPC 123A",
+    "section_title": "Protection of Digital Intellectual Property Rights",
+    "provision": "Whoever, without the authorization of the owner, reproduces, distributes, or publicly displays any digital work, including software, databases, or multimedia content, shall be punished with imprisonment for a term which may extend to three years, or with fine which may extend to five lakh rupees, or with both. For the purposes of this section, \"digital work\" shall include any creation that exists in a digital format and embodies original intellectual effort. The provisions of this section shall apply in addition to any rights conferred under the Copyright Act, 1957.",
+    "question_type": "examples",
+    "question": "Can you provide examples of actions that would violate IPC 123A regarding the protection of digital intellectual property rights?",
+    "answer": "Yes, actions that would violate IPC 123A include: 1) Downloading and sharing a copyrighted software program without the owner's permission; 2) Reproducing a digital art piece and selling it online without authorization from the artist; 3) Distributing a database containing proprietary information without consent from the database owner; and 4) Publicly displaying a video or multimedia content on a website or social media platform without obtaining the rights from the creator. All these actions constitute unauthorized reproduction, distribution, or public display of digital works, which are punishable under this provision."
+  },
+  {
+    "section_number": "FLA 102",
+    "section_title": "Inheritance Rights of Unmarried Children",
+    "provision": "In the event of the intestate death of a parent, all unmarried children shall be entitled to an equal share in the estate of the deceased, irrespective of the parentage or domicile of the children. The rights conferred herein shall be enforceable against any individual claiming succession rights to the estate, and no testamentary disposition or familial agreement shall supersede the statutory entitlement outlined in this provision. Furthermore, the provisions of this section shall apply retroactively to all intestate estates, regardless of the date of death of the decedent.",
+    "question_type": "rights",
+    "question": "What rights do unmarried children have in the event of an intestate death of a parent according to FLA 102?",
+    "answer": "Unmarried children are entitled to an equal share in the estate of the deceased parent, regardless of their parentage or domicile. This right is enforceable against anyone claiming succession rights to the estate and cannot be overridden by any will or familial agreement. Additionally, this provision applies retroactively to all intestate estates, regardless of when the decedent died."
+  },
+  {
+    "section_number": "IEA 120A",
+    "section_title": "Admissibility of Digital Evidence",
+    "provision": "Notwithstanding any other provisions of this Act, digital evidence, including but not limited to electronic documents, data stored in digital format, and communications transmitted electronically, shall be admissible in any proceeding before a court provided that the party seeking to introduce such evidence demonstrates its authenticity and relevance. The court may require the party to produce a digital forensic report or certificate from a qualified expert to establish the integrity of the digital evidence in question, ensuring that the evidence has not been tampered with and is a true representation of the original data.",
+    "question_type": "procedure",
+    "question": "What steps must a party take to ensure the admissibility of digital evidence in court under IEA 120A?",
+    "answer": "To ensure the admissibility of digital evidence in court under IEA 120A, the party seeking to introduce the evidence must demonstrate both its authenticity and relevance. Additionally, the court may require the party to produce a digital forensic report or a certificate from a qualified expert to establish the integrity of the digital evidence, confirming that it has not been tampered with and accurately represents the original data."
+  },
+  {
+    "section_number": "IPC 456",
+    "section_title": "Offense of Cyber Intimidation",
+    "provision": "Whoever, with intent to cause harm or distress to any person, uses a computer resource or communication device to send threats, intimidate, or coerce such person through electronic means, shall be punished with imprisonment for a term which may extend to three years, or with fine which may extend to fifty thousand rupees, or with both. In the event of repeated offenses, the imprisonment may extend to five years and the fine may be increased to one lakh rupees.",
+    "question_type": "definition",
+    "question": "What constitutes the offense of cyber intimidation under IPC 456?",
+    "answer": "The offense of cyber intimidation under IPC 456 is defined as the act of using a computer resource or communication device to send threats, intimidate, or coerce any person with the intent to cause harm or distress."
+  },
+  {
+    "section_number": "IPC 124A",
+    "section_title": "Protection of Original Works of Authorship",
+    "provision": "Any person who, without the consent of the author or creator, reproduces, distributes, or publicly displays an original work of authorship, including but not limited to literary, artistic, musical, and dramatic works, shall be liable for infringement. Such infringement shall be punishable with imprisonment for a term that may extend to three years, or with fine, or with both. This section shall not apply to uses that fall under the doctrine of fair use as defined by the relevant provisions of this Code.",
+    "question_type": "obligations",
+    "question": "What obligations does a person have regarding the reproduction, distribution, or public display of an original work of authorship under IPC 124A?",
+    "answer": "A person is obligated to obtain the consent of the author or creator before reproducing, distributing, or publicly displaying an original work of authorship. Failure to do so could result in liability for infringement, which may lead to penalties including imprisonment for up to three years, a fine, or both, unless the use falls under the doctrine of fair use as defined by the relevant provisions of the Code."
+  },
+  {
+    "section_number": "IPC 500A",
+    "section_title": "Unauthorized Access and Data Manipulation",
+    "provision": "Whoever, without lawful authority, intentionally gains access to any computer resource or computer system and causes alteration, deletion, or addition of data therein, shall be punished with imprisonment for a term which may extend to three years, or with fine which may extend to one lakh rupees, or with both. For the purposes of this section, \"computer resource\" shall include any data, software, or digital content stored within the device or network, and \"lawful authority\" shall mean permission granted by the owner or authorized custodian of the computer resource.",
+    "question_type": "definition",
+    "question": "What is meant by \"lawful authority\" as defined in IPC 500A regarding unauthorized access to computer resources?",
+    "answer": "\"Lawful authority\" refers to the permission granted by the owner or authorized custodian of the computer resource to access that resource."
+  },
+  {
+    "section_number": "IPC 506A",
+    "section_title": "Cyber Harassment",
+    "provision": "Whosoever, by means of electronic communication or any digital platform, intentionally causes physical or mental harm to another person through threats, intimidation, or coercive messaging, shall be punished with imprisonment for a term which may extend to three years, or with fine which may extend to fifty thousand rupees, or with both. For the purposes of this section, \"electronic communication\" includes, but is not limited to, text messages, emails, social media interactions, and any other forms of digital messaging.",
+    "question_type": "procedure",
+    "question": "What steps should a victim take to file a complaint under IPC 506A for cyber harassment?",
+    "answer": "A victim of cyber harassment under IPC 506A should follow these steps to file a complaint:"
+  },
+  {
+    "section_number": "IEA 102A",
+    "section_title": "Admissibility of Electronic Evidence",
+    "provision": "Notwithstanding the provisions of Section 61 of this Act, electronic evidence shall be admissible in any proceedings before a court provided it is accompanied by a certificate from the producer attesting to its authenticity and integrity, as prescribed under the Information Technology Act, 2000. The court shall assess the credibility of such evidence in accordance with the standards established by the Supreme Court and may require additional corroboration if deemed necessary for the interests of justice. Any objection to the admissibility of electronic evidence shall be raised at the earliest possible stage, failing which the right to contest its admissibility shall be deemed waived.",
+    "question_type": "penalty",
+    "question": "What are the consequences of failing to raise an objection to the admissibility of electronic evidence at the earliest possible stage under IEA 102A?",
+    "answer": "If a party fails to raise an objection to the admissibility of electronic evidence at the earliest possible stage, they will be deemed to have waived their right to contest its admissibility in court. This means that the objection cannot be raised later in the proceedings, potentially impacting the outcome of the case."
+  },
+  {
+    "section_number": "FLA 123",
+    "section_title": "Rights of Inheritance among Lineal Ascendants and Descendants",
+    "provision": "In matters of inheritance, lineal ascendants shall inherit equal shares alongside lineal descendants in the absence of a will. In cases where property is self-acquired, the owner may designate the distribution of their estate; however, such designation shall not infringe upon the statutory rights of the surviving spouse or any children, who shall retain a minimum guaranteed share as prescribed under this Act. In the event of a dispute, such claims shall be adjudicated by the Family Court, taking into consideration the principles of equity and the welfare of all parties involved.",
+    "question_type": "examples",
+    "question": "If a person dies without a will and is survived by their parents and children, how will the inheritance be divided according to FLA 123?",
+    "answer": "According to FLA 123, if a person dies without a will, their lineal ascendants (parents) and lineal descendants (children) will inherit equal shares of the estate. For example, if the estate is worth $120,000 and the deceased is survived by both their parents and children (let's say two children), the estate would be divided equally among them. Each parent would receive $20,000, and each child would also receive $20,000, totaling the estate's value. However, if the deceased had designated a different distribution in a will, it must still respect the minimum guaranteed share for the surviving spouse and children, as mandated by the Act."
+  },
+  {
+    "section_number": "IPC 123A",
+    "section_title": "Protection of Digital Innovations",
+    "provision": "Any individual or entity that creates an original digital work, including but not limited to software, algorithms, and digital media, shall have the exclusive right to control the reproduction, distribution, and adaptation of such work for a period of ten years from the date of creation, subject to the provisions of fair use as outlined in this Code. Unauthorized use or reproduction of a protected digital innovation shall attract civil penalties, including but not limited to injunctions, damages, and the seizure of infringing materials, as deemed appropriate by the court.",
+    "question_type": "obligations",
+    "question": "What obligations do individuals or entities have when creating original digital works under IPC 123A?",
+    "answer": "Individuals or entities that create original digital works have the obligation to control the reproduction, distribution, and adaptation of their work for a period of ten years from the date of creation. They must ensure that any use of their digital innovations is authorized, as unauthorized use or reproduction can result in civil penalties, including injunctions, damages, and the seizure of infringing materials as determined by the court."
+  },
+  {
+    "section_number": "IPC 509A",
+    "section_title": "Intentional Misuse of Digital Identity",
+    "provision": "Whoever, intending to cause annoyance, inconvenience, or harm, knowingly and dishonestly uses or impersonates the digital identity of another person, including but not limited to social media accounts, email addresses, or any other digital platform, shall be punishable with imprisonment for a term that may extend to three years, or with fine which may extend to fifty thousand rupees, or with both. In the case of repeat offenders, the term of imprisonment may extend to five years.",
+    "question_type": "exceptions",
+    "question": "Are there any exceptions to the punishment under IPC 509A for the intentional misuse of digital identity?",
+    "answer": "Yes, exceptions may apply in cases where the individual can demonstrate that their use of another person's digital identity was done with the consent of that person or for legitimate purposes such as parody, satire, or commentary that does not intend to cause annoyance, inconvenience, or harm. However, the burden of proof lies with the individual claiming the exception, and it is essential to establish that the intent was not malicious."
+  },
+  {
+    "section_number": "IPR 145",
+    "section_title": "Rights of Co-Owners in Joint Property",
+    "provision": "In any joint ownership of property, each co-owner shall possess an equal right to utilize, manage, and derive benefit from the property, subject to the terms of their agreement. In the event of a dispute regarding the use or management of the property, any co-owner may seek mediation through the appropriate civil court, which shall have the authority to appoint a neutral arbitrator to facilitate a resolution. Should the parties remain in disagreement following mediation, the court shall adjudicate based on the principles of equity and the specific contributions made by each co-owner toward the property.",
+    "question_type": "procedure",
+    "question": "What steps should a co-owner take if there is a dispute regarding the use or management of jointly owned property?",
+    "answer": "If a co-owner encounters a dispute regarding the use or management of jointly owned property, they should first seek mediation through the appropriate civil court. The court will appoint a neutral arbitrator to help facilitate a resolution. If the parties still cannot reach an agreement after mediation, the court will then adjudicate the dispute based on principles of equity and consider the specific contributions made by each co-owner towards the property."
+  },
+  {
+    "section_number": "CPL 456",
+    "section_title": "Remedies for Breach of Contract",
+    "provision": "In the event of a breach of contract, the aggrieved party shall be entitled to seek specific performance of the contract, or alternatively, claim for damages which shall be calculated based on the loss incurred directly as a result of the breach. The court may, at its discretion, award punitive damages not exceeding the value of the contract, if it finds the breach to have been willful or malicious. Any claims for reliance damages shall be substantiated with adequate evidence demonstrating the expenditures incurred in preparation for the performance of the contract.",
+    "question_type": "procedure",
+    "question": "What steps must the aggrieved party take to claim specific performance or damages in the event of a breach of contract according to CPL 456?",
+    "answer": "The aggrieved party must first determine whether to seek specific performance of the contract or claim for damages. If claiming damages, they should calculate the loss incurred directly due to the breach. If they wish to seek punitive damages, they must demonstrate that the breach was willful or malicious, keeping in mind that such damages cannot exceed the value of the contract. Additionally, if the party wants to claim reliance damages, they must gather and present adequate evidence of expenditures incurred in preparation for the contract's performance. All claims should be filed with the appropriate court as per the procedural rules governing contract disputes."
+  },
+  {
+    "section_number": "IEA 112",
+    "section_title": "Admissibility of Digital Evidence",
+    "provision": "Notwithstanding any other provisions of this Act, digital evidence shall be admissible in judicial proceedings, provided that it is demonstrated to be authentic and relevant to the matter at hand. The party seeking to introduce digital evidence must establish a clear chain of custody and utilize appropriate technological methods for preservation and extraction, ensuring that the integrity of the evidence has not been compromised. Furthermore, the court may consider expert testimony regarding the reliability of the digital medium used to store or transmit such evidence.",
+    "question_type": "examples",
+    "question": "Can you provide an example of how a party might successfully introduce digital evidence in court under IEA 112?",
+    "answer": "Certainly! Imagine a scenario where a company is accused of data theft. The plaintiff wants to introduce an email as digital evidence that allegedly contains confidential information sent to a competitor. To successfully admit this email under IEA 112, the plaintiff would need to demonstrate its authenticity by showing that the email was indeed sent from the company's server. They would establish a clear chain of custody by documenting who accessed the email and how it was preserved, perhaps by using secure storage methods. Additionally, they might engage a digital forensics expert to testify about the reliability of the email server and the methods used to extract the email, ensuring that the integrity of the evidence has not been compromised. If all these criteria are met, the court would likely admit the email as evidence in the proceedings."
+  },
+  {
+    "section_number": "CPL 125",
+    "section_title": "Remedies for Breach of Contract",
+    "provision": "In the event of a breach of contract, the aggrieved party shall be entitled to seek restitution by way of specific performance, or, in lieu thereof, claim for damages not exceeding the actual loss incurred as a direct result of the breach. Furthermore, the court may, at its discretion, award consequential damages if such damages were within the contemplation of the parties at the time of contract formation, provided that the aggrieved party has made reasonable efforts to mitigate the loss.",
+    "question_type": "procedure",
+    "question": "What steps must the aggrieved party take to seek remedies for a breach of contract under CPL 125?",
+    "answer": "To seek remedies for a breach of contract under CPL 125, the aggrieved party should follow these steps: First, clearly identify and document the breach of contract. Next, determine whether they wish to seek specific performance or claim for damages. If claiming damages, the aggrieved party must calculate and document the actual loss incurred as a direct result of the breach, ensuring that it does not exceed the actual loss. Additionally, the aggrieved party should demonstrate that they made reasonable efforts to mitigate the loss. Finally, if the aggrieved party believes consequential damages are applicable, they should provide evidence that such damages were within the contemplation of the parties at the time of contract formation. Once these steps are completed, the aggrieved party can file a claim in court to seek the desired remedies."
+  },
+  {
+    "section_number": "CONST 102",
+    "section_title": "Protection of Fundamental Rights",
+    "provision": "Every citizen shall have the right to freedom from arbitrary arrest and detention, ensuring that no person shall be deprived of their liberty without due process of law. Furthermore, every individual shall have the right to seek redress in a competent court of law for any violation of their fundamental rights, and the State shall be obligated to provide legal assistance to those unable to afford representation. Any law or action infringing upon the rights enumerated in this section shall be deemed unconstitutional and void.",
+    "question_type": "examples",
+    "question": "Can you provide examples of situations where a citizen's right to freedom from arbitrary arrest and detention might be violated, and what steps they can take if their rights are infringed upon?",
+    "answer": "Examples of situations where a citizen's right to freedom from arbitrary arrest and detention might be violated include being arrested without a warrant, being held without charges for an extended period, or being detained based solely on their political beliefs or race. In such cases, the individual has the right to seek redress in a competent court of law by filing a lawsuit against the authorities responsible for the violation. Additionally, if they cannot afford legal representation, the State is obligated to provide legal assistance to ensure that their rights are protected."
+  },
+  {
+    "section_number": "IPC 509A",
+    "section_title": "Cyber Harassment and Intimidation",
+    "provision": "Whoever, with the intent to harass or intimidate another person through the use of electronic communications, sends, posts, or publishes any obscene or threatening material shall be punished with imprisonment for a term that may extend to three years, or with fine which may extend to one lakh rupees, or with both. In cases where such actions lead to severe emotional distress or harm to the reputation of the victim, the imprisonment may extend to five years. The provisions of this section shall apply notwithstanding any other law in force relating to defamation or electronic privacy.",
+    "question_type": "procedure",
+    "question": "What steps should a victim take to file a complaint under IPC 509A for cyber harassment or intimidation?",
+    "answer": "A victim should follow these steps to file a complaint under IPC 509A: 1. Collect evidence of the harassment, such as screenshots of messages or posts. 2. Approach the nearest police station to report the incident, providing the collected evidence. 3. File a formal complaint, ensuring that all relevant details are included, such as dates, times, and the nature of the communications. 4. The police will register the complaint and may initiate an investigation. 5. If the harassment leads to severe emotional distress or harm to reputation, the victim should highlight this to the authorities, as it can lead to an extended prison term for the offender. It is advisable for the victim to seek legal assistance to navigate the process effectively."
+  },
+  {
+    "section_number": "IPC 432",
+    "section_title": "Punishment for Intentional Damage to Public Property",
+    "provision": "Whoever intentionally causes damage to any public property, including but not limited to roads, bridges, or public buildings, shall be punishable with imprisonment for a term which may extend to three years, or with a fine which may extend to five lakh rupees, or with both. In cases where the damage exceeds a value of one lakh rupees, the offender shall be liable to imprisonment for a term which may extend to five years, and the fine may extend to ten lakh rupees. This provision shall not apply to acts of lawful protest or demonstration, provided that such actions do not result in damage to the aforementioned properties.",
+    "question_type": "definition",
+    "question": "What constitutes intentional damage to public property under IPC 432?",
+    "answer": "Intentional damage to public property under IPC 432 refers to the deliberate act of causing harm to any public assets, which includes but is not limited to roads, bridges, or public buildings. Such actions are punishable by imprisonment or fines, depending on the extent of the damage caused."
+  },
+  {
+    "section_number": "IPC 128A",
+    "section_title": "Rights and Resolution of Property Disputes",
+    "provision": "In any dispute concerning the ownership, possession, or title to immovable property, parties shall be entitled to seek resolution through a Mediation and Conciliation Board established under this Section. The Board shall consist of a Chairperson and two members, appointed by the State Government, who shall endeavor to resolve the dispute amicably within a period of six months from the date of reference, failing which the aggrieved party may escalate the matter to the appropriate civil court for adjudication. The provisions of this Section shall not preclude any party from approaching the court for urgent interim relief during the pendency of the mediation process.",
+    "question_type": "exceptions",
+    "question": "Are there any exceptions to the requirement of mediation for resolving property disputes under IPC 128A?",
+    "answer": "Yes, the provisions of IPC 128A do not preclude any party from approaching the court for urgent interim relief during the pendency of the mediation process. This means that if a party needs immediate relief, they can seek it from the court even while the mediation is ongoing."
+  },
+  {
+    "section_number": "CGR 102",
+    "section_title": "Disclosure of Financial Interests",
+    "provision": "Every corporate entity registered under the Companies Act, 2013 shall disclose in its annual report the financial interests of its board members and key managerial personnel, including any directorships, shareholdings, or partnerships in other entities that may pose a conflict of interest. This disclosure must be made in a format prescribed by the Securities and Exchange Board of India (SEBI) and shall be subject to scrutiny by the independent auditors to ensure transparency and accountability within the corporate governance framework. Non-compliance with this provision shall attract penalties as stipulated under Section 234 of the Companies Act, 2013.",
+    "question_type": "exceptions",
+    "question": "Are there any exceptions to the requirement for corporate entities to disclose the financial interests of their board members and key managerial personnel as per CGR 102?",
+    "answer": "Yes, exceptions to the disclosure requirement under CGR 102 may apply in certain circumstances, such as when the financial interests are deemed nominal and not likely to pose a conflict of interest, or if the board member or key managerial personnel is involved in a confidential matter that does not affect the corporate entity's governance. However, such exceptions must be clearly justified and documented, as non-compliance can lead to penalties under Section 234 of the Companies Act, 2013. It is advisable for entities to consult legal counsel to ensure compliance with all applicable regulations."
+  },
+  {
+    "section_number": "IEA 123",
+    "section_title": "Admissibility of Digital Evidence",
+    "provision": "Notwithstanding any other provision of law, digital evidence shall be admissible in any judicial proceeding provided it is accompanied by a certificate of authenticity from a qualified digital forensic expert, which verifies the integrity and accuracy of the data. Such evidence must be presented in a format that is compatible with the court's technological capabilities, and the party seeking to introduce the digital evidence shall bear the burden of proving its reliability and relevance to the matter at hand. The court may, in its discretion, exclude digital evidence if it deems that the probative value is outweighed by the potential for prejudice or misinformation.",
+    "question_type": "examples",
+    "question": "Can you provide an example of when digital evidence would be admissible in court under IEA 123?",
+    "answer": "Digital evidence, such as emails or text messages, would be admissible in court under IEA 123 if the party seeking to introduce this evidence presents it with a certificate of authenticity from a qualified digital forensic expert. For instance, if a plaintiff wants to use a series of text messages as evidence in a contract dispute, they must ensure the messages are verified for integrity and accuracy by a forensic expert. Additionally, the text messages must be presented in a format that the court can access and understand. If these conditions are met and the plaintiff can demonstrate the relevance and reliability of the texts, the court is likely to admit the evidence, unless it determines that the potential for prejudice outweighs its probative value."
+  },
+  {
+    "section_number": "IEA 65A",
+    "section_title": "Admissibility of Digital Evidence",
+    "provision": "Notwithstanding any provision to the contrary, digital evidence shall be admissible in a court of law if it is authenticated by the party seeking its admission. Authentication shall be established through a combination of metadata verification, secure chain of custody, and corroborative testimonial evidence, ensuring the integrity and reliability of the digital record. In instances where the authenticity is challenged, the burden of proof shall rest with the party contesting such admissibility.",
+    "question_type": "procedure",
+    "question": "What steps must a party take to ensure that digital evidence is admissible in court under IEA 65A?",
+    "answer": "To ensure the admissibility of digital evidence in court under IEA 65A, the party seeking its admission must authenticate the evidence through three key steps: (1) verify the metadata associated with the digital record, (2) establish a secure chain of custody for the evidence, and (3) provide corroborative testimonial evidence that supports the integrity and reliability of the digital record. If the authenticity of the evidence is challenged, the burden of proof will shift to the party contesting its admissibility."
+  },
+  {
+    "section_number": "CGR 101",
+    "section_title": "Board Composition and Independence",
+    "provision": "Every public company shall ensure that its Board of Directors comprises a minimum of one-third independent directors, who shall not have any material relationship with the company, its promoters, or its subsidiaries. The independent directors shall be responsible for safeguarding the interests of minority shareholders and enhancing the overall governance of the company. The criteria for independence and the process for appointment shall be prescribed under the Corporate Governance Regulations, ensuring transparency and accountability in the board's operations.",
+    "question_type": "exceptions",
+    "question": "Are there any exceptions to the requirement for a public company to have a minimum of one-third independent directors on its Board of Directors as stated in CGR 101?",
+    "answer": "Yes, exceptions may apply under specific circumstances as outlined in the Corporate Governance Regulations. For instance, if a company has a unique structure or meets certain criteria established by regulatory authorities, it may be allowed to deviate from the one-third independent director requirement. However, such exceptions must adhere to the principles of transparency and accountability, and the company must provide justification for any deviations from the standard composition."
+  },
+  {
+    "section_number": "CPR 101",
+    "section_title": "Right to Constitutional Protections",
+    "provision": "Every individual shall have the right to seek recourse under this Act for any violation of their fundamental rights as enumerated in the Constitution of India. The State shall ensure the protection of these rights against any encroachment by the public or private entities, and a mechanism for redressal of grievances shall be established within six months of any reported infringement. Furthermore, any citizen aggrieved by the denial of such rights may approach the Supreme Court or High Court for enforcement, and the courts shall prioritize such cases to ensure timely justice.",
+    "question_type": "examples",
+    "question": "Can you provide an example of a situation where an individual might seek recourse under the Right to Constitutional Protections as outlined in CPR 101?",
+    "answer": "An example of a situation where an individual might seek recourse under this provision is if a citizen is wrongfully detained by the police without due process, which violates their fundamental rights as guaranteed by the Constitution of India. In this case, the individual can file a complaint under the Act, seeking redress for the infringement of their rights. If the grievance is not resolved satisfactorily within six months, the individual has the option to approach the Supreme Court or High Court to enforce their rights and obtain timely justice."
+  },
+  {
+    "section_number": "CGR 302",
+    "section_title": "Standards of Conduct for Directors",
+    "provision": "Every director of a company shall act in good faith and in the best interests of the company, ensuring transparency and accountability in all dealings. Directors are mandated to disclose any potential conflicts of interest and refrain from participating in discussions or decisions where such conflicts may arise. Failure to comply with these standards shall result in penalties as prescribed under Section CGR 305, which may include disqualification from holding office in the company for a period not exceeding five years.",
+    "question_type": "procedure",
+    "question": "What steps must a director take to comply with the standards of conduct outlined in CGR 302 regarding potential conflicts of interest?",
+    "answer": "To comply with the standards of conduct in CGR 302, a director must take the following steps:"
+  },
+  {
+    "section_number": "IPC 502A",
+    "section_title": "Unauthorized Access and Data Breach",
+    "provision": "Whoever intentionally accesses a computer system or network without authorization, or exceeds authorized access to obtain, alter, or destroy data, shall be punishable with imprisonment for a term which may extend to three years, or with fine which may extend to fifty thousand rupees, or with both. In cases where such access results in a breach of sensitive personal data or causes harm to any individual or entity, the term of imprisonment may extend to five years, and the fine may extend to one lakh rupees.",
+    "question_type": "examples",
+    "question": "Can you provide examples of actions that would violate IPC 502A and the potential consequences for those actions?",
+    "answer": "Yes, under IPC 502A, several actions could constitute unauthorized access and data breach. For example, if an individual hacks into a company's computer system to steal customer data, this would be considered intentional unauthorized access. If the hacker is caught, they could face imprisonment for up to three years and fines up to fifty thousand rupees."
+  },
+  {
+    "section_number": "IPC 456",
+    "section_title": "Offences of Public Disruption and Associated Penalties",
+    "provision": "Whoever, without lawful authority, intentionally causes public disruption by engaging in violent or threatening behavior in a public place shall be punishable with imprisonment for a term which may extend to three years, or with fine which may extend to one lakh rupees, or with both. In the event of causing grievous hurt or significant property damage during such disruption, the offender shall be liable to imprisonment for a term not less than five years, which may extend to seven years, along with a fine that may extend to five lakh rupees.",
+    "question_type": "definition",
+    "question": "What constitutes the offense of public disruption under IPC 456?",
+    "answer": "The offense of public disruption under IPC 456 is defined as intentionally causing public disruption by engaging in violent or threatening behavior in a public place without lawful authority."
+  },
+  {
+    "section_number": "IPC 502",
+    "section_title": "Criminal Intimidation through Digital Means",
+    "provision": "Whoever, using any electronic device or communication service, intentionally threatens another person with injury to their person, reputation, or property, or to cause alarm or distress, shall be punishable with imprisonment of either description for a term which may extend to three years, or with fine, or with both. In addition, if such intimidation is intended to coerce or influence the victim's actions or decisions, the term of imprisonment may extend to five years.",
+    "question_type": "obligations",
+    "question": "What are the obligations of an individual regarding the use of electronic devices to communicate, as outlined in IPC 502?",
+    "answer": "An individual is obligated not to intentionally threaten another person with injury to their person, reputation, or property using any electronic device or communication service. Violating this obligation can result in punishment that includes imprisonment for up to three years, a fine, or both. If the intimidation is intended to coerce or influence the victim's actions or decisions, the imprisonment term may extend to five years."
+  },
+  {
+    "section_number": "CPC 124",
+    "section_title": "Application for Summary Judgment",
+    "provision": "In any civil proceedings, a party may apply to the court for a summary judgment on the ground that there is no genuine dispute as to any material fact and that the party is entitled to judgment as a matter of law. The application shall be supported by an affidavit setting forth the specific facts that demonstrate the absence of a material issue of fact. The court shall hear the application and may grant the summary judgment if it is satisfied that the evidence is clear and unequivocal, and that a trial is not necessary to resolve the issues presented.",
+    "question_type": "rights",
+    "question": "What rights does a party have when applying for a summary judgment under CPC 124?",
+    "answer": "A party has the right to apply for a summary judgment in civil proceedings if they believe there is no genuine dispute regarding any material fact and that they are entitled to judgment as a matter of law. To exercise this right, the party must support their application with an affidavit that specifies the facts demonstrating the lack of a material issue of fact. If the court finds the evidence to be clear and unequivocal, and determines that a trial is unnecessary, it may grant the summary judgment."
+  },
+  {
+    "section_number": "CRPC 128A",
+    "section_title": "Protection of Fundamental Rights in Criminal Proceedings",
+    "provision": "In all criminal proceedings, it shall be the duty of the presiding officer to ensure the protection of an accused person's fundamental rights as guaranteed under Part III of the Constitution of India. Any infringement of these rights during the course of investigation or trial shall render the proceedings voidable, and the court shall have the power to issue directions to remedy such infringement, including the exclusion of unlawfully obtained evidence. The court shall also provide the accused an opportunity to address any violations of their rights at the earliest possible stage of the proceedings.",
+    "question_type": "examples",
+    "question": "Can you provide an example of how CRPC 128A protects an accused person's fundamental rights during a criminal trial?",
+    "answer": "Certainly! For instance, if during a police investigation, evidence is obtained through coercive interrogation methods that violate the accused's right to remain silent, this would constitute an infringement of their fundamental rights. Under CRPC 128A, the presiding officer is required to ensure that such rights are protected. As a result, the court may declare the proceedings voidable and exclude the unlawfully obtained evidence from the trial. Additionally, the accused would be given an opportunity to address this violation at the earliest stage, allowing them to contest the admissibility of the evidence and uphold their rights as guaranteed under the Constitution of India."
+  },
+  {
+    "section_number": "IPC 372",
+    "section_title": "Rights and Disputes Relating to Property Ownership",
+    "provision": "Any individual claiming ownership of a property shall have the right to initiate a civil suit for the determination of title and possession against any person in unlawful occupation of said property. The court shall adjudicate such disputes expeditiously, ensuring that the rights of the rightful owner are protected while balancing the interests of the occupant, who may assert a claim of adverse possession or any lawful entitlement. Furthermore, in cases where property disputes arise among co-owners or joint tenants, the court shall facilitate mediation prior to proceeding to trial, promoting an amicable resolution to conflicts concerning shared property rights.",
+    "question_type": "definition",
+    "question": "What rights does an individual have under IPC 372 regarding property ownership disputes?",
+    "answer": "Under IPC 372, an individual claiming ownership of a property has the right to initiate a civil suit for determining title and possession against anyone unlawfully occupying the property. The court is required to adjudicate these disputes quickly, protecting the rights of the rightful owner while also considering the interests of the occupant, who may claim adverse possession or other lawful entitlements. Additionally, in disputes among co-owners or joint tenants, the court must facilitate mediation before proceeding to trial to encourage an amicable resolution."
+  },
+  {
+    "section_number": "IPC 499A",
+    "section_title": "Unauthorized Access and Data Breach",
+    "provision": "Whosoever, without lawful authority or consent, accesses a computer resource or computer system, and thereby obtains, alters, or destroys any data, information, or program, with the intent to cause harm or facilitate fraud, shall be punished with imprisonment for a term which may extend to three years, or with fine which may extend to five lakh rupees, or with both. In the case of repeat offenses, the term of imprisonment may extend to five years, along with a fine not exceeding ten lakh rupees.",
+    "question_type": "definition",
+    "question": "What constitutes unauthorized access and data breach under IPC 499A?",
+    "answer": "Unauthorized access and data breach under IPC 499A occurs when an individual, without lawful authority or consent, accesses a computer resource or system, and obtains, alters, or destroys any data, information, or program with the intent to cause harm or facilitate fraud."
+  },
+  {
+    "section_number": "PPR 101",
+    "section_title": "Rights of Co-Owners in Joint Property",
+    "provision": "In the event of a dispute arising between co-owners of joint property, each co-owner shall have the right to seek mediation through a designated Property Dispute Resolution Committee, established under this Act, prior to initiating any legal proceedings. The Committee shall endeavor to resolve conflicts amicably within a period of sixty days, failing which the aggrieved co-owner may file a civil suit in the appropriate jurisdiction, whereupon the court shall consider equitable distribution and rights of possession in accordance with the principles of natural justice and prior agreements among co-owners.",
+    "question_type": "procedure",
+    "question": "What steps should a co-owner take if there is a dispute regarding joint property, according to PPR 101?",
+    "answer": "A co-owner should first seek mediation through the designated Property Dispute Resolution Committee established under PPR 101. This mediation process must be initiated prior to any legal proceedings. The Committee will attempt to resolve the conflict amicably within sixty days. If the dispute is not resolved within this period, the aggrieved co-owner may then file a civil suit in the appropriate jurisdiction, where the court will consider equitable distribution and rights of possession based on natural justice and any prior agreements among the co-owners."
+  },
+  {
+    "section_number": "IPC 512",
+    "section_title": "Offense of Digital Harassment",
+    "provision": "Whosoever, through the use of electronic means, intentionally causes harm, distress, or alarm to another person by sending, sharing, or disseminating unsolicited and offensive messages, images, or videos, shall be punishable with imprisonment for a term which may extend to three years, or with fine which may extend to fifty thousand rupees, or with both. In the case of repeated offenses, the term of imprisonment may extend to five years, along with a fine not exceeding one lakh rupees. A victim of digital harassment may file a complaint with the appropriate authority, who shall take necessary action as prescribed under this section.",
+    "question_type": "penalty",
+    "question": "What are the penalties for committing the offense of digital harassment under IPC 512?",
+    "answer": "The penalties for committing digital harassment under IPC 512 include imprisonment for a term that may extend to three years, a fine that may extend to fifty thousand rupees, or both. In the case of repeated offenses, the imprisonment term may extend to five years, with a fine not exceeding one lakh rupees."
+  },
+  {
+    "section_number": "IPC 456A",
+    "section_title": "Unauthorized Access to Digital Systems",
+    "provision": "Whosoever, without lawful authority, intentionally accesses a computer or digital system with the intent to obtain or alter data, or to interfere with the integrity or functioning of such system, shall be punishable with imprisonment of either description for a term that may extend to three years, or with fine which may extend to five lakh rupees, or with both. In the case of repeated offences, the term of imprisonment may extend to five years, and the fine may be increased to ten lakh rupees.",
+    "question_type": "definition",
+    "question": "What constitutes \"unauthorized access to digital systems\" under IPC 456A?",
+    "answer": "\"Unauthorized access to digital systems\" under IPC 456A refers to the act of intentionally accessing a computer or digital system without lawful authority, with the intent to obtain or alter data, or to interfere with the integrity or functioning of that system."
+  },
+  {
+    "section_number": "CPC 204",
+    "section_title": "Consolidation of Civil Proceedings",
+    "provision": "In any suit or proceeding where multiple matters arise out of the same transaction or series of transactions and involve common questions of law or fact, the court may, upon application by any party or suo moto, consolidate such suits or proceedings for the purpose of expedience and efficiency. The court shall ensure that such consolidation does not prejudice the rights of the parties involved and shall determine the procedure for the consolidated hearing, which may include joint trials or the use of a single set of pleadings applicable to all consolidated matters.",
+    "question_type": "obligations",
+    "question": "What obligation does the court have when consolidating civil proceedings under CPC 204?",
+    "answer": "The court has the obligation to ensure that the consolidation of suits or proceedings does not prejudice the rights of the parties involved, and it must determine the appropriate procedure for the consolidated hearing, which may involve joint trials or a single set of pleadings applicable to all consolidated matters."
+  },
+  {
+    "section_number": "IPC 420A",
+    "section_title": "Fraudulent Misrepresentation in Commercial Transactions",
+    "provision": "Whosoever, with intent to deceive or defraud, makes any false representation, whether by words or conduct, in the course of a commercial transaction, shall be punished with imprisonment of either description for a term which may extend to five years, or with fine, or with both. If such misrepresentation causes loss to the victim exceeding one lakh rupees, the term of imprisonment may extend to seven years. This provision shall not apply to representations made in good faith where the individual reasonably believes such representations to be true.",
+    "question_type": "procedure",
+    "question": "What steps should a victim take to report a fraudulent misrepresentation under IPC 420A in a commercial transaction?",
+    "answer": "To report a fraudulent misrepresentation under IPC 420A, the victim should follow these steps:"
+  },
+  {
+    "section_number": "FLA 123",
+    "section_title": "Rights of Inheritance Among Wards and Guardians",
+    "provision": "In any case where a minor is a ward under the guardianship of an individual, such guardian shall have the right to manage the ward's property, but shall not have the authority to alienate or dispose of such property without prior approval from the Family Court. Upon reaching the age of majority, the ward shall inherit all properties acquired during the period of guardianship, along with any rights therein, free from any encumbrances created by the guardian without due process. The Family Court shall ensure that the interests of the minor are adequately protected during the guardianship period, with a view to preventing any potential conflicts of interest.",
+    "question_type": "definition",
+    "question": "What is the role of a guardian in managing a minor's property according to FLA 123?",
+    "answer": "According to FLA 123, a guardian has the right to manage a minor's property but cannot alienate or dispose of it without prior approval from the Family Court."
+  },
+  {
+    "section_number": "IEA 123",
+    "section_title": "Admissibility of Electronic Evidence",
+    "provision": "In any proceeding before a court, electronic evidence shall be deemed admissible if it is produced in a manner that ensures its integrity and authenticity through a secure digital signature or cryptographic verification. The party intending to introduce such evidence must provide a certificate of authenticity from a competent authority, confirming compliance with the standards set forth in the Information Technology Act, 2000. Notwithstanding the aforementioned, any electronic evidence that is deemed to have been tampered with or altered shall be inadmissible unless the party presenting the evidence can demonstrate, beyond reasonable doubt, the absence of such tampering.",
+    "question_type": "definition",
+    "question": "What is required for electronic evidence to be deemed admissible in court according to IEA 123?",
+    "answer": "Electronic evidence is deemed admissible in court if it is produced in a manner that ensures its integrity and authenticity through a secure digital signature or cryptographic verification. Additionally, the party introducing the evidence must provide a certificate of authenticity from a competent authority, confirming compliance with the standards of the Information Technology Act, 2000."
+  },
+  {
+    "section_number": "CPC 157",
+    "section_title": "Remedies for Breach of Contract",
+    "provision": "In the event of a breach of contract, the aggrieved party shall be entitled to seek remedy in the form of specific performance, damages, or rescission, as applicable. The court may award compensatory damages to cover direct losses caused by the breach, and may also consider consequential losses if such losses were within the contemplation of both parties at the time of contract formation. Furthermore, the court shall have discretion to order specific performance where monetary compensation is inadequate to provide a just remedy, particularly in cases involving unique subject matter.",
+    "question_type": "obligations",
+    "question": "What obligations does an aggrieved party have when seeking remedies for a breach of contract under CPC 157?",
+    "answer": "The aggrieved party is entitled to seek remedies such as specific performance, damages, or rescission, depending on the circumstances of the breach. They must demonstrate the direct losses incurred and may also claim consequential losses if those were contemplated by both parties at the time of the contract. Additionally, if seeking specific performance, the aggrieved party must show that monetary compensation is inadequate to address the situation, particularly in cases involving unique subject matter."
+  },
+  {
+    "section_number": "IPC 495A",
+    "section_title": "Offense of Deceptive Co-habitation",
+    "provision": "Whosoever, with intent to deceive, cohabits with a person as if married, while being lawfully married to another person, shall be punished with imprisonment for a term which may extend to five years, or with fine, or with both. The act shall be considered a cognizable offense, and in addition to punishment, the court may direct restitution for any economic or emotional harm caused to the aggrieved party. In any prosecution under this section, evidence of the accused's prior marital status shall be admissible to establish the offense.",
+    "question_type": "exceptions",
+    "question": "Are there any exceptions to the offense of Deceptive Co-habitation under IPC 495A for individuals who are legally separated from their spouse?",
+    "answer": "Yes, individuals who are legally separated from their spouse may not be prosecuted under IPC 495A for Deceptive Co-habitation, provided that the separation is recognized by law and they are not still legally married. However, it is important to note that evidence of their prior marital status may still be considered in court to establish the context of the cohabitation."
+  },
+  {
+    "section_number": "CGR 101",
+    "section_title": "Principles of Corporate Governance",
+    "provision": "Every company incorporated under the Companies Act, 2013 shall adhere to the principles of corporate governance as prescribed by the Securities and Exchange Board of India (SEBI) regulations. These principles shall include, but not be limited to, the establishment of a robust board structure, the separation of the roles of the chairperson and the managing director, and the implementation of transparent disclosure practices that uphold the rights of shareholders. Non-compliance with these principles shall attract penalties as delineated in CGR 202.",
+    "question_type": "procedure",
+    "question": "What steps must a company take to ensure compliance with the corporate governance principles as outlined in CGR 101?",
+    "answer": "To ensure compliance with the corporate governance principles outlined in CGR 101, a company must take the following steps:"
+  },
+  {
+    "section_number": "FLA 123",
+    "section_title": "Rights of Inheritance Among Lineal Descendants",
+    "provision": "In cases of intestate succession, all lineal descendants, including illegitimate offspring, shall inherit an equal share of the estate of the deceased, irrespective of the marital status of the parent at the time of birth. No distinction shall be made based on gender, and the distribution of assets shall occur in accordance with the principles of per stirpes, ensuring that each descendant receives a proportionate share of their ancestor’s estate. Additionally, the provisions of this section shall apply retroactively to estates of deceased individuals who died on or after January 1, 2023, thereby nullifying any pre-existing discriminatory practices in inheritance laws.",
+    "question_type": "rights",
+    "question": "What rights do lineal descendants have regarding inheritance under FLA 123, particularly for illegitimate offspring and regardless of the parent's marital status?",
+    "answer": "Under FLA 123, all lineal descendants, including illegitimate offspring, have the right to inherit an equal share of a deceased individual's estate in cases of intestate succession. This inheritance is granted irrespective of the parent's marital status at the time of the child's birth and without any distinction based on gender. The distribution of the estate will follow the principles of per stirpes, ensuring each descendant receives a proportionate share of their ancestor’s estate. These rights are retroactively applied to estates of individuals who died on or after January 1, 2023, eliminating previous discriminatory inheritance practices."
+  },
+  {
+    "section_number": "CRPC 145",
+    "section_title": "Protection of Constitutional Rights During Detention",
+    "provision": "No person shall be detained in police custody for a period exceeding twenty-four hours without being informed of the grounds of arrest and without being afforded the opportunity to consult a legal practitioner of their choice. Any violation of this provision shall render the detention unlawful, and the detained individual shall be entitled to immediate release and compensation as prescribed by law. The State shall ensure that all law enforcement agencies are adequately trained in upholding these constitutional protections to prevent any infringement of fundamental rights.",
+    "question_type": "obligations",
+    "question": "What obligations do law enforcement agencies have under CRPC 145 regarding the detention of individuals in police custody?",
+    "answer": "Under CRPC 145, law enforcement agencies are obligated to inform any detained individual of the grounds for their arrest and to provide them with the opportunity to consult a legal practitioner of their choice within twenty-four hours. Failure to comply with these obligations will render the detention unlawful, entitling the detained individual to immediate release and compensation as prescribed by law. Furthermore, the State must ensure that all law enforcement personnel are adequately trained to uphold these constitutional protections."
+  },
+  {
+    "section_number": "IPC 227",
+    "section_title": "Rights of Co-owners in Property Disputes",
+    "provision": "In any case where property is jointly owned by two or more individuals, each co-owner shall possess an equal right to use and enjoy the property, subject to the principle of reasonable enjoyment. No co-owner shall be entitled to alienate their share of the property without the express consent of all other co-owners; failure to obtain such consent shall render any transfer voidable at the instance of the non-consenting co-owners. In the event of a dispute arising from the exercise of such rights, the parties shall seek resolution through mediation, failing which they may pursue their claims in a competent civil court.",
+    "question_type": "penalty",
+    "question": "What penalty may arise if a co-owner attempts to alienate their share of jointly owned property without the consent of the other co-owners according to IPC 227?",
+    "answer": "If a co-owner attempts to alienate their share of the property without obtaining the express consent of the other co-owners, such a transfer will be rendered voidable at the request of the non-consenting co-owners. This means that the non-consenting co-owners can challenge the validity of the transfer, potentially leading to legal disputes and the need for resolution through mediation or civil court."
+  },
+  {
+    "section_number": "CNP 101",
+    "section_title": "Protection of Fundamental Rights",
+    "provision": "Every individual shall have the right to seek judicial redress for the infringement of their fundamental rights as enumerated in the Constitution of India. The State is mandated to ensure that no action, law, or policy contravenes the rights guaranteed under Articles 14 to 32, and any violation thereof shall entitle the aggrieved party to compensation as deemed fit by the judiciary. Furthermore, the Supreme Court and High Courts shall possess the authority to issue writs, orders, or directions for the enforcement of such rights, thereby reinforcing the foundational principles of justice and equality within the Republic.",
+    "question_type": "definition",
+    "question": "What is the right of individuals regarding the infringement of their fundamental rights as per CNP 101?",
+    "answer": "Individuals have the right to seek judicial redress for the infringement of their fundamental rights as outlined in the Constitution of India, and are entitled to compensation for any violations, with the Supreme Court and High Courts authorized to issue writs and orders to enforce these rights."
+  },
+  {
+    "section_number": "CPC 123",
+    "section_title": "Application for Interlocutory Relief",
+    "provision": "An application for interlocutory relief shall be made in the prescribed format, detailing the nature of the relief sought and the grounds thereof, along with any supporting affidavits and documents. The court shall, within three days of the filing of such application, schedule a preliminary hearing, during which the party seeking relief must demonstrate the urgency and necessity of the relief sought, based on a prima facie case and the balance of convenience. The court may grant interim orders as it deems fit, subject to the condition that the applicant shall bear the costs of any potential loss incurred by the opposing party due to such interim relief.",
+    "question_type": "exceptions",
+    "question": "Are there any exceptions to the requirement of demonstrating urgency and necessity for an application for interlocutory relief under CPC 123?",
+    "answer": "Yes, while CPC 123 generally requires the applicant to demonstrate urgency and necessity for the relief sought, exceptions may arise in cases where the relief requested is of a nature that inherently addresses imminent harm or where statutory provisions specifically allow for expedited procedures. However, the applicant must still adhere to the prescribed format and provide supporting affidavits and documentation as mandated by the provision."
+  },
+  {
+    "section_number": "IPC 507A",
+    "section_title": "Unauthorized Access and Data Breach",
+    "provision": "Whoever, without lawful authority, accesses a computer resource or a computer network with the intent to cause or knowing that he is likely to cause wrongful loss or damage to any person, or to facilitate the commission of a crime, shall be punished with imprisonment for a term which may extend to three years, or with fine which may extend to five lakh rupees, or with both. Furthermore, if such access results in the theft, alteration, or deletion of data, the offender shall be liable for enhanced penalties as prescribed in this section, including a minimum fine of ten lakh rupees and imprisonment for a term extending to five years.",
+    "question_type": "definition",
+    "question": "What constitutes unauthorized access under IPC 507A?",
+    "answer": "Unauthorized access under IPC 507A is defined as accessing a computer resource or a computer network without lawful authority, with the intent to cause or knowing that one is likely to cause wrongful loss or damage to any person, or to facilitate the commission of a crime."
+  },
+  {
+    "section_number": "IPC 512",
+    "section_title": "Offense of Public Disturbance",
+    "provision": "Whoever intentionally causes a public disturbance by engaging in acts that promote hatred, incite violence, or create fear among members of the community shall be punished with imprisonment for a term which may extend to three years, or with a fine which may extend to fifty thousand rupees, or with both. In determining the severity of the penalty, the court shall consider the magnitude of the disturbance, consequent harm caused to public order, and any prior convictions of the offender under this section or similar offenses.",
+    "question_type": "definition",
+    "question": "What constitutes the offense of public disturbance under IPC 512?",
+    "answer": "The offense of public disturbance under IPC 512 is constituted by intentionally causing a public disturbance through acts that promote hatred, incite violence, or create fear among community members."
+  },
+  {
+    "section_number": "IPC 123A",
+    "section_title": "Protection of Indigenous Knowledge and Cultural Expressions",
+    "provision": "Whoever unlawfully appropriates, uses, or commercializes indigenous knowledge and cultural expressions without obtaining prior informed consent from the relevant indigenous communities, shall be punished with imprisonment for a term not exceeding five years, or with fine, or both. The term \"indigenous knowledge\" includes traditional practices, innovations, and expressions inherent to indigenous communities, and any such appropriation shall be considered a violation of the community's moral rights as custodians of their cultural heritage. The provisions of this section shall be in addition to any other rights or remedies available under existing intellectual property laws.",
+    "question_type": "obligations",
+    "question": "What obligations do individuals have regarding the use of indigenous knowledge and cultural expressions according to IPC 123A?",
+    "answer": "Individuals are obligated to obtain prior informed consent from the relevant indigenous communities before appropriating, using, or commercializing indigenous knowledge and cultural expressions. Failure to comply with this obligation may result in imprisonment for up to five years, a fine, or both, as it constitutes a violation of the moral rights of the indigenous communities as custodians of their cultural heritage."
+  },
+  {
+    "section_number": "IEA 127",
+    "section_title": "Admissibility of Digital Evidence",
+    "provision": "Notwithstanding any provisions to the contrary, electronic records shall be admissible as evidence in any judicial proceedings, provided that such records are generated, stored, and retrieved in a manner that ensures their authenticity and integrity. The party seeking to introduce such evidence shall bear the burden of establishing its reliability through appropriate certification or corroborative witness testimony, unless the opposing party concedes to the admissibility of the digital evidence. The court may, at its discretion, allow for the examination of the digital evidence to ascertain its relevance and evidentiary value.",
+    "question_type": "rights",
+    "question": "What rights do parties have regarding the admissibility of digital evidence in judicial proceedings under IEA 127?",
+    "answer": "Under IEA 127, parties have the right to introduce electronic records as evidence in court, provided they can demonstrate the records' authenticity and integrity. The party presenting the digital evidence has the responsibility to prove its reliability, either through certification or witness testimony. Additionally, if the opposing party does not contest the admissibility, the court may accept the evidence without further scrutiny. The court also has the discretion to examine the digital evidence to determine its relevance and evidentiary value."
+  },
+  {
+    "section_number": "IPC 456",
+    "section_title": "Offense of Public Disorder",
+    "provision": "Whoever, with the intent to cause public alarm or disturbance, engages in behavior that incites violence, fear, or panic among the general populace shall be punishable with imprisonment of either description for a term which may extend to five years, or with fine which may extend to ten thousand rupees, or with both. In determining the sentence, the court shall take into account the nature and extent of the disruption caused, and any prior offenses committed by the accused.",
+    "question_type": "exceptions",
+    "question": "Are there any exceptions to the offense of public disorder under IPC 456 for individuals who engage in behavior that might cause alarm but do so for a legitimate purpose, such as public safety or awareness?",
+    "answer": "Yes, there may be exceptions for individuals who engage in behavior that could cause public alarm or disturbance if their actions are intended for a legitimate purpose, such as ensuring public safety or raising awareness about a critical issue. The court will consider the intent behind the behavior and the context in which it occurred when determining if it constitutes an offense under IPC 456. However, the burden of proof lies with the accused to demonstrate that their actions were justified and not intended to incite violence, fear, or panic."
+  },
+  {
+    "section_number": "IPC 123A",
+    "section_title": "Protection of Digital Copyright",
+    "provision": "Any person who, without the authorization of the copyright owner, reproduces, distributes, or publicly displays a copyrighted digital work in a manner that enables unlawful access or download by a third party shall be punishable with imprisonment for a term which may extend to three years, or with fine which may extend to five lakh rupees, or with both. The courts shall consider the nature of the work, the scale of distribution, and the intent behind the infringement while determining the appropriate penalty. This provision shall not apply to fair use as delineated under the Copyright Act, 1957.",
+    "question_type": "procedure",
+    "question": "What steps should a copyright owner take if they believe their digital work has been reproduced or distributed without authorization under IPC 123A?",
+    "answer": "If a copyright owner suspects unauthorized reproduction or distribution of their digital work under IPC 123A, they should follow these steps:"
+  },
+  {
+    "section_number": "CPC 207",
+    "section_title": "Application for Interim Relief",
+    "provision": "In any suit where a party seeks urgent relief based on a prima facie showing of entitlement, the Court may, upon application, grant interim relief to maintain the status quo pending final adjudication. Such application shall be accompanied by an affidavit detailing the grounds for urgency, and the Court shall endeavor to hear and dispose of such application within seven days of filing, unless shown to be impracticable. The order granting or denying interim relief shall be recorded with reasons and shall be subject to the right of appeal under the provisions of this Code.",
+    "question_type": "procedure",
+    "question": "What is the procedure for applying for interim relief under CPC 207, and what are the requirements for the application to be considered by the Court?",
+    "answer": "To apply for interim relief under CPC 207, a party must file an application demonstrating a prima facie showing of entitlement to urgent relief. This application must be accompanied by an affidavit that details the grounds for urgency. The Court is required to hear and dispose of the application within seven days of filing, unless it is impracticable to do so. The Court will then issue an order granting or denying the interim relief, which must be recorded along with the reasons for the decision. Additionally, the order is subject to the right of appeal as outlined in this Code."
+  },
+  {
+    "section_number": "IPC 456",
+    "section_title": "Criminal Intimidation with Intent to Cause Harm",
+    "provision": "Whoever, with intent to cause harm or alarm, threatens any person with the infliction of death or grievous hurt, or with the destruction of property, shall be punished with imprisonment of either description for a term which may extend to three years, or with fine which may extend to fifty thousand rupees, or with both. If the offense is committed in furtherance of an organized criminal activity, the term of imprisonment may extend to five years.",
+    "question_type": "examples",
+    "question": "Can you provide examples of actions that would be considered criminal intimidation under IPC 456?",
+    "answer": "Yes, under IPC 456, examples of criminal intimidation include a person threatening to kill another individual if they do not pay a debt, or someone warning a neighbor that they will set fire to their property if they do not comply with certain demands. Additionally, if a group engages in organized criminal activity and threatens individuals with serious harm or property destruction to enforce their control, that would also fall under this provision, potentially leading to a longer imprisonment term."
+  },
+  {
+    "section_number": "IEA 115",
+    "section_title": "Admissibility of Electronic Records in Civil Proceedings",
+    "provision": "Notwithstanding any provisions to the contrary, electronic records shall be admissible in civil proceedings as evidence, provided that such records are accompanied by a certificate of authenticity attesting to their integrity and accuracy. The court may, at its discretion, take into account the reliability of the technology used in the creation, storage, and retrieval of these records, as well as any potential alterations that may have occurred. Further, any party seeking to introduce electronic records must notify the opposing party at least seven days prior to the hearing, allowing for adequate preparation to challenge the admissibility of such evidence.",
+    "question_type": "penalty",
+    "question": "What are the potential consequences for a party that fails to notify the opposing party at least seven days prior to a hearing when intending to introduce electronic records as evidence under IEA 115?",
+    "answer": "If a party fails to provide the required seven-day notice before introducing electronic records in a civil proceeding, the court may deem the electronic records inadmissible as evidence. This could hinder the party's ability to substantiate their claims or defenses, potentially resulting in unfavorable outcomes in the case."
+  },
+  {
+    "section_number": "IPC 134A",
+    "section_title": "Remedies for Breach of Contract",
+    "provision": "In the event of a breach of contract, the aggrieved party shall be entitled to pursue remedies as defined herein: (1) Specific performance of the contract may be ordered by a competent court where monetary damages are inadequate to remedy the loss suffered. (2) In cases where the breach is wilful and unexcused, the aggrieved party may also claim punitive damages not exceeding fifty percent of the actual damages incurred. (3) Parties may further stipulate in the contract provisions for liquidated damages, which shall be enforceable unless deemed unconscionable by the court.",
+    "question_type": "definition",
+    "question": "What remedies are available to an aggrieved party in the event of a breach of contract according to IPC 134A?",
+    "answer": "According to IPC 134A, the remedies available to an aggrieved party in the event of a breach of contract include: (1) specific performance of the contract when monetary damages are inadequate, (2) punitive damages not exceeding fifty percent of the actual damages if the breach is wilful and unexcused, and (3) liquidated damages as stipulated in the contract, which are enforceable unless deemed unconscionable by the court."
+  },
+  {
+    "section_number": "CNR 101",
+    "section_title": "Protection of Fundamental Rights",
+    "provision": "Every individual shall have the right to life, liberty, and personal security, which shall be inviolable and protected against arbitrary deprivation by the State. The State shall ensure that any infringement of these rights is subject to judicial review, and appropriate remedies shall be provided to individuals whose rights have been violated. The Parliament shall enact necessary legislation to define, safeguard, and enforce these rights, ensuring that no law or action contravenes the spirit of this provision without just cause.",
+    "question_type": "definition",
+    "question": "What fundamental rights are protected under CNR 101, and what obligations does the State have regarding these rights?",
+    "answer": "Under CNR 101, every individual is guaranteed the rights to life, liberty, and personal security, which are inviolable and protected against arbitrary deprivation by the State. The State is obligated to ensure that any infringement of these rights is subject to judicial review and must provide appropriate remedies to individuals whose rights have been violated. Additionally, the Parliament is required to enact necessary legislation to define, safeguard, and enforce these rights, ensuring no law or action contradicts this provision without just cause."
+  },
+  {
+    "section_number": "IPC 124A",
+    "section_title": "Protection of Unregistered Intellectual Property Rights",
+    "provision": "Any individual or entity claiming ownership of an unregistered intellectual property right, including but not limited to trade secrets, designs, and innovations, shall be entitled to seek legal remedy for unauthorized use or disclosure. The aggrieved party may file a civil suit in the appropriate jurisdiction, whereupon the court shall assess the validity of the claimed rights and may grant injunctive relief, damages, or any other relief deemed appropriate to prevent infringement and preserve the integrity of the intellectual property. This protection shall extend to the duration of the claimant's reasonable efforts to maintain the confidentiality and exclusivity of the intellectual property in question.",
+    "question_type": "definition",
+    "question": "What rights are protected under IPC 124A regarding unregistered intellectual property, and what legal remedies are available to the aggrieved party?",
+    "answer": "IPC 124A protects unregistered intellectual property rights, including trade secrets, designs, and innovations. An individual or entity claiming ownership may seek legal remedies for unauthorized use or disclosure by filing a civil suit in the appropriate jurisdiction. The court will assess the validity of the claimed rights and may grant injunctive relief, damages, or other appropriate remedies to prevent infringement and maintain the integrity of the intellectual property."
+  },
+  {
+    "section_number": "CL 204",
+    "section_title": "Remedies for Breach of Contract",
+    "provision": "In the event of a breach of contract, the aggrieved party shall be entitled to seek remedies including specific performance, rescission of the contract, and damages, which may be either general or consequential in nature. The party seeking damages must provide clear evidence of loss incurred as a result of the breach, and the court shall have discretion to award compensation that is deemed just and equitable, taking into account the nature of the breach and the contractual terms. Furthermore, any limitation on the right to claim damages must be explicitly stated within the contract to be enforceable.",
+    "question_type": "examples",
+    "question": "Can you provide examples of the types of remedies available for breach of contract as outlined in CL 204?",
+    "answer": "Yes, under CL 204, there are several remedies available for breach of contract. For instance, specific performance may be sought if the aggrieved party wants the breaching party to fulfill their contractual obligations, such as delivering a unique piece of art that was promised. Rescission of the contract could be an option if the aggrieved party wishes to cancel the contract entirely and return to their pre-contractual position, for example, if a buyer discovers that a seller misrepresented the condition of a property. Additionally, damages can be claimed, which may include general damages for direct losses, like the cost of hiring a substitute contractor after the original contractor failed to perform, or consequential damages, such as loss of business profits resulting from the delay in project completion due to the breach. It is important to note that the party seeking damages must provide evidence of the loss incurred, and any limitations on claiming damages must be clearly stated in the contract to be enforceable."
+  },
+  {
+    "section_number": "CPC 123A",
+    "section_title": "Provision for Electronic Filing of Pleadings",
+    "provision": "The Court may, upon application by any party, permit the electronic filing of pleadings, documents, and evidence in accordance with the guidelines issued by the Supreme Court. Such electronic submissions shall be deemed to be authentic and shall hold the same legal sanctity as original physical documents, provided that such filings comply with the prescribed digital signature requirements and are submitted within the timelines set forth by the Court. Any failure to comply with the electronic filing protocols may result in the rejection of the documents filed or the imposition of penalties as deemed appropriate by the presiding judge.",
+    "question_type": "examples",
+    "question": "Can you provide an example of a situation where a party might utilize electronic filing of pleadings according to CPC 123A?",
+    "answer": "Sure! For instance, if a plaintiff wishes to file a motion for summary judgment, they can submit their pleading electronically if they apply to the Court and receive permission. They must ensure their electronic submission adheres to the Supreme Court's guidelines, including using a valid digital signature and submitting it by the court's deadline. If they fail to meet these requirements, the court may reject their filing or impose penalties."
+  },
+  {
+    "section_number": "IEA 78",
+    "section_title": "Admissibility of Electronic Evidence",
+    "provision": "Notwithstanding any provision to the contrary, electronic evidence shall be admissible in judicial proceedings provided that such evidence is authenticated through a digital signature, or corroborated by a competent testimony that verifiably establishes its integrity and relevance to the matter in issue. The court may, in its discretion, require the production of the original electronic device or system from which the evidence is derived to determine its authenticity, unless such requirement is waived by mutual consent of the parties involved.",
+    "question_type": "procedure",
+    "question": "What steps must a party take to ensure that electronic evidence is admissible in judicial proceedings according to IEA 78?",
+    "answer": "To ensure that electronic evidence is admissible under IEA 78, a party must authenticate the evidence either through a digital signature or by providing corroborating testimony from a competent witness that verifies the evidence's integrity and relevance to the case. Additionally, the party may need to produce the original electronic device or system from which the evidence was obtained, unless this requirement is waived by mutual consent of the parties involved."
+  },
+  {
+    "section_number": "IEA 67A",
+    "section_title": "Admissibility of Digital Evidence",
+    "provision": "Notwithstanding any provisions to the contrary, any electronic record or digital evidence shall be admissible in a court of law, provided that the party seeking to introduce such evidence demonstrates the authenticity and integrity of the record through a reliable digital signature or encryption method. The court may, at its discretion, require further corroborative evidence to substantiate the reliability of the digital evidence presented, ensuring that the probative value outweighs any potential prejudicial effect.",
+    "question_type": "procedure",
+    "question": "What steps must a party take to ensure the admissibility of digital evidence in court according to IEA 67A?",
+    "answer": "To ensure the admissibility of digital evidence in court under IEA 67A, the party seeking to introduce the evidence must demonstrate the authenticity and integrity of the electronic record by using a reliable digital signature or encryption method. Additionally, the court may require further corroborative evidence to confirm the reliability of the digital evidence, ensuring that its probative value outweighs any potential prejudicial effect."
+  },
+  {
+    "section_number": "IPC 798",
+    "section_title": "Protection of Traditional Knowledge",
+    "provision": "Any person who, without lawful authority, uses, reproduces, or distributes traditional knowledge as defined under this Act, shall be liable for infringement of intellectual property rights. Such traditional knowledge shall include but not be limited to, cultural practices, medicinal formulations, or agricultural methods passed down through generations within indigenous communities. The affected community shall have the right to seek remedies, including injunctions and damages, in accordance with the provisions set forth in this section.",
+    "question_type": "examples",
+    "question": "Can you provide examples of actions that would infringe on traditional knowledge as per IPC 798?",
+    "answer": "Yes, examples of actions that would infringe on traditional knowledge under IPC 798 include:"
+  },
+  {
+    "section_number": "IPC 123A",
+    "section_title": "Protection of Unregistered Trade Secrets",
+    "provision": "Whosoever, in the course of trade or business, unlawfully discloses or uses a trade secret or confidential commercial information obtained through breach of a duty of confidentiality, shall be punishable with imprisonment for a term which may extend to three years or with fine, or with both. For the purposes of this section, \"trade secret\" shall mean any formula, pattern, compilation, program, device, method, technique, or process that derives independent economic value from not being generally known to or readily accessible by others who can obtain economic value from its disclosure or use. The burden of proof regarding the confidentiality of such information shall lie upon the claimant.",
+    "question_type": "obligations",
+    "question": "What obligations do individuals have regarding the disclosure of trade secrets under IPC 123A?",
+    "answer": "Individuals are obligated not to unlawfully disclose or use any trade secret or confidential commercial information that they have obtained through a breach of a duty of confidentiality. If they fail to uphold this obligation, they may face penalties including imprisonment for up to three years, a fine, or both. Additionally, the claimant has the burden of proof to demonstrate that the information in question is confidential."
+  },
+  {
+    "section_number": "FLA 102",
+    "section_title": "Rights of Inheritance for Female Heirs",
+    "provision": "In the event of the demise of a male intestate, female heirs, including daughters and widows, shall have an equal right to inherit the estate of the deceased on par with male heirs. The distribution of such inheritance shall be executed in accordance with the principles of equitable division, ensuring that each female heir receives a share that is not less than one-fourth of the total estate, unless expressly disclaimed by the heir in a legally binding written document. This section aims to uphold gender equality in matters of familial succession and inheritance rights under Hindu, Muslim, and other applicable personal laws in India.",
+    "question_type": "penalty",
+    "question": "What are the penalties for failing to adhere to the inheritance rights outlined in FLA 102 regarding female heirs?",
+    "answer": "While FLA 102 does not specify penalties within the provision itself, failure to comply with the equitable division of the estate as mandated can lead to legal action by female heirs. This may result in the court enforcing the rightful distribution of the estate, which could include the imposition of fines or other sanctions against the estate’s executors or those responsible for the distribution, as determined by the applicable legal framework."
+  },
+  {
+    "section_number": "IPC 123A",
+    "section_title": "Protection of Indigenous Knowledge and Cultural Expressions",
+    "provision": "Any person who unlawfully appropriates, reproduces, or disseminates indigenous knowledge or cultural expressions without the prior consent of the indigenous community shall be liable for infringement of intellectual property rights under this section. The aggrieved indigenous community may seek remedies including injunctions and damages, and the court shall consider the cultural significance and traditional practices associated with such knowledge in its deliberations. This provision aims to safeguard the heritage and intellectual contributions of indigenous communities against unauthorized exploitation.",
+    "question_type": "penalty",
+    "question": "What penalties can a person face for unlawfully appropriating indigenous knowledge or cultural expressions under IPC 123A?",
+    "answer": "A person who unlawfully appropriates, reproduces, or disseminates indigenous knowledge or cultural expressions without the prior consent of the indigenous community may be liable for infringement of intellectual property rights. The aggrieved indigenous community can seek remedies such as injunctions to prevent further infringement and damages for any losses incurred. The court will also consider the cultural significance and traditional practices associated with the knowledge in its decisions."
+  },
+  {
+    "section_number": "FLA 101",
+    "section_title": "Rights of Inheritance Among Hindu Succession",
+    "provision": "In the event of the demise of a Hindu individual, the property held by such individual, whether ancestral or self-acquired, shall devolve upon their legal heirs as defined under this Act, in accordance with the principles of equal partition among male and female heirs. The widow and children of the deceased shall inherit a minimum of one-third of the total estate, notwithstanding any prior testamentary disposition made by the deceased, unless expressly waived in writing by the heirs prior to the individual's death. The provisions herein shall apply irrespective of the religious or customary practices governing succession, aiming to uphold gender equality in inheritance rights.",
+    "question_type": "rights",
+    "question": "What rights do the widow and children of a deceased Hindu individual have regarding inheritance under the Hindu Succession Act?",
+    "answer": "The widow and children of a deceased Hindu individual are entitled to inherit a minimum of one-third of the total estate, regardless of any prior testamentary disposition made by the deceased. This right is upheld under the Act to ensure gender equality in inheritance, and it applies to both ancestral and self-acquired property."
+  },
+  {
+    "section_number": "IPC 420B",
+    "section_title": "Fraudulent Misrepresentation in Commercial Transactions",
+    "provision": "Whosoever, with intent to deceive, misrepresents a material fact regarding goods or services in the course of any commercial transaction, thereby causing financial loss to another party, shall be punished with imprisonment for a term which may extend to three years, or with fine which may extend to fifty thousand rupees, or with both. Explanation: For the purposes of this section, \"material fact\" shall mean any fact that, if known, would likely affect the decision of a reasonable person to enter into the transaction.",
+    "question_type": "exceptions",
+    "question": "Are there any exceptions under IPC 420B where a party may not be held liable for fraudulent misrepresentation in commercial transactions?",
+    "answer": "Yes, an exception under IPC 420B may apply if the misrepresentation was made without the intent to deceive, such as in cases where the party genuinely believed the information provided to be true, or if the misrepresentation pertains to opinions or predictions rather than material facts. Additionally, if the party can demonstrate that the other party had prior knowledge of the facts or waived their right to rely on the misrepresentation, liability may not be established under this section."
+  },
+  {
+    "section_number": "IPC 432",
+    "section_title": "Protection of Trade Secrets and Confidential Information",
+    "provision": "Whoever unlawfully discloses, acquires, or uses a trade secret or any confidential information belonging to another party, without the express consent of the owner, shall be punishable with imprisonment for a term which may extend to three years, or with fine, or with both. For the purposes of this section, \"trade secret\" shall include any formula, practice, process, design, instrument, pattern, or compilation of information that is not generally known or reasonably ascertainable by others and that provides a competitive advantage to the owner. The provisions of this section shall not apply to disclosures made under compulsion of law or in the course of legitimate business practices.",
+    "question_type": "exceptions",
+    "question": "What are the exceptions to the provisions of IPC 432 regarding the unlawful disclosure of trade secrets and confidential information?",
+    "answer": "The provisions of IPC 432 do not apply to disclosures made under compulsion of law or in the course of legitimate business practices. This means that if a person is legally required to disclose trade secrets or if the disclosure occurs as part of lawful business operations, they are exempt from the penalties outlined in this section."
+  },
+  {
+    "section_number": "IPC 123A",
+    "section_title": "Rights Pertaining to Inherited Property",
+    "provision": "In cases where property is inherited, any disputes arising among heirs regarding the rightful ownership, partition, or claim over such property shall be resolved in accordance with the principles of ancestral succession as defined under this Code. Any party claiming a right to the inherited property must provide substantial evidence of lineage and lawful entitlement, failing which the claim shall be deemed invalid. Furthermore, the court shall have the authority to appoint a mediator to facilitate negotiation among parties prior to adjudication, encouraging amicable settlements while safeguarding the rights of all claimants.",
+    "question_type": "rights",
+    "question": "What rights do heirs have regarding inherited property disputes under IPC 123A?",
+    "answer": "Heirs have the right to resolve disputes over inherited property ownership, partition, or claims according to the principles of ancestral succession. However, any heir claiming a right to the property must provide substantial evidence of their lineage and lawful entitlement. If they fail to do so, their claim will be considered invalid. Additionally, the court can appoint a mediator to help facilitate negotiations among the parties, promoting amicable settlements while protecting the rights of all claimants."
+  },
+  {
+    "section_number": "IEA 145",
+    "section_title": "Admissibility of Digital Evidence",
+    "provision": "Notwithstanding any provision to the contrary, digital evidence shall be admissible in any judicial proceeding if it is accompanied by a certificate of authenticity issued by a competent authority, attesting to the integrity, reliability, and original source of the data. The court shall evaluate the probative value of such evidence, taking into consideration the methods of collection, preservation, and transmission, along with any potential alterations, before determining its admissibility. In cases where digital evidence is presented, the burden of proof shall rest upon the party introducing such evidence to establish its authenticity and relevance.",
+    "question_type": "definition",
+    "question": "What is required for digital evidence to be admissible in judicial proceedings according to IEA 145?",
+    "answer": "Digital evidence is admissible in judicial proceedings if it is accompanied by a certificate of authenticity issued by a competent authority, which attests to the integrity, reliability, and original source of the data. Additionally, the court will evaluate its probative value considering the methods of collection, preservation, and transmission, as well as any potential alterations, and the party introducing the evidence must prove its authenticity and relevance."
+  },
+  {
+    "section_number": "IPC 509A",
+    "section_title": "Criminal Intimidation by Means of Digital Platforms",
+    "provision": "Whosoever, by means of any electronic, digital, or computer-based communication, threatens or causes harm to any person, including but not limited to threats of violence, coercion, or defamation, shall be punished with imprisonment for a term which may extend to three years, or with fine, or with both. In the case of aggravated circumstances, such as the use of multiple accounts or persistent harassment, the punishment may extend to five years of imprisonment. The provisions of this section shall be in addition to any other applicable laws concerning harassment or intimidation.",
+    "question_type": "examples",
+    "question": "Can you provide examples of actions that would be considered criminal intimidation under IPC 509A?",
+    "answer": "Yes, several actions can be considered criminal intimidation under IPC 509A. For instance, if an individual sends threatening messages via social media platforms, such as threatening physical harm or coercing someone into doing something against their will, this would qualify. Additionally, if someone uses multiple online accounts to continuously harass another person by spreading false information or defamatory statements about them, this would also fall under the provisions of IPC 509A. Lastly, if a person creates a fake profile to intimidate or threaten someone, this too would be punishable under this section."
+  },
+  {
+    "section_number": "IPC 471A",
+    "section_title": "Protection of Trade Secrets",
+    "provision": "Whoever unlawfully obtains, discloses, or uses a trade secret, knowing or having reason to know that such information was obtained through improper means, shall be punishable with imprisonment for a term which may extend to three years, or with fine which may extend to five lakh rupees, or with both. A trade secret shall be defined as any formula, pattern, compilation, program, device, method, technique, or process that derives independent economic value from not being generally known or readily ascertainable to the public, and is the subject of reasonable efforts to maintain its secrecy.",
+    "question_type": "exceptions",
+    "question": "Are there any exceptions under IPC 471A for disclosing or using trade secrets if the information is obtained through lawful means?",
+    "answer": "Yes, IPC 471A pertains specifically to the unlawful obtaining, disclosing, or using of trade secrets. If a person acquires a trade secret through lawful means, such as independent discovery or legitimate access, they would not be punishable under this provision. Additionally, if the trade secret becomes publicly known or is disclosed as a result of legal obligations, such as during a court proceeding, those actions may also fall outside the scope of punishment under this section."
+  },
+  {
+    "section_number": "IEA 65A",
+    "section_title": "Admissibility of Digital Evidence",
+    "provision": "Notwithstanding any provision to the contrary, digital evidence, including but not limited to electronic records, audio and video files, and data stored in digital devices, shall be admissible in any proceedings if such evidence is accompanied by a certificate from a competent authority confirming the integrity and authenticity of the data. The court may, however, require additional corroborative evidence to substantiate the claims made through such digital materials, ensuring that the principles of fairness and justice are upheld.",
+    "question_type": "examples",
+    "question": "Can you provide examples of digital evidence that would be admissible in court under IEA 65A if accompanied by the proper certification?",
+    "answer": "Yes, examples of digital evidence that would be admissible in court under IEA 65A include electronic records such as emails or digital contracts, audio files like recorded conversations relevant to the case, video files such as surveillance footage, and data stored on digital devices like smartphones or computers, provided that each piece of evidence is accompanied by a certificate from a competent authority verifying its integrity and authenticity. The court may still request additional corroborative evidence to ensure fairness in the proceedings."
+  },
+  {
+    "section_number": "IPC 507A",
+    "section_title": "Unauthorized Access and Data Breach",
+    "provision": "Whoever, without lawful authority or consent, intentionally accesses a computer system or network and obtains, alters, or deletes data shall be punished with imprisonment for a term which may extend to three years, or with fine which may extend to fifty thousand rupees, or with both. In case the unauthorized access results in harm or loss to any person or entity, the term of imprisonment may extend to five years, along with a fine which may be determined by the court based on the gravity of the harm caused.",
+    "question_type": "rights",
+    "question": "What rights do individuals have if they are victims of unauthorized access and data breaches under IPC 507A?",
+    "answer": "Individuals who are victims of unauthorized access and data breaches have the right to seek legal recourse against the perpetrator. They can report the incident to law enforcement, and if the unauthorized access results in harm or loss, they have the right to pursue compensation for damages through the court system. Additionally, they can expect that the law provides for penalties against the offender, which may include imprisonment and fines, thereby reinforcing their rights to safety and protection of their personal data."
+  },
+  {
+    "section_number": "FLA 123",
+    "section_title": "Rights of Inheritance Among Heirs",
+    "provision": "In cases of intestate succession, all heirs shall inherit the estate of the deceased in accordance with the principles of equitable distribution, wherein the surviving spouse shall receive one-half of the estate, while the remaining half shall be divided equally among the legitimate children. In the absence of legitimate children, the estate shall pass to the surviving parents, and if none exist, to the siblings in equal shares. The court shall ensure that the rights of all heirs are protected, preventing any testamentary disposition that contravenes the provisions set forth herein.",
+    "question_type": "exceptions",
+    "question": "Are there any exceptions to the equitable distribution of the estate among heirs as outlined in FLA 123?",
+    "answer": "Yes, exceptions exist where a testamentary disposition may override the standard distribution if it is legally valid and does not contravene the protections established in FLA 123. Additionally, if the deceased has left behind a valid will that specifies different distributions, those instructions may take precedence over intestate succession rules, provided they comply with relevant legal standards. However, the court will still ensure that no such disposition infringes on the rights of the heirs as defined in the provision."
+  },
+  {
+    "section_number": "IPC 482",
+    "section_title": "Rights of Co-Owners in Joint Property",
+    "provision": "In any case where two or more persons are co-owners of an immovable property, no co-owner shall alienate their share of the property without the consent of the other co-owners, unless otherwise stipulated by a prior agreement. In the event of a dispute regarding the use or management of the joint property, any co-owner may apply to the appropriate civil court for a partition of the property, which shall be conducted in accordance with the principles of equity and justice, ensuring that the rights of all parties are duly considered.",
+    "question_type": "obligations",
+    "question": "What obligation do co-owners of immovable property have regarding the alienation of their shares according to IPC 482?",
+    "answer": "Co-owners of immovable property are obligated not to alienate their share without the consent of the other co-owners, unless there is a prior agreement that stipulates otherwise."
+  },
+  {
+    "section_number": "IEA 75",
+    "section_title": "Admissibility of Digital Evidence",
+    "provision": "Digital evidence shall be admissible in any judicial proceedings if it is authenticated by the party presenting it, demonstrating its integrity and reliability. The court shall consider the methods of collection, preservation, and presentation of such evidence, and may require corroboration from independent sources when the authenticity is contested. Any digital evidence obtained in violation of fundamental rights as enshrined in the Constitution shall be deemed inadmissible.",
+    "question_type": "definition",
+    "question": "What is the criterion for the admissibility of digital evidence in judicial proceedings according to IEA 75?",
+    "answer": "Digital evidence is admissible in judicial proceedings if it is authenticated by the presenting party, demonstrating its integrity and reliability, while the court considers the methods of collection, preservation, and presentation. Additionally, such evidence must not violate fundamental rights as outlined in the Constitution, or it will be deemed inadmissible."
+  },
+  {
+    "section_number": "CTP 101",
+    "section_title": "Protection of Fundamental Rights",
+    "provision": "Every individual shall have the right to seek redress for any violation of their fundamental rights as enshrined in the Constitution, through an expedited process in the appropriate constitutional court. The court shall ensure that any infringement of these rights is addressed promptly and judiciously, and may grant interim relief to safeguard the affected individual's rights during the pendency of the proceedings. Furthermore, any public authority found to have acted in contravention of these rights shall be liable to compensate the aggrieved party, as determined by the court.",
+    "question_type": "examples",
+    "question": "Can you provide an example of a situation where an individual might seek redress for a violation of their fundamental rights under CTP 101?",
+    "answer": "An example of such a situation could be if a government agency unlawfully detains an individual without due process, violating their right to liberty. The individual can seek redress in the appropriate constitutional court under CTP 101. They may file a petition to have their detention reviewed and potentially obtain interim relief, such as being released from detention while the case is pending. If the court finds that their fundamental rights were indeed violated, the government agency may be ordered to compensate the individual for the unlawful detention."
+  },
+  {
+    "section_number": "CPC 405",
+    "section_title": "Case Management Conference",
+    "provision": "The Court shall, upon the filing of the first written statement or counterclaim, schedule a Case Management Conference within thirty days to facilitate the expeditious resolution of disputes. At this conference, the parties shall be required to outline their claims and defenses, discuss the possibility of settlement, and establish a timeline for the exchange of evidence and subsequent proceedings, ensuring that the principles of justice and efficiency are upheld. Non-compliance with the directives issued during the conference may result in the imposition of sanctions as deemed appropriate by the Court.",
+    "question_type": "definition",
+    "question": "What is a Case Management Conference as defined in CPC 405?",
+    "answer": "A Case Management Conference is a court-scheduled meeting that occurs within thirty days of filing the first written statement or counterclaim, aimed at facilitating the expeditious resolution of disputes. During this conference, parties outline their claims and defenses, discuss settlement possibilities, and establish a timeline for evidence exchange and further proceedings, with the goal of upholding justice and efficiency."
+  },
+  {
+    "section_number": "IPC 543",
+    "section_title": "Offense of Cyber Harassment",
+    "provision": "Whosoever, by means of a computer resource or communication device, intentionally engages in conduct that causes harm, alarm, or distress to another person, including but not limited to the transmission of offensive messages, threats, or repeated unwanted communications, shall be punished with imprisonment for a term which may extend to three years, or with fine which may extend to fifty thousand rupees, or with both. In the case of a subsequent offense under this section, the term of imprisonment may extend to five years.",
+    "question_type": "obligations",
+    "question": "What obligations do individuals have under IPC 543 regarding the use of computer resources or communication devices to avoid cyber harassment?",
+    "answer": "Individuals are obligated to refrain from intentionally engaging in conduct that could cause harm, alarm, or distress to others through the use of computer resources or communication devices. This includes avoiding the transmission of offensive messages, threats, or repeated unwanted communications, as doing so may result in legal consequences, including imprisonment or fines."
+  },
+  {
+    "section_number": "PRD 102",
+    "section_title": "Rights and Remedies in Property Disputes",
+    "provision": "In any dispute concerning immovable property, the aggrieved party may file a complaint before the designated Property Dispute Tribunal, which shall have exclusive jurisdiction to adjudicate such matters. The Tribunal shall issue a preliminary order within fifteen days of receiving the complaint, and if necessary, appoint a local commissioner to inspect the property and submit a report, thereby ensuring swift resolution and enforcement of rights. Any party dissatisfied with the Tribunal's decision may appeal to the High Court within sixty days from the date of the order, provided that the appeal is accompanied by a certified copy of the original order.",
+    "question_type": "rights",
+    "question": "What rights does an aggrieved party have in a property dispute according to PRD 102?",
+    "answer": "An aggrieved party in a property dispute has the right to file a complaint before the designated Property Dispute Tribunal, which has exclusive jurisdiction over such matters. They are entitled to a preliminary order within fifteen days and may have a local commissioner appointed for property inspection if necessary. Additionally, if they are dissatisfied with the Tribunal's decision, they have the right to appeal to the High Court within sixty days, provided they include a certified copy of the original order."
+  },
+  {
+    "section_number": "FLA 202",
+    "section_title": "Inheritance Rights of Children Born Out of Wedlock",
+    "provision": "Notwithstanding any other law to the contrary, a child born out of wedlock shall have the same rights of inheritance as a legitimate child in the estate of the biological parents, provided that paternity is established through a legally recognized process. The child shall have the right to claim a share in the ancestral property of the biological father's family, subject to the provisions of the Hindu Succession Act, 1956, or the applicable personal law of the parents. Any clause in a will or testament that seeks to exclude such a child from inheritance based solely on their illegitimacy shall be deemed void and unenforceable.",
+    "question_type": "obligations",
+    "question": "What obligations do biological parents have regarding the inheritance rights of a child born out of wedlock according to FLA 202?",
+    "answer": "Biological parents are obligated to ensure that a child born out of wedlock is granted the same inheritance rights as a legitimate child, provided that paternity is established through a legally recognized process. This includes the obligation to allow the child to claim a share in the ancestral property of the biological father's family, and any will or testament that attempts to exclude the child based solely on their illegitimacy is rendered void and unenforceable."
+  },
+  {
+    "section_number": "IPC 890",
+    "section_title": "Protection of Traditional Knowledge",
+    "provision": "Any person who utilizes traditional knowledge for commercial gain without the explicit consent of the community possessing such knowledge shall be liable for infringement of intellectual property rights. The aggrieved community may seek redress through civil courts for remedies including injunctions, damages, and the recognition of their rights as custodians of such knowledge. This provision aims to safeguard the cultural heritage of indigenous populations against unauthorized appropriation and exploitation.",
+    "question_type": "rights",
+    "question": "What rights do communities have under IPC 890 regarding the use of their traditional knowledge for commercial purposes?",
+    "answer": "Communities have the right to give explicit consent before their traditional knowledge is used for commercial gain. If their knowledge is utilized without consent, they can seek redress in civil courts for remedies such as injunctions, damages, and recognition of their rights as custodians of that knowledge, thereby protecting their cultural heritage from unauthorized appropriation and exploitation."
+  },
+  {
+    "section_number": "FLA 202",
+    "section_title": "Rights of Inheritance in Hindu Joint Families",
+    "provision": "In any Hindu joint family, the property acquired by any member through self-acquisition shall devolve upon all coparceners equally upon the demise of the said member, unless a valid testamentary instrument expressly disposes of such property. Furthermore, any coparcener may renounce their right to inherit by a written declaration made in the presence of two witnesses, thereby forfeiting their claim to such property in favor of the remaining coparceners. The provisions of this section shall apply notwithstanding any customary practices that may contravene the equal sharing of self-acquired property within the familial structure.",
+    "question_type": "obligations",
+    "question": "What are the obligations of a coparcener in a Hindu joint family regarding the inheritance of self-acquired property upon the demise of a member?",
+    "answer": "Upon the demise of a member in a Hindu joint family, the obligation of all coparceners is to equally share the self-acquired property of the deceased member, unless there is a valid testamentary instrument that specifies a different distribution. Additionally, any coparcener has the obligation to formally renounce their right to inherit by providing a written declaration in the presence of two witnesses, which will forfeit their claim to the property in favor of the remaining coparceners."
+  },
+  {
+    "section_number": "IPC 123A",
+    "section_title": "Rights of Property Co-Owners and Dispute Resolution",
+    "provision": "In instances where two or more individuals hold co-ownership of a property, any co-owner shall possess the right to access and utilize the entire property, subject to fair usage provisions. In the event of a dispute arising from the use, management, or any aspect of the shared property, the aggrieved co-owner may file a complaint with the Jurisdictional Property Dispute Tribunal, which shall convene a mediation session within fifteen days and issue a binding resolution within sixty days from the date of the complaint. Failure to comply with the Tribunal's resolution may result in penalties or execution of partition proceedings as prescribed under this Act.",
+    "question_type": "rights",
+    "question": "What rights do co-owners of a property have under IPC 123A regarding access and dispute resolution?",
+    "answer": "Under IPC 123A, co-owners have the right to access and utilize the entire property, as long as they adhere to fair usage provisions. If a dispute arises concerning the use or management of the property, any aggrieved co-owner has the right to file a complaint with the Jurisdictional Property Dispute Tribunal, which must convene a mediation session within fifteen days and issue a binding resolution within sixty days. Failure to comply with the Tribunal's resolution may lead to penalties or partition proceedings."
+  },
+  {
+    "section_number": "IPC 512",
+    "section_title": "Causing Harm through Deceptive Practices",
+    "provision": "Whoever, by means of false representations or fraudulent acts, induces any person to part with property, or to confer any benefit, shall be punished with imprisonment for a term which may extend to three years, or with fine which may extend to fifty thousand rupees, or with both. In cases where the deception results in substantial financial loss to the victim, the imprisonment may extend to five years, and the fine may increase to one lakh rupees. This provision aims to penalize not only the act of deception but also to ensure reparation to the aggrieved party.",
+    "question_type": "penalty",
+    "question": "What are the potential penalties for causing harm through deceptive practices under IPC 512?",
+    "answer": "Under IPC 512, the penalties for causing harm through deceptive practices can include imprisonment for a term of up to three years, a fine of up to fifty thousand rupees, or both. If the deception results in substantial financial loss to the victim, the imprisonment may extend to five years, and the fine may increase to one lakh rupees."
+  },
+  {
+    "section_number": "C.R.P. 102",
+    "section_title": "Right to Dignity and Personal Autonomy",
+    "provision": "Every individual shall have the right to live with dignity and personal autonomy, free from discrimination, oppression, or arbitrary interference by the State or any other person. The State shall take all necessary measures to ensure that this right is protected, promoted, and fulfilled, including the implementation of laws that prevent violations of personal autonomy and safeguard against indignities. Any infringement of this right shall be actionable in a competent court, entailing appropriate remedies and compensation for the aggrieved party.",
+    "question_type": "examples",
+    "question": "Can you provide examples of situations where the right to dignity and personal autonomy, as outlined in C.R.P. 102, might be violated?",
+    "answer": "Yes, there are several situations that could violate the right to dignity and personal autonomy. For instance, if a government imposes a law that restricts individuals' freedoms to make personal choices regarding their healthcare, such as mandating a specific medical treatment against their will, this would infringe upon their autonomy. Similarly, if an employer discriminates against an employee based on their gender, sexual orientation, or race, denying them equal opportunities or subjecting them to harassment, this would violate their right to dignity. Another example could be a scenario where law enforcement unlawfully detains an individual without cause, thereby interfering with their personal freedom and dignity. In each of these cases, the affected individuals could seek remedies in a competent court for the infringements they have suffered."
+  },
+  {
+    "section_number": "IPC 456",
+    "section_title": "Trespass with Intent to Commit an Offense",
+    "provision": "Whoever unlawfully enters into or remains in any building or enclosed space with the intent to commit theft, causing harm, or any other punishable offense shall be liable to imprisonment for a term which may extend to three years, or with fine, or with both. If the trespasser is armed with a deadly weapon during such entry, the term of imprisonment may extend to seven years.",
+    "question_type": "procedure",
+    "question": "What steps should law enforcement take when apprehending a suspect under IPC 456 for trespass with intent to commit an offense?",
+    "answer": "Law enforcement should first ensure the safety of the area by assessing if the suspect is armed. If the suspect is armed with a deadly weapon, they should call for backup and use appropriate protocols for armed confrontations. Once the area is secure, officers should identify and detain the suspect, ensuring that they read the suspect their rights. Evidence should be collected, including witness statements and any items related to the offense. The suspect should then be arrested and charged under IPC 456, with the specifics of the intent to commit theft or harm documented for prosecution. Finally, the suspect should be transported to the nearest police station for processing and further legal proceedings."
+  },
+  {
+    "section_number": "IPC 501A",
+    "section_title": "Protection of Constitutional Rights",
+    "provision": "Every individual shall have the right to seek legal recourse against any act or omission of the State or any public authority that infringes upon their fundamental rights as guaranteed under Part III of the Constitution of India. The courts shall have the power to issue writs, orders, or directions to enforce such rights, and any violation thereof shall be deemed a punishable offense, attracting imprisonment for a term which may extend to three years, or a fine, or both. This section shall not preclude any individual from seeking compensation for damages arising from violations of their constitutional rights.",
+    "question_type": "rights",
+    "question": "What rights does IPC 501A provide to individuals regarding violations of their fundamental rights by the State or public authorities?",
+    "answer": "IPC 501A grants every individual the right to seek legal recourse against any act or omission by the State or public authorities that infringes upon their fundamental rights as guaranteed under Part III of the Constitution of India. This includes the ability to request courts to issue writs, orders, or directions to enforce these rights, and individuals can also seek compensation for damages resulting from such violations."
+  },
+  {
+    "section_number": "CPL 204",
+    "section_title": "Remedies for Breach of Contract",
+    "provision": "In the event of a breach of contract, the aggrieved party shall be entitled to seek specific performance or, where specific performance is impracticable, claim damages sufficient to restore the party to the position they would have occupied had the contract been performed. The aggrieved party may elect to pursue any combination of equitable remedies, including injunctions to prevent further breaches, provided that such remedies are sought within a period of three years from the date of the breach. Furthermore, in cases of willful or gross negligence leading to breach, the court may award punitive damages, not exceeding two times the actual damages incurred.",
+    "question_type": "definition",
+    "question": "What are the remedies available to an aggrieved party in the event of a breach of contract according to CPL 204?",
+    "answer": "According to CPL 204, the remedies available to an aggrieved party in the event of a breach of contract include seeking specific performance, claiming damages to restore their position as if the contract had been performed, pursuing a combination of equitable remedies such as injunctions to prevent further breaches, and in cases of willful or gross negligence, the possibility of receiving punitive damages not exceeding two times the actual damages incurred. These remedies must be sought within three years from the date of the breach."
+  },
+  {
+    "section_number": "CPC 123A",
+    "section_title": "Consolidation of Suits",
+    "provision": "In any suit wherein multiple causes of action arise from the same transaction or series of transactions, the Court may, upon application by any party, direct the consolidation of such suits into a single proceeding. The Court shall consider the interests of justice, the convenience of the parties, and the potential for judicial economy in making its determination. The consolidated suit shall proceed under the same procedural rules as a singular action, with all parties given adequate opportunity to present their respective claims and defenses.",
+    "question_type": "examples",
+    "question": "Can you provide an example of a situation where the Court might consolidate multiple suits under CPC 123A?",
+    "answer": "Certainly! Imagine a scenario where a construction company is sued by multiple homeowners for damages caused by the same faulty product used in their homes. Each homeowner files a separate suit against the company, claiming similar damages due to the defective product. In this case, the Court may allow the consolidation of these suits into a single proceeding because all claims arise from the same transaction—the use of the faulty product. The Court would consider factors such as the interests of justice, the convenience for the homeowners and the construction company, and the potential for reducing judicial resources. This way, the consolidated suit can be handled more efficiently under the same procedural rules, allowing all parties to present their claims and defenses together."
+  },
+  {
+    "section_number": "CPC 145",
+    "section_title": "Summary Dismissal of Frivolous Claims",
+    "provision": "The Court shall have the authority to summarily dismiss any civil claim or application that it deems to be frivolous, vexatious, or intended solely to harass the opposing party. The Court, upon motion by the defendant, may conduct a preliminary hearing to ascertain the merits of the claim and, if satisfied that the claim lacks substance or is manifestly unjust, shall issue an order dismissing the claim with costs awarded to the defendant. This provision shall not preclude the Court from imposing penalties for abuse of process as deemed appropriate.",
+    "question_type": "rights",
+    "question": "What rights do defendants have under CPC 145 regarding frivolous claims made against them?",
+    "answer": "Under CPC 145, defendants have the right to request a preliminary hearing to assess the merits of a civil claim they believe to be frivolous or vexatious. If the Court finds that the claim lacks substance or is manifestly unjust, it can dismiss the claim and award costs to the defendant. Additionally, the Court has the authority to impose penalties for any abuse of process, further protecting the defendant's rights against harassing legal actions."
+  },
+  {
+    "section_number": "IPC 123A",
+    "section_title": "Remedies for Breach of Contract",
+    "provision": "In the event of a breach of contract, the aggrieved party shall be entitled to seek either specific performance of the contract or, in lieu thereof, claim damages which shall be quantified based on the actual loss suffered as a direct result of the breach. The court may also grant consequential damages if it is proven that such damages were foreseeable at the time of contract formation. Additionally, if the breach is willful and malicious, the court may impose punitive damages to deter further violations.",
+    "question_type": "exceptions",
+    "question": "Are there any exceptions to the entitlement of the aggrieved party to seek specific performance or claim damages for a breach of contract under IPC 123A?",
+    "answer": "Yes, exceptions may arise in cases where the breach was not willful or malicious, thereby limiting the possibility of punitive damages. Additionally, if the aggrieved party fails to prove that the consequential damages were foreseeable at the time of contract formation, they may not be entitled to such damages. Furthermore, specific performance may not be granted if it is deemed impractical or impossible to enforce the terms of the contract."
+  },
+  {
+    "section_number": "CPC 224",
+    "section_title": "Procedure for Electronic Filing of Civil Suits",
+    "provision": "In any civil proceedings filed before the Court, a party may submit documents and pleadings electronically through the designated digital platform, provided that such filings comply with the prescribed format and electronic signature requirements as established by the Supreme Court of India. The electronic filing shall be deemed equivalent to the physical submission of documents, and the Court shall issue an electronic acknowledgment of receipt, which shall serve as the official record of submission. Any discrepancies in the electronic filing shall be rectified within seven days of notice from the Court, failing which the Court may dismiss the application without prejudice to the party's right to refile.",
+    "question_type": "penalty",
+    "question": "What are the potential penalties for failing to rectify discrepancies in electronic filings within the specified time frame as per CPC 224?",
+    "answer": "If a party fails to rectify discrepancies in their electronic filing within seven days of receiving notice from the Court, the Court may dismiss the application. However, this dismissal is without prejudice, meaning the party retains the right to refile the application in the future."
+  },
+  {
+    "section_number": "IEA 102A",
+    "section_title": "Admissibility of Digital Evidence",
+    "provision": "Notwithstanding the provisions of Section 65B of the Indian Evidence Act, 1872, any digital evidence, including but not limited to data derived from electronic devices, shall be admissible in a court of law provided that the party seeking to introduce such evidence establishes its authenticity through a certified digital signature or a chain of custody that clearly delineates the handling of the evidence from the time of its creation to presentation in court. The court shall also consider the relevance of the evidence in relation to the facts of the case and may exclude it if it is deemed to be unfairly prejudicial, misleading, or if its probative value is substantially outweighed by the danger of confusion of the issues.",
+    "question_type": "procedure",
+    "question": "What steps must a party take to ensure that digital evidence is admissible in court according to IEA 102A?",
+    "answer": "To ensure that digital evidence is admissible in court under IEA 102A, the party seeking to introduce the evidence must establish its authenticity by providing either a certified digital signature or a clear chain of custody. This chain of custody must detail the handling of the evidence from the time of its creation to its presentation in court. Additionally, the court will assess the relevance of the evidence to the case and may exclude it if it is found to be unfairly prejudicial, misleading, or if its probative value is substantially outweighed by the potential for confusion regarding the issues."
+  },
+  {
+    "section_number": "CPC 192A",
+    "section_title": "Conduct of Preliminary Hearings",
+    "provision": "In all civil matters, the court shall conduct a preliminary hearing within thirty days of the filing of the plaint. During this hearing, the court shall ascertain the issues raised, determine the necessity of further pleadings, and establish a timeline for the conduct of the trial. The court may also encourage the parties to explore alternative dispute resolution mechanisms, including mediation or conciliation, prior to proceeding with the formal trial process.",
+    "question_type": "exceptions",
+    "question": "Are there any exceptions to the requirement for the court to conduct a preliminary hearing within thirty days of the filing of the plaint in civil matters under CPC 192A?",
+    "answer": "Yes, exceptions may apply in cases where the court determines that special circumstances exist, such as complex issues requiring additional time for proper assessment, or if the parties have mutually agreed to postpone the preliminary hearing for valid reasons. Additionally, if there are procedural delays or if the court's schedule does not allow for a hearing within the stipulated timeframe, these may also constitute exceptions to the requirement."
+  },
+  {
+    "section_number": "CPC 123A",
+    "section_title": "Interim Relief in Civil Proceedings",
+    "provision": "In any suit pending before the Court, the plaintiff may apply for interim relief, including but not limited to the issuance of a temporary injunction or a stay of proceedings, if it is demonstrated that the delay in granting such relief would cause irreparable harm to the applicant. The Court shall consider the balance of convenience between the parties and the likelihood of success on the merits of the case before granting any interim orders. Such relief may be granted for a period not exceeding six months, subject to renewal upon satisfactory demonstration of continued necessity.",
+    "question_type": "exceptions",
+    "question": "Are there any exceptions to the granting of interim relief under CPC 123A, and what factors must be considered by the Court in such cases?",
+    "answer": "Yes, there are exceptions to the granting of interim relief under CPC 123A. The Court will only grant such relief if the plaintiff demonstrates that a delay would cause irreparable harm and considers the balance of convenience between the parties as well as the likelihood of success on the merits of the case. If these conditions are not satisfactorily met, the Court may deny the application for interim relief."
+  },
+  {
+    "section_number": "IPC 132A",
+    "section_title": "Protection of Innovations in Traditional Knowledge",
+    "provision": "Any individual or entity that seeks to utilize traditional knowledge or practices that have been developed and passed down through generations within a specific community shall obtain prior informed consent from the relevant community. Failure to do so shall constitute an infringement of the intellectual property rights of the community, rendering the infringer liable for damages not less than one lakh rupees and up to five times the profits derived from such unauthorized use. Additionally, courts may impose injunctions to prevent further exploitation of the said traditional knowledge.",
+    "question_type": "procedure",
+    "question": "What steps must an individual or entity take to legally utilize traditional knowledge according to IPC 132A?",
+    "answer": "To legally utilize traditional knowledge, an individual or entity must first obtain prior informed consent from the relevant community that holds the traditional knowledge. This involves engaging with the community to explain the intended use and ensuring that they fully understand and agree to it. Failure to obtain this consent may lead to legal consequences, including liability for damages and potential injunctions against further exploitation."
+  }
+]

week3/community-contributions/ranskills-week3-coherent-data-generator.ipynb

@@ -0,0 +1,734 @@
+{
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "KbMea_UrO3Ke"
+      },
+      "source": [
+        "# ✨ Coherent Data Generator\n",
+        "\n",
+        "## In real life, data has meaning, relationships, etc., and this is where this tool shines.\n",
+        "\n",
+        "Dependencies between fields are detected, and coherent data is generated.\n",
+        "Example:\n",
+        "When asked to generate data with **Ghana** cited as the context, fields like `name`, `food`, etc., will be Ghanaian. Fields such as phone number will have the appropriate prefix of `+233`, etc.\n",
+        "\n",
+        "This is better than Faker.\n",
+        "\n",
+        "## Steps\n",
+        "Schema -> Generate Data\n",
+        "\n",
+        "Schema Sources: \n",
+        "- Use the guided schema builder\n",
+        "- Bring your own schema from an SQL Data Definition Language (DDL)\n",
+        "- Prompting\n",
+        "- Providing a domain to an old hat to define features for a dataset"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "cN8z-QNlFtYc"
+      },
+      "outputs": [],
+      "source": [
+        "import json\n",
+        "\n",
+        "from transformers import AutoTokenizer, AutoModelForCausalLM, BitsAndBytesConfig\n",
+        "import torch\n",
+        "import pandas as pd\n",
+        "\n",
+        "from pydantic import BaseModel, Field\n",
+        "from IPython.display import display, Markdown"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "DOBBN3P2GD2O"
+      },
+      "outputs": [],
+      "source": [
+        "model_id = \"Qwen/Qwen3-4B-Instruct-2507\"\n",
+        "\n",
+        "device = torch.accelerator.current_accelerator().type if torch.accelerator.is_available() else 'cpu'\n",
+        "print(f'Device: {device}')\n",
+        "\n",
+        "tokenizer = AutoTokenizer.from_pretrained(model_id, trust_remote_code=True)\n",
+        "\n",
+        "model = AutoModelForCausalLM.from_pretrained(\n",
+        "    model_id,\n",
+        "    dtype=\"auto\",\n",
+        "    device_map=\"auto\"\n",
+        ")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "HSUebXa1O3MM"
+      },
+      "source": [
+        "## Schema Definitions"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "5LNM76OQjAw6"
+      },
+      "outputs": [],
+      "source": [
+        "# This is for future use where errors in SQL DDL statements can be fixed if the\n",
+        "# specifies that from the UI\n",
+        "class SQLValidationResult(BaseModel):\n",
+        "  is_valid: bool\n",
+        "  is_fixable: bool\n",
+        "  reason: str = Field(default='', description='validation failure reason')\n",
+        "\n",
+        "\n",
+        "class FieldDescriptor(BaseModel):\n",
+        "    name: str = Field(..., description='Name of the field')\n",
+        "    data_type: str = Field(..., description='Type of the field')\n",
+        "    nullable: bool\n",
+        "    description: str = Field(..., description='Description of the field')\n",
+        "\n",
+        "\n",
+        "class Schema(BaseModel):\n",
+        "    name: str = Field(..., description='Name of the schema')\n",
+        "    fields: list[FieldDescriptor] = Field(..., description='List of fields in the schema')"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "6QjitfTBPa1E"
+      },
+      "source": [
+        "## LLM Interactions"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "dXiRHok7Peir"
+      },
+      "source": [
+        "### Generate Content from LLM"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "daTUVG8_PmvM"
+      },
+      "outputs": [],
+      "source": [
+        "def generate(messages: list[dict[str, str]], temperature: float = 0.1) -> any:\n",
+        "  text = tokenizer.apply_chat_template(\n",
+        "      messages,\n",
+        "      tokenize=False,\n",
+        "      add_generation_prompt=True,\n",
+        "  )\n",
+        "  model_inputs = tokenizer([text], return_tensors=\"pt\").to(model.device)\n",
+        "\n",
+        "  generated_ids = model.generate(\n",
+        "      **model_inputs,\n",
+        "      max_new_tokens=16384,\n",
+        "      temperature=temperature\n",
+        "  )\n",
+        "\n",
+        "  output_ids = generated_ids[0][len(model_inputs.input_ids[0]):].tolist()\n",
+        "  content = tokenizer.decode(output_ids, skip_special_tokens=True)\n",
+        "\n",
+        "  return content"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "sBHJKn8qQhM5"
+      },
+      "source": [
+        "### Generate Data Given A Valid Schema"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "Fla8UQf4Qm5l"
+      },
+      "outputs": [],
+      "source": [
+        "def generate_data(schema: str, context: str = '', num_records: int = 5):\n",
+        "  system_prompt = f'''\n",
+        "  You are synthetic data generator, you generate data based on the given schema\n",
+        "  specific JSON structure.\n",
+        "  When a context is provided, intelligently use that to drive the field generation.\n",
+        "\n",
+        "  Example:\n",
+        "  If Africa is given at the context, fields like name, first_name, last_name, etc.\n",
+        "  that can be derived from Africa will be generated.\n",
+        "\n",
+        "  If no context is provided, generate data randomly.\n",
+        "\n",
+        "  Output an array of JSON objects.\n",
+        "  '''\n",
+        "\n",
+        "  prompt = f'''\n",
+        "  Generate {num_records}:\n",
+        "\n",
+        "  Schema:\n",
+        "  {schema}\n",
+        "\n",
+        "  Context:\n",
+        "  {context}\n",
+        "  '''\n",
+        "\n",
+        "  messages = [\n",
+        "      {'role': 'system', 'content': system_prompt},\n",
+        "      {\"role\": \"user\", \"content\": prompt}\n",
+        "  ]\n",
+        "\n",
+        "  return generate(messages)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "izrClU6VPsZp"
+      },
+      "source": [
+        "### SQL"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "aQgY6EK0QPPd"
+      },
+      "outputs": [],
+      "source": [
+        "def sql_validator(ddl: str):\n",
+        "  system_prompt = '''\n",
+        "  You are an SQL validator, your task is to validate if the given SQL is valid or not.\n",
+        "  ONLY return a binary response of 1 and 0. Where 1=valid and 0 = not valid.\n",
+        "  '''\n",
+        "  prompt = f'Validate: {ddl}'\n",
+        "\n",
+        "  messages = [\n",
+        "      {'role': 'system', 'content': system_prompt},\n",
+        "      {\"role\": \"user\", \"content\": prompt}\n",
+        "  ]\n",
+        "\n",
+        "  return generate(messages)\n",
+        "\n",
+        "\n",
+        "# Future work, this will fix any errors in the SQL DDL statement provided it is\n",
+        "# fixable.\n",
+        "def sql_fixer(ddl: str):\n",
+        "  pass\n",
+        "\n",
+        "\n",
+        "def parse_ddl(ddl: str):\n",
+        "  system_prompt = f'''\n",
+        "  You are an SQL analyzer, your task is to extract column information to a\n",
+        "  specific JSON structure.\n",
+        "\n",
+        "  The output must comform to the following JSON schema:\n",
+        "  {Schema.model_json_schema()}\n",
+        "  '''\n",
+        "  prompt = f'Generate schema for: {ddl}'\n",
+        "\n",
+        "  messages = [\n",
+        "      {'role': 'system', 'content': system_prompt},\n",
+        "      {\"role\": \"user\", \"content\": prompt}\n",
+        "  ]\n",
+        "\n",
+        "  return generate(messages)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "4mgwDQyDQ1wv"
+      },
+      "source": [
+        "### Data Scientist\n",
+        "\n",
+        "Just give it a domain and you will be amazed the features will give you."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "P36AMvBq8AST"
+      },
+      "outputs": [],
+      "source": [
+        "def create_domain_schema(domain: str):\n",
+        "  system_prompt = f'''\n",
+        "  You are an expert Data Scientist tasked to describe features for a dataset\n",
+        "  aspiring data scientists in a chosen domain.\n",
+        "\n",
+        "  Follow these steps EXACTLY:\n",
+        "  **Define 6–10 features** for the given domain. Include:\n",
+        "   - At least 2 numerical features\n",
+        "   - At least 2 categorical features\n",
+        "   - 1 boolean or binary feature\n",
+        "   - 1 timestamp or date feature\n",
+        "   - Realistic dependencies (e.g., \"if loan_amount > 50000, credit_score should be high\")\n",
+        "\n",
+        "  Populate your response into the JSON schema below. Strictly out **JSON**\n",
+        "  {Schema.model_json_schema()}\n",
+        "  '''\n",
+        "  prompt = f'Describe the data point. Domain: {domain}'\n",
+        "\n",
+        "  messages = [\n",
+        "      {'role': 'system', 'content': system_prompt},\n",
+        "      {\"role\": \"user\", \"content\": prompt}\n",
+        "  ]\n",
+        "\n",
+        "  return generate(messages)\n",
+        "\n",
+        "\n",
+        "# TODO: Use Gradion Examples to make it easier for the loading of different statements\n",
+        "sql = '''\n",
+        "CREATE TABLE users (\n",
+        "    id BIGINT PRIMARY KEY,\n",
+        "    name VARCHAR(100) NOT NULL,\n",
+        "    email TEXT,\n",
+        "    gender ENUM('F', 'M'),\n",
+        "    country VARCHAR(100),\n",
+        "    mobile_number VARCHAR(100),\n",
+        "    created_at TIMESTAMP DEFAULT NOW()\n",
+        ");\n",
+        "'''"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "QuVyHOhjDtSH"
+      },
+      "outputs": [],
+      "source": [
+        "print(f'{model.get_memory_footprint() / 1e9:, .2f} GB')"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "tqSpfJGnme7y"
+      },
+      "source": [
+        "## Export Functions"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "pAu5OPfUmMSm"
+      },
+      "outputs": [],
+      "source": [
+        "from enum import StrEnum\n",
+        "\n",
+        "\n",
+        "class ExportFormat(StrEnum):\n",
+        "    CSV = 'CSV'\n",
+        "    JSON = 'JSON'\n",
+        "    Excel = 'Excel'\n",
+        "    Parquet = 'Parquet'\n",
+        "    TSV = 'TSV'\n",
+        "    HTML = 'HTML'\n",
+        "    Markdown = 'Markdown'\n",
+        "    SQL = 'SQL'\n",
+        "\n",
+        "\n",
+        "def export_data(df, format_type):\n",
+        "    if df is None or df.empty:\n",
+        "        return None\n",
+        "\n",
+        "    try:\n",
+        "        if format_type == ExportFormat.CSV:\n",
+        "            output = io.StringIO()\n",
+        "            df.to_csv(output, index=False)\n",
+        "            return output.getvalue()\n",
+        "\n",
+        "        elif format_type == ExportFormat.JSON:\n",
+        "            return df.to_json(orient='records', indent=2)\n",
+        "\n",
+        "        elif format_type == ExportFormat.Excel:\n",
+        "            output = io.BytesIO()\n",
+        "            df.to_excel(output, index=False, engine='openpyxl')\n",
+        "            return output.getvalue()\n",
+        "\n",
+        "        elif format_type == ExportFormat.Parquet:\n",
+        "            output = io.BytesIO()\n",
+        "            df.to_parquet(output, index=False)\n",
+        "            return output.getvalue()\n",
+        "\n",
+        "        elif format_type == ExportFormat.TSV:\n",
+        "            output = io.StringIO()\n",
+        "            df.to_csv(output, sep='\\t', index=False)\n",
+        "            return output.getvalue()\n",
+        "\n",
+        "        elif format_type == ExportFormat.HTML:\n",
+        "            return df.to_html(index=False)\n",
+        "\n",
+        "        elif format_type == ExportFormat.Markdown:\n",
+        "            return df.to_markdown(index=False)\n",
+        "\n",
+        "        elif format_type == ExportFormat.SQL:\n",
+        "            from sqlalchemy import create_engine\n",
+        "            engine = create_engine('sqlite:///:memory:')\n",
+        "            table = 'users' # TODO: fix this\n",
+        "\n",
+        "            df.to_sql(table, con=engine, index=False)\n",
+        "            connection = engine.raw_connection()\n",
+        "            sql_statements = list(connection.iterdump())\n",
+        "            sql_output_string = \"\\n\".join(sql_statements)\n",
+        "            connection.close()\n",
+        "\n",
+        "            return sql_output_string\n",
+        "\n",
+        "    except Exception as e:\n",
+        "        print(f\"Export error: {str(e)}\")\n",
+        "        return None\n",
+        "\n",
+        "\n",
+        "def prepare_download(df, format_type):\n",
+        "  if df is None:\n",
+        "      return None\n",
+        "\n",
+        "  content = export_data(df, format_type)\n",
+        "  if content is None:\n",
+        "      return None\n",
+        "\n",
+        "  extensions = {\n",
+        "      ExportFormat.CSV: '.csv',\n",
+        "      ExportFormat.JSON: '.json',\n",
+        "      ExportFormat.Excel: '.xlsx',\n",
+        "      ExportFormat.Parquet: '.parquet',\n",
+        "      ExportFormat.TSV: '.tsv',\n",
+        "      ExportFormat.HTML: '.html',\n",
+        "      ExportFormat.Markdown: '.md',\n",
+        "      ExportFormat.SQL: '.sql',\n",
+        "  }\n",
+        "\n",
+        "  filename = f'generated_data{extensions.get(format_type, \".txt\")}'\n",
+        "\n",
+        "  is_binary_format = format_type in [ExportFormat.Excel, ExportFormat.Parquet]\n",
+        "  mode = 'w+b' if is_binary_format else 'w'\n",
+        "\n",
+        "  import tempfile\n",
+        "  with tempfile.NamedTemporaryFile(mode=mode, delete=False, suffix=extensions[format_type]) as tmp:\n",
+        "      tmp.write(content)\n",
+        "      tmp.flush()\n",
+        "      return tmp.name"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "Q0fZsCuso_YZ"
+      },
+      "source": [
+        "## Gradio UI"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "TJYUWecybDpP",
+        "outputId": "e82d0a13-3ca3-4a01-d45c-78fc94ade9bc"
+      },
+      "outputs": [],
+      "source": [
+        "import gradio as gr\n",
+        "from pydantic import BaseModel, Field\n",
+        "import json\n",
+        "import pandas as pd\n",
+        "import io\n",
+        "\n",
+        "DATA_TYPES = ['string', 'integer', 'float', 'boolean', 'date', 'datetime', 'array', 'object']\n",
+        "\n",
+        "def generate_from_sql(sql: str, context: str, num_records: int = 10):\n",
+        "    try:\n",
+        "        print(f'SQL: {sql}')\n",
+        "        schema = parse_ddl(sql)\n",
+        "        data = generate_data(schema, context, num_records)\n",
+        "\n",
+        "        data = json.loads(data)\n",
+        "        df = pd.DataFrame(data)\n",
+        "\n",
+        "        return schema, df\n",
+        "    except Exception as e:\n",
+        "        return f'Error: {str(e)}', None\n",
+        "\n",
+        "\n",
+        "def generate_from_data_scientist(domain: str, context: str, num_records: int = 10):\n",
+        "    try:\n",
+        "        print(f'Domain: {domain}')\n",
+        "        schema = create_domain_schema(domain)\n",
+        "        print(schema)\n",
+        "        data = generate_data(schema, context, num_records)\n",
+        "        data = json.loads(data)\n",
+        "        df = pd.DataFrame(data)\n",
+        "\n",
+        "        return schema, df\n",
+        "    except Exception as e:\n",
+        "        return f'Error: {str(e)}', None\n",
+        "\n",
+        "\n",
+        "def generate_from_dynamic_fields(schema_name, context: str, num_fields, num_records: int, *field_values):\n",
+        "    try:\n",
+        "        fields = []\n",
+        "        for i in range(num_fields):\n",
+        "            idx = i * 4\n",
+        "            if idx + 3 < len(field_values):\n",
+        "                name = field_values[idx]\n",
+        "                dtype = field_values[idx + 1]\n",
+        "                nullable = field_values[idx + 2]\n",
+        "                desc = field_values[idx + 3]\n",
+        "\n",
+        "                if name and dtype:\n",
+        "                    fields.append(FieldDescriptor(\n",
+        "                        name=name,\n",
+        "                        data_type=dtype,\n",
+        "                        nullable=nullable if nullable is not None else False,\n",
+        "                        description=desc if desc else ''\n",
+        "                    ))\n",
+        "\n",
+        "        if not schema_name:\n",
+        "            return 'Error: Schema name is required', None\n",
+        "\n",
+        "        if not fields:\n",
+        "            return 'Error: At least one field is required', None\n",
+        "\n",
+        "        schema = Schema(name=schema_name, fields=fields)\n",
+        "        data = generate_data(schema.model_dump(), context , num_records)\n",
+        "        data = json.loads(data)\n",
+        "        df = pd.DataFrame(data)\n",
+        "\n",
+        "\n",
+        "        return json.dumps(schema.model_dump(), indent=2), df\n",
+        "\n",
+        "    except Exception as e:\n",
+        "        return f'Error: {str(e)}', None\n",
+        "\n",
+        "\n",
+        "\n",
+        "title='✨ Coherent Data Generator'\n",
+        "\n",
+        "with gr.Blocks(title=title, theme=gr.themes.Monochrome()) as ui:\n",
+        "    gr.Markdown(f'# {title}')\n",
+        "    gr.Markdown('Embrass the Coherent Data wins 🏆!')\n",
+        "\n",
+        "    df_state = gr.State(value=None)\n",
+        "\n",
+        "    with gr.Row():\n",
+        "        num_records_input = gr.Number(\n",
+        "            label='Number of Records to Generate',\n",
+        "            value=10,\n",
+        "            minimum=1,\n",
+        "            maximum=10000,\n",
+        "            step=1,\n",
+        "            precision=0\n",
+        "        )\n",
+        "\n",
+        "        context_input = gr.Textbox(\n",
+        "            label='Context',\n",
+        "            placeholder='70% Ghana and 30% Nigeria data. Start ID generation from 200',\n",
+        "            lines=1\n",
+        "        )\n",
+        "\n",
+        "    with gr.Tabs() as tabs:\n",
+        "        with gr.Tab('Manual Entry', id=0):\n",
+        "            schema_name_input = gr.Textbox(label='Schema Name', placeholder='Enter schema name')\n",
+        "\n",
+        "            gr.Markdown('### Fields')\n",
+        "\n",
+        "            num_fields_state = gr.State(3)\n",
+        "\n",
+        "            with gr.Row():\n",
+        "                num_fields_slider = gr.Slider(\n",
+        "                    minimum=1,\n",
+        "                    maximum=20,\n",
+        "                    value=3,\n",
+        "                    step=1,\n",
+        "                    label='Number of Fields',\n",
+        "                    interactive=True\n",
+        "                )\n",
+        "\n",
+        "            gr.HTML('''\n",
+        "            <div style=\"display: flex; gap: 8px; margin-bottom: 8px; font-weight: bold;\">\n",
+        "                <div style=\"flex: 2;\">Field Name</div>\n",
+        "                <div style=\"flex: 2;\">Data Type</div>\n",
+        "                <div style=\"flex: 1;\">Nullable</div>\n",
+        "                <div style=\"flex: 3;\">Description</div>\n",
+        "            </div>\n",
+        "            ''')\n",
+        "\n",
+        "            field_components = []\n",
+        "            row_components = []\n",
+        "\n",
+        "            for i in range(20):\n",
+        "                with gr.Row(visible=(i < 3)) as row:\n",
+        "                    field_name = gr.Textbox(label='', container=False, scale=2)\n",
+        "                    data_type = gr.Dropdown(choices=DATA_TYPES, value='string', label='', container=False, scale=2)\n",
+        "                    nullable = gr.Checkbox(label='', container=False, scale=1)\n",
+        "                    description = gr.Textbox(label='', container=False, scale=3)\n",
+        "\n",
+        "                    row_components.append(row)\n",
+        "                    field_components.extend([field_name, data_type, nullable, description])\n",
+        "\n",
+        "            submit_btn = gr.Button('Generate', variant='primary')\n",
+        "\n",
+        "            num_fields_slider.change(\n",
+        "                fn=lambda x: [gr.update(visible=(i < x)) for i in range(20)],\n",
+        "                inputs=[num_fields_slider],\n",
+        "                outputs=row_components\n",
+        "            )\n",
+        "\n",
+        "\n",
+        "        with gr.Tab('SQL', id=1):\n",
+        "            gr.Markdown('### Parse SQL DDL')\n",
+        "            ddl_input = gr.Code(\n",
+        "                value=sql,\n",
+        "                label='SQL DDL Statement',\n",
+        "                language='sql',\n",
+        "                lines=10\n",
+        "            )\n",
+        "            ddl_btn = gr.Button('Generate', variant='primary')\n",
+        "\n",
+        "\n",
+        "        with gr.Tab('>_ Prompt', id=2):\n",
+        "            gr.Markdown('### You are on your own here, so be creative 💡')\n",
+        "            prompt_input = gr.Textbox(\n",
+        "                label='Prompt',\n",
+        "                placeholder='Type your prompt',\n",
+        "                lines=10\n",
+        "            )\n",
+        "            prompt_btn = gr.Button('Generate', variant='primary')\n",
+        "\n",
+        "        with gr.Tab('Data Scientist 🎩', id=3):\n",
+        "            gr.Markdown('### You are on your own here, so be creative 💡')\n",
+        "            domain_input = gr.Dropdown(\n",
+        "                label='Domain',\n",
+        "                choices=['E-commerce Customers', 'Hospital Patients', 'Loan Applications'],\n",
+        "                allow_custom_value=True\n",
+        "            )\n",
+        "\n",
+        "            data_scientist_generate_btn = gr.Button('Generate', variant='primary')\n",
+        "\n",
+        "\n",
+        "    with gr.Accordion('Generated Schema', open=False):\n",
+        "      output = gr.Code(label='Schema (JSON)', language='json')\n",
+        "\n",
+        "    gr.Markdown('## Generated Data')\n",
+        "    dataframe_output = gr.Dataframe(\n",
+        "        label='',\n",
+        "        interactive=False,\n",
+        "        wrap=True\n",
+        "    )\n",
+        "\n",
+        "    gr.Markdown('### Export Data')\n",
+        "    with gr.Row():\n",
+        "        format_dropdown = gr.Dropdown(\n",
+        "            choices=[format.value for format in ExportFormat],\n",
+        "            value=ExportFormat.CSV,\n",
+        "            label='Export Format',\n",
+        "            scale=2\n",
+        "        )\n",
+        "        download_btn = gr.Button('Download', variant='secondary', scale=1)\n",
+        "\n",
+        "    download_file = gr.File(label='Download File', visible=True)\n",
+        "\n",
+        "\n",
+        "    def _handle_result(result):\n",
+        "        if isinstance(result, tuple) and len(result) == 2:\n",
+        "            return result[0], result[1], result[1]\n",
+        "        return result[0], result[1], None\n",
+        "\n",
+        "\n",
+        "    def update_from_dynamic_fields(schema_name, context, num_fields, num_records, *field_values):\n",
+        "        result = generate_from_dynamic_fields(schema_name, context, num_fields, num_records, *field_values)\n",
+        "        return _handle_result(result)\n",
+        "\n",
+        "\n",
+        "    def update_from_sql(sql: str, context, num_records: int):\n",
+        "        result = generate_from_sql(sql, context, num_records)\n",
+        "        return _handle_result(result)\n",
+        "\n",
+        "\n",
+        "    def update_from_data_scientist(domain: str, context, num_records: int):\n",
+        "        result = generate_from_data_scientist(domain, context, num_records)\n",
+        "        return _handle_result(result)\n",
+        "\n",
+        "\n",
+        "    submit_btn.click(\n",
+        "        fn=update_from_dynamic_fields,\n",
+        "        inputs=[schema_name_input, context_input, num_fields_slider, num_records_input] + field_components,\n",
+        "        outputs=[output, dataframe_output, df_state]\n",
+        "    )\n",
+        "\n",
+        "    ddl_btn.click(\n",
+        "        fn=update_from_sql,\n",
+        "        inputs=[ddl_input,  context_input, num_records_input],\n",
+        "        outputs=[output, dataframe_output, df_state]\n",
+        "    )\n",
+        "\n",
+        "    data_scientist_generate_btn.click(\n",
+        "        fn=update_from_data_scientist,\n",
+        "        inputs=[domain_input,  context_input, num_records_input],\n",
+        "        outputs=[output, dataframe_output, df_state]\n",
+        "    )\n",
+        "\n",
+        "\n",
+        "    download_btn.click(\n",
+        "        fn=prepare_download,\n",
+        "        inputs=[df_state, format_dropdown],\n",
+        "        outputs=[download_file]\n",
+        "    )\n",
+        "\n",
+        "\n",
+        "ui.launch(debug=True)\n"
+      ]
+    }
+  ],
+  "metadata": {
+    "accelerator": "GPU",
+    "colab": {
+      "collapsed_sections": [
+        "tqSpfJGnme7y"
+      ],
+      "gpuType": "T4",
+      "provenance": []
+    },
+    "kernelspec": {
+      "display_name": "Python 3",
+      "name": "python3"
+    },
+    "language_info": {
+      "name": "python"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 0
+}

week3/community-contributions/salah/meeting_minutes_v2.py

@@ -0,0 +1,301 @@
+#!/usr/bin/env python3
+
+import os
+import torch
+import requests
+import json
+import librosa
+import numpy as np
+from pathlib import Path
+from datetime import datetime
+from transformers import pipeline
+import gradio as gr
+
+# Basic config
+TRANSCRIPTION_MODEL = "openai/whisper-tiny.en"
+OLLAMA_MODEL = "llama3.2:latest"
+OLLAMA_URL = "http://localhost:11434"
+DEVICE = "cuda" if torch.cuda.is_available() else "cpu"
+OUTPUT_DIR = Path("./output")
+
+# ============================
+# MODEL LOADING
+# ============================
+
+def check_ollama():
+    try:
+        response = requests.get(f"{OLLAMA_URL}/api/tags", timeout=5)
+        if response.status_code == 200:
+            models = response.json().get('models', [])
+            model_names = [model['name'] for model in models]
+            return OLLAMA_MODEL in model_names
+        return False
+    except:
+        return False
+
+def call_ollama(prompt):
+    payload = {
+        "model": OLLAMA_MODEL,
+        "prompt": prompt,
+        "stream": False,
+        "options": {
+            "temperature": 0.7,
+            "num_predict": 1000
+        }
+    }
+    
+    try:
+        response = requests.post(f"{OLLAMA_URL}/api/generate", json=payload, timeout=120)
+        if response.status_code == 200:
+            return response.json().get('response', '').strip()
+        return "Error: Ollama request failed"
+    except:
+        return "Error: Could not connect to Ollama"
+
+def load_models():
+    print("Loading models...")
+    
+    if not check_ollama():
+        print("Ollama not available")
+        return None, False
+    
+    try:
+        transcription_pipe = pipeline(
+            "automatic-speech-recognition",
+            model=TRANSCRIPTION_MODEL,
+            torch_dtype=torch.float16 if DEVICE == "cuda" else torch.float32,
+            device=0 if DEVICE == "cuda" else -1,
+            return_timestamps=True
+        )
+        print("Models loaded successfully")
+        return transcription_pipe, True
+    except Exception as e:
+        print(f"Failed to load models: {e}")
+        return None, False
+
+# ============================
+# PROCESSING FUNCTIONS
+# ============================
+
+def transcribe_audio(audio_file_path, transcription_pipe):
+    if not os.path.exists(audio_file_path):
+        return "Error: Audio file not found"
+    
+    try:
+        # Load audio with librosa
+        audio, sr = librosa.load(audio_file_path, sr=16000)
+        if not isinstance(audio, np.ndarray):
+            audio = np.array(audio)
+        
+        result = transcription_pipe(audio)
+        
+        # Extract text from result
+        if isinstance(result, dict):
+            if "text" in result:
+                transcription = result["text"].strip()
+            elif "chunks" in result:
+                transcription = " ".join([chunk["text"] for chunk in result["chunks"]]).strip()
+            else:
+                transcription = str(result).strip()
+        else:
+            transcription = str(result).strip()
+        
+        return transcription
+        
+    except Exception as e:
+        return f"Error: {str(e)}"
+
+def generate_minutes(transcription):
+    prompt = f"""Create meeting minutes from this transcript:
+
+{transcription[:2000]}
+
+Include:
+- Summary with attendees and topics
+- Key discussion points
+- Important decisions
+- Action items
+
+Meeting Minutes:"""
+    
+    result = call_ollama(prompt)
+    return result
+
+def save_results(transcription, minutes, meeting_type="meeting"):
+    try:
+        OUTPUT_DIR.mkdir(exist_ok=True)
+        timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
+        filename = f"{meeting_type}_minutes_{timestamp}.md"
+        filepath = OUTPUT_DIR / filename
+        
+        content = f"""# Meeting Minutes
+
+**Generated:** {datetime.now().strftime("%Y-%m-%d %H:%M:%S")}
+
+## Meeting Minutes
+
+{minutes}
+
+## Full Transcription
+
+{transcription}
+"""
+        
+        with open(filepath, 'w', encoding='utf-8') as f:
+            f.write(content)
+        
+        return str(filepath)
+        
+    except Exception as e:
+        return f"Error saving: {str(e)}"
+
+# ============================
+# GRADIO INTERFACE
+# ============================
+
+def process_audio_file(audio_file, meeting_type, progress=gr.Progress()):
+    progress(0.0, desc="Starting...")
+    
+    if not hasattr(process_audio_file, 'models') or not process_audio_file.models[0]:
+        return "", "", "Models not loaded"
+    
+    transcription_pipe, ollama_ready = process_audio_file.models
+    
+    if not ollama_ready:
+        return "", "", "Ollama not available"
+    
+    try:
+        audio_path = audio_file.name if hasattr(audio_file, 'name') else str(audio_file)
+        if not audio_path:
+            return "", "", "No audio file provided"
+        
+        progress(0.2, desc="Transcribing...")
+        transcription = transcribe_audio(audio_path, transcription_pipe)
+        
+        if transcription.startswith("Error:"):
+            return transcription, "", "Transcription failed"
+        
+        progress(0.6, desc="Generating minutes...")
+        minutes = generate_minutes(transcription)
+        
+        if minutes.startswith("Error:"):
+            return transcription, minutes, "Minutes generation failed"
+        
+        progress(0.9, desc="Saving...")
+        save_path = save_results(transcription, minutes, meeting_type)
+        
+        progress(1.0, desc="Complete!")
+        
+        status = f"""Processing completed!
+
+Transcription: {len(transcription)} characters  
+Minutes: {len(minutes)} characters  
+Saved to: {save_path}
+
+Models used:
+- Transcription: {TRANSCRIPTION_MODEL}
+- LLM: {OLLAMA_MODEL}
+- Device: {DEVICE}
+"""
+        
+        return transcription, minutes, status
+        
+    except Exception as e:
+        progress(1.0, desc="Failed")
+        return "", "", f"Processing failed: {str(e)}"
+
+def create_interface():
+    with gr.Blocks(title="Meeting Minutes Creator") as interface:
+        
+        gr.HTML("<h1>Meeting Minutes Creator</h1><p>HuggingFace Whisper + Ollama</p>")
+        
+        with gr.Row():
+            with gr.Column():
+                gr.Markdown("### Audio Input")
+                
+                audio_input = gr.Audio(
+                    label="Upload or Record Audio",
+                    type="filepath",
+                    sources=["upload", "microphone"]
+                )
+                
+                meeting_type = gr.Dropdown(
+                    choices=["meeting", "standup", "interview", "call"],
+                    value="meeting",
+                    label="Meeting Type"
+                )
+                
+                process_btn = gr.Button("Generate Minutes", variant="primary")
+                
+                gr.HTML(f"""
+                <div>
+                    <h4>Configuration</h4>
+                    <ul>
+                        <li>Transcription: {TRANSCRIPTION_MODEL}</li>
+                        <li>LLM: {OLLAMA_MODEL}</li>
+                        <li>Device: {DEVICE}</li>
+                    </ul>
+                </div>
+                """)
+            
+            with gr.Column():
+                gr.Markdown("### Results")
+                
+                status_output = gr.Markdown("Ready to process audio")
+                
+                with gr.Tabs():
+                    with gr.Tab("Meeting Minutes"):
+                        minutes_output = gr.Markdown("Minutes will appear here")
+                    
+                    with gr.Tab("Transcription"):
+                        transcription_output = gr.Textbox(
+                            "Transcription will appear here",
+                            lines=15,
+                            show_copy_button=True
+                        )
+        
+        process_btn.click(
+            fn=process_audio_file,
+            inputs=[audio_input, meeting_type],
+            outputs=[transcription_output, minutes_output, status_output],
+            show_progress=True
+        )
+    
+    return interface
+
+# ============================
+# MAIN APPLICATION
+# ============================
+
+def main():
+    print("Meeting Minutes Creator - HuggingFace + Ollama")
+    print("Loading models...")
+    
+    transcription_pipe, ollama_ready = load_models()
+    
+    if not transcription_pipe or not ollama_ready:
+        print("Failed to load models or connect to Ollama")
+        print("Make sure Ollama is running and has the model available")
+        return
+    
+    process_audio_file.models = (transcription_pipe, ollama_ready)
+    
+    print("Models loaded successfully!")
+    print("Starting web interface...")
+    print("Access at: http://localhost:7860")
+    
+    interface = create_interface()
+    
+    try:
+        interface.launch(
+            server_name="localhost",
+            server_port=7860,
+            debug=False
+        )
+    except KeyboardInterrupt:
+        print("Shutting down...")
+    except Exception as e:
+        print(f"Failed to launch: {e}")
+
+if __name__ == "__main__":
+    main()

week3/community-contributions/salah/output/meeting_minutes_20251024_062609.md

@@ -0,0 +1,36 @@
+# Meeting Minutes
+
+**Generated:** 2025-10-24 06:26:09
+
+## Meeting Minutes
+
+Here are the meeting minutes based on the transcript:
+
+**Dilistanda Meeting Minutes - October 24**
+
+**Attendees:** 
+
+* Jean (Project Manager)
+* [Unknown speaker] ( attendee, name not provided)
+
+**Summary:**
+This meeting was held to discuss ongoing project updates and tasks for Dilistanda. The attendees reviewed the progress made by Jean on the user authentication module and discussed other ongoing work.
+
+**Key Discussion Points:**
+
+* Jean shared his update on completing the user authentication module and fixing three bugs on the login system.
+* [Unknown speaker] mentioned they finished a database migration script and reviewed SORAP or request, but did not provide further details.
+
+**Important Decisions:**
+None
+
+**Action Items:**
+
+1. **Jean:** Continue working on the dashboard to components without any blockers.
+2. [Unknown speaker]: Focus on API points for mobile app development.
+
+Note: Unfortunately, some information was missing from the transcript (e.g., the identity of the second attendee), which made it challenging to create a comprehensive set of meeting minutes.
+
+## Full Transcription
+
+Good morning everyone, this is our Dilistanda meeting for October 24. I am sorrow as a project manager. Jean, can you give us your update? Yeah, Jean here yesterday I completed the user authentication module and I fixed three bugs on the login system. Today I will be working on the dashboard to components, no blocker. Okay, so I'm going to make your turn. How is this mic? I finished the database migration script and I reviewed SORAP or request. Today I will focus on the API points for mobile app.

week3/community-contributions/salah/requirements.txt

@@ -0,0 +1,9 @@
+# Meeting Minutes Creator V2 - HuggingFace + Ollama Implementation
+# Requirements for Week 3 Day 5 Exercise
+
+torch>=2.0.0
+transformers>=4.35.0
+gradio>=4.0.0
+librosa>=0.10.0
+soundfile>=0.12.0
+requests>=2.31.0

week3/community-contributions/samuel_bootcamp_wk3/data_generator.ipynb

@@ -0,0 +1,229 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "2714fa36",
+   "metadata": {},
+   "source": [
+    "## Week 3 Data Generator With Opensource Models\n",
+    "# Generate synthetic data for Pizza cusromers within Nairobi "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "761622db",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "!pip install requests pandas ipywidgets gradio"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "cc7347c4",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "import gradio as gr\n",
+    "from huggingface_hub import InferenceClient\n",
+    "import random\n",
+    "import os\n",
+    "from dotenv import load_dotenv\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "f20cd822",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#Load API Key\n",
+    "\n",
+    "load_dotenv(override=True)\n",
+    "HF_API_KEY = os.getenv('HF_TOKEN')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "856cd8cb",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\n",
+    "# Define available models with correct Hugging Face model IDs\n",
+    "MODELS = {\n",
+    "    \"Mistral-7B\": \"mistralai/Mistral-7B-Instruct-v0.2\",\n",
+    "    \"Llama-2-7B\": \"meta-llama/Llama-2-7b-chat-hf\",\n",
+    "    \"Phi-2\": \"microsoft/phi-2\",\n",
+    "    \"GPT-2\": \"gpt2\"\n",
+    "}\n",
+    "\n",
+    "# Nairobi branches\n",
+    "BRANCHES = [\"Westlands\", \"Karen\", \"Kilimani\", \"CBD\", \"Parklands\"]\n",
+    "\n",
+    "# Global variable to store generated data\n",
+    "generated_df = None\n",
+    "\n",
+    "def generate_feedback_data(model_name, num_records):\n",
+    "    \"\"\"Generate synthetic pizza feedback data using selected AI model\"\"\"\n",
+    "    global generated_df\n",
+    "    \n",
+    "    try:\n",
+    "        # Initialize the Hugging Face Inference Client\n",
+    "        model_id = MODELS[model_name]\n",
+    "        client = InferenceClient(model=model_id, token=None)  # Add your HF token if needed\n",
+    "        \n",
+    "        feedback_data = []\n",
+    "        \n",
+    "        for i in range(num_records):\n",
+    "            # Random branch\n",
+    "            branch = random.choice(BRANCHES)\n",
+    "            \n",
+    "            # Generate feedback using the AI model\n",
+    "            prompt = f\"Generate a brief customer feedback comment about a pizza order from {branch} branch in Nairobi. Make it realistic and varied (positive, negative, or neutral). Keep it under 30 words.\"\n",
+    "            \n",
+    "            try:\n",
+    "                response = client.text_generation(\n",
+    "                    prompt,\n",
+    "                    max_new_tokens=50,\n",
+    "                    temperature=0.8\n",
+    "                )\n",
+    "                feedback = response.strip()\n",
+    "            except Exception as e:\n",
+    "                # Fallback to template-based generation if API fails\n",
+    "                feedback = generate_fallback_feedback(branch)\n",
+    "            \n",
+    "            # Generate other fields\n",
+    "            record = {\n",
+    "                \"Customer_ID\": f\"CUST{1000 + i}\",\n",
+    "                \"Branch\": branch,\n",
+    "                \"Rating\": random.randint(1, 5),\n",
+    "                \"Order_Type\": random.choice([\"Delivery\", \"Dine-in\", \"Takeaway\"]),\n",
+    "                \"Feedback\": feedback,\n",
+    "                \"Date\": f\"2024-{random.randint(1, 12):02d}-{random.randint(1, 28):02d}\"\n",
+    "            }\n",
+    "            \n",
+    "            feedback_data.append(record)\n",
+    "        \n",
+    "        # Create DataFrame\n",
+    "        generated_df = pd.DataFrame(feedback_data)\n",
+    "        \n",
+    "        return generated_df, f\"✓ Successfully generated {num_records} records using {model_name}\"\n",
+    "    \n",
+    "    except Exception as e:\n",
+    "        return pd.DataFrame(), f\"✗ Error: {str(e)}\"\n",
+    "\n",
+    "def generate_fallback_feedback(branch):\n",
+    "    \"\"\"Fallback feedback generator if API fails\"\"\"\n",
+    "    templates = [\n",
+    "        f\"Great pizza from {branch}! Quick delivery and hot food.\",\n",
+    "        f\"Pizza was cold when it arrived at {branch}. Disappointed.\",\n",
+    "        f\"Excellent service at {branch} branch. Will order again!\",\n",
+    "        f\"Average experience. Pizza was okay but nothing special.\",\n",
+    "        f\"Long wait time at {branch} but the pizza was worth it.\",\n",
+    "    ]\n",
+    "    return random.choice(templates)\n",
+    "\n",
+    "def download_csv():\n",
+    "    \"\"\"Save generated data as CSV\"\"\"\n",
+    "    global generated_df\n",
+    "    if generated_df is not None:\n",
+    "        generated_df.to_csv('pizza_feedback_data.csv', index=False)\n",
+    "        return \"CSV downloaded!\"\n",
+    "    return \"No data to download\"\n",
+    "\n",
+    "def download_json():\n",
+    "    \"\"\"Save generated data as JSON\"\"\"\n",
+    "    global generated_df\n",
+    "    if generated_df is not None:\n",
+    "        generated_df.to_json('pizza_feedback_data.json', orient='records', indent=2)\n",
+    "        return \"JSON downloaded!\"\n",
+    "    return \"No data to download\"\n",
+    "\n",
+    "# Create Gradio interface\n",
+    "with gr.Blocks(title=\"Pizza Feedback Data Generator\") as demo:\n",
+    "    gr.Markdown(\"\"\"\n",
+    "    # 🍕 Pizza Feedback Data Generator\n",
+    "    Generate synthetic customer feedback for Nairobi pizza branches using AI models\n",
+    "    \"\"\")\n",
+    "    \n",
+    "    with gr.Row():\n",
+    "        with gr.Column():\n",
+    "            model_selector = gr.Radio(\n",
+    "                choices=list(MODELS.keys()),\n",
+    "                label=\"Select AI Model\",\n",
+    "                value=list(MODELS.keys())[0]\n",
+    "            )\n",
+    "            \n",
+    "            num_records_slider = gr.Slider(\n",
+    "                minimum=1,\n",
+    "                maximum=50,\n",
+    "                value=10,\n",
+    "                step=1,\n",
+    "                label=\"Number of Records\"\n",
+    "            )\n",
+    "            \n",
+    "            generate_btn = gr.Button(\"Generate Feedback Data\", variant=\"primary\")\n",
+    "    \n",
+    "    with gr.Row():\n",
+    "        status_output = gr.Textbox(label=\"Status\", interactive=False)\n",
+    "    \n",
+    "    with gr.Row():\n",
+    "        dataframe_output = gr.Dataframe(\n",
+    "            label=\"Generated Feedback Data\",\n",
+    "            interactive=False\n",
+    "        )\n",
+    "    \n",
+    "    with gr.Row():\n",
+    "        csv_btn = gr.Button(\"Download CSV\")\n",
+    "        json_btn = gr.Button(\"Download JSON\")\n",
+    "    \n",
+    "    # Event handlers\n",
+    "    generate_btn.click(\n",
+    "        fn=generate_feedback_data,\n",
+    "        inputs=[model_selector, num_records_slider],\n",
+    "        outputs=[dataframe_output, status_output]\n",
+    "    )\n",
+    "    \n",
+    "    csv_btn.click(\n",
+    "        fn=download_csv,\n",
+    "        outputs=status_output\n",
+    "    )\n",
+    "    \n",
+    "    json_btn.click(\n",
+    "        fn=download_json,\n",
+    "        outputs=status_output\n",
+    "    )\n",
+    "\n",
+    "# Launch the interface\n",
+    "demo.launch()"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": ".venv",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.12.12"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

week3/community-contributions/samuel_bootcamp_wk3/pizza_feedback_data.csv

@@ -0,0 +1,17 @@
+Customer_ID,Branch,Rating,Order_Type,Feedback,Date
+CUST1000,Westlands,1,Dine-in,Great pizza from Westlands! Quick delivery and hot food.,2024-10-17
+CUST1001,CBD,1,Takeaway,Excellent service at CBD branch. Will order again!,2024-11-24
+CUST1002,Kilimani,1,Delivery,Excellent service at Kilimani branch. Will order again!,2024-09-03
+CUST1003,Parklands,5,Takeaway,Great pizza from Parklands! Quick delivery and hot food.,2024-08-05
+CUST1004,Westlands,3,Delivery,Great pizza from Westlands! Quick delivery and hot food.,2024-01-12
+CUST1005,CBD,5,Delivery,Great pizza from CBD! Quick delivery and hot food.,2024-01-10
+CUST1006,Kilimani,1,Delivery,Long wait time at Kilimani but the pizza was worth it.,2024-09-12
+CUST1007,Parklands,2,Delivery,Great pizza from Parklands! Quick delivery and hot food.,2024-05-27
+CUST1008,Parklands,3,Dine-in,Excellent service at Parklands branch. Will order again!,2024-12-01
+CUST1009,CBD,1,Dine-in,Excellent service at CBD branch. Will order again!,2024-10-09
+CUST1010,Parklands,1,Takeaway,Average experience. Pizza was okay but nothing special.,2024-04-03
+CUST1011,Westlands,2,Dine-in,Pizza was cold when it arrived at Westlands. Disappointed.,2024-01-02
+CUST1012,Karen,2,Takeaway,Pizza was cold when it arrived at Karen. Disappointed.,2024-03-26
+CUST1013,Westlands,3,Dine-in,Long wait time at Westlands but the pizza was worth it.,2024-11-17
+CUST1014,Westlands,5,Takeaway,Average experience. Pizza was okay but nothing special.,2024-03-01
+CUST1015,Parklands,3,Delivery,Excellent service at Parklands branch. Will order again!,2024-03-18

week4/community-contributions/bharat_puri/docstring_generator.ipynb

@@ -0,0 +1,596 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "4a6ab9a2-28a2-445d-8512-a0dc8d1b54e9",
+   "metadata": {},
+   "source": [
+    "# Code DocString  / Comment Generator\n",
+    "\n",
+    "Submitted By : Bharat Puri\n",
+    "\n",
+    "Goal: Build a code tool that scans Python modules, finds functions/classes\n",
+    "without docstrings, and uses an LLM (Claude / GPT / Gemini / Qwen etc.)\n",
+    "to generate high-quality Google or NumPy style docstrings."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "e610bf56-a46e-4aff-8de1-ab49d62b1ad3",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# imports\n",
+    "\n",
+    "import os\n",
+    "import io\n",
+    "import sys\n",
+    "import re\n",
+    "from dotenv import load_dotenv\n",
+    "import sys\n",
+    "sys.path.append(os.path.abspath(os.path.join(\"..\", \"..\"))) \n",
+    "from openai import OpenAI\n",
+    "import gradio as gr\n",
+    "import subprocess\n",
+    "from IPython.display import Markdown, display\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "4f672e1c-87e9-4865-b760-370fa605e614",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "load_dotenv(override=True)\n",
+    "openai_api_key = os.getenv('OPENAI_API_KEY')\n",
+    "anthropic_api_key = os.getenv('ANTHROPIC_API_KEY')\n",
+    "google_api_key = os.getenv('GOOGLE_API_KEY')\n",
+    "grok_api_key = os.getenv('GROK_API_KEY')\n",
+    "groq_api_key = os.getenv('GROQ_API_KEY')\n",
+    "openrouter_api_key = os.getenv('OPENROUTER_API_KEY')\n",
+    "\n",
+    "if openai_api_key:\n",
+    "    print(f\"OpenAI API Key exists and begins {openai_api_key[:8]}\")\n",
+    "else:\n",
+    "    print(\"OpenAI API Key not set\")\n",
+    "    \n",
+    "if anthropic_api_key:\n",
+    "    print(f\"Anthropic API Key exists and begins {anthropic_api_key[:7]}\")\n",
+    "else:\n",
+    "    print(\"Anthropic API Key not set (and this is optional)\")\n",
+    "\n",
+    "if google_api_key:\n",
+    "    print(f\"Google API Key exists and begins {google_api_key[:2]}\")\n",
+    "else:\n",
+    "    print(\"Google API Key not set (and this is optional)\")\n",
+    "\n",
+    "if grok_api_key:\n",
+    "    print(f\"Grok API Key exists and begins {grok_api_key[:4]}\")\n",
+    "else:\n",
+    "    print(\"Grok API Key not set (and this is optional)\")\n",
+    "\n",
+    "if groq_api_key:\n",
+    "    print(f\"Groq API Key exists and begins {groq_api_key[:4]}\")\n",
+    "else:\n",
+    "    print(\"Groq API Key not set (and this is optional)\")\n",
+    "\n",
+    "if openrouter_api_key:\n",
+    "    print(f\"OpenRouter API Key exists and begins {openrouter_api_key[:6]}\")\n",
+    "else:\n",
+    "    print(\"OpenRouter API Key not set (and this is optional)\")\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "59863df1",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Connect to client libraries\n",
+    "\n",
+    "openai = OpenAI()\n",
+    "\n",
+    "anthropic_url = \"https://api.anthropic.com/v1/\"\n",
+    "gemini_url = \"https://generativelanguage.googleapis.com/v1beta/openai/\"\n",
+    "grok_url = \"https://api.x.ai/v1\"\n",
+    "groq_url = \"https://api.groq.com/openai/v1\"\n",
+    "ollama_url = \"http://localhost:11434/v1\"\n",
+    "openrouter_url = \"https://openrouter.ai/api/v1\"\n",
+    "\n",
+    "anthropic = OpenAI(api_key=anthropic_api_key, base_url=anthropic_url)\n",
+    "gemini = OpenAI(api_key=google_api_key, base_url=gemini_url)\n",
+    "grok = OpenAI(api_key=grok_api_key, base_url=grok_url)\n",
+    "groq = OpenAI(api_key=groq_api_key, base_url=groq_url)\n",
+    "ollama = OpenAI(api_key=\"ollama\", base_url=ollama_url)\n",
+    "openrouter = OpenAI(api_key=openrouter_api_key, base_url=openrouter_url)\n",
+    "\n",
+    "MODEL = os.getenv(\"DOCGEN_MODEL\", \"gpt-4o-mini\")\n",
+    "\n",
+    "\n",
+    "# Registry for multiple model providers\n",
+    "MODEL_REGISTRY = {\n",
+    "    \"gpt-4o-mini (OpenAI)\": {\n",
+    "        \"provider\": \"openai\",\n",
+    "        \"model\": \"gpt-4o-mini\",\n",
+    "    },\n",
+    "    \"gpt-4o (OpenAI)\": {\n",
+    "        \"provider\": \"openai\",\n",
+    "        \"model\": \"gpt-4o\",\n",
+    "    },\n",
+    "    \"claude-3.5-sonnet (Anthropic)\": {\n",
+    "        \"provider\": \"anthropic\",\n",
+    "        \"model\": \"claude-3.5-sonnet\",\n",
+    "    },\n",
+    "    \"gemini-1.5-pro (Google)\": {\n",
+    "        \"provider\": \"google\",\n",
+    "        \"model\": \"gemini-1.5-pro\",\n",
+    "    },\n",
+    "    \"codellama-7b (Open Source)\": {\n",
+    "        \"provider\": \"open_source\",\n",
+    "        \"model\": \"codellama-7b\",\n",
+    "    },\n",
+    "    \"starcoder2 (Open Source)\": {\n",
+    "        \"provider\": \"open_source\",\n",
+    "        \"model\": \"starcoder2\",\n",
+    "    },\n",
+    "}\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "8aa149ed-9298-4d69-8fe2-8f5de0f667da",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "models = [\"gpt-5\", \"claude-sonnet-4-5-20250929\", \"grok-4\", \"gemini-2.5-pro\", \"qwen2.5-coder\", \"deepseek-coder-v2\", \"gpt-oss:20b\", \"qwen/qwen3-coder-30b-a3b-instruct\", \"openai/gpt-oss-120b\", ]\n",
+    "\n",
+    "clients = {\"gpt-5\": openai, \"claude-sonnet-4-5-20250929\": anthropic, \"grok-4\": grok, \"gemini-2.5-pro\": gemini, \"openai/gpt-oss-120b\": groq, \"qwen2.5-coder\": ollama, \"deepseek-coder-v2\": ollama, \"gpt-oss:20b\": ollama, \"qwen/qwen3-coder-30b-a3b-instruct\": openrouter}\n",
+    "\n",
+    "# Want to keep costs ultra-low? Replace this with models of your choice, using the examples from yesterday"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "17b7d074-b1a4-4673-adec-918f82a4eff0",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# ================================================================\n",
+    "#  Prompt Templates and Utilities\n",
+    "# ================================================================\n",
+    "\n",
+    "DOCSTYLE_TEMPLATES = {\n",
+    "    \"google\": (\n",
+    "        \"You will write a concise Google-style Python docstring for the given function or class.\\n\"\n",
+    "        \"Rules:\\n\"\n",
+    "        \"- One-line summary followed by short details.\\n\"\n",
+    "        \"- Include Args:, Returns:, Raises: only if relevant.\\n\"\n",
+    "        \"- Keep under 12 lines, no code fences or markdown formatting.\\n\"\n",
+    "        \"Return ONLY the text between triple quotes.\"\n",
+    "    ),\n",
+    "}\n",
+    "\n",
+    "SYSTEM_PROMPT = (\n",
+    "    \"You are a senior Python engineer and technical writer. \"\n",
+    "    \"Write precise, helpful docstrings.\"\n",
+    ")\n",
+    "\n",
+    "\n",
+    "def make_user_prompt(style: str, module_name: str, signature: str, code_context: str) -> str:\n",
+    "    \"\"\"Build the user message for the model based on template and context.\"\"\"\n",
+    "    instr = DOCSTYLE_TEMPLATES.get(style, DOCSTYLE_TEMPLATES[\"google\"])\n",
+    "    prompt = (\n",
+    "        f\"{instr}\\n\\n\"\n",
+    "        f\"Module: {module_name}\\n\"\n",
+    "        f\"Signature:\\n{signature}\\n\\n\"\n",
+    "        f\"Code context:\\n{code_context}\\n\\n\"\n",
+    "        \"Return ONLY a triple-quoted docstring, for example:\\n\"\n",
+    "        '\"\"\"One-line summary.\\n\\n'\n",
+    "        \"Args:\\n\"\n",
+    "        \"    x: Description\\n\"\n",
+    "        \"Returns:\\n\"\n",
+    "        \"    y: Description\\n\"\n",
+    "        '\"\"\"'\n",
+    "    )\n",
+    "    return prompt\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "16b3c10f-f7bc-4a2f-a22f-65c6807b7574",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# ================================================================\n",
+    "# LLM Chat Helper — OpenAI GPT\n",
+    "# ================================================================\n",
+    "def llm_generate_docstring(signature: str, context: str, style: str = \"google\", \n",
+    "                           module_name: str = \"module\", model_choice: str = \"gpt-4o-mini (OpenAI)\") -> str:\n",
+    "    \"\"\"\n",
+    "    Generate a Python docstring using the selected model provider.\n",
+    "    \"\"\"\n",
+    "    user_prompt = make_user_prompt(style, module_name, signature, context)\n",
+    "    model_info = MODEL_REGISTRY.get(model_choice, MODEL_REGISTRY[\"gpt-4o-mini (OpenAI)\"])\n",
+    "\n",
+    "    provider = model_info[\"provider\"]\n",
+    "    model_name = model_info[\"model\"]\n",
+    "\n",
+    "    if provider == \"openai\":\n",
+    "        response = openai.chat.completions.create(\n",
+    "            model=model_name,\n",
+    "            temperature=0.2,\n",
+    "            messages=[\n",
+    "                {\"role\": \"system\", \"content\": \"You are a senior Python engineer and technical writer.\"},\n",
+    "                {\"role\": \"user\", \"content\": user_prompt},\n",
+    "            ],\n",
+    "        )\n",
+    "        text = response.choices[0].message.content.strip()\n",
+    "\n",
+    "    elif provider == \"anthropic\":\n",
+    "        # Future: integrate Anthropic SDK\n",
+    "        text = \"Claude response simulation: \" + user_prompt[:200]\n",
+    "\n",
+    "    elif provider == \"google\":\n",
+    "        # Future: integrate Gemini API\n",
+    "        text = \"Gemini response simulation: \" + user_prompt[:200]\n",
+    "\n",
+    "    else:\n",
+    "        # Simulated open-source fallback\n",
+    "        text = f\"[Simulated output from {model_name}]\\nAuto-generated docstring for {signature}\"\n",
+    "\n",
+    "    import re\n",
+    "    match = re.search(r'\"\"\"(.*?)\"\"\"', text, re.S)\n",
+    "    return match.group(1).strip() if match else text\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "82da91ac-e563-4425-8b45-1b94880d342f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# ================================================================\n",
+    "# 🧱 AST Parsing Utilities — find missing docstrings\n",
+    "# ================================================================\n",
+    "import ast\n",
+    "\n",
+    "def node_signature(node: ast.AST) -> str:\n",
+    "    \"\"\"\n",
+    "    Build a readable signature string from a FunctionDef or ClassDef node.\n",
+    "    Example: def add(x, y) -> int:\n",
+    "    \"\"\"\n",
+    "    if isinstance(node, (ast.FunctionDef, ast.AsyncFunctionDef)):\n",
+    "        args = [a.arg for a in node.args.args]\n",
+    "        if node.args.vararg:\n",
+    "            args.append(\"*\" + node.args.vararg.arg)\n",
+    "        for a in node.args.kwonlyargs:\n",
+    "            args.append(a.arg + \"=?\")\n",
+    "        if node.args.kwarg:\n",
+    "            args.append(\"**\" + node.args.kwarg.arg)\n",
+    "        ret = \"\"\n",
+    "        if getattr(node, \"returns\", None):\n",
+    "            try:\n",
+    "                ret = f\" -> {ast.unparse(node.returns)}\"\n",
+    "            except Exception:\n",
+    "                pass\n",
+    "        return f\"def {node.name}({', '.join(args)}){ret}:\"\n",
+    "\n",
+    "    elif isinstance(node, ast.ClassDef):\n",
+    "        return f\"class {node.name}:\"\n",
+    "\n",
+    "    return \"\"\n",
+    "\n",
+    "\n",
+    "def context_snippet(src: str, node: ast.AST, max_lines: int = 60) -> str:\n",
+    "    \"\"\"\n",
+    "    Extract a small snippet of source code around a node for context.\n",
+    "    This helps the LLM understand what the function/class does.\n",
+    "    \"\"\"\n",
+    "    lines = src.splitlines()\n",
+    "    start = getattr(node, \"lineno\", 1) - 1\n",
+    "    end = getattr(node, \"end_lineno\", start + 1)\n",
+    "    snippet = lines[start:end]\n",
+    "    if len(snippet) > max_lines:\n",
+    "        snippet = snippet[:max_lines] + [\"# ... (truncated) ...\"]\n",
+    "    return \"\\n\".join(snippet)\n",
+    "\n",
+    "\n",
+    "def find_missing_docstrings(src: str):\n",
+    "    \"\"\"\n",
+    "    Parse the Python source code and return a list of nodes\n",
+    "    (module, class, function) that do NOT have docstrings.\n",
+    "    \"\"\"\n",
+    "    tree = ast.parse(src)\n",
+    "    missing = []\n",
+    "\n",
+    "    # Module-level docstring check\n",
+    "    if ast.get_docstring(tree) is None:\n",
+    "        missing.append((\"module\", tree))\n",
+    "\n",
+    "    # Walk through the AST for classes and functions\n",
+    "    for node in ast.walk(tree):\n",
+    "        if isinstance(node, (ast.ClassDef, ast.FunctionDef, ast.AsyncFunctionDef)):\n",
+    "            if ast.get_docstring(node) is None:\n",
+    "                kind = \"class\" if isinstance(node, ast.ClassDef) else \"function\"\n",
+    "                missing.append((kind, node))\n",
+    "\n",
+    "    return missing\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "ea69108f-e4ca-4326-89fe-97c5748c0e79",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "## Quick Test ##\n",
+    "\n",
+    "code = '''\n",
+    "def add(x, y):\n",
+    "    return x + y\n",
+    "\n",
+    "class Counter:\n",
+    "    def inc(self):\n",
+    "        self.total += 1\n",
+    "'''\n",
+    "\n",
+    "for kind, node in find_missing_docstrings(code):\n",
+    "    print(f\"Missing docstring → {kind}: {node_signature(node)}\")\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "00d65b96-e65d-4e11-89be-06f265a5f2e3",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# ================================================================\n",
+    "# Insert Generated Docstrings into Code\n",
+    "# ================================================================\n",
+    "import difflib\n",
+    "import textwrap\n",
+    "\n",
+    "def insert_docstring(src: str, node: ast.AST, docstring: str) -> str:\n",
+    "    \"\"\"\n",
+    "    Insert a generated docstring inside a function/class node.\n",
+    "    Keeps indentation consistent with the original code.\n",
+    "    \"\"\"\n",
+    "    lines = src.splitlines()\n",
+    "    if not hasattr(node, \"body\") or not node.body:\n",
+    "        return src  # nothing to insert into\n",
+    "\n",
+    "    start_idx = node.body[0].lineno - 1\n",
+    "    indent = re.match(r\"\\s*\", lines[start_idx]).group(0)\n",
+    "    ds_lines = textwrap.indent(f'\"\"\"{docstring.strip()}\"\"\"', indent).splitlines()\n",
+    "\n",
+    "    new_lines = lines[:start_idx] + ds_lines + [\"\"] + lines[start_idx:]\n",
+    "    return \"\\n\".join(new_lines)\n",
+    "\n",
+    "\n",
+    "def insert_module_docstring(src: str, docstring: str) -> str:\n",
+    "    \"\"\"Insert a module-level docstring at the top of the file.\"\"\"\n",
+    "    lines = src.splitlines()\n",
+    "    ds_block = f'\"\"\"{docstring.strip()}\"\"\"\\n'\n",
+    "    return ds_block + \"\\n\".join(lines)\n",
+    "\n",
+    "\n",
+    "def diff_text(a: str, b: str) -> str:\n",
+    "    \"\"\"Show unified diff of original vs updated code.\"\"\"\n",
+    "    return \"\".join(\n",
+    "        difflib.unified_diff(\n",
+    "            a.splitlines(keepends=True),\n",
+    "            b.splitlines(keepends=True),\n",
+    "            fromfile=\"original.py\",\n",
+    "            tofile=\"updated.py\",\n",
+    "        )\n",
+    "    )\n",
+    "\n",
+    "\n",
+    "def generate_docstrings_for_source(src: str, style: str = \"google\", module_name: str = \"module\", model_choice: str = \"gpt-4o-mini (OpenAI)\"):\n",
+    "    targets = find_missing_docstrings(src)\n",
+    "    updated = src\n",
+    "    report = []\n",
+    "\n",
+    "    for kind, node in sorted(targets, key=lambda t: 0 if t[0] == \"module\" else 1):\n",
+    "        sig = \"module \" + module_name if kind == \"module\" else node_signature(node)\n",
+    "        ctx = src if kind == \"module\" else context_snippet(src, node)\n",
+    "        doc = llm_generate_docstring(sig, ctx, style=style, module_name=module_name, model_choice=model_choice)\n",
+    "\n",
+    "        if kind == \"module\":\n",
+    "            updated = insert_module_docstring(updated, doc)\n",
+    "        else:\n",
+    "            updated = insert_docstring(updated, node, doc)\n",
+    "\n",
+    "        report.append({\"kind\": kind, \"signature\": sig, \"model\": model_choice, \"doc_preview\": doc[:150]})\n",
+    "\n",
+    "    return updated, report\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "d00cf4b7-773d-49cb-8262-9d11d787ee10",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "## Quick Test ##\n",
+    "new_code, report = generate_docstrings_for_source(code, style=\"google\", module_name=\"demo\")\n",
+    "\n",
+    "print(\"=== Generated Docstrings ===\")\n",
+    "for r in report:\n",
+    "    print(f\"- {r['kind']}: {r['signature']}\")\n",
+    "    print(\"  \", r['doc_preview'])\n",
+    "print(\"\\n=== Updated Source ===\")\n",
+    "print(new_code)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "id": "b318db41-c05d-48ce-9990-b6f1a0577c68",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# ================================================================\n",
+    "# 📂 File-Based Workflow — preview or apply docstrings\n",
+    "# ================================================================\n",
+    "from pathlib import Path\n",
+    "import pandas as pd\n",
+    "\n",
+    "def process_file(path: str, style: str = \"google\", apply: bool = False) -> pd.DataFrame:\n",
+    "    \"\"\"\n",
+    "    Process a .py file: find missing docstrings, generate them via GPT,\n",
+    "    and either preview the diff or apply the updates in place.\n",
+    "    \"\"\"\n",
+    "    p = Path(path)\n",
+    "    src = p.read_text(encoding=\"utf-8\")\n",
+    "    updated, rows = generate_docstrings_for_source(src, style=style, module_name=p.stem)\n",
+    "\n",
+    "    if apply:\n",
+    "        p.write_text(updated, encoding=\"utf-8\")\n",
+    "        print(f\"✅ Updated file written → {p}\")\n",
+    "    else:\n",
+    "        print(\"🔍 Diff preview:\")\n",
+    "        print(diff_text(src, updated))\n",
+    "\n",
+    "    return pd.DataFrame(rows)\n",
+    "\n",
+    "# Example usage:\n",
+    "# df = process_file(\"my_script.py\", style=\"google\", apply=False)  # preview\n",
+    "# df = process_file(\"my_script.py\", style=\"google\", apply=True)   # overwrite with docstrings\n",
+    "# df\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "id": "8962cf0e-9255-475e-bbc1-21500be0cd78",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# ================================================================\n",
+    "# 📂 File-Based Workflow — preview or apply docstrings\n",
+    "# ================================================================\n",
+    "from pathlib import Path\n",
+    "import pandas as pd\n",
+    "\n",
+    "def process_file(path: str, style: str = \"google\", apply: bool = False) -> pd.DataFrame:\n",
+    "    \"\"\"\n",
+    "    Process a .py file: find missing docstrings, generate them via GPT,\n",
+    "    and either preview the diff or apply the updates in place.\n",
+    "    \"\"\"\n",
+    "    p = Path(path)\n",
+    "    src = p.read_text(encoding=\"utf-8\")\n",
+    "    updated, rows = generate_docstrings_for_source(src, style=style, module_name=p.stem)\n",
+    "\n",
+    "    if apply:\n",
+    "        p.write_text(updated, encoding=\"utf-8\")\n",
+    "        print(f\"✅ Updated file written → {p}\")\n",
+    "    else:\n",
+    "        print(\"🔍 Diff preview:\")\n",
+    "        print(diff_text(src, updated))\n",
+    "\n",
+    "    return pd.DataFrame(rows)\n",
+    "\n",
+    "# Example usage:\n",
+    "# df = process_file(\"my_script.py\", style=\"google\", apply=False)  # preview\n",
+    "# df = process_file(\"my_script.py\", style=\"google\", apply=True)   # overwrite with docstrings\n",
+    "# df\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "b0b0f852-982f-4918-9b5d-89880cc12003",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# ================================================================\n",
+    "# 🎨 Enhanced Gradio Interface with Model Selector\n",
+    "# ================================================================\n",
+    "import gradio as gr\n",
+    "\n",
+    "def gradio_generate(code_text: str, style: str, model_choice: str):\n",
+    "    \"\"\"Wrapper for Gradio — generates docstrings using selected model.\"\"\"\n",
+    "    if not code_text.strip():\n",
+    "        return \"⚠️ Please paste some Python code first.\"\n",
+    "    try:\n",
+    "        updated, _ = generate_docstrings_for_source(\n",
+    "            code_text, style=style, module_name=\"gradio_snippet\", model_choice=model_choice\n",
+    "        )\n",
+    "        return updated\n",
+    "    except Exception as e:\n",
+    "        return f\"❌ Error: {e}\"\n",
+    "\n",
+    "with gr.Blocks(theme=gr.themes.Soft()) as doc_ui:\n",
+    "    gr.Markdown(\"## 🧠 Auto Docstring Generator — by Bharat Puri\\nChoose your model and generate high-quality docstrings.\")\n",
+    "\n",
+    "    with gr.Row():\n",
+    "        code_input = gr.Code(label=\"Paste your Python code\", language=\"python\", lines=18)\n",
+    "        code_output = gr.Code(label=\"Generated code with docstrings\", language=\"python\", lines=18)\n",
+    "\n",
+    "    with gr.Row():\n",
+    "        style_choice = gr.Radio([\"google\"], value=\"google\", label=\"Docstring Style\")\n",
+    "        model_choice = gr.Dropdown(\n",
+    "            list(MODEL_REGISTRY.keys()),\n",
+    "            value=\"gpt-4o-mini (OpenAI)\",\n",
+    "            label=\"Select Model\",\n",
+    "        )\n",
+    "\n",
+    "    generate_btn = gr.Button(\"🚀 Generate Docstrings\")\n",
+    "    generate_btn.click(\n",
+    "        fn=gradio_generate,\n",
+    "        inputs=[code_input, style_choice, model_choice],\n",
+    "        outputs=[code_output],\n",
+    "    )\n",
+    "\n",
+    "doc_ui.launch(share=False)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "5e6d6720-de8e-4cbb-be9f-82bac3dcc71a",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.14"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

week4/community-contributions/hopeogbons/README.md

@@ -0,0 +1,255 @@
+# 🔶 Multi-Language Code Complexity Annotator
+
+An automated tool that analyzes source code and annotates it with Big-O complexity estimates, complete with syntax highlighting and optional AI-powered code reviews.
+
+## 🎯 What It Does
+
+Understanding time complexity (Big-O notation) is crucial for writing efficient algorithms, identifying bottlenecks, making informed optimization decisions, and passing technical interviews.
+
+Analyzing complexity manually is tedious and error-prone. This tool **automates** the entire process—detecting loops, recursion, and functions, then annotating code with Big-O estimates and explanations.
+
+### Core Features
+
+- 📊 **Automatic Detection** - Identifies loops, recursion, and functions across 13+ programming languages
+- 🧮 **Complexity Estimation** - Calculates Big-O complexity (O(1), O(n), O(n²), O(log n), etc.)
+- 💬 **Inline Annotations** - Inserts explanatory comments directly into your code
+- 🎨 **Syntax Highlighting** - Generates beautiful HTML previews with orange-colored complexity comments
+- 🤖 **AI Code Review** - Optional LLaMA-powered analysis for optimization suggestions
+- 💾 **Export Options** - Download annotated source code and Markdown previews
+
+## 🌐 Supported Languages
+
+Python • JavaScript • TypeScript • Java • C • C++ • C# • Go • PHP • Swift • Ruby • Kotlin • Rust
+
+## 🛠️ Tech Stack
+
+- **HuggingFace Transformers** - LLM model loading and inference
+- **LLaMA 3.2** - AI-powered code review
+- **Gradio** - Interactive web interface
+- **Pygments** - Syntax highlighting
+- **PyTorch** - Deep learning framework
+- **Regex Analysis** - Heuristic complexity detection
+
+## 📋 Prerequisites
+
+- Python 3.12+
+- `uv` package manager (or `pip`)
+- 4GB+ RAM (for basic use without AI)
+- 14GB+ RAM (for AI code review with LLaMA models)
+- Optional: NVIDIA GPU with CUDA (for model quantization)
+
+## 🚀 Installation
+
+### 1. Clone the Repository
+
+```bash
+cd week4
+```
+
+### 2. Install Dependencies
+
+```bash
+uv pip install -U pip
+uv pip install transformers accelerate gradio torch --extra-index-url https://download.pytorch.org/whl/cpu
+uv pip install bitsandbytes pygments python-dotenv
+```
+
+> **Note:** This installs the CPU-only version of PyTorch. For GPU support, remove the `--extra-index-url` flag.
+
+### 3. Set Up HuggingFace Token (Optional - for AI Features)
+
+Create a `.env` file in the `week4` directory:
+
+```env
+HF_TOKEN=hf_your_token_here
+```
+
+Get your token at: https://huggingface.co/settings/tokens
+
+> **Required for:** LLaMA models (requires accepting Meta's license agreement)
+
+## 💡 Usage
+
+### Option 1: Jupyter Notebook
+
+Open and run `week4 EXERCISE_hopeogbons.ipynb`:
+
+```bash
+jupyter notebook "week4 EXERCISE_hopeogbons.ipynb"
+```
+
+Run all cells in order. The Gradio interface will launch at `http://127.0.0.1:7861`
+
+### Option 2: Web Interface
+
+Once the Gradio app is running:
+
+#### **Without AI Review (No Model Needed)**
+
+1. Upload a code file (.py, .js, .java, etc.)
+2. Uncheck "Generate AI Code Review"
+3. Click "🚀 Process & Annotate"
+4. View syntax-highlighted code with Big-O annotations
+5. Download the annotated source + Markdown
+
+#### **With AI Review (Requires Model)**
+
+1. Click "🔄 Load Model" (wait 2-5 minutes for first download)
+2. Upload your code file
+3. Check "Generate AI Code Review"
+4. Adjust temperature/tokens if needed
+5. Click "🚀 Process & Annotate"
+6. Read AI-generated optimization suggestions
+
+## 📊 How It Works
+
+### Complexity Detection Algorithm
+
+The tool uses **heuristic pattern matching** to estimate Big-O complexity:
+
+1. **Detect Blocks** - Regex patterns find functions, loops, and recursion
+2. **Analyze Loops** - Count nesting depth:
+   - 1 loop = O(n)
+   - 2 nested loops = O(n²)
+   - 3 nested loops = O(n³)
+3. **Analyze Recursion** - Pattern detection:
+   - Divide-and-conquer (binary search) = O(log n)
+   - Single recursive call = O(n)
+   - Multiple recursive calls = O(2^n)
+4. **Aggregate** - Functions inherit worst-case complexity of inner operations
+
+### Example Output
+
+**Input (Python):**
+
+```python
+def bubble_sort(arr):
+    for i in range(len(arr)):
+        for j in range(len(arr) - i - 1):
+            if arr[j] > arr[j + 1]:
+                arr[j], arr[j + 1] = arr[j + 1], arr[j]
+```
+
+**Output (Annotated):**
+
+```python
+def bubble_sort(arr):
+# Big-O: O(n^2)
+# Explanation: Nested loops indicate quadratic time.
+    for i in range(len(arr)):
+        for j in range(len(arr) - i - 1):
+            if arr[j] > arr[j + 1]:
+                arr[j], arr[j + 1] = arr[j + 1], arr[j]
+```
+
+## 🧠 AI Model Options
+
+### CPU/Mac (No GPU)
+
+- `meta-llama/Llama-3.2-1B` (Default, ~1GB, requires HF approval)
+- `gpt2` (No approval needed, ~500MB)
+- `microsoft/DialoGPT-medium` (~1GB)
+
+### GPU Users
+
+- Any model with 8-bit or 4-bit quantization enabled
+- `meta-llama/Llama-2-7b-chat-hf` (requires approval)
+
+### Memory Requirements
+
+- **Without quantization:** ~14GB RAM (7B models) or ~26GB (13B models)
+- **With 8-bit quantization:** ~50% reduction (GPU required)
+- **With 4-bit quantization:** ~75% reduction (GPU required)
+
+## ⚙️ Configuration
+
+### File Limits
+
+- Max file size: **2 MB**
+- Supported extensions: `.py`, `.js`, `.ts`, `.java`, `.c`, `.cpp`, `.cs`, `.go`, `.php`, `.swift`, `.rb`, `.kt`, `.rs`
+
+### Model Parameters
+
+- **Temperature** (0.0 - 1.5): Controls randomness
+  - Lower = more deterministic
+  - Higher = more creative
+- **Max Tokens** (16 - 1024): Maximum length of AI review
+
+## 📁 Project Structure
+
+```
+week4/
+├── week4 EXERCISE_hopeogbons.ipynb  # Main application notebook
+├── README.md                         # This file
+└── .env                             # HuggingFace token (create this)
+```
+
+## 🐛 Troubleshooting
+
+### Model Loading Issues
+
+**Error:** "Model not found" or "Access denied"
+
+- **Solution:** Accept Meta's license at https://huggingface.co/meta-llama/Llama-3.2-1B
+- Ensure your `.env` file contains a valid HF_TOKEN
+
+### Memory Issues
+
+**Error:** "Out of memory" during model loading
+
+- **Solution:** Use a smaller model like `gpt2` or `microsoft/DialoGPT-medium`
+- Try 8-bit or 4-bit quantization (GPU required)
+
+### Quantization Requires GPU
+
+**Error:** "Quantization requires CUDA"
+
+- **Solution:** Disable both 4-bit and 8-bit quantization checkboxes
+- Run on CPU with smaller models
+
+### File Upload Issues
+
+**Error:** "Unsupported file extension"
+
+- **Solution:** Ensure your file has one of the supported extensions
+- Check that the file size is under 2MB
+
+## 🎓 Use Cases
+
+- **Code Review** - Automated complexity analysis for pull requests
+- **Interview Prep** - Understand algorithm efficiency before coding interviews
+- **Performance Optimization** - Identify bottlenecks in existing code
+- **Education** - Learn Big-O notation through practical examples
+- **Documentation** - Auto-generate complexity documentation
+
+## 📝 Notes
+
+- First model load downloads weights (~1-14GB depending on model)
+- Subsequent runs load from cache (much faster)
+- Complexity estimates are heuristic-based, not formally verified
+- For production use, consider manual verification of critical algorithms
+
+## 🤝 Contributing
+
+This is a learning project from the Andela LLM Engineering course (Week 4). Feel free to extend it with:
+
+- Additional language support
+- More sophisticated complexity detection
+- Integration with CI/CD pipelines
+- Support for space complexity analysis
+
+## 📄 License
+
+Educational project - use as reference for learning purposes.
+
+## 🙏 Acknowledgments
+
+- **OpenAI Whisper** for inspiration on model integration
+- **HuggingFace** for providing the Transformers library
+- **Meta** for LLaMA models
+- **Gradio** for the excellent UI framework
+- **Andela** for the LLM Engineering curriculum
+
+---
+
+**Built with ❤️ as part of Week 4 LLM Engineering coursework**

week4/community-contributions/hopeogbons/python sample.py

@@ -0,0 +1,9 @@
+# Python Function
+
+# This function takes a list of items and returns all possible pairs of items
+def all_pairs(items):
+    pairs = []
+    for i in range(len(items)):
+        for j in range(i + 1, len(items)):
+            pairs.append((items[i], items[j]))
+    return pairs

week4/community-contributions/pytest_generator/pytest_generator.ipynb

@@ -0,0 +1,498 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "b8be8252",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "!uv pip install pytest"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "ba193fd5",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "import re\n",
+    "import ast\n",
+    "import sys\n",
+    "import uuid\n",
+    "import json\n",
+    "import textwrap\n",
+    "import subprocess\n",
+    "from pathlib import Path\n",
+    "from dataclasses import dataclass\n",
+    "from typing import List, Protocol, Tuple, Dict, Optional\n",
+    "\n",
+    "from dotenv import load_dotenv\n",
+    "from openai import OpenAI\n",
+    "from openai import BadRequestError as _OpenAIBadRequest\n",
+    "import gradio as gr\n",
+    "\n",
+    "load_dotenv(override=True)\n",
+    "\n",
+    "# --- Provider base URLs (Gemini & Groq speak OpenAI-compatible API) ---\n",
+    "GEMINI_BASE = \"https://generativelanguage.googleapis.com/v1beta/openai/\"\n",
+    "GROQ_BASE   = \"https://api.groq.com/openai/v1\"\n",
+    "\n",
+    "# --- API Keys (add these in your .env) ---\n",
+    "openai_api_key = os.getenv(\"OPENAI_API_KEY\")   # OpenAI\n",
+    "google_api_key = os.getenv(\"GOOGLE_API_KEY\")   # Gemini\n",
+    "groq_api_key   = os.getenv(\"GROQ_API_KEY\")     # Groq\n",
+    "\n",
+    "# --- Clients ---\n",
+    "openai_client = OpenAI()  # OpenAI default (reads OPENAI_API_KEY)\n",
+    "gemini_client = OpenAI(api_key=google_api_key, base_url=GEMINI_BASE) if google_api_key else None\n",
+    "groq_client   = OpenAI(api_key=groq_api_key,   base_url=GROQ_BASE)   if groq_api_key   else None\n",
+    "\n",
+    "# --- Model registry: label -> { client, model } ---\n",
+    "MODEL_REGISTRY: Dict[str, Dict[str, object]] = {}\n",
+    "\n",
+    "def _register(label: str, client: Optional[OpenAI], model_id: str):\n",
+    "    \"\"\"Add a model to the registry only if its client is configured.\"\"\"\n",
+    "    if client is not None:\n",
+    "        MODEL_REGISTRY[label] = {\"client\": client, \"model\": model_id}\n",
+    "\n",
+    "# OpenAI\n",
+    "_register(\"OpenAI • GPT-5\",        openai_client, \"gpt-5\")\n",
+    "_register(\"OpenAI • GPT-5 Nano\",   openai_client, \"gpt-5-nano\")\n",
+    "_register(\"OpenAI • GPT-4o-mini\",  openai_client, \"gpt-4o-mini\")\n",
+    "\n",
+    "# Gemini (Google)\n",
+    "_register(\"Gemini • 2.5 Pro\",      gemini_client, \"gemini-2.5-pro\")\n",
+    "_register(\"Gemini • 2.5 Flash\",    gemini_client, \"gemini-2.5-flash\")\n",
+    "\n",
+    "# Groq\n",
+    "_register(\"Groq • Llama 3.1 8B\",   groq_client,   \"llama-3.1-8b-instant\")\n",
+    "_register(\"Groq • Llama 3.3 70B\",  groq_client,   \"llama-3.3-70b-versatile\")\n",
+    "_register(\"Groq • GPT-OSS 20B\",    groq_client,   \"openai/gpt-oss-20b\")\n",
+    "_register(\"Groq • GPT-OSS 120B\",   groq_client,   \"openai/gpt-oss-120b\")\n",
+    "\n",
+    "DEFAULT_MODEL = next(iter(MODEL_REGISTRY.keys()), None)\n",
+    "\n",
+    "print(f\"Providers configured → OpenAI:{bool(openai_api_key)}  Gemini:{bool(google_api_key)}  Groq:{bool(groq_api_key)}\")\n",
+    "print(\"Models available     →\", \", \".join(MODEL_REGISTRY.keys()) or \"None (add API keys in .env)\")\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "e5d6b0f2",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "class CompletionClient(Protocol):\n",
+    "    \"\"\"Any LLM client provides a .complete() method using a registry label.\"\"\"\n",
+    "    def complete(self, *, model_label: str, system: str, user: str) -> str: ...\n",
+    "\n",
+    "\n",
+    "def _extract_code_or_text(s: str) -> str:\n",
+    "    \"\"\"Prefer fenced python if present; otherwise return raw text.\"\"\"\n",
+    "    m = re.search(r\"```(?:python)?\\s*(.*?)```\", s, flags=re.S | re.I)\n",
+    "    return m.group(1).strip() if m else s.strip()\n",
+    "\n",
+    "\n",
+    "class MultiModelChatClient:\n",
+    "    \"\"\"Routes requests to the right provider/client based on model label.\"\"\"\n",
+    "    def __init__(self, registry: Dict[str, Dict[str, object]]):\n",
+    "        self._registry = registry\n",
+    "\n",
+    "    def _call(self, *, client: OpenAI, model_id: str, system: str, user: str) -> str:\n",
+    "        params = {\n",
+    "            \"model\": model_id,\n",
+    "            \"messages\": [\n",
+    "                {\"role\": \"system\", \"content\": system},\n",
+    "                {\"role\": \"user\",   \"content\": user},\n",
+    "            ],\n",
+    "        }\n",
+    "        resp = client.chat.completions.create(**params)  # do NOT send temperature for strict providers\n",
+    "        text = (resp.choices[0].message.content or \"\").strip()\n",
+    "        return _extract_code_or_text(text)\n",
+    "\n",
+    "    def complete(self, *, model_label: str, system: str, user: str) -> str:\n",
+    "        if model_label not in self._registry:\n",
+    "            raise ValueError(f\"Unknown model label: {model_label}\")\n",
+    "        info   = self._registry[model_label]\n",
+    "        client = info[\"client\"]\n",
+    "        model  = info[\"model\"]\n",
+    "        try:\n",
+    "            return self._call(client=client, model_id=str(model), system=system, user=user)\n",
+    "        except _OpenAIBadRequest as e:\n",
+    "            # Providers may reject stray params; we don't send any, but retry anyway.\n",
+    "            if \"temperature\" in str(e).lower():\n",
+    "                return self._call(client=client, model_id=str(model), system=system, user=user)\n",
+    "            raise\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "31558bf0",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "@dataclass(frozen=True)\n",
+    "class SymbolInfo:\n",
+    "    kind: str      # \"function\" | \"class\" | \"method\"\n",
+    "    name: str\n",
+    "    signature: str\n",
+    "    lineno: int\n",
+    "\n",
+    "class PublicAPIExtractor:\n",
+    "    \"\"\"Extract concise 'public API' summary from a Python module.\"\"\"\n",
+    "    def extract(self, source: str) -> List[SymbolInfo]:\n",
+    "        tree = ast.parse(source)\n",
+    "        out: List[SymbolInfo] = []\n",
+    "        for node in tree.body:\n",
+    "            if isinstance(node, ast.FunctionDef) and not node.name.startswith(\"_\"):\n",
+    "                out.append(SymbolInfo(\"function\", node.name, self._sig(node), node.lineno))\n",
+    "            elif isinstance(node, ast.ClassDef) and not node.name.startswith(\"_\"):\n",
+    "                out.append(SymbolInfo(\"class\", node.name, node.name, node.lineno))\n",
+    "                for sub in node.body:\n",
+    "                    if isinstance(sub, ast.FunctionDef) and not sub.name.startswith(\"_\"):\n",
+    "                        out.append(SymbolInfo(\"method\",\n",
+    "                                              f\"{node.name}.{sub.name}\",\n",
+    "                                              self._sig(sub),\n",
+    "                                              sub.lineno))\n",
+    "        return sorted(out, key=lambda s: (s.kind, s.name.lower(), s.lineno))\n",
+    "\n",
+    "    def _sig(self, fn: ast.FunctionDef) -> str:\n",
+    "        args = [a.arg for a in fn.args.args]\n",
+    "        if fn.args.vararg:\n",
+    "            args.append(\"*\" + fn.args.vararg.arg)\n",
+    "        args.extend(a.arg + \"=?\" for a in fn.args.kwonlyargs)\n",
+    "        if fn.args.kwarg:\n",
+    "            args.append(\"**\" + fn.args.kwarg.arg)\n",
+    "        ret = \"\"\n",
+    "        if fn.returns is not None:\n",
+    "            try:\n",
+    "                ret = f\" -> {ast.unparse(fn.returns)}\"\n",
+    "            except Exception:\n",
+    "                pass\n",
+    "        return f\"def {fn.name}({', '.join(args)}){ret}:\"\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "3aeadedc",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "class PromptBuilder:\n",
+    "    \"\"\"Builds deterministic prompts for pytest generation.\"\"\"\n",
+    "    SYSTEM = (\n",
+    "        \"You are a senior Python engineer. Produce a single, self-contained pytest file.\\n\"\n",
+    "        \"Rules:\\n\"\n",
+    "        \"- Output only Python test code (no prose, no markdown fences).\\n\"\n",
+    "        \"- Use plain pytest tests (functions), no classes unless unavoidable.\\n\"\n",
+    "        \"- Deterministic: avoid network/IO; seed randomness if used.\\n\"\n",
+    "        \"- Import the target module by module name only.\\n\"\n",
+    "        \"- Cover every public function and method with at least one tiny test.\\n\"\n",
+    "        \"- Prefer straightforward, fast assertions.\\n\"\n",
+    "    )\n",
+    "\n",
+    "    def build_user(self, *, module_name: str, source: str, symbols: List[SymbolInfo]) -> str:\n",
+    "        summary = \"\\n\".join(f\"- {s.kind:<6}  {s.signature}\" for s in symbols) or \"- (no public symbols)\"\n",
+    "        return textwrap.dedent(f\"\"\"\n",
+    "        Create pytest tests for module `{module_name}`.\n",
+    "\n",
+    "        Public API Summary:\n",
+    "        {summary}\n",
+    "\n",
+    "        Constraints:\n",
+    "        - Import as: `import {module_name} as mod`\n",
+    "        - Keep tests tiny, fast, and deterministic.\n",
+    "\n",
+    "        Full module source (for reference):\n",
+    "        # --- BEGIN SOURCE {module_name}.py ---\n",
+    "        {source}\n",
+    "        # --- END SOURCE ---\n",
+    "        \"\"\").strip()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "a45ac5be",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def _ensure_header_and_import(code: str, module_name: str) -> str:\n",
+    "    \"\"\"Ensure tests import pytest and the target module as 'mod'.\"\"\"\n",
+    "    code = code.strip()\n",
+    "    needs_pytest = \"import pytest\" not in code\n",
+    "    has_mod = (f\"import {module_name} as mod\" in code) or (f\"from {module_name} import\" in code)\n",
+    "    needs_import = not has_mod\n",
+    "\n",
+    "    header = []\n",
+    "    if needs_pytest:\n",
+    "        header.append(\"import pytest\")\n",
+    "    if needs_import:\n",
+    "        header.append(f\"import {module_name} as mod\")\n",
+    "\n",
+    "    return (\"\\n\".join(header) + \"\\n\\n\" + code) if header else code\n",
+    "\n",
+    "\n",
+    "def build_module_name_from_path(path: str) -> str:\n",
+    "    return Path(path).stem\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "787e58b6",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "class TestGenerator:\n",
+    "    \"\"\"Extraction → prompt → model → polish.\"\"\"\n",
+    "    def __init__(self, llm: CompletionClient):\n",
+    "        self._llm = llm\n",
+    "        self._extractor = PublicAPIExtractor()\n",
+    "        self._prompts = PromptBuilder()\n",
+    "\n",
+    "    def generate_tests(self, model_label: str, module_name: str, source: str) -> str:\n",
+    "        symbols = self._extractor.extract(source)\n",
+    "        user = self._prompts.build_user(module_name=module_name, source=source, symbols=symbols)\n",
+    "        raw = self._llm.complete(model_label=model_label, system=self._prompts.SYSTEM, user=user)\n",
+    "        return _ensure_header_and_import(raw, module_name)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "8402f62f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def _parse_pytest_summary(output: str) -> Tuple[str, Dict[str, int]]:\n",
+    "    \"\"\"\n",
+    "    Parse the final summary line like:\n",
+    "      '3 passed, 1 failed, 2 skipped in 0.12s'\n",
+    "    Return (summary_line, counts_dict).\n",
+    "    \"\"\"\n",
+    "    summary_line = \"\"\n",
+    "    for line in output.strip().splitlines()[::-1]:  # scan from end\n",
+    "        if \" passed\" in line or \" failed\" in line or \" error\" in line or \" skipped\" in line or \" deselected\" in line:\n",
+    "            summary_line = line.strip()\n",
+    "            break\n",
+    "\n",
+    "    counts = {\"passed\": 0, \"failed\": 0, \"errors\": 0, \"skipped\": 0, \"xfail\": 0, \"xpassed\": 0}\n",
+    "    m = re.findall(r\"(\\d+)\\s+(passed|failed|errors?|skipped|xfailed|xpassed)\", summary_line)\n",
+    "    for num, kind in m:\n",
+    "        if kind.startswith(\"error\"):\n",
+    "            counts[\"errors\"] += int(num)\n",
+    "        elif kind == \"passed\":\n",
+    "            counts[\"passed\"] += int(num)\n",
+    "        elif kind == \"failed\":\n",
+    "            counts[\"failed\"] += int(num)\n",
+    "        elif kind == \"skipped\":\n",
+    "            counts[\"skipped\"] += int(num)\n",
+    "        elif kind == \"xfailed\":\n",
+    "            counts[\"xfail\"] += int(num)\n",
+    "        elif kind == \"xpassed\":\n",
+    "            counts[\"xpassed\"] += int(num)\n",
+    "\n",
+    "    return summary_line or \"(no summary line found)\", counts\n",
+    "\n",
+    "\n",
+    "def run_pytest_on_snippet(module_name: str, module_code: str, tests_code: str) -> Tuple[str, str]:\n",
+    "    \"\"\"\n",
+    "    Create an isolated temp workspace, write module + tests, run pytest,\n",
+    "    and return (human_summary, full_cli_output).\n",
+    "    \"\"\"\n",
+    "    if not module_name or not module_code.strip() or not tests_code.strip():\n",
+    "        return \"❌ Provide module name, module code, and tests.\", \"\"\n",
+    "\n",
+    "    run_id = uuid.uuid4().hex[:8]\n",
+    "    base = Path(\".pytest_runs\") / f\"run_{run_id}\"\n",
+    "    tests_dir = base / \"tests\"\n",
+    "    tests_dir.mkdir(parents=True, exist_ok=True)\n",
+    "\n",
+    "    # Write module and tests\n",
+    "    (base / f\"{module_name}.py\").write_text(module_code, encoding=\"utf-8\")\n",
+    "    (tests_dir / f\"test_{module_name}.py\").write_text(tests_code, encoding=\"utf-8\")\n",
+    "\n",
+    "    # Run pytest with this temp dir on PYTHONPATH\n",
+    "    env = os.environ.copy()\n",
+    "    env[\"PYTHONPATH\"] = str(base) + os.pathsep + env.get(\"PYTHONPATH\", \"\")\n",
+    "    cmd = [sys.executable, \"-m\", \"pytest\", \"-q\"]  # quiet output, but still includes summary\n",
+    "    proc = subprocess.run(cmd, cwd=base, env=env, text=True, capture_output=True)\n",
+    "\n",
+    "    full_out = (proc.stdout or \"\") + (\"\\n\" + proc.stderr if proc.stderr else \"\")\n",
+    "    summary_line, counts = _parse_pytest_summary(full_out)\n",
+    "\n",
+    "    badges = []\n",
+    "    for key in (\"passed\", \"failed\", \"errors\", \"skipped\", \"xpassed\", \"xfail\"):\n",
+    "        val = counts.get(key, 0)\n",
+    "        if val:\n",
+    "            badges.append(f\"**{key}: {val}**\")\n",
+    "    badges = \"  •  \".join(badges) if badges else \"no tests collected?\"\n",
+    "\n",
+    "    human = f\"{summary_line}\\n\\n{badges}\"\n",
+    "    return human, full_out\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "5d240ce5",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "LLM = MultiModelChatClient(MODEL_REGISTRY)\n",
+    "SERVICE = TestGenerator(LLM)\n",
+    "\n",
+    "def generate_from_code(model_label: str, module_name: str, code: str, save: bool, out_dir: str) -> Tuple[str, str]:\n",
+    "    if not model_label or model_label not in MODEL_REGISTRY:\n",
+    "        return \"\", \"❌ Pick a model (or add API keys for providers in .env).\"\n",
+    "    if not module_name.strip():\n",
+    "        return \"\", \"❌ Please provide a module name.\"\n",
+    "    if not code.strip():\n",
+    "        return \"\", \"❌ Please paste some Python code.\"\n",
+    "\n",
+    "    tests_code = SERVICE.generate_tests(model_label=model_label, module_name=module_name.strip(), source=code)\n",
+    "    saved = \"\"\n",
+    "    if save:\n",
+    "        out = Path(out_dir or \"tests\")\n",
+    "        out.mkdir(parents=True, exist_ok=True)\n",
+    "        out_path = out / f\"test_{module_name}.py\"\n",
+    "        out_path.write_text(tests_code, encoding=\"utf-8\")\n",
+    "        saved = f\"✅ Saved to {out_path}\"\n",
+    "    return tests_code, saved\n",
+    "\n",
+    "\n",
+    "def generate_from_file(model_label: str, file_obj, save: bool, out_dir: str) -> Tuple[str, str]:\n",
+    "    if file_obj is None:\n",
+    "        return \"\", \"❌ Please upload a .py file.\"\n",
+    "    code = file_obj.decode(\"utf-8\")\n",
+    "    module_name = build_module_name_from_path(\"uploaded_module.py\")\n",
+    "    return generate_from_code(model_label, module_name, code, save, out_dir)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "e3e1401a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "EXAMPLE_CODE = \"\"\"\\\n",
+    "def add(a: int, b: int) -> int:\n",
+    "    return a + b\n",
+    "\n",
+    "def divide(a: float, b: float) -> float:\n",
+    "    if b == 0:\n",
+    "        raise ZeroDivisionError(\"b must be non-zero\")\n",
+    "    return a / b\n",
+    "\n",
+    "class Counter:\n",
+    "    def __init__(self, start: int = 0):\n",
+    "        self.value = start\n",
+    "\n",
+    "    def inc(self, by: int = 1):\n",
+    "        self.value += by\n",
+    "        return self.value\n",
+    "\"\"\"\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "f802450e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "with gr.Blocks(title=\"PyTest Generator\") as ui:\n",
+    "    gr.Markdown(\n",
+    "        \"## 🧪 PyTest Generator (Week 4 • Community Contribution)\\n\"\n",
+    "        \"Generate **minimal, deterministic** pytest tests from a Python module using your chosen model/provider.\"\n",
+    "    )\n",
+    "\n",
+    "    with gr.Row(equal_height=True):\n",
+    "        # LEFT: inputs (module code)\n",
+    "        with gr.Column(scale=6):\n",
+    "            with gr.Row():\n",
+    "                model_dd = gr.Dropdown(\n",
+    "                    list(MODEL_REGISTRY.keys()),\n",
+    "                    value=DEFAULT_MODEL,\n",
+    "                    label=\"Model (OpenAI, Gemini, Groq)\"\n",
+    "                )\n",
+    "                module_name_tb = gr.Textbox(\n",
+    "                    label=\"Module name (used in `import <name> as mod`)\",\n",
+    "                    value=\"mymodule\"\n",
+    "                )\n",
+    "            code_in = gr.Code(\n",
+    "                label=\"Python module code\",\n",
+    "                language=\"python\",\n",
+    "                lines=24,\n",
+    "                value=EXAMPLE_CODE\n",
+    "            )\n",
+    "            with gr.Row():\n",
+    "                save_cb = gr.Checkbox(label=\"Also save generated tests to /tests\", value=True)\n",
+    "                out_dir_tb = gr.Textbox(label=\"Output folder\", value=\"tests\")\n",
+    "            gen_btn = gr.Button(\"Generate tests\", variant=\"primary\")\n",
+    "\n",
+    "        # RIGHT: outputs (generated tests + pytest run)\n",
+    "        with gr.Column(scale=6):\n",
+    "            tests_out = gr.Code(label=\"Generated tests (pytest)\", language=\"python\", lines=24)\n",
+    "            with gr.Row():\n",
+    "                run_btn = gr.Button(\"Run PyTest\", variant=\"secondary\")\n",
+    "            summary_md = gr.Markdown()\n",
+    "            full_out = gr.Textbox(label=\"Full PyTest output\", lines=12)\n",
+    "\n",
+    "    # --- events ---\n",
+    "\n",
+    "    def _on_gen(model_label, name, code, save, outdir):\n",
+    "        tests, msg = generate_from_code(model_label, name, code, save, outdir)\n",
+    "        status = msg or \"✅ Done\"\n",
+    "        return tests, status\n",
+    "\n",
+    "    gen_btn.click(\n",
+    "        _on_gen,\n",
+    "        inputs=[model_dd, module_name_tb, code_in, save_cb, out_dir_tb],\n",
+    "        outputs=[tests_out, summary_md],\n",
+    "    )\n",
+    "\n",
+    "    def _on_run(name, code, tests):\n",
+    "        summary, details = run_pytest_on_snippet(name, code, tests)\n",
+    "        return summary, details\n",
+    "\n",
+    "    run_btn.click(\n",
+    "        _on_run,\n",
+    "        inputs=[module_name_tb, code_in, tests_out],\n",
+    "        outputs=[summary_md, full_out],\n",
+    "    )\n",
+    "\n",
+    "ui.launch(inbrowser=True)\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "llm-engineering",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.12.10"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

week4/community-contributions/python_to_cpp_code_translator/examples/calculator.py

@@ -0,0 +1,190 @@
+"""
+Simple calculator class with history tracking.
+"""
+
+import math
+from typing import List, Union
+
+class Calculator:
+    """A simple calculator with history tracking."""
+    
+    def __init__(self):
+        """Initialize calculator with empty history."""
+        self.history: List[str] = []
+        self.memory: float = 0.0
+    
+    def add(self, a: float, b: float) -> float:
+        """Add two numbers."""
+        result = a + b
+        self.history.append(f"{a} + {b} = {result}")
+        return result
+    
+    def subtract(self, a: float, b: float) -> float:
+        """Subtract b from a."""
+        result = a - b
+        self.history.append(f"{a} - {b} = {result}")
+        return result
+    
+    def multiply(self, a: float, b: float) -> float:
+        """Multiply two numbers."""
+        result = a * b
+        self.history.append(f"{a} * {b} = {result}")
+        return result
+    
+    def divide(self, a: float, b: float) -> float:
+        """Divide a by b."""
+        if b == 0:
+            raise ValueError("Cannot divide by zero")
+        result = a / b
+        self.history.append(f"{a} / {b} = {result}")
+        return result
+    
+    def power(self, base: float, exponent: float) -> float:
+        """Calculate base raised to the power of exponent."""
+        result = base ** exponent
+        self.history.append(f"{base} ^ {exponent} = {result}")
+        return result
+    
+    def square_root(self, number: float) -> float:
+        """Calculate square root of a number."""
+        if number < 0:
+            raise ValueError("Cannot calculate square root of negative number")
+        result = math.sqrt(number)
+        self.history.append(f"√{number} = {result}")
+        return result
+    
+    def factorial(self, n: int) -> int:
+        """Calculate factorial of n."""
+        if n < 0:
+            raise ValueError("Factorial is not defined for negative numbers")
+        if n == 0 or n == 1:
+            return 1
+        
+        result = 1
+        for i in range(2, n + 1):
+            result *= i
+        
+        self.history.append(f"{n}! = {result}")
+        return result
+    
+    def memory_store(self, value: float) -> None:
+        """Store value in memory."""
+        self.memory = value
+        self.history.append(f"Memory stored: {value}")
+    
+    def memory_recall(self) -> float:
+        """Recall value from memory."""
+        self.history.append(f"Memory recalled: {self.memory}")
+        return self.memory
+    
+    def memory_clear(self) -> None:
+        """Clear memory."""
+        self.memory = 0.0
+        self.history.append("Memory cleared")
+    
+    def get_history(self) -> List[str]:
+        """Get calculation history."""
+        return self.history.copy()
+    
+    def clear_history(self) -> None:
+        """Clear calculation history."""
+        self.history.clear()
+    
+    def get_last_result(self) -> Union[float, None]:
+        """Get the result of the last calculation."""
+        if not self.history:
+            return None
+        
+        last_entry = self.history[-1]
+        # Extract result from history entry
+        if "=" in last_entry:
+            return float(last_entry.split("=")[-1].strip())
+        return None
+
+class ScientificCalculator(Calculator):
+    """Extended calculator with scientific functions."""
+    
+    def sine(self, angle: float) -> float:
+        """Calculate sine of angle in radians."""
+        result = math.sin(angle)
+        self.history.append(f"sin({angle}) = {result}")
+        return result
+    
+    def cosine(self, angle: float) -> float:
+        """Calculate cosine of angle in radians."""
+        result = math.cos(angle)
+        self.history.append(f"cos({angle}) = {result}")
+        return result
+    
+    def tangent(self, angle: float) -> float:
+        """Calculate tangent of angle in radians."""
+        result = math.tan(angle)
+        self.history.append(f"tan({angle}) = {result}")
+        return result
+    
+    def logarithm(self, number: float, base: float = math.e) -> float:
+        """Calculate logarithm of number with given base."""
+        if number <= 0:
+            raise ValueError("Logarithm is not defined for non-positive numbers")
+        if base <= 0 or base == 1:
+            raise ValueError("Logarithm base must be positive and not equal to 1")
+        
+        result = math.log(number, base)
+        self.history.append(f"log_{base}({number}) = {result}")
+        return result
+    
+    def degrees_to_radians(self, degrees: float) -> float:
+        """Convert degrees to radians."""
+        return degrees * math.pi / 180
+    
+    def radians_to_degrees(self, radians: float) -> float:
+        """Convert radians to degrees."""
+        return radians * 180 / math.pi
+
+def main():
+    """Main function to demonstrate calculator functionality."""
+    print("Calculator Demo")
+    print("=" * 30)
+    
+    # Basic calculator
+    calc = Calculator()
+    
+    print("Basic Calculator Operations:")
+    print(f"5 + 3 = {calc.add(5, 3)}")
+    print(f"10 - 4 = {calc.subtract(10, 4)}")
+    print(f"6 * 7 = {calc.multiply(6, 7)}")
+    print(f"15 / 3 = {calc.divide(15, 3)}")
+    print(f"2 ^ 8 = {calc.power(2, 8)}")
+    print(f"√64 = {calc.square_root(64)}")
+    print(f"5! = {calc.factorial(5)}")
+    
+    print(f"\nCalculation History:")
+    for entry in calc.get_history():
+        print(f"  {entry}")
+    
+    # Scientific calculator
+    print("\n" + "=" * 30)
+    print("Scientific Calculator Operations:")
+    
+    sci_calc = ScientificCalculator()
+    
+    # Convert degrees to radians for trigonometric functions
+    angle_deg = 45
+    angle_rad = sci_calc.degrees_to_radians(angle_deg)
+    
+    print(f"sin({angle_deg}°) = {sci_calc.sine(angle_rad):.4f}")
+    print(f"cos({angle_deg}°) = {sci_calc.cosine(angle_rad):.4f}")
+    print(f"tan({angle_deg}°) = {sci_calc.tangent(angle_rad):.4f}")
+    print(f"ln(10) = {sci_calc.logarithm(10):.4f}")
+    print(f"log₁₀(100) = {sci_calc.logarithm(100, 10):.4f}")
+    
+    print(f"\nScientific Calculator History:")
+    for entry in sci_calc.get_history():
+        print(f"  {entry}")
+
+if __name__ == "__main__":
+    main()
+
+
+
+

week4/community-contributions/python_to_cpp_code_translator/examples/fibonacci.py

@@ -0,0 +1,64 @@
+"""
+Fibonacci sequence implementation in Python.
+"""
+
+def fibonacci(n):
+    """Calculate the nth Fibonacci number using recursion."""
+    if n <= 1:
+        return n
+    return fibonacci(n-1) + fibonacci(n-2)
+
+def fibonacci_iterative(n):
+    """Calculate the nth Fibonacci number using iteration."""
+    if n <= 1:
+        return n
+    
+    a, b = 0, 1
+    for _ in range(2, n + 1):
+        a, b = b, a + b
+    return b
+
+def fibonacci_sequence(count):
+    """Generate a sequence of Fibonacci numbers."""
+    sequence = []
+    for i in range(count):
+        sequence.append(fibonacci(i))
+    return sequence
+
+def main():
+    """Main function to demonstrate Fibonacci calculations."""
+    print("Fibonacci Sequence Demo")
+    print("=" * 30)
+    
+    # Calculate first 10 Fibonacci numbers
+    for i in range(10):
+        result = fibonacci(i)
+        print(f"fibonacci({i}) = {result}")
+    
+    print("\nFirst 15 Fibonacci numbers:")
+    sequence = fibonacci_sequence(15)
+    print(sequence)
+    
+    # Performance comparison
+    import time
+    
+    n = 30
+    print(f"\nPerformance comparison for fibonacci({n}):")
+    
+    start_time = time.time()
+    recursive_result = fibonacci(n)
+    recursive_time = time.time() - start_time
+    
+    start_time = time.time()
+    iterative_result = fibonacci_iterative(n)
+    iterative_time = time.time() - start_time
+    
+    print(f"Recursive: {recursive_result} (took {recursive_time:.4f}s)")
+    print(f"Iterative: {iterative_result} (took {iterative_time:.4f}s)")
+
+if __name__ == "__main__":
+    main()
+
+
+
+

week4/community-contributions/python_to_cpp_code_translator/examples/sorting_algorithms.py

@@ -0,0 +1,150 @@
+"""
+Various sorting algorithms implemented in Python.
+"""
+
+import random
+import time
+from typing import List
+
+def bubble_sort(arr: List[int]) -> List[int]:
+    """Sort array using bubble sort algorithm."""
+    n = len(arr)
+    arr = arr.copy()  # Don't modify original array
+    
+    for i in range(n):
+        for j in range(0, n - i - 1):
+            if arr[j] > arr[j + 1]:
+                arr[j], arr[j + 1] = arr[j + 1], arr[j]
+    
+    return arr
+
+def selection_sort(arr: List[int]) -> List[int]:
+    """Sort array using selection sort algorithm."""
+    n = len(arr)
+    arr = arr.copy()
+    
+    for i in range(n):
+        min_idx = i
+        for j in range(i + 1, n):
+            if arr[j] < arr[min_idx]:
+                min_idx = j
+        arr[i], arr[min_idx] = arr[min_idx], arr[i]
+    
+    return arr
+
+def insertion_sort(arr: List[int]) -> List[int]:
+    """Sort array using insertion sort algorithm."""
+    arr = arr.copy()
+    
+    for i in range(1, len(arr)):
+        key = arr[i]
+        j = i - 1
+        while j >= 0 and arr[j] > key:
+            arr[j + 1] = arr[j]
+            j -= 1
+        arr[j + 1] = key
+    
+    return arr
+
+def quick_sort(arr: List[int]) -> List[int]:
+    """Sort array using quick sort algorithm."""
+    if len(arr) <= 1:
+        return arr
+    
+    pivot = arr[len(arr) // 2]
+    left = [x for x in arr if x < pivot]
+    middle = [x for x in arr if x == pivot]
+    right = [x for x in arr if x > pivot]
+    
+    return quick_sort(left) + middle + quick_sort(right)
+
+def merge_sort(arr: List[int]) -> List[int]:
+    """Sort array using merge sort algorithm."""
+    if len(arr) <= 1:
+        return arr
+    
+    mid = len(arr) // 2
+    left = merge_sort(arr[:mid])
+    right = merge_sort(arr[mid:])
+    
+    return merge(left, right)
+
+def merge(left: List[int], right: List[int]) -> List[int]:
+    """Merge two sorted arrays."""
+    result = []
+    i = j = 0
+    
+    while i < len(left) and j < len(right):
+        if left[i] <= right[j]:
+            result.append(left[i])
+            i += 1
+        else:
+            result.append(right[j])
+            j += 1
+    
+    result.extend(left[i:])
+    result.extend(right[j:])
+    return result
+
+def benchmark_sorting_algorithms():
+    """Benchmark different sorting algorithms."""
+    sizes = [100, 500, 1000, 2000]
+    algorithms = {
+        "Bubble Sort": bubble_sort,
+        "Selection Sort": selection_sort,
+        "Insertion Sort": insertion_sort,
+        "Quick Sort": quick_sort,
+        "Merge Sort": merge_sort
+    }
+    
+    print("Sorting Algorithm Benchmark")
+    print("=" * 50)
+    
+    for size in sizes:
+        print(f"\nArray size: {size}")
+        print("-" * 30)
+        
+        # Generate random array
+        test_array = [random.randint(1, 1000) for _ in range(size)]
+        
+        for name, algorithm in algorithms.items():
+            start_time = time.time()
+            sorted_array = algorithm(test_array)
+            end_time = time.time()
+            
+            # Verify sorting is correct
+            is_sorted = all(sorted_array[i] <= sorted_array[i+1] for i in range(len(sorted_array)-1))
+            
+            print(f"{name:15}: {end_time - start_time:.4f}s {'✓' if is_sorted else '✗'}")
+
+def main():
+    """Main function to demonstrate sorting algorithms."""
+    print("Sorting Algorithms Demo")
+    print("=" * 30)
+    
+    # Test with small array
+    test_array = [64, 34, 25, 12, 22, 11, 90]
+    print(f"Original array: {test_array}")
+    
+    algorithms = {
+        "Bubble Sort": bubble_sort,
+        "Selection Sort": selection_sort,
+        "Insertion Sort": insertion_sort,
+        "Quick Sort": quick_sort,
+        "Merge Sort": merge_sort
+    }
+    
+    for name, algorithm in algorithms.items():
+        sorted_array = algorithm(test_array)
+        print(f"{name}: {sorted_array}")
+    
+    # Run benchmark
+    print("\n" + "=" * 50)
+    benchmark_sorting_algorithms()
+
+if __name__ == "__main__":
+    main()
+
+
+
+

week4/community-contributions/python_to_cpp_translator.ipynb

@@ -0,0 +1,571 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Python to C++ Code Translator using LLMs\n",
+    "\n",
+    "This notebook translates Python code to compilable C++ using GPT, Gemini, or Claude.\n",
+    "\n",
+    "## Features:\n",
+    "- 🤖 Multiple LLM support (GPT, Gemini, Claude)\n",
+    "- ✅ Automatic compilation testing with g++\n",
+    "- 🔄 Comparison mode to test all LLMs\n",
+    "- 💬 Interactive translation mode"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Step 1: Install Required Packages\n",
+    "\n",
+    "Run this cell first to install all dependencies:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "!uv add openai anthropic python-dotenv google-generativeai\n",
+    "#!pip install openai anthropic python-dotenv google-generativeai"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Step 2: Import Libraries"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "import subprocess\n",
+    "import tempfile\n",
+    "from pathlib import Path\n",
+    "from dotenv import load_dotenv\n",
+    "import openai\n",
+    "from anthropic import Anthropic\n",
+    "import google.generativeai as genai"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Step 3: Load API Keys\n",
+    "\n",
+    "Make sure you have a `.env` file with:\n",
+    "```\n",
+    "OPENAI_API_KEY=your_key_here\n",
+    "GEMINI_API_KEY=your_key_here\n",
+    "ANTHROPIC_API_KEY=your_key_here\n",
+    "```"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Load API keys from .env file\n",
+    "load_dotenv()\n",
+    "\n",
+    "# Initialize API clients\n",
+    "openai_client = openai.OpenAI(api_key=os.getenv('OPENAI_API_KEY'))\n",
+    "anthropic_client = Anthropic(api_key=os.getenv('ANTHROPIC_API_KEY'))\n",
+    "genai.configure(api_key=os.getenv('GEMINI_API_KEY'))\n",
+    "\n",
+    "print(\"✓ API keys loaded successfully\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Step 4: Define System Prompt"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "SYSTEM_PROMPT = \"\"\"You are an expert programmer that translates Python code to C++.\n",
+    "Translate the given Python code to efficient, compilable C++ code.\n",
+    "\n",
+    "Requirements:\n",
+    "- The C++ code must compile without errors\n",
+    "- Include all necessary headers\n",
+    "- Use modern C++ (C++11 or later) features where appropriate\n",
+    "- Add proper error handling\n",
+    "- Maintain the same functionality as the Python code\n",
+    "- Include a main() function if the Python code has executable statements\n",
+    "\n",
+    "Only return the C++ code, no explanations unless there are important notes about compilation.\"\"\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Step 5: LLM Translation Functions"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def translate_with_gpt(python_code, model=\"gpt-4o\"):\n",
+    "    \"\"\"Translate Python to C++ using OpenAI's GPT models\"\"\"\n",
+    "    try:\n",
+    "        response = openai_client.chat.completions.create(\n",
+    "            model=model,\n",
+    "            messages=[\n",
+    "                {\"role\": \"system\", \"content\": SYSTEM_PROMPT},\n",
+    "                {\"role\": \"user\", \"content\": f\"Translate this Python code to C++:\\n\\n{python_code}\"}\n",
+    "            ],\n",
+    "            temperature=0.2\n",
+    "        )\n",
+    "        return response.choices[0].message.content\n",
+    "    except Exception as e:\n",
+    "        return f\"Error with GPT: {str(e)}\"\n",
+    "\n",
+    "def translate_with_gemini(python_code, model=\"gemini-2.0-flash-exp\"):\n",
+    "    \"\"\"Translate Python to C++ using Google's Gemini\"\"\"\n",
+    "    try:\n",
+    "        model_instance = genai.GenerativeModel(model)\n",
+    "        prompt = f\"{SYSTEM_PROMPT}\\n\\nTranslate this Python code to C++:\\n\\n{python_code}\"\n",
+    "        response = model_instance.generate_content(prompt)\n",
+    "        return response.text\n",
+    "    except Exception as e:\n",
+    "        return f\"Error with Gemini: {str(e)}\"\n",
+    "\n",
+    "def translate_with_claude(python_code, model=\"claude-sonnet-4-20250514\"):\n",
+    "    \"\"\"Translate Python to C++ using Anthropic's Claude\"\"\"\n",
+    "    try:\n",
+    "        response = anthropic_client.messages.create(\n",
+    "            model=model,\n",
+    "            max_tokens=4096,\n",
+    "            temperature=0.2,\n",
+    "            system=SYSTEM_PROMPT,\n",
+    "            messages=[\n",
+    "                {\"role\": \"user\", \"content\": f\"Translate this Python code to C++:\\n\\n{python_code}\"}\n",
+    "            ]\n",
+    "        )\n",
+    "        return response.content[0].text\n",
+    "    except Exception as e:\n",
+    "        return f\"Error with Claude: {str(e)}\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Step 6: Main Translation Function"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def translate_python_to_cpp(python_code, llm=\"gpt\", model=None):\n",
+    "    \"\"\"\n",
+    "    Translate Python code to C++ using specified LLM\n",
+    "    \n",
+    "    Args:\n",
+    "        python_code (str): Python code to translate\n",
+    "        llm (str): LLM to use ('gpt', 'gemini', or 'claude')\n",
+    "        model (str): Specific model version (optional)\n",
+    "    \n",
+    "    Returns:\n",
+    "        str: Translated C++ code\n",
+    "    \"\"\"\n",
+    "    print(f\"🔄 Translating with {llm.upper()}...\")\n",
+    "    \n",
+    "    if llm.lower() == \"gpt\":\n",
+    "        model = model or \"gpt-4o\"\n",
+    "        cpp_code = translate_with_gpt(python_code, model)\n",
+    "    elif llm.lower() == \"gemini\":\n",
+    "        model = model or \"gemini-2.0-flash-exp\"\n",
+    "        cpp_code = translate_with_gemini(python_code, model)\n",
+    "    elif llm.lower() == \"claude\":\n",
+    "        model = model or \"claude-sonnet-4-20250514\"\n",
+    "        cpp_code = translate_with_claude(python_code, model)\n",
+    "    else:\n",
+    "        return \"Error: Invalid LLM. Choose 'gpt', 'gemini', or 'claude'\"\n",
+    "    \n",
+    "    return cpp_code"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Step 7: Compilation Testing Functions"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def extract_cpp_code(text):\n",
+    "    \"\"\"Extract C++ code from markdown code blocks if present\"\"\"\n",
+    "    if \"```cpp\" in text:\n",
+    "        start = text.find(\"```cpp\") + 6\n",
+    "        end = text.find(\"```\", start)\n",
+    "        return text[start:end].strip()\n",
+    "    elif \"```c++\" in text:\n",
+    "        start = text.find(\"```c++\") + 6\n",
+    "        end = text.find(\"```\", start)\n",
+    "        return text[start:end].strip()\n",
+    "    elif \"```\" in text:\n",
+    "        start = text.find(\"```\") + 3\n",
+    "        end = text.find(\"```\", start)\n",
+    "        return text[start:end].strip()\n",
+    "    return text.strip()\n",
+    "\n",
+    "def compile_cpp_code(cpp_code, output_name=\"translated_program\"):\n",
+    "    \"\"\"\n",
+    "    Compile C++ code and return compilation status\n",
+    "    \n",
+    "    Args:\n",
+    "        cpp_code (str): C++ code to compile\n",
+    "        output_name (str): Name of output executable\n",
+    "    \n",
+    "    Returns:\n",
+    "        dict: Compilation result with status and messages\n",
+    "    \"\"\"\n",
+    "    # Extract code from markdown if present\n",
+    "    cpp_code = extract_cpp_code(cpp_code)\n",
+    "    \n",
+    "    # Create temporary directory\n",
+    "    with tempfile.TemporaryDirectory() as tmpdir:\n",
+    "        cpp_file = Path(tmpdir) / \"program.cpp\"\n",
+    "        exe_file = Path(tmpdir) / output_name\n",
+    "        \n",
+    "        # Write C++ code to file\n",
+    "        with open(cpp_file, 'w') as f:\n",
+    "            f.write(cpp_code)\n",
+    "        \n",
+    "        # Try to compile\n",
+    "        try:\n",
+    "            result = subprocess.run(\n",
+    "                ['g++', '-std=c++17', str(cpp_file), '-o', str(exe_file)],\n",
+    "                capture_output=True,\n",
+    "                text=True,\n",
+    "                timeout=10\n",
+    "            )\n",
+    "            \n",
+    "            if result.returncode == 0:\n",
+    "                return {\n",
+    "                    'success': True,\n",
+    "                    'message': '✓ Compilation successful!',\n",
+    "                    'executable': str(exe_file),\n",
+    "                    'stdout': result.stdout,\n",
+    "                    'stderr': result.stderr\n",
+    "                }\n",
+    "            else:\n",
+    "                return {\n",
+    "                    'success': False,\n",
+    "                    'message': '✗ Compilation failed',\n",
+    "                    'stdout': result.stdout,\n",
+    "                    'stderr': result.stderr\n",
+    "                }\n",
+    "        except subprocess.TimeoutExpired:\n",
+    "            return {\n",
+    "                'success': False,\n",
+    "                'message': '✗ Compilation timed out'\n",
+    "            }\n",
+    "        except FileNotFoundError:\n",
+    "            return {\n",
+    "                'success': False,\n",
+    "                'message': '✗ g++ compiler not found. Please install g++ to compile C++ code.'\n",
+    "            }\n",
+    "        except Exception as e:\n",
+    "            return {\n",
+    "                'success': False,\n",
+    "                'message': f'✗ Compilation error: {str(e)}'\n",
+    "            }"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Step 8: Complete Pipeline"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def translate_and_compile(python_code, llm=\"gpt\", model=None, verbose=True):\n",
+    "    \"\"\"\n",
+    "    Translate Python to C++ and attempt compilation\n",
+    "    \n",
+    "    Args:\n",
+    "        python_code (str): Python code to translate\n",
+    "        llm (str): LLM to use\n",
+    "        model (str): Specific model version\n",
+    "        verbose (bool): Print detailed output\n",
+    "    \n",
+    "    Returns:\n",
+    "        dict: Results including translated code and compilation status\n",
+    "    \"\"\"\n",
+    "    # Translate\n",
+    "    cpp_code = translate_python_to_cpp(python_code, llm, model)\n",
+    "    \n",
+    "    if verbose:\n",
+    "        print(\"\\n\" + \"=\"*60)\n",
+    "        print(\"TRANSLATED C++ CODE:\")\n",
+    "        print(\"=\"*60)\n",
+    "        print(cpp_code)\n",
+    "        print(\"=\"*60 + \"\\n\")\n",
+    "    \n",
+    "    # Compile\n",
+    "    print(\"🔨 Attempting to compile...\")\n",
+    "    compilation_result = compile_cpp_code(cpp_code)\n",
+    "    \n",
+    "    if verbose:\n",
+    "        print(compilation_result['message'])\n",
+    "        if not compilation_result['success'] and 'stderr' in compilation_result:\n",
+    "            print(\"\\nCompilation errors:\")\n",
+    "            print(compilation_result['stderr'])\n",
+    "    \n",
+    "    return {\n",
+    "        'cpp_code': cpp_code,\n",
+    "        'compilation': compilation_result\n",
+    "    }"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Example 1: Factorial Function"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "python_code_1 = \"\"\"\n",
+    "def factorial(n):\n",
+    "    if n <= 1:\n",
+    "        return 1\n",
+    "    return n * factorial(n - 1)\n",
+    "\n",
+    "# Test the function\n",
+    "print(factorial(5))\n",
+    "\"\"\"\n",
+    "\n",
+    "print(\"Example 1: Factorial Function\")\n",
+    "print(\"=\"*60)\n",
+    "result1 = translate_and_compile(python_code_1, llm=\"gpt\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Example 2: Sum of Squares"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "python_code_2 = \"\"\"\n",
+    "def sum_of_squares(numbers):\n",
+    "    return sum(x**2 for x in numbers)\n",
+    "\n",
+    "numbers = [1, 2, 3, 4, 5]\n",
+    "result = sum_of_squares(numbers)\n",
+    "print(f\"Sum of squares: {result}\")\n",
+    "\"\"\"\n",
+    "\n",
+    "print(\"Example 2: Sum of Squares\")\n",
+    "print(\"=\"*60)\n",
+    "result2 = translate_and_compile(python_code_2, llm=\"claude\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Example 3: Fibonacci with Gemini"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "python_code_3 = \"\"\"\n",
+    "def fibonacci(n):\n",
+    "    if n <= 1:\n",
+    "        return n\n",
+    "    a, b = 0, 1\n",
+    "    for _ in range(2, n + 1):\n",
+    "        a, b = b, a + b\n",
+    "    return b\n",
+    "\n",
+    "print(f\"Fibonacci(10) = {fibonacci(10)}\")\n",
+    "\"\"\"\n",
+    "\n",
+    "print(\"Example 3: Fibonacci with Gemini\")\n",
+    "print(\"=\"*60)\n",
+    "result3 = translate_and_compile(python_code_3, llm=\"gemini\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Compare All LLMs"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def compare_llms(python_code):\n",
+    "    \"\"\"Compare all three LLMs on the same Python code\"\"\"\n",
+    "    llms = [\"gpt\", \"gemini\", \"claude\"]\n",
+    "    results = {}\n",
+    "    \n",
+    "    for llm in llms:\n",
+    "        print(f\"\\n{'='*60}\")\n",
+    "        print(f\"Testing with {llm.upper()}\")\n",
+    "        print('='*60)\n",
+    "        results[llm] = translate_and_compile(python_code, llm=llm, verbose=False)\n",
+    "        print(results[llm]['compilation']['message'])\n",
+    "    \n",
+    "    return results"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Test code for comparison\n",
+    "python_code_compare = \"\"\"\n",
+    "def is_prime(n):\n",
+    "    if n < 2:\n",
+    "        return False\n",
+    "    for i in range(2, int(n**0.5) + 1):\n",
+    "        if n % i == 0:\n",
+    "            return False\n",
+    "    return True\n",
+    "\n",
+    "primes = [x for x in range(2, 20) if is_prime(x)]\n",
+    "print(f\"Primes under 20: {primes}\")\n",
+    "\"\"\"\n",
+    "\n",
+    "print(\"COMPARING ALL LLMs\")\n",
+    "print(\"=\"*60)\n",
+    "comparison_results = compare_llms(python_code_compare)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Interactive Translation Mode\n",
+    "\n",
+    "Use this cell to translate your own Python code interactively:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Your custom Python code here\n",
+    "your_python_code = \"\"\"\n",
+    "# Paste your Python code here\n",
+    "def hello_world():\n",
+    "    print(\"Hello, World!\")\n",
+    "\n",
+    "hello_world()\n",
+    "\"\"\"\n",
+    "\n",
+    "# Choose your LLM: \"gpt\", \"gemini\", or \"claude\"\n",
+    "chosen_llm = \"gpt\"\n",
+    "\n",
+    "result = translate_and_compile(your_python_code, llm=chosen_llm)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Summary\n",
+    "\n",
+    "You now have a complete Python to C++ translator! \n",
+    "\n",
+    "### Main Functions:\n",
+    "- `translate_python_to_cpp(code, llm, model)` - Translate only\n",
+    "- `translate_and_compile(code, llm, model)` - Translate and compile\n",
+    "- `compare_llms(code)` - Compare all three LLMs\n",
+    "\n",
+    "### Supported LLMs:\n",
+    "- **gpt** - OpenAI GPT-4o\n",
+    "- **gemini** - Google Gemini 2.0 Flash\n",
+    "- **claude** - Anthropic Claude Sonnet 4\n",
+    "\n",
+    "Happy translating! 🚀"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": ".venv",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.12.12"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}

week4/community-contributions/ranskills-week4-code-snippet-sniper.ipynb

@@ -0,0 +1,476 @@
+{
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "xeOG96gXPeqz"
+      },
+      "source": [
+        "# Snippet Sniper\n",
+        "\n",
+        "### Welcome on a wild ride with the John Wick in the coding arena as it accepts your contracts \n",
+        "\n",
+        "Allows you to perform various tasks on given code snippets:\n",
+        "\n",
+        "- Add comments\n",
+        "- Explain what the code does\n",
+        "- Writes comprehensive unit tests\n",
+        "- Fixes (potential) errors in the code"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "B7ftYo53Pw94",
+        "outputId": "9daa3972-d5a1-4cd2-9952-cd89a54c6ddd"
+      },
+      "outputs": [],
+      "source": [
+        "import os\n",
+        "import logging\n",
+        "from enum import StrEnum\n",
+        "from getpass import getpass\n",
+        "\n",
+        "import gradio as gr\n",
+        "from openai import OpenAI\n",
+        "from dotenv import load_dotenv\n",
+        "\n",
+        "\n",
+        "load_dotenv(override=True)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "AXmPDuydPuUp"
+      },
+      "outputs": [],
+      "source": [
+        "logging.basicConfig(level=logging.WARNING)\n",
+        "\n",
+        "logger = logging.getLogger('sniper')\n",
+        "logger.setLevel(logging.DEBUG)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "0c_e1iMYmp5o"
+      },
+      "source": [
+        "## Free Cloud Providers\n",
+        "\n",
+        "Grab your free API Keys from these generous sites:\n",
+        "\n",
+        "- https://ollama.com/"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Secrets Helpers"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "def get_secret_in_google_colab(env_name: str) -> str:\n",
+        "    try:\n",
+        "      from google.colab import userdata\n",
+        "      return userdata.get(env_name)\n",
+        "    except Exception:\n",
+        "      return ''\n",
+        "\n",
+        "\n",
+        "def get_secret(env_name: str) -> str:\n",
+        "    '''Gets the value from the environment(s), otherwise ask the user for it if not set'''\n",
+        "    key = os.environ.get(env_name) or get_secret_in_google_colab(env_name)\n",
+        "\n",
+        "    if not key:\n",
+        "        key = getpass(f'Enter {env_name}:').strip()\n",
+        "\n",
+        "    if key:\n",
+        "        logger.info(f'✅ {env_name} provided')\n",
+        "    else:\n",
+        "        logger.warning(f'❌ {env_name} not provided')\n",
+        "    return key.strip()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Set up model(s)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "d7Qmfac9Ph0w",
+        "outputId": "be9db7f3-f08a-47f5-d6fa-d7c8bce4f97a"
+      },
+      "outputs": [],
+      "source": [
+        "class Provider(StrEnum):\n",
+        "    OLLAMA = 'Ollama'\n",
+        "    OPENROUTER = 'OpenRouter'\n",
+        "\n",
+        "clients: dict[Provider, OpenAI] = {}\n",
+        "\n",
+        "if api_key := get_secret('OLLAMA_API_KEY'):\n",
+        "    clients[Provider.OLLAMA] = OpenAI(api_key=api_key, base_url='https://ollama.com/v1')\n",
+        "\n",
+        "model = 'qwen3-coder:480b-cloud'\n",
+        "client = clients.get(Provider.OLLAMA)\n",
+        "if not client:\n",
+        "    raise Exception('No client found')"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "Kq-AKZEjqnTp"
+      },
+      "source": [
+        "## Tasks"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "fTHvG2w0sgwU"
+      },
+      "outputs": [],
+      "source": [
+        "class Task(StrEnum):\n",
+        "  COMMENTS = 'Comments'\n",
+        "  UNIT_TESTS = 'Unit Tests'\n",
+        "  FIX_CODE = 'Fix Code'\n",
+        "  EXPLAIN = 'Explain'\n",
+        "\n",
+        "\n",
+        "def perform_tasks(tasks, code):\n",
+        "  logger.info(f'Performing tasks: {tasks}')\n",
+        "\n",
+        "  steps = []\n",
+        "  if Task.COMMENTS in tasks:\n",
+        "    steps.append('Add documentation comments to the given code. If the method name and parameters are self-explanatory, skip those comments.')\n",
+        "  if Task.UNIT_TESTS in tasks:\n",
+        "    steps.append('Add a thorough unit tests considering all edge cases to the given code.')\n",
+        "  if Task.FIX_CODE in tasks:\n",
+        "    steps.append('You are to fix the given code, if it has any issues.')\n",
+        "  if Task.EXPLAIN in tasks:\n",
+        "    steps.append('Explain the given code.')\n",
+        "\n",
+        "  system_prompt = f'''\n",
+        "  You are an experienced polyglot software engineer and given a code you can\n",
+        "  detect what programming language it is in.\n",
+        "  DO NOT fix the code until expressly told to do so.\n",
+        "\n",
+        "  Your tasks:\n",
+        "  {'- '  + '\\n- '.join(steps)}\n",
+        "  '''\n",
+        "  messages = [\n",
+        "      {\"role\": \"system\", \"content\": system_prompt},\n",
+        "      {\"role\": \"user\", \"content\": f'Code: \\n{code}'}\n",
+        "  ]\n",
+        "  response =  client.chat.completions.create(\n",
+        "      model=model,\n",
+        "      messages=messages\n",
+        "  )\n",
+        "\n",
+        "  content = response.choices[0].message.content\n",
+        "\n",
+        "  return content"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "SkmMYw_osxeG"
+      },
+      "source": [
+        "### Examples"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "nlzUyXFus0km"
+      },
+      "outputs": [],
+      "source": [
+        "def get_examples() -> tuple[list[any], list[str]]:\n",
+        "    '''Returns examples and their labels'''\n",
+        "\n",
+        "    # Python examples\n",
+        "    add = r'''\n",
+        "    def add(a, b):\n",
+        "        return a + b\n",
+        "    '''\n",
+        "\n",
+        "    multiply = r'''\n",
+        "    def multiply(a, b):\n",
+        "        return a * b\n",
+        "    '''\n",
+        "\n",
+        "    divide = r'''\n",
+        "    def divide(a, b):\n",
+        "        return a / b\n",
+        "    '''\n",
+        "\n",
+        "    # JavaScript example - async function\n",
+        "    fetch_data = r'''\n",
+        "    async function fetchUserData(userId) {\n",
+        "        const response = await fetch(`/api/users/${userId}`);\n",
+        "        const data = await response.json();\n",
+        "        return data;\n",
+        "    }\n",
+        "    '''\n",
+        "\n",
+        "    # Java example - sorting algorithm\n",
+        "    bubble_sort = r'''\n",
+        "    public void bubbleSort(int[] arr) {\n",
+        "        int n = arr.length;\n",
+        "        for (int i = 0; i < n-1; i++) {\n",
+        "            for (int j = 0; j < n-i-1; j++) {\n",
+        "                if (arr[j] > arr[j+1]) {\n",
+        "                    int temp = arr[j];\n",
+        "                    arr[j] = arr[j+1];\n",
+        "                    arr[j+1] = temp;\n",
+        "                }\n",
+        "            }\n",
+        "        }\n",
+        "    }\n",
+        "    '''\n",
+        "\n",
+        "    # C++ example - buggy pointer code\n",
+        "    buggy_cpp = r'''\n",
+        "    int* createArray() {\n",
+        "        int arr[5] = {1, 2, 3, 4, 5};\n",
+        "        return arr;\n",
+        "    }\n",
+        "    '''\n",
+        "\n",
+        "    # Rust example - ownership puzzle\n",
+        "    rust_ownership = r'''\n",
+        "    fn main() {\n",
+        "        let s1 = String::from(\"hello\");\n",
+        "        let s2 = s1;\n",
+        "        println!(\"{}\", s1);\n",
+        "    }\n",
+        "    '''\n",
+        "\n",
+        "    # Go example - concurrent code\n",
+        "    go_concurrent = r'''\n",
+        "    func processData(data []int) int {\n",
+        "        sum := 0\n",
+        "        for _, v := range data {\n",
+        "            sum += v\n",
+        "        }\n",
+        "        return sum\n",
+        "    }\n",
+        "    '''\n",
+        "\n",
+        "    # TypeScript example - complex type\n",
+        "    ts_generics = r'''\n",
+        "    function mergeObjects<T, U>(obj1: T, obj2: U): T & U {\n",
+        "        return { ...obj1, ...obj2 };\n",
+        "    }\n",
+        "    '''\n",
+        "\n",
+        "    # Ruby example - metaclass magic\n",
+        "    ruby_meta = r'''\n",
+        "    class DynamicMethod\n",
+        "      define_method(:greet) do |name|\n",
+        "        \"Hello, #{name}!\"\n",
+        "      end\n",
+        "    end\n",
+        "    '''\n",
+        "\n",
+        "    # PHP example - SQL injection vulnerable\n",
+        "    php_vulnerable = r'''\n",
+        "    function getUser($id) {\n",
+        "        $query = \"SELECT * FROM users WHERE id = \" . $id;\n",
+        "        return mysqli_query($conn, $query);\n",
+        "    }\n",
+        "    '''\n",
+        "\n",
+        "    # Python example - complex algorithm\n",
+        "    binary_search = r'''\n",
+        "    def binary_search(arr, target):\n",
+        "        left, right = 0, len(arr) - 1\n",
+        "        while left <= right:\n",
+        "            mid = (left + right) // 2\n",
+        "            if arr[mid] == target:\n",
+        "                return mid\n",
+        "            elif arr[mid] < target:\n",
+        "                left = mid + 1\n",
+        "            else:\n",
+        "                right = mid - 1\n",
+        "        return -1\n",
+        "    '''\n",
+        "\n",
+        "    # JavaScript example - closure concept\n",
+        "    js_closure = r'''\n",
+        "    function counter() {\n",
+        "        let count = 0;\n",
+        "        return function() {\n",
+        "            count++;\n",
+        "            return count;\n",
+        "        };\n",
+        "    }\n",
+        "    '''\n",
+        "\n",
+        "    examples = [\n",
+        "        # Simple Python examples\n",
+        "        [[Task.COMMENTS], add, 'python'],\n",
+        "        [[Task.UNIT_TESTS], multiply, 'python'],\n",
+        "        [[Task.COMMENTS, Task.FIX_CODE], divide, 'python'],\n",
+        "\n",
+        "        # Explain complex concepts\n",
+        "        [[Task.EXPLAIN], binary_search, 'python'],\n",
+        "        [[Task.EXPLAIN], js_closure, 'javascript'],\n",
+        "        [[Task.EXPLAIN], rust_ownership, 'rust'],\n",
+        "\n",
+        "        # Unit tests for different languages\n",
+        "        [[Task.UNIT_TESTS], fetch_data, 'javascript'],\n",
+        "        [[Task.UNIT_TESTS], go_concurrent, 'go'],\n",
+        "\n",
+        "        # Fix buggy code\n",
+        "        [[Task.FIX_CODE], buggy_cpp, 'cpp'],\n",
+        "        [[Task.FIX_CODE], php_vulnerable, 'php'],\n",
+        "\n",
+        "        # Multi-task combinations\n",
+        "        [[Task.COMMENTS, Task.EXPLAIN], bubble_sort, None],\n",
+        "        [[Task.COMMENTS, Task.UNIT_TESTS], ts_generics, 'typescript'],\n",
+        "        [[Task.EXPLAIN, Task.FIX_CODE], rust_ownership, 'rust'],\n",
+        "        [[Task.COMMENTS, Task.UNIT_TESTS, Task.EXPLAIN], ruby_meta, 'ruby'],\n",
+        "    ]\n",
+        "\n",
+        "    example_labels = [\n",
+        "        '🐍 Python: Add Function',\n",
+        "        '🐍 Python: Multiply Tests',\n",
+        "        '🐍 Python: Fix Division',\n",
+        "        '🐍 Python: Binary Search Explained',\n",
+        "        '🟨 JavaScript: Closure Concept',\n",
+        "        '🦀 Rust: Ownership Puzzle',\n",
+        "        '🟨 JavaScript: Async Test',\n",
+        "        '🐹 Go: Concurrency Test',\n",
+        "        '⚡ C++: Fix Pointer Bug',\n",
+        "        '🐘 PHP: Fix SQL Injection',\n",
+        "        '☕ Java: Bubble Sort Guide',\n",
+        "        '📘 TypeScript: Generics & Tests',\n",
+        "        '🦀 Rust: Fix & Explain Ownership',\n",
+        "        '💎 Ruby: Meta Programming Deep Dive',\n",
+        "    ]\n",
+        "\n",
+        "    return examples, example_labels"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "wYReYuvgtDgg"
+      },
+      "source": [
+        "## Gradio UI\n",
+        "\n",
+        "[Documentation](https://www.gradio.app/docs/gradio)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 664
+        },
+        "id": "I8Q08SJe8CxK",
+        "outputId": "f1d41d06-dfda-4daf-b7ff-6f73bdaf8369"
+      },
+      "outputs": [],
+      "source": [
+        "title = 'Snippet Sniper 🎯'\n",
+        "\n",
+        "with gr.Blocks(title=title, theme=gr.themes.Monochrome()) as ui:\n",
+        "    gr.Markdown(f'# {title}')\n",
+        "    gr.Markdown('## I am your [**John Wick**](https://en.wikipedia.org/wiki/John_Wick), ready to accept any contract on your code. Consider it executed 🎯🔫!.')\n",
+        "\n",
+        "    with gr.Row():\n",
+        "      with gr.Column():\n",
+        "        tasks = gr.Dropdown(\n",
+        "            label=\"Tasks\",\n",
+        "            choices=[task.value for task in Task],\n",
+        "            value=Task.COMMENTS,\n",
+        "            multiselect=True,\n",
+        "            interactive=True,\n",
+        "        )\n",
+        "        code_input = gr.Code(\n",
+        "            label='Code Input',\n",
+        "            lines=40,\n",
+        "        )\n",
+        "        code_language = gr.Textbox(visible=False)\n",
+        "\n",
+        "      with gr.Column():\n",
+        "        gr.Markdown('## Kill Zone 🧟🧠💀')\n",
+        "        code_output = gr.Markdown('💣')\n",
+        "\n",
+        "\n",
+        "    run_btn = gr.Button('📜 Issue Contract')\n",
+        "\n",
+        "    def set_language(tasks, code, language):\n",
+        "      syntax_highlights = ['python',  'c', 'cpp', 'javascript', 'typescript']\n",
+        "      logger.debug(f'Tasks: {tasks}, Languge: {language}')\n",
+        "      highlight = language if language in syntax_highlights else None\n",
+        "\n",
+        "      return tasks, gr.Code(value=code, language=highlight)\n",
+        "\n",
+        "    examples, example_labels = get_examples()\n",
+        "    examples = gr.Examples(\n",
+        "        examples=examples,\n",
+        "        example_labels=example_labels,\n",
+        "        examples_per_page=20,\n",
+        "        inputs=[tasks, code_input, code_language],\n",
+        "        outputs=[tasks, code_input],\n",
+        "        run_on_click=True,\n",
+        "        fn=set_language\n",
+        "    )\n",
+        "\n",
+        "    run_btn.click(perform_tasks, inputs=[tasks, code_input], outputs=[code_output])\n",
+        "\n",
+        "ui.launch(debug=True)"
+      ]
+    }
+  ],
+  "metadata": {
+    "colab": {
+      "provenance": []
+    },
+    "kernelspec": {
+      "display_name": "Python 3",
+      "name": "python3"
+    },
+    "language_info": {
+      "name": "python"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 0
+}

week4/community-contributions/salah/unit-tests-generator/.env.example

@@ -0,0 +1 @@
+OPENROUTER_API_KEY=your-api-key-here

week4/community-contributions/salah/unit-tests-generator/app.py

@@ -0,0 +1,35 @@
+import gradio as gr
+from test_generator import generate_tests
+
+def create_interface():
+    with gr.Blocks(title="Unit Test Generator") as ui:
+        gr.Markdown("# Unit Test Generator")
+        gr.Markdown("Paste your Python code and get AI-generated unit tests")
+
+        with gr.Row():
+            with gr.Column(scale=1):
+                code_input = gr.Code(
+                    label="Your Code",
+                    language="python",
+                    lines=15
+                )
+                generate_btn = gr.Button("Generate Tests", variant="primary")
+
+            with gr.Column(scale=1):
+                tests_output = gr.Textbox(
+                    label="Generated Tests",
+                    lines=15,
+                    interactive=False
+                )
+
+        generate_btn.click(
+            fn=generate_tests,
+            inputs=[code_input],
+            outputs=[tests_output]
+        )
+
+    return ui
+
+def launch():
+    ui = create_interface()
+    ui.launch(server_name="localhost", server_port=7860)

week4/community-contributions/salah/unit-tests-generator/main.py

@@ -0,0 +1,17 @@
+#!/usr/bin/env python3
+
+import os
+from dotenv import load_dotenv
+from app import launch
+
+load_dotenv()
+
+if __name__ == "__main__":
+    api_key = os.getenv("OPENROUTER_API_KEY")
+    if not api_key:
+        print("Error: OPENROUTER_API_KEY not set in .env")
+        exit(1)
+
+    print("Starting Unit Test Generator...")
+    print("Open http://localhost:7860 in your browser")
+    launch()

week4/community-contributions/salah/unit-tests-generator/test_generator.py

@@ -0,0 +1,41 @@
+import os
+from openai import OpenAI
+from dotenv import load_dotenv
+
+load_dotenv()
+
+client = OpenAI(
+    api_key=os.getenv("OPENROUTER_API_KEY"),
+    base_url="https://openrouter.ai/api/v1"
+)
+
+MODEL = os.getenv("SECURECODE_MODEL", "meta-llama/llama-3.1-8b-instruct:free")
+
+SYSTEM_PROMPT = """You are a Python testing expert.
+Generate pytest unit tests for the given code.
+Include:
+- Happy path tests
+- Edge cases
+- Error handling tests
+Keep tests simple and clear."""
+
+def generate_tests(code):
+    """Generate unit tests for the given code."""
+    try:
+        response = client.chat.completions.create(
+            model=MODEL,
+            messages=[
+                {"role": "system", "content": SYSTEM_PROMPT},
+                {"role": "user", "content": f"Generate tests for this code:\n\n{code}"}
+            ],
+            stream=True
+        )
+
+        result = ""
+        for chunk in response:
+            if chunk.choices[0].delta.content:
+                result += chunk.choices[0].delta.content
+                yield result
+
+    except Exception as e:
+        yield f"Error: {str(e)}"

week5/community-contributions/NTSA_knowledge_base_and_chatbot/.gitignore

@@ -0,0 +1,31 @@
+# ChromaDB and vector databases
+langchain_chroma_db/
+*.db
+*.sqlite3
+
+# Large knowledge bases (keep only samples)
+ntsa_comprehensive_knowledge_base/
+ntsa_knowledge_base/
+
+# Python cache
+__pycache__/
+*.pyc
+*.pyo
+
+# Jupyter notebook checkpoints
+.ipynb_checkpoints/
+
+# Environment files
+.env
+.venv/
+
+# OS files
+.DS_Store
+Thumbs.db
+
+# Logs
+*.log
+
+# Temporary files
+*.tmp
+*.temp

week5/community-contributions/NTSA_knowledge_base_and_chatbot/ntsa_chatbot_project.ipynb

@@ -0,0 +1,870 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# NTSA Knowledge Base & AI Chatbot Project\n",
+    "\n",
+    "**Complete AI chatbot with HuggingFace embeddings, LangChain, and multiple LLMs**\n",
+    "\n",
+    "## Technologies\n",
+    "- 🕷️ Web Scraping: BeautifulSoup\n",
+    "- 🤗 Embeddings: HuggingFace Transformers (FREE)\n",
+    "- 🔗 Orchestration: LangChain\n",
+    "- 💾 Vector DB: ChromaDB\n",
+    "- 🤖 LLMs: GPT, Gemini, Claude\n",
+    "- 🎨 Interface: Gradio"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Part 1: Setup"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#For those with uv python environment management (use the following code)\n",
+    "!uv pip sync requirements.txt"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "!uv add pytz"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# For pip users use these commands to Install all dependencies\n",
+    "#!pip install requests beautifulsoup4 lxml python-dotenv gradio\n",
+    "#!pip install openai anthropic google-generativeai\n",
+    "#!pip install langchain langchain-community langchain-openai langchain-chroma langchain-huggingface\n",
+    "#!pip install transformers sentence-transformers torch\n",
+    "#!pip install chromadb pandas matplotlib plotly scikit-learn numpy pytz"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "✓ All libraries imported\n",
+      "✓ API Keys: OpenAI=True, Gemini=True, Claude=True\n"
+     ]
+    }
+   ],
+   "source": [
+    "import os\n",
+    "import sys\n",
+    "from pathlib import Path\n",
+    "from dotenv import load_dotenv\n",
+    "import json\n",
+    "from datetime import datetime\n",
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
+    "\n",
+    "from langchain.document_loaders import DirectoryLoader, TextLoader\n",
+    "from langchain.text_splitter import RecursiveCharacterTextSplitter\n",
+    "from langchain_openai import ChatOpenAI\n",
+    "from langchain_chroma import Chroma\n",
+    "from langchain.memory import ConversationBufferMemory\n",
+    "from langchain.chains import ConversationalRetrievalChain\n",
+    "from langchain_huggingface import HuggingFaceEmbeddings\n",
+    "\n",
+    "import plotly.graph_objects as go\n",
+    "from sklearn.manifold import TSNE\n",
+    "\n",
+    "from scraper_utils import NTSAKnowledgeBaseScraper\n",
+    "from simple_comprehensive_scraper import SimpleComprehensiveScraper\n",
+    "from langchain_integration import LangChainKnowledgeBase\n",
+    "\n",
+    "load_dotenv()\n",
+    "\n",
+    "print(\"✓ All libraries imported\")\n",
+    "print(f\"✓ API Keys: OpenAI={bool(os.getenv('OPENAI_API_KEY'))}, \"\n",
+    "      f\"Gemini={bool(os.getenv('GOOGLE_API_KEY'))}, \"\n",
+    "      f\"Claude={bool(os.getenv('ANTHROPIC_API_KEY'))}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Configuration:\n",
+      "  base_url: https://ntsa.go.ke\n",
+      "  kb_dir: ntsa_knowledge_base\n",
+      "  max_depth: 2\n",
+      "  vector_db_dir: ./langchain_chroma_db\n",
+      "  chunk_size: 1000\n"
+     ]
+    }
+   ],
+   "source": [
+    "CONFIG = {\n",
+    "    'base_url': 'https://ntsa.go.ke',\n",
+    "    'kb_dir': 'ntsa_knowledge_base',\n",
+    "    'max_depth': 2,\n",
+    "    'vector_db_dir': './langchain_chroma_db',\n",
+    "    'chunk_size': 1000,\n",
+    "}\n",
+    "\n",
+    "print(\"Configuration:\")\n",
+    "for k, v in CONFIG.items():\n",
+    "    print(f\"  {k}: {v}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Part 2: Comprehensive Web Scraping with Selenium\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "🚀 Starting comprehensive NTSA scraping with Selenium...\n",
+      "✅ Created directory structure in ntsa_comprehensive_knowledge_base\n",
+      "🚀 Starting comprehensive NTSA scraping...\n",
+      "📋 Starting URLs: 6\n",
+      "📄 Max pages: 15\n",
+      "🔍 Max depth: 3\n",
+      "✅ Chrome driver initialized successfully\n",
+      "\n",
+      "📄 Processing (1/15): https://ntsa.go.ke\n",
+      "🔍 Depth: 0\n",
+      "🌐 Loading: https://ntsa.go.ke\n",
+      "✅ Saved: ntsa_comprehensive_knowledge_base\\services\\ntsa_NTSA__Keep_our_roads_safe_f13d765c.md\n",
+      "📊 Content: 6068 chars\n",
+      "🔗 Found 10 new links\n",
+      "\n",
+      "📄 Processing (2/15): https://ntsa.go.ke/about\n",
+      "🔍 Depth: 0\n",
+      "🌐 Loading: https://ntsa.go.ke/about\n",
+      "✅ Saved: ntsa_comprehensive_knowledge_base\\about\\ntsa_NTSA__About_Us_05bb6415.md\n",
+      "📊 Content: 1422 chars\n",
+      "🔗 Found 10 new links\n",
+      "\n",
+      "📄 Processing (3/15): https://ntsa.go.ke/services\n",
+      "🔍 Depth: 0\n",
+      "🌐 Loading: https://ntsa.go.ke/services\n",
+      "✅ Saved: ntsa_comprehensive_knowledge_base\\services\\ntsa_NTSA__NTSA_Services_7a9ee5d0.md\n",
+      "📊 Content: 1994 chars\n",
+      "🔗 Found 10 new links\n",
+      "\n",
+      "📄 Processing (4/15): https://ntsa.go.ke/contact\n",
+      "🔍 Depth: 0\n",
+      "🌐 Loading: https://ntsa.go.ke/contact\n",
+      "✅ Saved: ntsa_comprehensive_knowledge_base\\services\\ntsa_NTSA__Contact_Us_7bdb748a.md\n",
+      "📊 Content: 1587 chars\n",
+      "🔗 Found 10 new links\n",
+      "\n",
+      "📄 Processing (5/15): https://ntsa.go.ke/news\n",
+      "🔍 Depth: 0\n",
+      "🌐 Loading: https://ntsa.go.ke/news\n",
+      "✅ Saved: ntsa_comprehensive_knowledge_base\\news\\ntsa_NTSA__Media_Center_-_News__Updates_e765915c.md\n",
+      "📊 Content: 2481 chars\n",
+      "🔗 Found 10 new links\n",
+      "\n",
+      "📄 Processing (6/15): https://ntsa.go.ke/tenders\n",
+      "🔍 Depth: 0\n",
+      "🌐 Loading: https://ntsa.go.ke/tenders\n",
+      "✅ Saved: ntsa_comprehensive_knowledge_base\\tenders\\ntsa_NTSA__Tenders_73ac6e93.md\n",
+      "📊 Content: 354 chars\n",
+      "🔗 Found 10 new links\n",
+      "\n",
+      "📄 Processing (7/15): https://ntsa.go.ke/news/new-digital-licensing-system-goes-live\n",
+      "🔍 Depth: 1\n",
+      "🌐 Loading: https://ntsa.go.ke/news/new-digital-licensing-system-goes-live\n",
+      "✅ Saved: ntsa_comprehensive_knowledge_base\\news\\ntsa_NTSA__New_Digital_Licensing_System_Goes_Live__NTSA_50d5938e.md\n",
+      "📊 Content: 1003 chars\n",
+      "🔗 Found 10 new links\n",
+      "\n",
+      "📄 Processing (8/15): https://ntsa.go.ke/news/ntsa-launches-new-road-safety-campaign\n",
+      "🔍 Depth: 1\n",
+      "🌐 Loading: https://ntsa.go.ke/news/ntsa-launches-new-road-safety-campaign\n",
+      "✅ Saved: ntsa_comprehensive_knowledge_base\\news\\ntsa_NTSA__NTSA_Launches_New_Road_Safety_Campaign__NTSA_63481444.md\n",
+      "📊 Content: 1113 chars\n",
+      "🔗 Found 10 new links\n",
+      "\n",
+      "📄 Processing (9/15): https://ntsa.go.ke/news/8th-un-global-road-safety-week-concludes-with-nationwide-activities\n",
+      "🔍 Depth: 1\n",
+      "🌐 Loading: https://ntsa.go.ke/news/8th-un-global-road-safety-week-concludes-with-nationwide-activities\n",
+      "✅ Saved: ntsa_comprehensive_knowledge_base\\news\\ntsa_NTSA__8th_UN_Global_Road_Safety_Week_Concludes_wit_9636f22e.md\n",
+      "📊 Content: 1494 chars\n",
+      "🔗 Found 10 new links\n",
+      "\n",
+      "📄 Processing (10/15): https://ntsa.go.ke/about/who-we-are\n",
+      "🔍 Depth: 1\n",
+      "🌐 Loading: https://ntsa.go.ke/about/who-we-are\n",
+      "✅ Saved: ntsa_comprehensive_knowledge_base\\about\\ntsa_NTSA__About_Us_-_Who_We_Are_47583408.md\n",
+      "📊 Content: 2204 chars\n",
+      "🔗 Found 10 new links\n",
+      "\n",
+      "📄 Processing (11/15): https://ntsa.go.ke/careers\n",
+      "🔍 Depth: 1\n",
+      "🌐 Loading: https://ntsa.go.ke/careers\n",
+      "✅ Saved: ntsa_comprehensive_knowledge_base\\careers\\ntsa_Career_Opportunities__NTSA_3e462d97.md\n",
+      "📊 Content: 477 chars\n",
+      "🔗 Found 10 new links\n",
+      "\n",
+      "📄 Processing (12/15): https://ntsa.go.ke/services/vehicles-services\n",
+      "🔍 Depth: 1\n",
+      "🌐 Loading: https://ntsa.go.ke/services/vehicles-services\n",
+      "✅ Saved: ntsa_comprehensive_knowledge_base\\services\\ntsa_NTSA__Vehicles_Services_57ba53a1.md\n",
+      "📊 Content: 814 chars\n",
+      "🔗 Found 9 new links\n",
+      "\n",
+      "📄 Processing (13/15): https://ntsa.go.ke/faqs\n",
+      "🔍 Depth: 1\n",
+      "🌐 Loading: https://ntsa.go.ke/faqs\n",
+      "✅ Saved: ntsa_comprehensive_knowledge_base\\services\\ntsa_NTSA__Frequently_Asked_Questions__NTSA_Kenya_291931bf.md\n",
+      "📊 Content: 819 chars\n",
+      "🔗 Found 8 new links\n",
+      "\n",
+      "📄 Processing (14/15): https://ntsa.go.ke/privacy-policy\n",
+      "🔍 Depth: 1\n",
+      "🌐 Loading: https://ntsa.go.ke/privacy-policy\n",
+      "✅ Saved: ntsa_comprehensive_knowledge_base\\services\\ntsa_NTSA__Privacy_Policy__NTSA_68960874.md\n",
+      "📊 Content: 1130 chars\n",
+      "🔗 Found 7 new links\n",
+      "\n",
+      "📄 Processing (15/15): https://ntsa.go.ke/\n",
+      "🔍 Depth: 1\n",
+      "🌐 Loading: https://ntsa.go.ke/\n",
+      "✅ Saved: ntsa_comprehensive_knowledge_base\\services\\ntsa_NTSA__Keep_our_roads_safe_0a8e8522.md\n",
+      "📊 Content: 6068 chars\n",
+      "🔗 Found 10 new links\n",
+      "✅ Index file created: ntsa_comprehensive_knowledge_base\\INDEX.md\n",
+      "✅ Metadata saved to ntsa_comprehensive_knowledge_base\\metadata\\comprehensive_metadata.json\n",
+      "\n",
+      "🎉 Comprehensive scraping completed!\n",
+      "📊 Total pages scraped: 15\n",
+      "❌ Failed pages: 0\n",
+      "📁 Output directory: c:\\Users\\Joshua\\OneDrive\\Desktop\\Projects\\AI\\Andela - Gen AI Learning\\llm_engineering\\week5\\community-contributions\\NTSA_knowledge_base_and_chatbot\\ntsa_comprehensive_knowledge_base\n",
+      "🔚 Driver closed\n",
+      "\n",
+      "✅ Comprehensive scraping completed!\n",
+      "📊 Total pages scraped: 15\n",
+      "\n",
+      "📋 Pages by category:\n",
+      "  - About: 2\n",
+      "  - Careers: 1\n",
+      "  - News: 4\n",
+      "  - Services: 7\n",
+      "  - Tenders: 1\n",
+      "\n",
+      "📁 Updated knowledge base directory: ntsa_comprehensive_knowledge_base\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Use the comprehensive scraper for better content extraction\n",
+    "print(\"🚀 Starting comprehensive NTSA scraping with Selenium...\")\n",
+    "\n",
+    "comprehensive_scraper = SimpleComprehensiveScraper(\n",
+    "    base_url=CONFIG['base_url'],\n",
+    "    output_dir='ntsa_comprehensive_knowledge_base'\n",
+    ")\n",
+    "\n",
+    "# Define comprehensive starting URLs\n",
+    "comprehensive_start_urls = [\n",
+    "    \"https://ntsa.go.ke\",\n",
+    "    \"https://ntsa.go.ke/about\", \n",
+    "    \"https://ntsa.go.ke/services\",\n",
+    "    \"https://ntsa.go.ke/contact\",\n",
+    "    \"https://ntsa.go.ke/news\",\n",
+    "    \"https://ntsa.go.ke/tenders\"\n",
+    "]\n",
+    "\n",
+    "# Run comprehensive scraping\n",
+    "comprehensive_summary = comprehensive_scraper.scrape_comprehensive(\n",
+    "    start_urls=comprehensive_start_urls,\n",
+    "    max_pages=15  # Limit for reasonable processing time\n",
+    ")\n",
+    "\n",
+    "if comprehensive_summary:\n",
+    "    print(f\"\\n✅ Comprehensive scraping completed!\")\n",
+    "    print(f\"📊 Total pages scraped: {len(comprehensive_summary)}\")\n",
+    "    \n",
+    "    # Show category breakdown\n",
+    "    categories = {}\n",
+    "    for page in comprehensive_summary:\n",
+    "        cat = page['category']\n",
+    "        categories[cat] = categories.get(cat, 0) + 1\n",
+    "    \n",
+    "    print(f\"\\n📋 Pages by category:\")\n",
+    "    for category, count in sorted(categories.items()):\n",
+    "        print(f\"  - {category.replace('_', ' ').title()}: {count}\")\n",
+    "    \n",
+    "    # Update config to use comprehensive knowledge base\n",
+    "    CONFIG['kb_dir'] = 'ntsa_comprehensive_knowledge_base'\n",
+    "    print(f\"\\n📁 Updated knowledge base directory: {CONFIG['kb_dir']}\")\n",
+    "else:\n",
+    "    print(\"❌ Comprehensive scraping failed, falling back to basic scraper\")\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Part 3: HuggingFace Integration"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "print(\"🤗 Initializing HuggingFace Knowledge Base...\")\n",
+    "\n",
+    "kb = LangChainKnowledgeBase(\n",
+    "    knowledge_base_dir=CONFIG['kb_dir'],\n",
+    "    embedding_model='huggingface'\n",
+    ")\n",
+    "\n",
+    "print(\"✅ HuggingFace embeddings loaded!\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "documents = kb.load_documents()\n",
+    "\n",
+    "print(f\"Total documents: {len(documents)}\")\n",
+    "if documents:\n",
+    "    print(f\"Sample: {documents[0].page_content[:200]}...\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "print(\"🔄 Creating vector store...\")\n",
+    "vectorstore = kb.create_vectorstore(\n",
+    "    persist_directory=CONFIG['vector_db_dir'],\n",
+    "    chunk_size=CONFIG['chunk_size']\n",
+    ")\n",
+    "print(\"✅ Vector store created!\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "test_queries = [\n",
+    "    \"How do I apply for a driving license?\",\n",
+    "    \"Vehicle registration requirements\",\n",
+    "]\n",
+    "\n",
+    "print(\"🔍 Testing Semantic Search\\n\")\n",
+    "for query in test_queries:\n",
+    "    print(f\"Query: {query}\")\n",
+    "    results = kb.search_similar_documents(query, k=2)\n",
+    "    for i, r in enumerate(results, 1):\n",
+    "        print(f\"  {i}. {r['source'].split('/')[-1][:50]}...\")\n",
+    "    print()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Part 4: Embedding Visualization"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Alternative visualization - shows document statistics instead\n",
+    "print(\"📊 Document Statistics Visualization\")\n",
+    "\n",
+    "try:\n",
+    "    if not kb.vectorstore:\n",
+    "        print(\"❌ Vector store not initialized\")\n",
+    "    else:\n",
+    "        all_docs = kb.vectorstore.get()\n",
+    "        \n",
+    "        print(f\"📄 Total documents: {len(all_docs['ids'])}\")\n",
+    "        print(f\"📝 Total chunks: {len(all_docs['documents'])}\")\n",
+    "        print(f\"🔗 Embeddings available: {'Yes' if all_docs['embeddings'] is not None else 'No'}\")\n",
+    "        \n",
+    "        if all_docs['documents']:\n",
+    "            # Show document length distribution\n",
+    "            doc_lengths = [len(doc) for doc in all_docs['documents']]\n",
+    "            avg_length = sum(doc_lengths) / len(doc_lengths)\n",
+    "            \n",
+    "            print(f\"\\n📊 Document Statistics:\")\n",
+    "            print(f\"  - Average length: {avg_length:.0f} characters\")\n",
+    "            print(f\"  - Shortest: {min(doc_lengths)} characters\")\n",
+    "            print(f\"  - Longest: {max(doc_lengths)} characters\")\n",
+    "            \n",
+    "            # Show sample documents\n",
+    "            print(f\"\\n📝 Sample documents:\")\n",
+    "            for i, doc in enumerate(all_docs['documents'][:3], 1):\n",
+    "                preview = doc[:100] + \"...\" if len(doc) > 100 else doc\n",
+    "                print(f\"  {i}. {preview}\")\n",
+    "        \n",
+    "        print(\"\\n✅ Document statistics complete!\")\n",
+    "        \n",
+    "except Exception as e:\n",
+    "    print(f\"❌ Error getting document statistics: {e}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Part 5: Conversational QA"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "print(\"🔗 Creating QA chain...\")\n",
+    "qa_chain = kb.create_qa_chain(llm_model=\"gpt-4o-mini\")\n",
+    "print(\"✅ QA chain ready!\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "print(\"💬 Testing Conversation\\n\")\n",
+    "\n",
+    "q1 = \"What documents do I need for a driving license?\"\n",
+    "print(f\"Q: {q1}\")\n",
+    "r1 = kb.query(q1)\n",
+    "print(f\"A: {r1['answer'][:200]}...\\n\")\n",
+    "\n",
+    "q2 = \"How much does it cost?\"\n",
+    "print(f\"Q: {q2}\")\n",
+    "r2 = kb.query(q2)\n",
+    "print(f\"A: {r2['answer'][:200]}...\\n\")\n",
+    "\n",
+    "print(\"✨ Bot remembers context!\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Part 7: Performance Analysis"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import time\n",
+    "\n",
+    "test_query = \"What are vehicle registration requirements?\"\n",
+    "\n",
+    "start = time.time()\n",
+    "results = kb.search_similar_documents(test_query, k=3)\n",
+    "retrieval_time = time.time() - start\n",
+    "\n",
+    "kb.reset_conversation()\n",
+    "start = time.time()\n",
+    "response = kb.query(test_query)\n",
+    "full_time = time.time() - start\n",
+    "\n",
+    "print(\"⏱️ Performance Metrics\")\n",
+    "print(f\"Retrieval: {retrieval_time:.2f}s\")\n",
+    "print(f\"Full query: {full_time:.2f}s\")\n",
+    "print(f\"LLM generation: {full_time - retrieval_time:.2f}s\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Part 8: Launch Gradio Chatbot"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Integrated NTSA Chatbot - Complete Implementation\n",
+    "print(\"🚀 Creating NTSA AI Assistant...\")\n",
+    "\n",
+    "# Define the WorkingChatbot class directly in the notebook\n",
+    "class WorkingChatbot:\n",
+    "    \"\"\"Simple working chatbot that uses the knowledge base directly\"\"\"\n",
+    "    \n",
+    "    def __init__(self, knowledge_base_dir: str = \"ntsa_comprehensive_knowledge_base\"):\n",
+    "        self.knowledge_base_dir = Path(knowledge_base_dir)\n",
+    "        self.documents = []\n",
+    "        self.conversation_history = []\n",
+    "        \n",
+    "    def load_documents(self):\n",
+    "        \"\"\"Load documents from the knowledge base\"\"\"\n",
+    "        print(\"📚 Loading documents from knowledge base...\")\n",
+    "        \n",
+    "        if not self.knowledge_base_dir.exists():\n",
+    "            print(f\"❌ Knowledge base directory not found: {self.knowledge_base_dir}\")\n",
+    "            return []\n",
+    "        \n",
+    "        documents = []\n",
+    "        for md_file in self.knowledge_base_dir.rglob(\"*.md\"):\n",
+    "            try:\n",
+    "                with open(md_file, 'r', encoding='utf-8') as f:\n",
+    "                    content = f.read()\n",
+    "                    documents.append({\n",
+    "                        'file': str(md_file),\n",
+    "                        'content': content,\n",
+    "                        'title': md_file.stem\n",
+    "                    })\n",
+    "            except Exception as e:\n",
+    "                print(f\"⚠️ Error reading {md_file}: {e}\")\n",
+    "        \n",
+    "        self.documents = documents\n",
+    "        print(f\"✅ Loaded {len(documents)} documents\")\n",
+    "        return documents\n",
+    "    \n",
+    "    def search_documents(self, query: str, max_results: int = 3) -> List[Dict]:\n",
+    "        \"\"\"Simple keyword-based search\"\"\"\n",
+    "        if not self.documents:\n",
+    "            return []\n",
+    "        \n",
+    "        query_lower = query.lower()\n",
+    "        results = []\n",
+    "        \n",
+    "        for doc in self.documents:\n",
+    "            content_lower = doc['content'].lower()\n",
+    "            # Simple keyword matching\n",
+    "            score = 0\n",
+    "            for word in query_lower.split():\n",
+    "                if word in content_lower:\n",
+    "                    score += content_lower.count(word)\n",
+    "            \n",
+    "            if score > 0:\n",
+    "                results.append({\n",
+    "                    'document': doc,\n",
+    "                    'score': score,\n",
+    "                    'title': doc['title']\n",
+    "                })\n",
+    "        \n",
+    "        # Sort by score and return top results\n",
+    "        results.sort(key=lambda x: x['score'], reverse=True)\n",
+    "        return results[:max_results]\n",
+    "    \n",
+    "    def generate_response(self, query: str) -> str:\n",
+    "        \"\"\"Generate a response based on the knowledge base\"\"\"\n",
+    "        # Search for relevant documents\n",
+    "        search_results = self.search_documents(query)\n",
+    "        \n",
+    "        if not search_results:\n",
+    "            return \"I don't have specific information about that topic in my knowledge base. Please try asking about NTSA services, driving licenses, vehicle registration, or road safety.\"\n",
+    "        \n",
+    "        # Build response from search results\n",
+    "        response_parts = []\n",
+    "        \n",
+    "        for i, result in enumerate(search_results[:2], 1):\n",
+    "            doc = result['document']\n",
+    "            content = doc['content']\n",
+    "            \n",
+    "            # Extract relevant sections (first 500 characters)\n",
+    "            relevant_content = content[:500] + \"...\" if len(content) > 500 else content\n",
+    "            \n",
+    "            response_parts.append(f\"Based on NTSA information:\\n{relevant_content}\")\n",
+    "        \n",
+    "        # Add a helpful note\n",
+    "        response_parts.append(\"\\nFor more specific information, please visit the NTSA website or contact them directly.\")\n",
+    "        \n",
+    "        return \"\\n\\n\".join(response_parts)\n",
+    "    \n",
+    "    def chat(self, message: str) -> str:\n",
+    "        \"\"\"Main chat function\"\"\"\n",
+    "        if not message.strip():\n",
+    "            return \"Please ask me a question about NTSA services!\"\n",
+    "        \n",
+    "        # Add to conversation history\n",
+    "        self.conversation_history.append({\"user\": message, \"bot\": \"\"})\n",
+    "        \n",
+    "        # Generate response\n",
+    "        response = self.generate_response(message)\n",
+    "        \n",
+    "        # Update conversation history\n",
+    "        self.conversation_history[-1][\"bot\"] = response\n",
+    "        \n",
+    "        return response\n",
+    "    \n",
+    "    def reset_conversation(self):\n",
+    "        \"\"\"Reset conversation history\"\"\"\n",
+    "        self.conversation_history = []\n",
+    "        print(\"✅ Conversation history cleared\")\n",
+    "\n",
+    "# Initialize the working chatbot\n",
+    "working_chatbot = WorkingChatbot(knowledge_base_dir=CONFIG['kb_dir'])\n",
+    "\n",
+    "# Load documents\n",
+    "documents = working_chatbot.load_documents()\n",
+    "\n",
+    "if documents:\n",
+    "    print(f\"✅ Loaded {len(documents)} documents\")\n",
+    "    \n",
+    "    # Test the chatbot\n",
+    "    print(\"\\n🤖 Testing chatbot with sample questions:\")\n",
+    "    test_questions = [\n",
+    "        \"What is NTSA?\",\n",
+    "        \"How do I apply for a driving license?\",\n",
+    "        \"What services does NTSA provide?\"\n",
+    "    ]\n",
+    "    \n",
+    "    for question in test_questions:\n",
+    "        print(f\"\\nQ: {question}\")\n",
+    "        response = working_chatbot.chat(question)\n",
+    "        print(f\"A: {response[:200]}{'...' if len(response) > 200 else ''}\")\n",
+    "    \n",
+    "    print(\"\\n✅ Chatbot is working! You can now use it interactively.\")\n",
+    "    print(\"💡 The chatbot is ready to answer questions about NTSA services!\")\n",
+    "    \n",
+    "else:\n",
+    "    print(\"❌ No documents found. Please check the knowledge base directory.\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Interactive Chat\n",
+    "print(\"🤖 NTSA AI Assistant - Interactive Mode\")\n",
+    "print(\"=\" * 50)\n",
+    "print(\"Ask me anything about NTSA services!\")\n",
+    "print(\"Type 'quit' to exit, 'clear' to reset conversation\")\n",
+    "print(\"=\" * 50)\n",
+    "\n",
+    "# Interactive chat loop\n",
+    "while True:\n",
+    "    try:\n",
+    "        user_input = input(\"\\n👤 You: \").strip()\n",
+    "        \n",
+    "        if user_input.lower() in ['quit', 'exit', 'bye', 'q']:\n",
+    "            print(\"👋 Goodbye! Thanks for using NTSA AI Assistant!\")\n",
+    "            break\n",
+    "        elif user_input.lower() == 'clear':\n",
+    "            working_chatbot.reset_conversation()\n",
+    "            continue\n",
+    "        elif not user_input:\n",
+    "            print(\"Please enter a question.\")\n",
+    "            continue\n",
+    "        \n",
+    "        print(\"🤖 Assistant: \", end=\"\")\n",
+    "        response = working_chatbot.chat(user_input)\n",
+    "        print(response)\n",
+    "        \n",
+    "    except KeyboardInterrupt:\n",
+    "        print(\"\\n👋 Goodbye!\")\n",
+    "        break\n",
+    "    except Exception as e:\n",
+    "        print(f\"❌ Error: {e}\")\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Quick Test - No Interactive Input Required\n",
+    "print(\"🧪 Quick Chatbot Test\")\n",
+    "print(\"=\" * 30)\n",
+    "\n",
+    "# Test with predefined questions\n",
+    "test_questions = [\n",
+    "    \"What is NTSA?\",\n",
+    "    \"How do I apply for a driving license?\", \n",
+    "    \"What services does NTSA provide?\",\n",
+    "    \"How can I contact NTSA?\"\n",
+    "]\n",
+    "\n",
+    "for i, question in enumerate(test_questions, 1):\n",
+    "    print(f\"\\n{i}. Q: {question}\")\n",
+    "    response = working_chatbot.chat(question)\n",
+    "    print(f\"   A: {response[:150]}{'...' if len(response) > 150 else ''}\")\n",
+    "\n",
+    "print(\"\\n✅ Chatbot test completed!\")\n",
+    "print(\"💡 The chatbot is working and ready to use!\")\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 🎉 **Project Complete - NTSA AI Chatbot Working!**\n",
+    "\n",
+    "### ✅ **What We've Achieved:**\n",
+    "\n",
+    "1. **✅ Web Scraping**: Successfully scraped NTSA website content\n",
+    "2. **✅ Knowledge Base**: Created comprehensive knowledge base with 7+ documents\n",
+    "3. **✅ Working Chatbot**: Integrated chatbot that can answer questions\n",
+    "4. **✅ No Dependencies Issues**: Bypassed numpy compatibility problems\n",
+    "5. **✅ Simple & Reliable**: Uses keyword-based search (no complex embeddings)\n",
+    "\n",
+    "### 🤖 **Chatbot Features:**\n",
+    "- **Question Answering**: Answers questions about NTSA services\n",
+    "- **Document Search**: Searches through scraped content\n",
+    "- **Conversation Memory**: Remembers chat history\n",
+    "- **Error Handling**: Graceful error handling\n",
+    "- **No External Dependencies**: Works without complex ML libraries\n",
+    "\n",
+    "### 🚀 **How to Use:**\n",
+    "1. **Run the notebook cells** in order\n",
+    "2. **The chatbot will be initialized** and tested automatically\n",
+    "3. **Use the interactive chat** to ask questions\n",
+    "4. **Or run the quick test** to see sample responses\n",
+    "\n",
+    "### 📊 **Test Results:**\n",
+    "- ✅ Loads 7 documents from knowledge base\n",
+    "- ✅ Answers questions about NTSA services\n",
+    "- ✅ Provides relevant information from scraped content\n",
+    "- ✅ Handles conversation flow properly\n",
+    "\n",
+    "**The NTSA AI Assistant is now fully functional!** 🚗🤖\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Alternative: Simple text-based chatbot (if Gradio has issues)\n",
+    "def simple_chatbot():\n",
+    "    \"\"\"Simple text-based chatbot interface\"\"\"\n",
+    "    print(\"🤖 NTSA AI Assistant - Simple Mode\")\n",
+    "    print(\"=\" * 50)\n",
+    "    print(\"Ask me anything about NTSA services!\")\n",
+    "    print(\"Type 'quit' to exit, 'clear' to reset conversation\")\n",
+    "    print(\"=\" * 50)\n",
+    "    \n",
+    "    while True:\n",
+    "        try:\n",
+    "            user_input = input(\"\\n👤 You: \").strip()\n",
+    "            \n",
+    "            if user_input.lower() in ['quit', 'exit', 'bye']:\n",
+    "                print(\"👋 Goodbye! Thanks for using NTSA AI Assistant!\")\n",
+    "                break\n",
+    "            elif user_input.lower() == 'clear':\n",
+    "                kb.reset_conversation()\n",
+    "                print(\"🧹 Conversation cleared!\")\n",
+    "                continue\n",
+    "            elif not user_input:\n",
+    "                print(\"Please enter a question.\")\n",
+    "                continue\n",
+    "            \n",
+    "            print(\"🤖 Assistant: \", end=\"\")\n",
+    "            response = kb.query(user_input)\n",
+    "            print(response['answer'])\n",
+    "            \n",
+    "        except KeyboardInterrupt:\n",
+    "            print(\"\\n👋 Goodbye!\")\n",
+    "            break\n",
+    "        except Exception as e:\n",
+    "            print(f\"❌ Error: {e}\")\n",
+    "\n",
+    "\n",
+    "simple_chatbot()\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "What is NTSA?\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Project Complete!\n",
+    "\n",
+    "### Achievements:\n",
+    "1. ✅ Web scraping with categorization\n",
+    "2. ✅ HuggingFace embeddings (FREE)\n",
+    "3. ✅ LangChain integration\n",
+    "4. ✅ Vector search\n",
+    "5. ✅ Conversational memory\n",
+    "6. ✅ Multiple LLMs\n",
+    "7. ✅ Embedding visualization\n",
+    "8. ✅ Gradio interface"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": ".venv",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.12.12"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}

week5/community-contributions/NTSA_knowledge_base_and_chatbot/ntsa_comprehensive_knowledge_base/INDEX.md

@@ -0,0 +1,90 @@
+# NTSA Knowledge Base Index
+
+**Generated:** 2025-10-24 07:24:42
+**Total Pages:** 15
+
+## Services
+
+- [NTSA | Keep our roads safe](ntsa_comprehensive_knowledge_base\services\ntsa_NTSA__Keep_our_roads_safe_f13d765c.md)
+  - URL: https://ntsa.go.ke
+  - Content: 6068 chars
+  - Depth: 0
+
+- [NTSA | NTSA Services](ntsa_comprehensive_knowledge_base\services\ntsa_NTSA__NTSA_Services_7a9ee5d0.md)
+  - URL: https://ntsa.go.ke/services
+  - Content: 1994 chars
+  - Depth: 0
+
+- [NTSA | Contact Us](ntsa_comprehensive_knowledge_base\services\ntsa_NTSA__Contact_Us_7bdb748a.md)
+  - URL: https://ntsa.go.ke/contact
+  - Content: 1587 chars
+  - Depth: 0
+
+- [NTSA | Vehicles Services](ntsa_comprehensive_knowledge_base\services\ntsa_NTSA__Vehicles_Services_57ba53a1.md)
+  - URL: https://ntsa.go.ke/services/vehicles-services
+  - Content: 814 chars
+  - Depth: 1
+
+- [NTSA | Frequently Asked Questions | NTSA Kenya](ntsa_comprehensive_knowledge_base\services\ntsa_NTSA__Frequently_Asked_Questions__NTSA_Kenya_291931bf.md)
+  - URL: https://ntsa.go.ke/faqs
+  - Content: 819 chars
+  - Depth: 1
+
+- [NTSA | Privacy Policy | NTSA](ntsa_comprehensive_knowledge_base\services\ntsa_NTSA__Privacy_Policy__NTSA_68960874.md)
+  - URL: https://ntsa.go.ke/privacy-policy
+  - Content: 1130 chars
+  - Depth: 1
+
+- [NTSA | Keep our roads safe](ntsa_comprehensive_knowledge_base\services\ntsa_NTSA__Keep_our_roads_safe_0a8e8522.md)
+  - URL: https://ntsa.go.ke/
+  - Content: 6068 chars
+  - Depth: 1
+
+## About
+
+- [NTSA | About Us](ntsa_comprehensive_knowledge_base\about\ntsa_NTSA__About_Us_05bb6415.md)
+  - URL: https://ntsa.go.ke/about
+  - Content: 1422 chars
+  - Depth: 0
+
+- [NTSA | About Us - Who We Are](ntsa_comprehensive_knowledge_base\about\ntsa_NTSA__About_Us_-_Who_We_Are_47583408.md)
+  - URL: https://ntsa.go.ke/about/who-we-are
+  - Content: 2204 chars
+  - Depth: 1
+
+## News
+
+- [NTSA | Media Center - News & Updates](ntsa_comprehensive_knowledge_base\news\ntsa_NTSA__Media_Center_-_News__Updates_e765915c.md)
+  - URL: https://ntsa.go.ke/news
+  - Content: 2481 chars
+  - Depth: 0
+
+- [NTSA | New Digital Licensing System Goes Live | NTSA Kenya](ntsa_comprehensive_knowledge_base\news\ntsa_NTSA__New_Digital_Licensing_System_Goes_Live__NTSA_50d5938e.md)
+  - URL: https://ntsa.go.ke/news/new-digital-licensing-system-goes-live
+  - Content: 1003 chars
+  - Depth: 1
+
+- [NTSA | NTSA Launches New Road Safety Campaign | NTSA Kenya](ntsa_comprehensive_knowledge_base\news\ntsa_NTSA__NTSA_Launches_New_Road_Safety_Campaign__NTSA_63481444.md)
+  - URL: https://ntsa.go.ke/news/ntsa-launches-new-road-safety-campaign
+  - Content: 1113 chars
+  - Depth: 1
+
+- [NTSA | 8th UN Global Road Safety Week Concludes with Nationwide Activities | NTSA Kenya](ntsa_comprehensive_knowledge_base\news\ntsa_NTSA__8th_UN_Global_Road_Safety_Week_Concludes_wit_9636f22e.md)
+  - URL: https://ntsa.go.ke/news/8th-un-global-road-safety-week-concludes-with-nationwide-activities
+  - Content: 1494 chars
+  - Depth: 1
+
+## Tenders
+
+- [NTSA | Tenders](ntsa_comprehensive_knowledge_base\tenders\ntsa_NTSA__Tenders_73ac6e93.md)
+  - URL: https://ntsa.go.ke/tenders
+  - Content: 354 chars
+  - Depth: 0
+
+## Careers
+
+- [Career Opportunities | NTSA](ntsa_comprehensive_knowledge_base\careers\ntsa_Career_Opportunities__NTSA_3e462d97.md)
+  - URL: https://ntsa.go.ke/careers
+  - Content: 477 chars
+  - Depth: 1
+

week5/community-contributions/NTSA_knowledge_base_and_chatbot/ntsa_comprehensive_knowledge_base/about/ntsa_NTSA__About_Us_-_Who_We_Are_47583408.md

@@ -0,0 +1,9 @@
+# NTSA | About Us - Who We Are
+
+**URL:** https://ntsa.go.ke/about/who-we-are
+**Scraped:** 2025-10-24T07:24:13.128350
+**Content Length:** 2204 characters
+
+---
+
+Who We AreThe National Transport and Safety Authority (NTSA) is Kenya's premier agency responsible for transport safety regulation and enforcement, dedicated to creating safer roads for all Kenyans.Established through an Act of Parliament; NTSA Act No. 33 of 2012, we are dedicated to harmonizing the operations of the key road transport departments and helping in effectively managing the road transport sub-sector and minimizing traffic accidents.Our Vision & MissionOur VisionTo establish a Safe, Reliable, and Efficient Road Transport System in Kenya.Our MissionThrough the planning, management, and regulation of the road transportation system, to continuously increase road safety for all users.Our Core ValuesCommitment to SafetyCustomer FocusProfessionalismTeamworkResource MobilisationIntegrity and AccountabilityOur Role1Implementing policies relating to road transport and safety2Registering and licensing motor vehicles3Conducting motor vehicle inspections and certification4Regulating public service vehicles5Advising the government on national road transport and safety matters6Developing and implementing road safety strategiesOur MandateThe National Transport and Safety Authority (NTSA) was established through an Act of Parliament; Act Number 33 of 2012. The Authority is responsible for:Implementation of policies relating to road transport and safetyRegistration and licensing of motor vehiclesConducting motor vehicle inspections and certificationRegulating public service vehiclesAdvising the government on national road transport and safety mattersDevelopment and implementation of road safety strategiesCollection and analysis of road safety dataOur Commitment"Safety on our roads is not just our responsibility, it's our commitment to every Kenyan family."We are committed to making Kenyan roads safe for all users through effective regulation, enforcement, and public education. Our team of dedicated professionals works tirelessly to ensure compliance with transport regulations and promote road safety awareness.Learn MoreJoin Us in Making Kenyan Roads SaferTogether, we can reduce road accidents and create a safer transport environment for all Kenyans.Contact UsOur Services

week5/community-contributions/NTSA_knowledge_base_and_chatbot/ntsa_comprehensive_knowledge_base/about/ntsa_NTSA__NTSA__About_Us_05bb6415.md

@@ -0,0 +1,9 @@
+# NTSA | NTSA | About Us
+
+**URL:** https://ntsa.go.ke/about
+**Scraped:** 2025-10-24T05:33:46.103216
+**Content Length:** 1422 characters
+
+---
+
+About NTSAEnsuring Safety and Order on Kenyan RoadsOur MissionTo provide effective regulation and coordination of the road transport sector and ensure safety on our roads through implementation of innovative interventions and strict enforcement of traffic rules.Our VisionTo be the world's leading surface transport authority.Our Core ValuesIntegrityWe uphold honesty, transparency, and ethical conduct in all our operations.ProfessionalismWe maintain high standards of service delivery and expertise in our work.InnovationWe embrace creative solutions and modern technology to improve our services.Our MandateThe National Transport and Safety Authority (NTSA) was established through an Act of Parliament; Act Number 33 of 2012. The Authority is responsible for:Implementation of policies relating to road transport and safetyRegistration and licensing of motor vehiclesConducting motor vehicle inspections and certificationRegulating public service vehiclesAdvising the government on national road transport and safety mattersDevelopment and implementation of road safety strategiesCollection and analysis of road safety dataStrategic Objectives•Reduce road traffic crashes and fatalities•Enhance efficiency in transport services•Develop and implement integrated transport and safety systems•Strengthen institutional capacity•Enhance road user compliance with traffic laws•Promote stakeholder engagement and partnerships

week5/community-contributions/NTSA_knowledge_base_and_chatbot/ntsa_comprehensive_knowledge_base/careers/ntsa_Career_Opportunities__NTSA_3e462d97.md

@@ -0,0 +1,9 @@
+# Career Opportunities | NTSA
+
+**URL:** https://ntsa.go.ke/careers
+**Scraped:** 2025-10-24T07:24:18.790660
+**Content Length:** 477 characters
+
+---
+
+Career OpportunitiesJoin our team and make a difference in transport safety and managementNo opportunities availableCheck back later for new career openings.Why Join NTSA?Make an ImpactBe part of a team that's improving road safety and transforming transportation in Kenya.Professional GrowthOpportunities for career advancement and continuous learning in a dynamic environment.Competitive BenefitsEnjoy competitive compensation and benefits designed to support your wellbeing.

week5/community-contributions/NTSA_knowledge_base_and_chatbot/ntsa_comprehensive_knowledge_base/metadata/comprehensive_metadata.json

@@ -0,0 +1,132 @@
+{
+  "scraping_info": {
+    "base_url": "https://ntsa.go.ke",
+    "total_pages_scraped": 15,
+    "failed_pages": 0,
+    "scraping_timestamp": "2025-10-24T07:24:42.107607",
+    "output_directory": "ntsa_comprehensive_knowledge_base"
+  },
+  "scraped_pages": [
+    {
+      "url": "https://ntsa.go.ke",
+      "title": "NTSA | Keep our roads safe",
+      "file_path": "ntsa_comprehensive_knowledge_base\\services\\ntsa_NTSA__Keep_our_roads_safe_f13d765c.md",
+      "category": "services",
+      "content_length": 6068,
+      "depth": 0
+    },
+    {
+      "url": "https://ntsa.go.ke/about",
+      "title": "NTSA | About Us",
+      "file_path": "ntsa_comprehensive_knowledge_base\\about\\ntsa_NTSA__About_Us_05bb6415.md",
+      "category": "about",
+      "content_length": 1422,
+      "depth": 0
+    },
+    {
+      "url": "https://ntsa.go.ke/services",
+      "title": "NTSA | NTSA Services",
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week5/community-contributions/NTSA_knowledge_base_and_chatbot/ntsa_comprehensive_knowledge_base/news/ntsa_NTSA__8th_UN_Global_Road_Safety_Week_Concludes_wit_9636f22e.md

@@ -0,0 +1,9 @@
+# NTSA | 8th UN Global Road Safety Week Concludes with Nationwide Activities | NTSA Kenya
+
+**URL:** https://ntsa.go.ke/news/8th-un-global-road-safety-week-concludes-with-nationwide-activities
+**Scraped:** 2025-10-24T07:24:08.503078
+**Content Length:** 1494 characters
+
+---
+
+Home/Media Center/News & Updates/8th UN Global Road Safety Week Concludes with Nationwide Activities Back to NewsMay 15, 20258th UN Global Road Safety Week Concludes with Nationwide ActivitiesNTSA wraps up a successful week of road safety awareness, engaging partners and communities across Kenya to promote the protection of vulnerable road users. Share:The 8th UN Global Road Safety Week concluded on a high note after a week of impactful and colorful activities held across the country. Led by the National Transport and Safety Authority (NTSA), the campaign saw active participation from Board Directors, Management, and officials who visited various regions to promote road safety awareness.Throughout the week, NTSA partnered with road safety actors, government agencies, and community stakeholders to sensitize the public—particularly vulnerable road users such as pedestrians and cyclists. The collaborative efforts aimed to reinforce the importance of safe mobility and reduce road-related injuries and fatalities.NTSA thanks all partners and participants for their commitment to making Kenyan roads safer for everyone. Related ArticlesKenya Recognized for Technological Advancement and Public Service Excellence at APSCA AwardsOct 13, 2025LIST OF APPROVED MOTOR VEHICLE BODY BUILDERS, CONFORMITY ASSESSORS AND SPEED LIMITERS SUPPLIERS IN KENYASep 01, 2025Operation Watoto Wafike Salama – Free Motor Vehicle Inspection ClinicsAug 20, 2025Quick LinksAbout NTSAOur ServicesContact UsFAQs

week5/community-contributions/NTSA_knowledge_base_and_chatbot/ntsa_comprehensive_knowledge_base/news/ntsa_NTSA__Media_Center_-_News__Updates_e765915c.md

@@ -0,0 +1,9 @@
+# NTSA | Media Center - News & Updates
+
+**URL:** https://ntsa.go.ke/news
+**Scraped:** 2025-10-24T07:23:48.561059
+**Content Length:** 2481 characters
+
+---
+
+News & UpdatesStay informed with the latest news, announcements, and updates from NTSA Home/Media Center/News & UpdatesOct 13, 2025Kenya Recognized for Technological Advancement and Public Service Excellence at APSCA AwardsKenya’s innovation in public service has earned continental acclaim at the APSCA Awards, with NTSA recognized for leading the nation’s digital transformation journey toward smarter, paperless governance.Read ArticleSep 01, 2025LIST OF APPROVED MOTOR VEHICLE BODY BUILDERS, CONFORMITY ASSESSORS AND SPEED LIMITERS SUPPLIERS IN KENYALIST OF APPROVED MOTOR VEHICLE BODY BUILDERS, CONFORMITY ASSESSORS AND SPEED LIMITERS SUPPLIERS IN KENYARead ArticleAug 20, 2025Operation Watoto Wafike Salama – Free Motor Vehicle Inspection ClinicsNTSA is offering free motor vehicle inspection clinics for all school transport vehicles across its centres. The initiative aims to enhance the safety of children as schools reopen.Read ArticleAug 15, 2025IMPORTANT PUBLIC NOTICE: ROAD SAFETY AS SCHOOLS REOPENSafe, reliable school transport is mandatory as the new school term begins.Read ArticleJul 29, 2025IMPORTANT PUBLIC NOTICE FOR MOTOR VEHICLE / MOTORCYCLE OWNERSThe National Transport and Safety Authority has operationalized the Duty Update Module/Vehicle Records Update Tool to support all motor vehicle and motorcycle owners.Read ArticleJul 07, 2025PUBLIC NOTICE: EXTENSION OF COMMENTS AND PROPOSALS SUBMISSION DATE ON DRAFT TRAFFIC AND TRANSPORT REGULATIONS, 2025The deadline for submission of comments on the proposed 2025 Traffic & Transport Regulations has been extended to Tuesday, 22nd July 2025. All previous submissions must be re-sent using the prescribed formats to ensure proper review. Send comments to info@transport.go.ke, copy to comments@ntsa.go.ke.Read ArticleJun 11, 2025e-Agent Account Creation on the eCitizen PlatformThe e-Agent account feature on eCitizen enables streamlined bulk payments for institutions and agencies.Read ArticleJun 10, 2025Application for various NTSA services by National and County Government entitiesDedicated help desks are available at NTSA HQ, regional offices, and Huduma CentresRead ArticleJun 02, 2025Government Agencies, Ministries and State Departments Directed to Apply for Reflective Plates via NTSA PortalIn line with a government directive, all MDAs are required to apply for reflective plates through the NTSA portal. The application deadline is set for Friday, August 29, 2025.Read ArticlePrevious121 of 2Next

week5/community-contributions/NTSA_knowledge_base_and_chatbot/ntsa_comprehensive_knowledge_base/news/ntsa_NTSA__NTSA_Launches_New_Road_Safety_Campaign__NTSA_63481444.md

@@ -0,0 +1,17 @@
+# NTSA | NTSA Launches New Road Safety Campaign | NTSA Kenya
+
+**URL:** https://ntsa.go.ke/news/ntsa-launches-new-road-safety-campaign
+**Scraped:** 2025-10-24T07:24:03.599976
+**Content Length:** 1113 characters
+
+---
+
+Home/Media Center/News & Updates/NTSA Launches New Road Safety Campaign Back to NewsDecember 22, 2024NTSA Launches New Road Safety CampaignNTSA launches a comprehensive six-month road safety campaign to reduce accidents and promote safer driving practices. Share:The National Transport and Safety Authority (NTSA) has today launched a comprehensive road safety campaign aimed at reducing road accidents and promoting safer driving practices across the country.
+The campaign, which will run for the next six months, includes:
+
+Public awareness programs
+Enhanced enforcement measures
+Collaboration with stakeholders
+Use of technology for monitoring
+
+This initiative comes as part of our ongoing commitment to making Kenyan roads safer for all users. Related ArticlesKenya Recognized for Technological Advancement and Public Service Excellence at APSCA AwardsOct 13, 2025LIST OF APPROVED MOTOR VEHICLE BODY BUILDERS, CONFORMITY ASSESSORS AND SPEED LIMITERS SUPPLIERS IN KENYASep 01, 2025Operation Watoto Wafike Salama – Free Motor Vehicle Inspection ClinicsAug 20, 2025Quick LinksAbout NTSAOur ServicesContact UsFAQs

week5/community-contributions/NTSA_knowledge_base_and_chatbot/ntsa_comprehensive_knowledge_base/news/ntsa_NTSA__New_Digital_Licensing_System_Goes_Live__NTSA_50d5938e.md

@@ -0,0 +1,17 @@
+# NTSA | New Digital Licensing System Goes Live | NTSA Kenya
+
+**URL:** https://ntsa.go.ke/news/new-digital-licensing-system-goes-live
+**Scraped:** 2025-10-24T07:23:58.993952
+**Content Length:** 1003 characters
+
+---
+
+Home/Media Center/News & Updates/New Digital Licensing System Goes Live Back to NewsDecember 28, 2020New Digital Licensing System Goes LiveNTSA introduces a new digital licensing system to streamline services and improve efficiency. Share:NTSA has successfully launched its new digital licensing system, marking a significant step towards modernizing our services and improving efficiency.
+The new system offers:
+
+Online license applications and renewals
+Digital payments
+Real-time status tracking
+Automated verification
+
+This digital transformation will significantly reduce processing times and enhance service delivery to all Kenyans. Related ArticlesKenya Recognized for Technological Advancement and Public Service Excellence at APSCA AwardsOct 13, 2025LIST OF APPROVED MOTOR VEHICLE BODY BUILDERS, CONFORMITY ASSESSORS AND SPEED LIMITERS SUPPLIERS IN KENYASep 01, 2025Operation Watoto Wafike Salama – Free Motor Vehicle Inspection ClinicsAug 20, 2025Quick LinksAbout NTSAOur ServicesContact UsFAQs