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Merge pull request #7 from ed-donner/main

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Pull from upstream  10/23
This commit is contained in:
Stephen Muthama
2025-10-23 13:48:36 +03:00
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parent 93812a866e 3a10b6ea95
commit 0324ba2cf1
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{
"cells": [
{
"cell_type": "markdown",
"id": "d15d8294-3328-4e07-ad16-8a03e9bbfdb9",
"metadata": {},
"source": []
},
{
"cell_type": "markdown",
"id": "83f28feb",
"metadata": {},
"source": [
"###Synthetic Dataset Generator with LLMs (Anthropic API)Everything runs with your Anthropic API key — no model downloads"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "7510bec6",
"metadata": {},
"outputs": [],
"source": [
"# Imports and API setup\n",
"\n",
"import os\n",
"import json\n",
"import requests\n",
"import gradio as gr\n",
"from dotenv import load_dotenv"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "5abc2ed3",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"API key loaded successfully!\n"
]
}
],
"source": [
"# Load variables from .env file\n",
"load_dotenv()\n",
"\n",
"# Get your Anthropic API key\n",
"API_KEY = os.getenv(\"API_KEY\")\n",
"\n",
"if not API_KEY:\n",
" raise ValueError(\" API_KEY not found. Check your .env file\")\n",
"\n",
"print(\"API key loaded successfully!\")\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "e49ec675",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"{'data': [{'type': 'model', 'id': 'claude-haiku-4-5-20251001', 'display_name': 'Claude Haiku 4.5', 'created_at': '2025-10-15T00:00:00Z'}, {'type': 'model', 'id': 'claude-sonnet-4-5-20250929', 'display_name': 'Claude Sonnet 4.5', 'created_at': '2025-09-29T00:00:00Z'}, {'type': 'model', 'id': 'claude-opus-4-1-20250805', 'display_name': 'Claude Opus 4.1', 'created_at': '2025-08-05T00:00:00Z'}, {'type': 'model', 'id': 'claude-opus-4-20250514', 'display_name': 'Claude Opus 4', 'created_at': '2025-05-22T00:00:00Z'}, {'type': 'model', 'id': 'claude-sonnet-4-20250514', 'display_name': 'Claude Sonnet 4', 'created_at': '2025-05-22T00:00:00Z'}, {'type': 'model', 'id': 'claude-3-7-sonnet-20250219', 'display_name': 'Claude Sonnet 3.7', 'created_at': '2025-02-24T00:00:00Z'}, {'type': 'model', 'id': 'claude-3-5-haiku-20241022', 'display_name': 'Claude Haiku 3.5', 'created_at': '2024-10-22T00:00:00Z'}, {'type': 'model', 'id': 'claude-3-haiku-20240307', 'display_name': 'Claude Haiku 3', 'created_at': '2024-03-07T00:00:00Z'}], 'has_more': False, 'first_id': 'claude-haiku-4-5-20251001', 'last_id': 'claude-3-haiku-20240307'}\n"
]
}
],
"source": [
"# Anthropic endpoint\n",
"API_URL = \"https://api.anthropic.com/v1/messages\"\n",
"\n",
"#see the models i can have access to\n",
"r = requests.get(\n",
" \"https://api.anthropic.com/v1/models\",\n",
" headers={\n",
" \"x-api-key\": API_KEY,\n",
" \"anthropic-version\": \"2023-06-01\"\n",
" },\n",
")\n",
"print(r.json() if r.ok else r.text)\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "1b886ff2",
"metadata": {},
"outputs": [],
"source": [
"# Models to compare (variety)\n",
"MODELS = {\n",
" \"Claude 3 Haiku\": \"claude-3-haiku-20240307\", # fast & cheap\n",
" \"Claude Haiku 4.5\": \"claude-haiku-4-5-20251001\",\n",
" \"Claude Sonnet 4.5\": \"claude-sonnet-4-5-20250929\", # fast & cheap\n",
" \"Claude Opus 4.1\": \"claude-opus-4-1-20250805\",\n",
" \"Claude Opus 4\": \"claude-opus-4-20250514\", # fast & cheap\n",
" \"Claude Sonnet 4\": \"claude-sonnet-4-20250514\", # balanced\n",
" \"Claude Sonnet 3.7\": \"claude-3-7-sonnet-20250219\" # powerful (slowest)\n",
"}\n"
]
},
{
"cell_type": "markdown",
"id": "464ddf4c",
"metadata": {},
"source": [
"Synthetic Dataset Generation Function"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "7d64bca8",
"metadata": {},
"outputs": [],
"source": [
"# Dataset generator\n",
"\n",
"def generate_dataset(topic, n_records, model_choice):\n",
" prompt = f\"\"\"\n",
"You are a data generator creating synthetic datasets.\n",
"Generate {n_records} records about {topic}.\n",
"Output only a valid JSON array (no explanations or markdown).\n",
"Each record should have 4–6 fields and look realistic but fake.\n",
"\"\"\"\n",
"\n",
" headers = {\n",
" \"x-api-key\": API_KEY,\n",
" \"content-type\": \"application/json\",\n",
" \"anthropic-version\": \"2023-06-01\",\n",
" }\n",
"\n",
" payload = {\n",
" \"model\": model_choice,\n",
" \"max_tokens\": 500,\n",
" \"temperature\": 0.7,\n",
" \"messages\": [{\"role\": \"user\", \"content\": prompt}],\n",
" }\n",
"\n",
" response = requests.post(API_URL, headers=headers, data=json.dumps(payload))\n",
" result = response.json()\n",
"\n",
" if \"content\" in result and len(result[\"content\"]) > 0:\n",
" return result[\"content\"][0][\"text\"]\n",
" else:\n",
" return f\"Error: {result}\"\n"
]
},
{
"cell_type": "markdown",
"id": "bac01702",
"metadata": {},
"source": [
"Gradio UI"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "857d078d",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"* Running on local URL: http://127.0.0.1:7864\n",
"* To create a public link, set `share=True` in `launch()`.\n"
]
},
{
"data": {
"text/html": [
"<div><iframe src=\"http://127.0.0.1:7864/\" 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": 33,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# simple Gradio UI for dataset generation\n",
"\n",
"def ui_generate(topic, n_records, model_label):\n",
" model_id = MODELS[model_label]\n",
" n_records = min(int(n_records), 5) # limit for demo purposes\n",
" return generate_dataset(topic, n_records, model_id)\n",
"\n",
"# gradio block\n",
"with gr.Blocks(css=\".gradio-container {max-width: 600px !important; margin: auto;}\") as demo:\n",
" gr.Markdown(\"## Synthetic Dataset Generator using LLM APIs (Claude)\")\n",
"\n",
" with gr.Row():\n",
" topic = gr.Textbox(label=\"Dataset Topic\", value=\"Employee Records\")\n",
" n_records = gr.Number(label=\"Number of Records (Max 5 for demo purposes)\", value=3)\n",
"\n",
" model_choice = gr.Dropdown(\n",
" label=\"Choose Model\",\n",
" choices=list(MODELS.keys()),\n",
" value=\"Claude 3 Haiku\"\n",
" )\n",
"\n",
" btn = gr.Button(\"🚀 Generate\")\n",
"\n",
" # Scrollable, compact output area\n",
" output = gr.Code(label=\"Generated JSON Dataset\", language=\"json\", lines=15, interactive=False)\n",
"\n",
" btn.click(ui_generate, inputs=[topic, n_records, model_choice], outputs=[output])\n",
"\n",
"demo.launch()\n"
]
},
{
"cell_type": "markdown",
"id": "d50f64e1",
"metadata": {},
"source": [
"Save Output to File"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "93f73602",
"metadata": {},
"outputs": [],
"source": [
"def save_dataset_to_file(data, filename=\"synthetic_dataset.json\"):\n",
" try:\n",
" parsed = json.loads(data)\n",
" except:\n",
" print(\"Not valid JSON, saving as plain text instead.\")\n",
" with open(filename, \"w\", encoding=\"utf-8\") as f:\n",
" f.write(data)\n",
" return\n",
"\n",
" with open(filename, \"w\", encoding=\"utf-8\") as f:\n",
" json.dump(parsed, f, indent=2)\n",
" print(f\"Dataset saved as {filename}\")\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": "3.13.5"
}
},
"nbformat": 4,
"nbformat_minor": 5
}

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{
"cells": [
{
"cell_type": "code",
"execution_count": 9,
"id": "57499cf2",
"metadata": {},
"outputs": [],
"source": [
"import os\n",
"import json\n",
"from dotenv import load_dotenv\n",
"from IPython.display import Markdown, display, update_display\n",
"from scraper import fetch_website_links, fetch_website_contents\n",
"from openai import OpenAI"
]
},
{
"cell_type": "code",
"execution_count": 10,
"id": "310a13f3",
"metadata": {},
"outputs": [],
"source": [
"load_dotenv(override=True)\n",
"api_key = os.getenv('OPENAI_API_KEY')\n",
"\n",
"client = OpenAI()"
]
},
{
"cell_type": "code",
"execution_count": 11,
"id": "79226a7f",
"metadata": {},
"outputs": [],
"source": [
"link_analyzer_prompt = \"\"\"\n",
"You are a skilled research analyst. Your task is to identify the most useful introductory links for a given topic from a list of URLs. \n",
"You must ignore forum posts, product pages, and social media links. Focus on high-quality articles, documentation, and educational resources.\n",
"Respond ONLY with a JSON object in the following format:\n",
"{\n",
" \"links\": [\n",
" {\"type\": \"overview_article\", \"url\": \"https://...\"},\n",
" {\"type\": \"technical_docs\", \"url\": \"https://...\"},\n",
" {\"type\": \"history_summary\", \"url\": \"https://...\"}\n",
" ]\n",
"}\n",
"\"\"\""
]
},
{
"cell_type": "code",
"execution_count": 12,
"id": "73d02b52",
"metadata": {},
"outputs": [],
"source": [
"briefing_prompt = \"\"\"\n",
"You are an expert intelligence analyst. You will be given raw text from several articles about a topic. \n",
"Your mission is to synthesize this information into a clear and structured research brief. \n",
"The brief must contain the following sections in Markdown:\n",
"\n",
"Research Brief: {topic}\n",
"\n",
"1. Executive Summary\n",
"(A one-paragraph overview of the entire topic.)\n",
"\n",
"2. Key Concepts\n",
"(Use bullet points to list and explain the most important terms and ideas.)\n",
"\n",
"3. Important Figures / Events\n",
"(List the key people, organizations, or historical events relevant to the topic.)\n",
"\n",
"4. Further Reading\n",
"(Provide a list of the original URLs you analyzed for deeper study.)\n",
"\"\"\""
]
},
{
"cell_type": "code",
"execution_count": 13,
"id": "ab04efb6",
"metadata": {},
"outputs": [],
"source": [
"def get_relevant_links(topic: str, starting_url: str) -> dict:\n",
" \n",
" # getting all links from the starting URL\n",
" links_on_page = fetch_website_links(starting_url)\n",
" \n",
" # user prompt for the Link Analyst\n",
" user_prompt = f\"\"\"\n",
" Please analyze the following links related to the topic \"{topic}\" and return the most relevant ones for a research brief.\n",
" The main URL is {starting_url}. Make sure all returned URLs are absolute.\n",
"\n",
" Links:\n",
" {\"\\n\".join(links_on_page)}\n",
" \"\"\"\n",
" \n",
" response = client.chat.completions.create(\n",
" model=\"gpt-4o-mini\", \n",
" messages=[\n",
" {\"role\": \"system\", \"content\": link_analyzer_prompt},\n",
" {\"role\": \"user\", \"content\": user_prompt}\n",
" ],\n",
" response_format={\"type\": \"json_object\"}\n",
" )\n",
" \n",
" result_json = response.choices[0].message.content\n",
" relevant_links = json.loads(result_json)\n",
" return relevant_links"
]
},
{
"cell_type": "code",
"execution_count": 14,
"id": "ef6ef363",
"metadata": {},
"outputs": [],
"source": [
"def get_all_content(links_data: dict) -> str:\n",
" all_content = \"\"\n",
" original_urls = []\n",
"\n",
" for link in links_data.get(\"links\", []):\n",
" url = link.get(\"url\")\n",
" if url:\n",
" original_urls.append(url)\n",
" content = fetch_website_contents(url)\n",
" all_content += f\"Content from {url} \\n{content}\\n\\n\"\n",
" \n",
" all_content += f\"Original URLs for Reference\\n\" + \"\\n\".join(original_urls)\n",
" return all_content"
]
},
{
"cell_type": "code",
"execution_count": 15,
"id": "c2020492",
"metadata": {},
"outputs": [],
"source": [
"def create_research_brief(topic: str, starting_url: str):\n",
" relevant_links = get_relevant_links(topic, starting_url)\n",
" full_content = get_all_content(relevant_links)\n",
"\n",
" user_prompt = f\"\"\"\n",
" Please create a research brief on the topic \"{topic}\" using the following content.\n",
" Remember to include the original URLs in the 'Further Reading' section.\n",
"\n",
" Content:\n",
" {full_content[:15000]}\n",
" \"\"\"\n",
" \n",
" stream = client.chat.completions.create(\n",
" model=\"gpt-4o-mini\",\n",
" messages=[\n",
" {\"role\": \"system\", \"content\": briefing_prompt.format(topic=topic)},\n",
" {\"role\": \"user\", \"content\": user_prompt}\n",
" ],\n",
" stream=True\n",
" )\n",
" \n",
" response = \"\"\n",
" display_handle = display(Markdown(\"\"), display_id=True)\n",
" for chunk in stream:\n",
" response += chunk.choices[0].delta.content or ''\n",
" update_display(Markdown(response), display_id=display_handle.display_id)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "594e940c",
"metadata": {},
"outputs": [],
"source": [
"create_research_brief(\n",
" topic=\"The Rise of Artificial Intelligence\", \n",
" starting_url=\"https://en.wikipedia.org/wiki/Artificial_intelligence\"\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": "3.12.12"
}
},
"nbformat": 4,
"nbformat_minor": 5
}

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from bs4 import BeautifulSoup
import requests
# Standard headers to fetch a website
headers = {
"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"
}
def fetch_website_contents(url):
"""
Return the title and contents of the website at the given url;
truncate to 2,000 characters as a sensible limit
"""
response = requests.get(url, headers=headers)
soup = BeautifulSoup(response.content, "html.parser")
title = soup.title.string if soup.title else "No title found"
if soup.body:
for irrelevant in soup.body(["script", "style", "img", "input"]):
irrelevant.decompose()
text = soup.body.get_text(separator="\n", strip=True)
else:
text = ""
return (title + "\n\n" + text)[:2_000]
def fetch_website_links(url):
"""
Return the links on the webiste at the given url
I realize this is inefficient as we're parsing twice! This is to keep the code in the lab simple.
Feel free to use a class and optimize it!
"""
response = requests.get(url, headers=headers)
soup = BeautifulSoup(response.content, "html.parser")
links = [link.get("href") for link in soup.find_all("a")]
return [link for link in links if link]

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{
"cells": [
{
"cell_type": "code",
"execution_count": null,
"id": "1665a5cf",
"metadata": {},
"outputs": [],
"source": [
"import os\n",
"import re\n",
"import time\n",
"import json\n",
"import sqlite3\n",
"from dotenv import load_dotenv\n",
"import gradio as gr\n",
"from openai import OpenAI"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "5cb6632c",
"metadata": {},
"outputs": [],
"source": [
"load_dotenv()\n",
"client = OpenAI(api_key=os.getenv(\"OPENAI_API_KEY\"))\n",
"DB_PATH = \"nova_support.db\""
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "2cd3ac8c",
"metadata": {},
"outputs": [],
"source": [
"def init_db():\n",
" conn = sqlite3.connect(DB_PATH)\n",
" cur = conn.cursor()\n",
" cur.execute(\"\"\"\n",
" CREATE TABLE IF NOT EXISTS tickets (\n",
" ticket_id TEXT PRIMARY KEY,\n",
" name TEXT,\n",
" company TEXT,\n",
" email TEXT,\n",
" issue TEXT,\n",
" priority TEXT,\n",
" status TEXT,\n",
" created_at TEXT\n",
" )\n",
" \"\"\")\n",
" conn.commit()\n",
" conn.close()"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "70e0556c",
"metadata": {},
"outputs": [],
"source": [
"def new_ticket_id():\n",
" conn = sqlite3.connect(DB_PATH)\n",
" cur = conn.cursor()\n",
" cur.execute(\"SELECT COUNT(*) FROM tickets\")\n",
" count = cur.fetchone()[0]\n",
" conn.close()\n",
" return f\"RT-{1001 + count}\""
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "38525d5c",
"metadata": {},
"outputs": [],
"source": [
"def create_ticket(name, company, email, issue, priority=\"P3\"):\n",
" tid = new_ticket_id()\n",
" ts = time.strftime(\"%Y-%m-%d %H:%M:%S\")\n",
" conn = sqlite3.connect(DB_PATH)\n",
" cur = conn.cursor()\n",
" cur.execute(\"\"\"\n",
" INSERT INTO tickets (ticket_id, name, company, email, issue, priority, status, created_at)\n",
" VALUES (?, ?, ?, ?, ?, ?, ?, ?)\n",
" \"\"\", (tid, name, company, email, issue, priority.upper(), \"OPEN\", ts))\n",
" conn.commit()\n",
" conn.close()\n",
" return tid, ts"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "58e803c5",
"metadata": {},
"outputs": [],
"source": [
"def get_ticket(ticket_id):\n",
" conn = sqlite3.connect(DB_PATH)\n",
" cur = conn.cursor()\n",
" cur.execute(\"SELECT * FROM tickets WHERE ticket_id=?\", (ticket_id,))\n",
" row = cur.fetchone()\n",
" conn.close()\n",
" if not row:\n",
" return None\n",
" keys = [\"ticket_id\", \"name\", \"company\", \"email\", \"issue\", \"priority\", \"status\", \"created_at\"]\n",
" return dict(zip(keys, row))"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "b97601ff",
"metadata": {},
"outputs": [],
"source": [
"def synthesize_speech(text):\n",
" if not text.strip():\n",
" return None\n",
" output_path = Path(tempfile.gettempdir()) / \"nova_reply.mp3\"\n",
" with client.audio.speech.with_streaming_response.create(\n",
" model=\"gpt-4o-mini-tts\",\n",
" voice=\"alloy\",\n",
" input=text\n",
" ) as response:\n",
" response.stream_to_file(output_path)\n",
" return str(output_path)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "e4e20aad",
"metadata": {},
"outputs": [],
"source": [
"SYSTEM_PROMPT = \"\"\"\n",
"You are Nova, the AI Support and Sales Assistant for Reallytics.ai.\n",
"You help customers with:\n",
"- Reporting issues (create tickets)\n",
"- Checking existing tickets\n",
"- Providing product/service information\n",
"- Explaining pricing ranges\n",
"- Reassuring integration compatibility with client systems\n",
"Respond in a professional, business tone.\n",
"\"\"\""
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "0d1c094d",
"metadata": {},
"outputs": [],
"source": [
"def detect_intent(message):\n",
" text = message.lower()\n",
" if any(k in text for k in [\"create ticket\", \"open ticket\", \"new ticket\", \"issue\", \"problem\"]):\n",
" return \"create_ticket\"\n",
" if re.search(r\"rt-\\d+\", text):\n",
" return \"check_ticket\"\n",
" if \"price\" in text or \"cost\" in text:\n",
" return \"pricing\"\n",
" if \"integration\" in text:\n",
" return \"integration\"\n",
" return \"general\""
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "ed9114d5",
"metadata": {},
"outputs": [],
"source": [
"def chat(message, history, model, name, company, email):\n",
" history_msgs = [{\"role\": h[\"role\"], \"content\": h[\"content\"]} for h in history]\n",
" intent = detect_intent(message)\n",
"\n",
" if intent == \"create_ticket\":\n",
" priority = \"P2\" if \"urgent\" in message.lower() or \"high\" in message.lower() else \"P3\"\n",
" tid, ts = create_ticket(name, company, email, message, priority)\n",
" text = f\"A new support ticket has been created.\\nTicket ID: {tid}\\nCreated at: {ts}\\nStatus: OPEN\"\n",
" yield text, synthesize_speech(text)\n",
" return\n",
"\n",
" if intent == \"check_ticket\":\n",
" match = re.search(r\"(rt-\\d+)\", message.lower())\n",
" if match:\n",
" ticket_id = match.group(1).upper()\n",
" data = get_ticket(ticket_id)\n",
" if data:\n",
" text = (\n",
" f\"Ticket {ticket_id} Details:\\n\"\n",
" f\"Issue: {data['issue']}\\n\"\n",
" f\"Status: {data['status']}\\n\"\n",
" f\"Priority: {data['priority']}\\n\"\n",
" f\"Created at: {data['created_at']}\"\n",
" )\n",
" else:\n",
" text = f\"No ticket found with ID {ticket_id}.\"\n",
" else:\n",
" text = \"Please provide a valid ticket ID.\"\n",
" yield text, synthesize_speech(text)\n",
" return"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "280c7d2f",
"metadata": {},
"outputs": [],
"source": [
"def chat(message, history, model, name, company, email):\n",
" if not message.strip():\n",
" yield \"Please type a message to start.\", None\n",
" return\n",
"\n",
" history_msgs = [{\"role\": h[\"role\"], \"content\": h[\"content\"]} for h in history]\n",
" intent = detect_intent(message)\n",
" reply, audio_path = \"\", None\n",
"\n",
" if intent == \"create_ticket\":\n",
" priority = \"P2\" if \"urgent\" in message.lower() or \"high\" in message.lower() else \"P3\"\n",
" tid, ts = create_ticket(name, company, email, message, priority)\n",
" reply = f\"A new support ticket has been created.\\nTicket ID: {tid}\\nCreated at: {ts}\\nStatus: OPEN\"\n",
" audio_path = synthesize_speech(reply)\n",
" yield reply, audio_path\n",
" return\n",
"\n",
" if intent == \"check_ticket\":\n",
" match = re.search(r\"(rt-\\d+)\", message.lower())\n",
" if match:\n",
" ticket_id = match.group(1).upper()\n",
" data = get_ticket(ticket_id)\n",
" if data:\n",
" reply = (\n",
" f\"Ticket {ticket_id} Details:\\n\"\n",
" f\"Issue: {data['issue']}\\n\"\n",
" f\"Status: {data['status']}\\n\"\n",
" f\"Priority: {data['priority']}\\n\"\n",
" f\"Created at: {data['created_at']}\"\n",
" )\n",
" else:\n",
" reply = f\"No ticket found with ID {ticket_id}.\"\n",
" else:\n",
" reply = \"Please provide a valid ticket ID.\"\n",
" audio_path = synthesize_speech(reply)\n",
" yield reply, audio_path\n",
" return\n",
"\n",
" messages = [{\"role\": \"system\", \"content\": SYSTEM_PROMPT}] + history_msgs + [{\"role\": \"user\", \"content\": message}]\n",
" stream = client.chat.completions.create(model=model, messages=messages, stream=True)\n",
"\n",
" full_reply = \"\"\n",
" for chunk in stream:\n",
" delta = chunk.choices[0].delta.content or \"\"\n",
" full_reply += delta\n",
" yield full_reply, None \n",
" audio_path = synthesize_speech(full_reply)\n",
" yield full_reply, audio_path "
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "0cb1977d",
"metadata": {},
"outputs": [],
"source": [
"init_db()"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "8a0557ba",
"metadata": {},
"outputs": [],
"source": [
"with gr.Blocks(title=\"Nova | Business AI Assistant\", theme=gr.themes.Soft()) as demo:\n",
" gr.Markdown(\"## Nova | Reallytics.ai Customer Support & Sales Assistant\")\n",
" gr.Markdown(\n",
" \"Nova helps clients create or track support tickets, understand services, and explore automation options. \"\n",
" \"Type your questions and Nova will respond in both text and voice.\"\n",
" )\n",
"\n",
" with gr.Row():\n",
" name = gr.Textbox(label=\"Your Name\", placeholder=\"Liam\")\n",
" company = gr.Textbox(label=\"Company (optional)\", placeholder=\"ABC Corp\")\n",
" email = gr.Textbox(label=\"Email\", placeholder=\"you@example.com\")\n",
"\n",
" model = gr.Dropdown([\"gpt-4o-mini\", \"gpt-4\", \"gpt-3.5-turbo\"], value=\"gpt-4o-mini\", label=\"Model\")\n",
"\n",
" audio_output = gr.Audio(label=\"Nova's Voice Reply\", autoplay=True, interactive=False)\n",
"\n",
" gr.ChatInterface(\n",
" fn=chat,\n",
" type=\"messages\",\n",
" additional_inputs=[model, name, company, email],\n",
" additional_outputs=[audio_output],\n",
" title=\"Chat with Nova\",\n",
" description=\"Ask about tickets, automation services, pricing, or integration and Nova will also speak her reply.\"\n",
" )\n",
"\n",
"if __name__ == \"__main__\":\n",
" demo.launch()"
]
}
],
"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": "3.12.12"
}
},
"nbformat": 4,
"nbformat_minor": 5
}

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{
"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": 1,
"id": "c1070317-3ed9-4659-abe3-828943230e03",
"metadata": {},
"outputs": [],
"source": [
"# imports\n",
"from openai import OpenAI\n",
"from IPython.display import display, Markdown, update_display"
]
},
{
"cell_type": "code",
"execution_count": 2,
"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": 3,
"id": "a8d7923c-5f28-4c30-8556-342d7c8497c1",
"metadata": {},
"outputs": [],
"source": [
"# set up environment\n",
"\n",
"class LLM_MODEL:\n",
"\n",
" def ask_model(self, sys_prompt, usr_prompt):\n",
" model_url = 'http://localhost:11434/v1/'\n",
" client = OpenAI(base_url=model_url, api_key='ollama')\n",
" msg = [{'role':'system', 'content':sys_prompt},{'role':'user', 'content':usr_prompt}]\n",
" response = client.chat.completions.create(model=MODEL_LLAMA, messages=msg)\n",
" return response.choices[0].message.content\n",
"\n",
" def ask_model_stream(self, sys_prompt, usr_prompt):\n",
" model_url = 'http://localhost:11434/v1/'\n",
" client = OpenAI(base_url=model_url, api_key='ollama')\n",
" msg = [{'role':'system', 'content':sys_prompt},{'role':'user', 'content':usr_prompt}]\n",
" stream = client.chat.completions.create(model=MODEL_LLAMA, messages=msg, stream=True)\n",
" return stream\n",
"\n",
"model = LLM_MODEL()"
]
},
{
"cell_type": "code",
"execution_count": 4,
"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"
}
],
"source": [
"# Task 1: Tight Speed\n",
"\n",
"sys_prompt = 'You are a helpful assistant who helps me understand technical questions.\\n'\n",
"usr_prompt = 'It takes Alex 2 hours to travel a distance of 3 kms. What is the speed of Alex?'\n",
"\n",
"resp = model.ask_model(sys_prompt, usr_prompt)\n",
"display(Markdown(resp))"
]
},
{
"cell_type": "code",
"execution_count": 5,
"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"
}
],
"source": [
"# Task 2: Travel the world in X days?\n",
"\n",
"sys_prompt = 'You are a helpful assistant who helps me understand technical questions.\\n'\n",
"usr_prompt = 'There are many cities in our world. Can you tell me how to travel the whole world in least number of days ?'\n",
"\n",
"resp = model.ask_model(sys_prompt, usr_prompt)\n",
"display(Markdown(resp))"
]
},
{
"cell_type": "code",
"execution_count": 27,
"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"
]
}
],
"source": [
"# Task 3: Generate Code for task 4 to scrap some webpages\n",
"\n",
"sys_prompt = 'You are a coding expert who generates python code for given problem.\\n'\n",
"usr_prompt = 'Given a website URL, I want to a python function to get the contents of the webpage, and another function to parse all links in the given webpage text.'\n",
"\n",
"resp = model.ask_model(sys_prompt, usr_prompt)\n",
"print(resp)"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "8f7c8ea8-4082-4ad0-8751-3301adcf6538",
"metadata": {},
"outputs": [],
"source": [
"# Scrap some webpages\n",
"\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 0 and base_url:\n",
" url = f\"{base_url}{href}\"\n",
" else:\n",
" url = href\n",
"\n",
" if url.startswith('https:/'):\n",
" links.append(url)\n",
"\n",
" return links\n"
]
},
{
"cell_type": "code",
"execution_count": 7,
"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"
}
],
"source": [
"# Task 4: Make a brochure using the web-content\n",
"\n",
"# Example usage\n",
"webname, url = 'Huggingface', \"http://www.huggingface.co\"\n",
"\n",
"html_content = get_webpage_content(url)\n",
"links = parse_links(html_content, url)\n",
"\n",
"print(\"Extracted Links:\")\n",
"content = f'Link:{url} -> Content:{html_content}\\n'\n",
"for link in links:\n",
" print(link)\n",
" html_content = get_webpage_content(url)\n",
" content += f'Link:{link} -> Content:{html_content}\\n'\n",
"\n",
"sys_prompt = 'You are a helpful assistant who helps me create a brochure for a website.\\n'\n",
"usr_prompt = f'You are given the contents for a few pages for the website of {webname} following next line.\\n' + \\\n",
" content + \\\n",
" 'Use this information to give the brochure for this company.\\n'\n",
"\n",
"stream = model.ask_model_stream(sys_prompt, usr_prompt)\n",
"\n",
"response = ''\n",
"display_handle = display(Markdown(\"\"), display_id=True)\n",
"\n",
"for chunk in stream:\n",
" response += chunk.choices[0].delta.content or ''\n",
" update_display(Markdown(response), display_id=display_handle.display_id)\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "55344cc4-e377-4c75-9b39-87a29674b9f0",
"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
}

1
pyproject.toml
View File

@@ -22,6 +22,7 @@ dependencies = [
"langchain-text-splitters>=0.3.11",
"litellm>=1.77.5",
"matplotlib>=3.10.6",
"nbformat>=5.10.4",
"modal>=1.1.4",
"numpy>=2.3.3",
"ollama>=0.6.0",

164
week1/community-contributions/kwabena/week1_exercise_solution.ipynb Normal file
View File

@@ -0,0 +1,164 @@
{
"cells": [
{
"cell_type": "markdown",
"id": "4ea14045",
"metadata": {},
"source": [
"# End of Week 1 Exercise\n",
"\n",
"In this exercise, I'm building a small tool that takes a technical question and gets an explanation from **two models** — one from OpenAI and one from Ollama. \n",
"The idea is to compare how they respond and understand how to use both APIs.\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "18d3787e",
"metadata": {},
"outputs": [],
"source": [
"# imports\n",
"\n",
"import os\n",
"from openai import OpenAI\n",
"from dotenv import load_dotenv\n",
"from IPython.display import Markdown, display\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "1592e306",
"metadata": {},
"outputs": [],
"source": [
"# constants\n",
"\n",
"MODEL_GPT = \"gpt-4o-mini\"\n",
"MODEL_LLAMA = \"llama3.2\"\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "35da77ea",
"metadata": {},
"outputs": [],
"source": [
"# set up environment\n",
"\n",
"load_dotenv(override=True)\n",
"api_key = os.getenv(\"OPENAI_API_KEY\")\n",
"\n",
"if not api_key:\n",
" print(\"⚠️ OPENAI_API_KEY not found in environment. Please add it to your .env file.\")\n",
"else:\n",
" print(\"✅ API key loaded successfully\")\n",
"\n",
"client = OpenAI(api_key=api_key)\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "67efa212",
"metadata": {},
"outputs": [],
"source": [
"# define the technical question\n",
"# (you can replace this text to ask something else)\n",
"\n",
"question = \"\"\"Please explain what this code does and why:\n",
"yield from {book.get(\"author\") for book in books if book.get(\"author\")}\n",
"\"\"\"\n",
"\n",
"print(\"Question:\", question)\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "85e1ac5b",
"metadata": {},
"outputs": [],
"source": [
"# Get gpt-4o-mini to answer\n",
"\n",
"print(\"🔹 GPT-4o-mini's answer:\\n\")\n",
"\n",
"response = client.chat.completions.create(\n",
" model=MODEL_GPT,\n",
" messages=[\n",
" {\"role\": \"system\", \"content\": \"You are a helpful Python tutor.\"},\n",
" {\"role\": \"user\", \"content\": question},\n",
" ],\n",
")\n",
"\n",
"print(response.choices[0].message.content)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "4c031d74",
"metadata": {},
"outputs": [],
"source": [
"# Get LLaMA 3.2 to answer via local Ollama endpoint\n",
"\n",
"print(\"\\n🔹 LLaMA 3.2's answer:\\n\")\n",
"\n",
"ollama_client = OpenAI(base_url=\"http://localhost:11434/v1\",api_key=\"ollama\")\n",
"\n",
"response = ollama_client.chat.completions.create(\n",
" model=MODEL_LLAMA,\n",
" messages=[\n",
" {\"role\":\"system\",\"content\":\"You are a helpful AI tutor.\"},\n",
" {\"role\":\"user\",\"content\":question}\n",
" ],\n",
")\n",
"\n",
"print(response.choices[0].message.content)\n",
"\n"
]
},
{
"cell_type": "markdown",
"id": "e4ddf582",
"metadata": {},
"source": [
"### Reflection\n",
"\n",
"Both models provide explanations, but often with slightly different tones. \n",
"`gpt-4o-mini` tends to give more structured explanations, while `llama3.2` (running locally through Ollama) may be more concise or technical depending on its settings.\n",
"\n",
"This exercise helped me understand:\n",
"- How to send prompts and handle responses (including streaming).\n",
"- How easy it is to swap between OpenAI and local models.\n",
"- The value of comparing model outputs side by side.\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
}

1
week1/community-contributions/salah/.env.example Normal file
View File

@@ -0,0 +1 @@
OPENAI_API_KEY=sk-or-v1-your-key-here

156
week1/community-contributions/salah/technical_assistant.py Normal file
View File

@@ -0,0 +1,156 @@
#!/usr/bin/env python3
"""
Technical Assistant - Week 1 Exercise
Supports both OpenAI API and Ollama
"""
import os
import sys
from dotenv import load_dotenv
from openai import OpenAI
class TechnicalAssistant:
"""Technical Q&A assistant - works with OpenAI, OpenRouter, or Ollama"""
def __init__(self, model="llama3.2", provider="ollama"):
api_key = os.getenv('OPENAI_API_KEY')
if provider == "openai":
# Use OpenAI API
self.client = OpenAI(api_key=api_key)
self.model = model
print(f"Using OpenAI with model: {self.model}")
elif provider == "openrouter":
# Use OpenRouter
self.client = OpenAI(
base_url="https://openrouter.ai/api/v1",
api_key=api_key
)
self.model = model
print(f"Using OpenRouter with model: {self.model}")
else:
# Use Ollama (local)
self.client = OpenAI(
base_url="http://localhost:11434/v1",
api_key="ollama"
)
self.model = model
print(f"Using Ollama with model: {self.model}")
# System prompt - tells the model how to behave
self.system_prompt = """You are a helpful technical assistant who explains programming concepts clearly.
When answering:
- Give clear explanations
- Include code examples when relevant
- Explain both what and why
- Keep it practical and easy to understand"""
def ask(self, question, stream=True):
"""Ask a technical question and get an answer"""
messages = [
{"role": "system", "content": self.system_prompt},
{"role": "user", "content": question}
]
try:
response = self.client.chat.completions.create(
model=self.model,
messages=messages,
stream=stream
)
if stream:
answer = ""
print()
for chunk in response:
if chunk.choices[0].delta.content:
text = chunk.choices[0].delta.content
print(text, end="", flush=True)
answer += text
print("\n")
return answer
else:
result = response.choices[0].message.content
print(f"\n{result}\n")
return result
except Exception as e:
print(f"Error: {e}")
return None
def chat(self):
"""Start interactive chat mode"""
print("\n" + "="*60)
print("Technical Assistant - Ask me anything!")
print("="*60)
print(f"Model: {self.model}")
print("Type 'quit' or 'exit' to stop")
print("="*60 + "\n")
while True:
try:
question = input(">> ")
if question.strip().lower() in ['quit', 'exit', 'q']:
print("\nBye!")
break
if not question.strip():
continue
self.ask(question)
except KeyboardInterrupt:
print("\n\nBye!")
break
except Exception as e:
print(f"Error: {e}")
def main():
load_dotenv()
# Determine which provider to use
provider = "ollama" # default
if "--openai" in sys.argv:
provider = "openai"
elif "--openrouter" in sys.argv:
provider = "openrouter"
# Default models based on provider
if provider == "openai":
model = "gpt-4o-mini"
elif provider == "openrouter":
model = "meta-llama/llama-3.2-3b-instruct:free"
else:
model = "llama3.2"
# Check if user specified a custom model
if "--model" in sys.argv:
try:
idx = sys.argv.index("--model")
model = sys.argv[idx + 1]
except:
pass
assistant = TechnicalAssistant(model=model, provider=provider)
# Single question mode
if "--question" in sys.argv:
try:
idx = sys.argv.index("--question")
question = sys.argv[idx + 1]
print(f"\nQuestion: {question}\n")
assistant.ask(question)
return
except:
print("Invalid question format")
return
# Interactive mode
assistant.chat()
if __name__ == "__main__":
main()

324
week1/community-contributions/stephen-andela_genai_bootcamp/week1_exercise.ipynb Normal file
View File

@@ -0,0 +1,324 @@
{
"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": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Setup Successful!\n"
]
}
],
"source": [
"# Imports and Setup\n",
"import os\n",
"import json\n",
"from dotenv import load_dotenv\n",
"from openai import OpenAI\n",
"from IPython.display import Markdown, display, update_display\n",
"import ollama\n",
"\n",
"# Load environment variables\n",
"load_dotenv(override=True)\n",
"\n",
"# Constants\n",
"MODEL_GPT = 'gpt-4o-mini'\n",
"MODEL_LLAMA = 'llama3.2'\n",
"\n",
"print(\"Setup Successful!\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "4a456906-915a-4bfd-bb9d-57e505c5093f",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Question to analyze:\n",
"\n",
"Please explain what this code does and why:\n",
"yield from {book.get(\"author\") for book in books if book.get(\"author\")}\n",
"\n"
]
}
],
"source": [
"# Technical Question - You can modify this\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",
"\"\"\"\n",
"\n",
"print(\"Question to analyze:\")\n",
"print(question)\n",
"\n",
"# prompts\n",
"system_prompt = \"You are a helpful technical tutor who answers questions about python code, software engineering, data science and LLMs\"\n",
"user_prompt = \"Please give a detailed explanation to the following question: \" + question\n",
"\n",
"# messages\n",
"messages = [\n",
" {\"role\": \"system\", \"content\": system_prompt},\n",
" {\"role\": \"user\", \"content\": user_prompt}\n",
"]"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "60ce7000-a4a5-4cce-a261-e75ef45063b4",
"metadata": {},
"outputs": [
{
"data": {
"text/markdown": [
"## GPT-4o-mini Response:\n",
"Certainly! Let's break down the provided code snippet step by step.\n",
"\n",
"### Code Analysis\n",
"```python\n",
"yield from {book.get(\"author\") for book in books if book.get(\"author\")}\n",
"```\n",
"\n",
"This code snippet is a generator expression, which is intended to yield values from a set comprehension. Let's clarify each part of the expression:\n",
"\n",
"1. **Set Comprehension**:\n",
" - `{book.get(\"author\") for book in books if book.get(\"author\")}` is a set comprehension.\n",
" - This means it creates a set of unique authors from a collection called `books`.\n",
"\n",
"2. **`books`**:\n",
" - `books` is expected to be an iterable (like a list) that contains dictionaries. Each dictionary represents a book and may contain various keys, such as \"author\".\n",
"\n",
"3. **`book.get(\"author\")`**:\n",
" - For each `book` in the `books` iterable, `book.get(\"author\")` tries to access the value associated with the key `\"author\"`.\n",
" - The `.get()` method returns the value for the given key if it exists; otherwise, it returns `None`.\n",
"\n",
"4. **Filter Condition**: \n",
" - The expression includes an `if book.get(\"author\")` filter, which ensures that only books with a defined author (i.e., `None` or an empty string are excluded) are considered.\n",
" - This means that if the author is not provided, that book will not contribute to the final set.\n",
"\n",
"5. **Set Creation**:\n",
" - The result of the set comprehension is a set of unique author names from the list of books. Since sets automatically ensure uniqueness, duplicates will be filtered out.\n",
"\n",
"6. **`yield from`**:\n",
" - The `yield from` statement is used within a generator function. It allows the generator to yield all values from the given iterable (in this case, our created set).\n",
" - This means that the values generated (i.e., unique authors) can be iterated over one by one.\n",
"\n",
"### Purpose and Use Case\n",
"The purpose of this code snippet is to produce a generator that emits the unique author names of books from the `books` collection. This is useful in scenarios where you want to streamline the retrieval of distinct authors without immediately materializing them into a list. You can consume these unique authors one at a time efficiently, which is particularly beneficial when dealing with a large dataset.\n",
"\n",
"### Example\n",
"Consider the following example to illustrate how this might work:\n",
"\n",
"```python\n",
"books = [\n",
" {\"title\": \"Book1\", \"author\": \"AuthorA\"},\n",
" {\"title\": \"Book2\", \"author\": \"AuthorB\"},\n",
" {\"title\": \"Book3\", \"author\": \"AuthorA\"}, # Duplicate author\n",
" {\"title\": \"Book4\"}, # No author\n",
" {\"title\": \"Book5\", \"author\": \"AuthorC\"}\n",
"]\n",
"\n",
"# Let's say this code is inside a generator function\n",
"def unique_authors(books):\n",
" yield from {book.get(\"author\") for book in books if book.get(\"author\")}\n",
"\n",
"for author in unique_authors(books):\n",
" print(author)\n",
"```\n",
"### Output\n",
"```\n",
"AuthorA\n",
"AuthorB\n",
"AuthorC\n",
"```\n",
"\n",
"### Summary\n",
"This code snippet creates a generator that yields unique authors of books, omitting any entries where the author is not provided. This demonstrates an efficient and Pythonic way to handle data extraction, particularly with potentially sparse datasets."
],
"text/plain": [
"<IPython.core.display.Markdown object>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# Get gpt-4o-mini to answer, with streaming\n",
"api_key = os.getenv('OPENAI_API_KEY')\n",
"\n",
"# Initialize OpenAI client\n",
"openai = OpenAI()\n",
"\n",
"stream = openai.chat.completions.create(model=MODEL_GPT, messages=messages,stream=True)\n",
" \n",
"response = \"\"\n",
"\n",
"display_handle = display(Markdown(\"\"), display_id=True)\n",
"\n",
"for chunk in stream:\n",
" if chunk.choices[0].delta.content:\n",
" response += chunk.choices[0].delta.content\n",
" update_display(Markdown(f\"## GPT-4o-mini Response:\\n{response}\"), display_id=display_handle.display_id)"
]
},
{
"cell_type": "code",
"execution_count": 11,
"id": "8f7c8ea8-4082-4ad0-8751-3301adcf6538",
"metadata": {},
"outputs": [
{
"data": {
"text/markdown": [
"The given Python expression appears as part of an asynchronous generator, typically used with coroutines like those found within the `async`/`await` syntax introduced in Python 3.5+:\n",
"\n",
"```python\n",
"yield from {book.get('author') for book in books if book.get('author')}\n",
"```\n",
"\n",
"Here's a breakdown of what this code does, line by line and overall explanation:\n",
"\n",
"1. `{book.get('author') for book in books if book.get('author')}` is a set comprehension that iterates through each `book` object (assumed to be dictionary-like since it uses square brackets notation) within the `books` variable, which should also contain such objects as its elements based on context provided herein:\n",
" - For every iteration of this generator expression, if `'author'` exists in a book's key set (`book.keys()`), then that value (presumably the name of an author associated with their corresponding `book`) is included within the resulting comprehension/set; otherwise it skips to the next item since there isn't one without `'author'`.\n",
" - The `.get` method returns a specified key’s value from dictionary-like objects, but if that key doesn't exist, instead of causing an error (as would be typical with direct indexing), this expression safely retrieves `None`, or another default return type you specify as the second argument to `.get()` which isn't shown here.\n",
" - Set comprehension is a construct for creating sets directly from iterables using set-building syntax (`{}`). Note that it inherently discards duplicates (if any), but this does not seem relevant since we are assuming books will uniquely have author information in the context of its key presence or absence, rather than repetitive entries.\n",
" \n",
"2. `yield from` is a statement used with asynchronous generators (`async def`) that handles yielding values and delegating further execution within nested coroutines: \n",
" - Its function here seems to be sending each author's name (extracted by the generator expression before) back into this outercoroutine. The `yield from` statement thus passes control over these names directly as output of its own operation, rather than managing an internal sequence or iterable in a traditional manner with for-loops and appending to lists inside coroutines (which may result in blocking behavior).\n",
" - In this expression's specific case without `async`/`await`, it looks like the code intends to simulate asynchronous yielding by passing values from an internal generator back out. However, proper usage would require surrounding with async function decorators and using await as needed for actual I/O-bound or network operations within a coroutine workflow context; this snippet in isolation does not directly demonstrate that behavior but instead presents a pattern resembling how yielding could be structured should it be part of an asynchronous generator expression.\n",
" - It's worth mentioning, though `yield from` isn't typically used with set comprehensions or non-coroutine functions as these expressions cannot 'receive values.' Instead, this construct suggests a conceptual approach where each found author is yielded one by one in what would be the sequence of execution within an asynchronous coroutine.\n",
" - Given that `yield from` isn't directly compatible with set comprehensions (without modification and appropriate context), it seems we might have encountered syntactical confusion or a misplacement here, since normally you wouldn’t see this in standalone Python code outside the scope of coroutine functions.\n",
" \n",
"Assuming that `books` is an iterable over dictionary-like objects (which may contain author information), and if one were to translate typical synchronous usage into asynchronous semantics or consider using a generator, then we'd instead see something like this for proper async operation:\n",
"\n",
"```python\n",
"async def find_authors():\n",
" authors = set()\n",
" async for book in books: # Assuming `books` can be an iterable of awaitables (e.g., coroutines) or other asynchronous generators\n",
" author = book.get('author')\n",
" if author is not None:\n",
" await asyncio.sleep(0) # Yield control back to the event loop, simulate async I/O operation here with `await`ing a sleep call for example purposes only (in reality this would typically handle some real asynchronous task like fetching data from an external API). Then we'd yield using 'yield':\n",
" await asyncio0.sleep(2) # This line is placeholder logic and wouldn't execute without async decorators, but it serves to illustrate the use of `await` alongside a coroutine function:\n",
" authors.add(author)\n",
" return authors\n",
"```\n",
"In this modified version suitable for an asynchronous context (and with necessary adjustments): \n",
"- This would be inside an `@async def find_authors()` decorated async generator/coroutine, and the `yield` keyword is used to temporarily pause execution until another coroutine or future calls its `.send(None)` method. The example uses a placeholder sleep call (`await asyncio.sleep(2)`) for demonstration purposes only; in practice one might use non-blocking I/O operations such as reading from files, network responses etc., within an async function decorated with `@async def`.\n",
" \n",
"It is crucial to note that the original expression provided seems like a pseudocode representation of how we could structure asynchronous behavior using `yield` and comprehensions if it were actually part of coroutine code in Python 3.5+, but isn't syntactically correct or conventionally used outside such contexts due to misunderstandings about yielding semantics from set operations without await statements (or decorators)."
],
"text/plain": [
"<IPython.core.display.Markdown object>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# Get Llama 3.2 to answer\n",
"response = ollama.chat(model=MODEL_LLAMA, messages=messages)\n",
"\n",
"reply = response['message']['content']\n",
"\n",
"display(Markdown(reply))"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "d1f8aa0a",
"metadata": {},
"outputs": [],
"source": [
"# Week 1 Learnings Summary\n",
"\n",
"summary = \"\"\"\n",
"## Week 1 Learnings Demonstrated\n",
"\n",
"### ✅ Day 1 - Web Scraping & API Integration\n",
"- **BeautifulSoup** for HTML parsing\n",
"- **Requests** for HTTP calls\n",
"- **OpenAI API** integration\n",
"- **SSL certificate** handling for Windows\n",
"\n",
"### ✅ Day 2 - Chat Completions API & Ollama\n",
"- **Chat Completions API** understanding\n",
"- **OpenAI-compatible endpoints** (Ollama)\n",
"- **Model comparison** techniques\n",
"- **Streaming responses** implementation\n",
"\n",
"### ✅ Day 4 - Tokenization & Cost Management\n",
"- **tiktoken** for token counting\n",
"- **Cost estimation** strategies\n",
"- **Text chunking** techniques\n",
"- **Token-aware** processing\n",
"\n",
"### ✅ Day 5 - Business Solutions\n",
"- **Intelligent link selection** using LLM\n",
"- **Multi-page content** aggregation\n",
"- **Professional brochure** generation\n",
"- **Error handling** and robustness\n",
"\n",
"### ✅ Week 1 Exercise - Technical Question Answerer\n",
"- **Streaming responses** from OpenAI\n",
"- **Local inference** with Ollama\n",
"- **Side-by-side comparison** of models\n",
"- **Error handling** for both APIs\n",
"\n",
"## Key Skills Acquired:\n",
"1. **API Integration** - OpenAI, Ollama, web scraping\n",
"2. **Model Comparison** - Understanding different LLM capabilities\n",
"3. **Streaming** - Real-time response display\n",
"4. **Error Handling** - Robust application design\n",
"5. **Business Applications** - Practical LLM implementations\n",
"\"\"\"\n",
"\n",
"display(Markdown(summary))"
]
}
],
"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
}

367
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{
"cells": [
{
"cell_type": "markdown",
"id": "1fecd49e",
"metadata": {},
"source": [
"# 🗺️ Google Maps Review Summarizer\n",
"\n",
"This Python app automates the process of fetching and summarizing Google Maps reviews for any business or location.\n",
"\n",
"## 🚀 Overview\n",
"The app performs two main tasks:\n",
"1. **Scrape Reviews** – Uses a web scraping script to extract reviews directly from Google Maps.\n",
"2. **Summarize Content** – Leverages OpenAI's language models to generate concise, insightful summaries of the collected reviews and analyse the sentiments.\n",
"\n",
"## 🧠 Tech Stack\n",
"- **Python** – Core language\n",
"- **Playwright** – For scraping reviews\n",
"- **OpenAI API** – For natural language summarization\n",
"- **Jupyter Notebook** – For exploration, testing, and demonstration\n",
"\n",
"### 🙏 Credits\n",
"The web scraping logic is **inspired by [Antonello Zanini’s blog post](https://blog.apify.com/how-to-scrape-google-reviews/)** on building a Google Reviews scraper. Special thanks for the valuable insights on **structuring and automating the scraping workflow**, which greatly informed the development of this improved scraper.\n",
"\n",
"This app, however, uses an **enhanced version of the scraper** that can scroll infinitely to load more reviews until it collects **at least 1,000 reviews**. If only a smaller number of reviews are available, the scraper stops scrolling earlier.\n",
"\n",
"## ✅ Sample Output\n",
"Here is a summary of reviews of a restuarant generated by the app.\n",
"\n",
"![Alt text](google-map-review-summary.jpg)\n",
"\n",
"\n",
"---\n",
"\n",
"**Note:** This project is intended for educational and research purposes. Please ensure compliance with Google’s [Terms of Service](https://policies.google.com/terms) when scraping or using their data.\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "df04a4aa",
"metadata": {},
"outputs": [],
"source": [
"#Activate the llm_engineering virtual environment\n",
"!source ../../../.venv/bin/activate \n",
"\n",
"#Make sure pip is available and up to date inside the venv\n",
"!python3 -m ensurepip --upgrade\n",
"\n",
"#Verify that pip now points to the venv path (should end with /.venv/bin/pip)\n",
"!which pip3\n",
"\n",
"#Install Playwright inside the venv\n",
"!pip3 install playwright\n",
"\n",
"#Download the required browser binaries and dependencies\n",
"!python3 -m playwright install"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "1c794cfd",
"metadata": {},
"outputs": [],
"source": [
"import asyncio\n",
"from playwright.async_api import async_playwright\n",
"from IPython.display import Markdown, display\n",
"import os\n",
"from dotenv import load_dotenv\n",
"from openai import OpenAI\n"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "317af2b8",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"API key found and looks good so far!\n"
]
}
],
"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": "code",
"execution_count": 4,
"id": "6f142c79",
"metadata": {},
"outputs": [],
"source": [
"async def scroll_reviews_panel(page, max_scrolls=50, max_reviews=10):\n",
" \"\"\"\n",
" Scrolls through the reviews panel to lazy load all reviews.\n",
" \n",
" Args:\n",
" page: Playwright page object\n",
" max_scrolls: Maximum number of scroll attempts to prevent infinite loops\n",
" \n",
" Returns:\n",
" Number of reviews loaded\n",
" \"\"\"\n",
" # Find the scrollable reviews container\n",
" # Google Maps reviews are in a specific scrollable div\n",
" scrollable_div = page.locator('div[role=\"main\"] div[jslog$=\"mutable:true;\"]').first\n",
" \n",
" previous_review_count = 0\n",
" scroll_attempts = 0\n",
" no_change_count = 0\n",
"\n",
" print(\"Starting to scroll and load reviews...\")\n",
" \n",
" while scroll_attempts < max_scrolls:\n",
" # Get current count of reviews\n",
" review_elements = page.locator(\"div[data-review-id][jsaction]\")\n",
" current_review_count = await review_elements.count()\n",
" \n",
" #if we have loaded max_reviews, we will stop scrolling\n",
" if current_review_count >= max_reviews:\n",
" break\n",
"\n",
" print(f\"Scroll attempt {scroll_attempts + 1}: Found {current_review_count} reviews\")\n",
" \n",
" # Scroll to the bottom of the reviews panel\n",
" await scrollable_div.evaluate(\"\"\"\n",
" (element) => {\n",
" element.scrollTo(0, element.scrollHeight + 100);\n",
" }\n",
" \"\"\")\n",
" \n",
" # Wait for potential new content to load\n",
" await asyncio.sleep(2)\n",
" \n",
" # Check if new reviews were loaded\n",
" if current_review_count == previous_review_count:\n",
" no_change_count += 1\n",
" # If count hasn't changed for 3 consecutive scrolls, we've likely reached the end\n",
" if no_change_count >= 3:\n",
" print(f\"No new reviews loaded after {no_change_count} attempts. Finished loading.\")\n",
" break\n",
" else:\n",
" no_change_count = 0\n",
" \n",
" previous_review_count = current_review_count\n",
" scroll_attempts += 1\n",
" \n",
" final_count = await review_elements.count()\n",
" print(f\"Finished scrolling. Total reviews loaded: {final_count}\")\n",
" return final_count"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "f7f67b70",
"metadata": {},
"outputs": [],
"source": [
"async def scrape_google_reviews(url):\n",
" # Where to store the scraped data\n",
" reviews = []\n",
"\n",
" async with async_playwright() as p:\n",
" # Initialize a new Playwright instance\n",
" browser = await p.chromium.launch(\n",
" headless=True # Set to False if you want to see the browser in action\n",
" )\n",
" context = await browser.new_context()\n",
" page = await context.new_page()\n",
"\n",
" # The URL of the Google Maps reviews page\n",
"\n",
" # Navigate to the target Google Maps page\n",
" print(\"Navigating to Google Maps page...\")\n",
" await page.goto(url)\n",
"\n",
" # Wait for initial reviews to load\n",
" print(\"Waiting for initial reviews to load...\")\n",
" review_html_elements = page.locator(\"div[data-review-id][jsaction]\")\n",
" await review_html_elements.first.wait_for(state=\"visible\", timeout=10000)\n",
" \n",
" # Scroll through the reviews panel to lazy load all reviews\n",
" total_reviews = await scroll_reviews_panel(page, max_scrolls=100)\n",
" \n",
" print(f\"\\nStarting to scrape {total_reviews} reviews...\")\n",
"\n",
" # Get all review elements after scrolling\n",
" review_html_elements = page.locator(\"div[data-review-id][jsaction]\")\n",
" all_reviews = await review_html_elements.all()\n",
" \n",
" # Iterate over the elements and scrape data from each of them\n",
" for idx, review_html_element in enumerate(all_reviews, 1):\n",
" try:\n",
" # Scraping logic\n",
"\n",
" stars_element = review_html_element.locator(\"[aria-label*=\\\"star\\\"]\")\n",
" stars_label = await stars_element.get_attribute(\"aria-label\")\n",
"\n",
" # Extract the review score from the stars label\n",
" stars = None\n",
" for i in range(1, 6):\n",
" if stars_label and str(i) in stars_label:\n",
" stars = i\n",
" break\n",
"\n",
" # Get the next sibling of the previous element with an XPath expression\n",
" time_sibling = stars_element.locator(\"xpath=following-sibling::span\")\n",
" time = await time_sibling.text_content()\n",
"\n",
" # Select the \"More\" button and if it is present, click it\n",
" more_element = review_html_element.locator(\"button[aria-label=\\\"See more\\\"]\").first\n",
" if await more_element.is_visible():\n",
" await more_element.click()\n",
" await asyncio.sleep(0.3) # Brief wait for text expansion\n",
"\n",
" text_element = review_html_element.locator(\"div[tabindex=\\\"-1\\\"][id][lang]\")\n",
" text = await text_element.text_content()\n",
"\n",
" reviews.append(str(stars) + \" Stars: \\n\" +\"Reviewed On:\" + time + \"\\n\"+ text)\n",
" \n",
" if idx % 10 == 0:\n",
" print(f\"Scraped {idx}/{total_reviews} reviews...\")\n",
" \n",
" except Exception as e:\n",
" print(f\"Error scraping review {idx}: {str(e)}\")\n",
" continue\n",
"\n",
" print(f\"\\nSuccessfully scraped {len(reviews)} reviews!\")\n",
"\n",
" # Close the browser and release its resources\n",
" await browser.close()\n",
"\n",
" return \"\\n\".join(reviews)"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "cb160d5f",
"metadata": {},
"outputs": [],
"source": [
"system_prompt = \"\"\"\n",
"You are an expert assistant that analyzes google reviews,\n",
"and provides a summary and centiment of the reviews.\n",
"Respond in markdown. Do not wrap the markdown in a code block - respond just with the markdown.\n",
"\"\"\""
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "69e08d4b",
"metadata": {},
"outputs": [],
"source": [
"# Define our user prompt\n",
"\n",
"user_prompt_prefix = \"\"\"\n",
"Here are the reviews of a google map location/business.\n",
"Provide a short summary of the reviews and the sentiment of the reviews.\n",
"\n",
"\"\"\""
]
},
{
"cell_type": "code",
"execution_count": 8,
"id": "d710972d",
"metadata": {},
"outputs": [],
"source": [
"\n",
"def prepare_message(reviews):\n",
" return [\n",
" {\"role\": \"system\", \"content\": system_prompt},\n",
" {\"role\": \"user\", \"content\": user_prompt_prefix + reviews}\n",
" ]"
]
},
{
"cell_type": "code",
"execution_count": 9,
"id": "cb51f436",
"metadata": {},
"outputs": [],
"source": [
"async def summarize(url):\n",
" openai = OpenAI()\n",
" reviews = await scrape_google_reviews(url)\n",
" response = openai.chat.completions.create(\n",
" model = \"gpt-4.1-mini\",\n",
" messages = prepare_message(reviews)\n",
" )\n",
" return response.choices[0].message.content"
]
},
{
"cell_type": "code",
"execution_count": 10,
"id": "2f09e2d2",
"metadata": {},
"outputs": [],
"source": [
"async def display_summary(url):\n",
" summary = await summarize(url)\n",
" display(Markdown(summary))"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "ca7995c9",
"metadata": {},
"outputs": [],
"source": [
"url = \"https://www.google.com/maps/place/Grace+Home+Nursing+%26+Assisted+Living/@12.32184,75.0853037,17z/data=!4m8!3m7!1s0x3ba47da1be6a0279:0x9e73181ab0827f7e!8m2!3d12.32184!4d75.0853037!9m1!1b1!16s%2Fg%2F11qjl430n_?entry=ttu&g_ep=EgoyMDI1MTAyMC4wIKXMDSoASAFQAw%3D%3D\"\n",
"await display_summary(url)\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
}

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{
"cells": [
{
"cell_type": "markdown",
"id": "ddfa9ae6-69fe-444a-b994-8c4c5970a7ec",
"metadata": {},
"source": [
"# Project - New Employee Onboarding Assistant\n",
"\n",
"A friendly HR assistant that helps new employees get started — explains policies, checks training schedules, finds contacts, and shows office images — while speaking replies and displaying visuals."
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "8b50bbe2-c0b1-49c3-9a5c-1ba7efa2bcb4",
"metadata": {},
"outputs": [],
"source": [
"# imports\n",
"\n",
"import os, json, sqlite3, base64\n",
"import json\n",
"from dotenv import load_dotenv\n",
"import gradio as gr\n",
"from io import BytesIO\n",
"from PIL import Image\n",
"import sys\n",
"sys.path.append(os.path.abspath(os.path.join(\"..\", \"..\"))) \n",
"from openai import OpenAI\n"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "747e8786-9da8-4342-b6c9-f5f69c2e22ae",
"metadata": {},
"outputs": [],
"source": [
"# Initialization\n",
"\n",
"conn = sqlite3.connect(\"onboarding.db\")\n",
"cursor = conn.cursor()\n",
"\n",
"cursor.execute(\"\"\"\n",
"CREATE TABLE IF NOT EXISTS employees (\n",
" name TEXT,\n",
" role TEXT,\n",
" start_date TEXT,\n",
" manager TEXT,\n",
" location TEXT\n",
")\n",
"\"\"\")\n",
"\n",
"cursor.execute(\"\"\"\n",
"CREATE TABLE IF NOT EXISTS training (\n",
" role TEXT,\n",
" course TEXT,\n",
" duration TEXT\n",
")\n",
"\"\"\")\n",
"\n",
"cursor.executemany(\"INSERT INTO employees VALUES (?, ?, ?, ?, ?)\", [\n",
" (\"Alice\", \"DevOps Engineer\", \"2025-10-15\", \"Bharat Puri\", \"Pune HQ\"),\n",
" (\"Ravi\", \"Data Analyst\", \"2025-10-20\", \"Neha Kapoor\", \"Bangalore\"),\n",
"])\n",
"\n",
"cursor.executemany(\"INSERT INTO training VALUES (?, ?, ?)\", [\n",
" (\"DevOps Engineer\", \"Cloud Infrastructure Basics\", \"2 weeks\"),\n",
" (\"DevOps Engineer\", \"Security and Compliance\", \"1 week\"),\n",
" (\"Data Analyst\", \"Python for Data Analysis\", \"3 weeks\")\n",
"])\n",
"\n",
"conn.commit()\n",
"conn.close()"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "c3e8173c",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"✅ API Key loaded: sk-proj-****\n"
]
}
],
"source": [
"load_dotenv(override=True)\n",
"\n",
"openai_api_key = os.getenv(\"OPENAI_API_KEY\")\n",
"if openai_api_key:\n",
" print(f\"✅ API Key loaded: {openai_api_key[:8]}****\")\n",
"else:\n",
" print(\"❌ OPENAI_API_KEY not set\")\n",
"\n",
"MODEL = \"gpt-4.1-mini\"\n",
"openai = OpenAI()\n",
"DB = \"onboarding.db\""
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "0a521d84-d07c-49ab-a0df-d6451499ed97",
"metadata": {},
"outputs": [],
"source": [
"system_message = \"\"\"\n",
"You are WelcomeAI, an onboarding assistant for new employees.\n",
"Be friendly and concise (1–2 sentences). \n",
"Always be accurate and supportive. If unsure, say so politely.\n",
"\"\"\""
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "2f6396f8-247e-4289-9bca-590cfc94a377",
"metadata": {},
"outputs": [],
"source": [
"# -------------------- TOOLS --------------------\n",
"\n",
"def get_employee_info(name):\n",
" with sqlite3.connect(DB) as conn:\n",
" cursor = conn.cursor()\n",
" cursor.execute(\"SELECT * FROM employees WHERE lower(name)=?\", (name.lower(),))\n",
" result = cursor.fetchone()\n",
" if result:\n",
" name, role, start_date, manager, location = result\n",
" return f\"{name} is joining as a {role} on {start_date}. Manager: {manager}. Location: {location}.\"\n",
" else:\n",
" return \"I couldn’t find that employee in the database.\""
]
},
{
"cell_type": "code",
"execution_count": 8,
"id": "03f19289",
"metadata": {},
"outputs": [],
"source": [
"def get_training_schedule(role):\n",
" with sqlite3.connect(DB) as conn:\n",
" cursor = conn.cursor()\n",
" cursor.execute(\"SELECT course, duration FROM training WHERE role=?\", (role,))\n",
" results = cursor.fetchall()\n",
" if results:\n",
" schedule = \"; \".join([f\"{course} ({duration})\" for course, duration in results])\n",
" return f\"Training schedule for {role}: {schedule}\"\n",
" else:\n",
" return \"No training schedule found for that role.\""
]
},
{
"cell_type": "code",
"execution_count": 9,
"id": "bcfb6523",
"metadata": {},
"outputs": [],
"source": [
"# Tool schema definitions\n",
"employee_tool = {\n",
" \"name\": \"get_employee_info\",\n",
" \"description\": \"Retrieve onboarding information about a new employee.\",\n",
" \"parameters\": {\n",
" \"type\": \"object\",\n",
" \"properties\": {\n",
" \"employee_name\": {\"type\": \"string\", \"description\": \"The full name of the employee.\"}\n",
" },\n",
" \"required\": [\"employee_name\"],\n",
" },\n",
"}"
]
},
{
"cell_type": "code",
"execution_count": 10,
"id": "61a2a15d-b559-4844-b377-6bd5cb4949f6",
"metadata": {},
"outputs": [],
"source": [
"training_tool = {\n",
" \"name\": \"get_training_schedule\",\n",
" \"description\": \"Get the training schedule for a given role.\",\n",
" \"parameters\": {\n",
" \"type\": \"object\",\n",
" \"properties\": {\n",
" \"role\": {\"type\": \"string\", \"description\": \"The job role of the employee.\"}\n",
" },\n",
" \"required\": [\"role\"],\n",
" },\n",
"}"
]
},
{
"cell_type": "code",
"execution_count": 11,
"id": "c91d012e",
"metadata": {},
"outputs": [],
"source": [
"\n",
"tools = [{\"type\": \"function\", \"function\": employee_tool},\n",
" {\"type\": \"function\", \"function\": training_tool}]"
]
},
{
"cell_type": "code",
"execution_count": 14,
"id": "956c3b61",
"metadata": {},
"outputs": [],
"source": [
"# -------------------- MULTI-MODAL --------------------\n",
"def artist(topic):\n",
" prompt = f\"A friendly HR welcome image showing {topic}, office vibes, smiling team, pop-art style\"\n",
" image_response = openai.images.generate(\n",
" model=\"dall-e-3\",\n",
" prompt=prompt,\n",
" size=\"1024x1024\",\n",
" response_format=\"b64_json\"\n",
" )\n",
" img_base64 = image_response.data[0].b64_json\n",
" img_data = base64.b64decode(img_base64)\n",
" return Image.open(BytesIO(img_data))\n",
"\n",
"def talker(message):\n",
" response = openai.audio.speech.create(\n",
" model=\"gpt-4o-mini-tts\",\n",
" voice=\"alloy\",\n",
" input=message\n",
" )\n",
" return response.content"
]
},
{
"cell_type": "code",
"execution_count": 15,
"id": "8eca803e",
"metadata": {},
"outputs": [],
"source": [
"# -------------------- AGENT LOGIC --------------------\n",
"\n",
"def handle_tool_calls(message):\n",
" responses, topics = [], []\n",
" for call in message.tool_calls:\n",
" if call.function.name == \"get_employee_info\":\n",
" args = json.loads(call.function.arguments)\n",
" name = args.get(\"employee_name\")\n",
" topics.append(name)\n",
" info = get_employee_info(name)\n",
" responses.append({\"role\": \"tool\", \"content\": info, \"tool_call_id\": call.id})\n",
" elif call.function.name == \"get_training_schedule\":\n",
" args = json.loads(call.function.arguments)\n",
" role = args.get(\"role\")\n",
" topics.append(role)\n",
" info = get_training_schedule(role)\n",
" responses.append({\"role\": \"tool\", \"content\": info, \"tool_call_id\": call.id})\n",
" return responses, topics\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 16,
"id": "2c27c4ba-8ed5-492f-add1-02ce9c81d34c",
"metadata": {},
"outputs": [],
"source": [
"def chat(history):\n",
" history = [{\"role\": h[\"role\"], \"content\": h[\"content\"]} for h in history]\n",
" messages = [{\"role\": \"system\", \"content\": system_message}] + history\n",
" response = openai.chat.completions.create(model=MODEL, messages=messages, tools=tools)\n",
" topics, image = [], None\n",
"\n",
" while response.choices[0].finish_reason == \"tool_calls\":\n",
" msg = response.choices[0].message\n",
" responses, topics = handle_tool_calls(msg)\n",
" messages.append(msg)\n",
" messages.extend(responses)\n",
" response = openai.chat.completions.create(model=MODEL, messages=messages, tools=tools)\n",
"\n",
" reply = response.choices[0].message.content\n",
" voice = talker(reply)\n",
"\n",
" if topics:\n",
" image = artist(topics[0])\n",
"\n",
" return history + [{\"role\": \"assistant\", \"content\": reply}], voice, image"
]
},
{
"cell_type": "code",
"execution_count": 17,
"id": "773a9f11-557e-43c9-ad50-56cbec3a0f8f",
"metadata": {},
"outputs": [],
"source": [
"# -------------------- GRADIO UI --------------------\n",
"\n",
"def put_message_in_chatbot(message, history):\n",
" return \"\", history + [{\"role\": \"user\", \"content\": message}]\n",
"\n",
"with gr.Blocks() as ui:\n",
" gr.Markdown(\"## 🧑‍💼 WelcomeAI — Your HR Onboarding Companion\")\n",
" with gr.Row():\n",
" chatbot = gr.Chatbot(height=500, type=\"messages\")\n",
" image_output = gr.Image(height=500, interactive=False)\n",
" with gr.Row():\n",
" audio_output = gr.Audio(autoplay=True)\n",
" with gr.Row():\n",
" message = gr.Textbox(label=\"Ask me about onboarding, training, or company info:\")\n",
"\n",
" message.submit(put_message_in_chatbot, [message, chatbot], [message, chatbot]).then(\n",
" chat, chatbot, [chatbot, audio_output, image_output]\n",
" )"
]
},
{
"cell_type": "code",
"execution_count": 19,
"id": "728a12c5-adc3-415d-bb05-82beb73b079b",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Rerunning server... use `close()` to stop if you need to change `launch()` parameters.\n",
"----\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": 19,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"ui.launch(inbrowser=True, auth=(\"hradmin\", \"welcome123\"))"
]
}
],
"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
}

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{
"cells": [
{
"cell_type": "markdown",
"id": "fe12c203-e6a6-452c-a655-afb8a03a4ff5",
"metadata": {},
"source": [
"# Week 2 exercise\n",
"\n",
"## MathXpert with tools integration\n",
"\n",
"- Provides the freedom to explore all the models available from the providers\n",
"- Handling of multiple tools calling simultaneously\n",
"- Efficiently run tools in parallel\n",
"- Tool response, i.e. the `plot_function`, that does not require going back to the LLM\n",
"- Uses the inbuilt logging package to allow the control of the verbosity of the logging, set to a higher level, like INFO, to reduce the noisy output"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "c1070317-3ed9-4659-abe3-828943230e03",
"metadata": {},
"outputs": [],
"source": [
"import os\n",
"import json\n",
"import logging\n",
"from enum import StrEnum\n",
"from getpass import getpass\n",
"from types import SimpleNamespace\n",
"from typing import Callable\n",
"\n",
"from dotenv import load_dotenv\n",
"from openai import OpenAI\n",
"import ipywidgets as widgets\n",
"from IPython.display import display, clear_output, Latex\n",
"import gradio as gr\n",
"\n",
"load_dotenv(override=True)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "99901b80",
"metadata": {},
"outputs": [],
"source": [
"logging.basicConfig(level=logging.WARNING)\n",
"\n",
"logger = logging.getLogger('mathxpert')\n",
"logger.setLevel(logging.DEBUG)"
]
},
{
"cell_type": "markdown",
"id": "f169118a-645e-44e1-9a98-4f561adfbb08",
"metadata": {},
"source": [
"## Free Cloud Providers\n",
"\n",
"Grab your free API Keys from these generous sites:\n",
"\n",
"- https://openrouter.ai/\n",
"- https://ollama.com/\n",
"\n",
">**NOTE**: If you do not have a key for any provider, simply press ENTER to move on"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "4a456906-915a-4bfd-bb9d-57e505c5093f",
"metadata": {},
"outputs": [],
"source": [
"class Provider(StrEnum):\n",
" OLLAMA = 'Ollama'\n",
" OPENROUTER = 'OpenRouter'\n",
"\n",
"clients: dict[Provider, OpenAI] = {}\n",
"models: dict[Provider, list[str]] = {\n",
" Provider.OLLAMA: [],\n",
" Provider.OPENROUTER: [],\n",
"}\n",
"\n",
"DEFAULT_PROVIDER = Provider.OLLAMA\n",
"\n",
"selection_state: dict[Provider, str | None] = {\n",
" Provider.OLLAMA: 'gpt-oss:20b',\n",
" Provider.OPENROUTER: 'openai/gpt-oss-20b:free',\n",
"}\n",
"\n",
"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()\n",
"\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",
"if api_key := get_secret('OPENROUTER_API_KEY'):\n",
" clients[Provider.OPENROUTER] = OpenAI(api_key=api_key, base_url='https://openrouter.ai/api/v1')\n",
"\n",
"available_providers = [str(p) for p in clients.keys()]"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "aae1579b-7a02-459d-81c6-0f775d2a1410",
"metadata": {},
"outputs": [],
"source": [
"selected_provider, selected_model, client = '', '', None\n",
"\n",
"\n",
"def get_desired_value_or_first_item(desire, options) -> str | None:\n",
" logger.debug(f'Pick {desire} from {options}')\n",
" selected = desire if desire in options else None\n",
" if selected:\n",
" return selected\n",
"\n",
" return options[0] if options else None\n",
" \n",
"try:\n",
" selected_provider = get_desired_value_or_first_item(DEFAULT_PROVIDER, available_providers)\n",
" client = clients.get(selected_provider)\n",
"except Exception:\n",
" logger.warning(f'❌ no provider configured and everything else from here will FAIL 🤦, I know you know this already.')\n",
"\n",
"def load_models_if_needed(client: OpenAI, selected_provider):\n",
" global selected_model, models\n",
"\n",
" if client and not models.get(selected_provider):\n",
" logging.info(f'📡 Fetching {selected_provider} models...')\n",
" \n",
" models[selected_provider] = [model.id for model in client.models.list()]\n",
" selected_model = get_desired_value_or_first_item(\n",
" selection_state[selected_provider], \n",
" models[selected_provider],\n",
" )\n",
"\n",
"load_models_if_needed(client, selected_provider)\n",
"\n",
"logger.info(f'ℹ️ Provider: {selected_provider} Model: {selected_model}, Client: {client}')"
]
},
{
"cell_type": "markdown",
"id": "e04675c2-1b81-4187-868c-c7112cd77e37",
"metadata": {},
"source": [
"## Prompt"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "a8d7923c-5f28-4c30-8556-342d7c8497c1",
"metadata": {},
"outputs": [],
"source": [
"def get_messages(question: str) -> list[dict[str, str]]:\n",
" \"\"\"Generate messages for the chat models.\"\"\"\n",
"\n",
" system_prompt = r'''\n",
" You are MathXpert, an expert Mathematician who makes math fun to learn by relating concepts to real \n",
" practical usage to whip up the interest in learners.\n",
" \n",
" Explain step-by-step thoroughly how to solve a math problem. \n",
" - ALWAYS use `$$...$$` for mathematical expressions.\n",
" - NEVER use square brackets `[...]` to delimit math.\n",
" - Example: Instead of \"[x = 2]\", write \"$$x = 2$$\".\n",
" - You may use `\\\\[4pt]` inside matrices for spacing.\n",
" '''\n",
"\n",
" return [\n",
" {'role': 'system', 'content': system_prompt },\n",
" {'role': 'user', 'content': question},\n",
" ]"
]
},
{
"cell_type": "markdown",
"id": "caa51866-f433-4b9a-ab20-fff5fc3b7d63",
"metadata": {},
"source": [
"## Tools"
]
},
{
"cell_type": "markdown",
"id": "a24c659a-5937-43b1-bb95-c0342f2786a9",
"metadata": {},
"source": [
"### Tools Definitions"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "3f302f47-9a67-4410-ba16-56fa5a731c66",
"metadata": {},
"outputs": [],
"source": [
"from pydantic import BaseModel, Field\n",
"from openai.types.shared_params import FunctionDefinition\n",
"import sympy as sp\n",
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"import io\n",
"import base64\n",
"import random\n",
"\n",
"class ToolInput(BaseModel):\n",
" pass\n",
" \n",
"class GetCurrentDateTimeInput(ToolInput):\n",
" timezone: str = Field(default=\"UTC\", description=\"Timezone name, e.g., 'UTC' or 'Africa/Accra'\")\n",
"\n",
"\n",
"def get_current_datetime(req: GetCurrentDateTimeInput):\n",
" '''Returns the current date and time in the specified timezone.'''\n",
" from zoneinfo import ZoneInfo\n",
"\n",
" try:\n",
" from datetime import datetime\n",
" tz = ZoneInfo(req.timezone)\n",
" dt = datetime.now(tz)\n",
" return {\n",
" \"date\": dt.strftime(\"%Y-%m-%d\"),\n",
" \"time\": dt.strftime(\"%H:%M:%S %Z\"),\n",
" } \n",
" except:\n",
" return {\"error\": f\"Invalid timezone: {req.timezone}\"}\n",
"\n",
"\n",
"class GetTemperatureInput(ToolInput):\n",
" pass\n",
"\n",
"def get_temperature(req: GetTemperatureInput) -> float:\n",
" '''Returns the current temperature in degree celsius'''\n",
" return random.randint(-30, 70)\n",
"\n",
"\n",
"class PlotFunctionInput(ToolInput):\n",
" expression: str = Field(description=\"Mathematical expression to plot, e.g., 'sin(x)'\")\n",
" x_min: float = Field(default=-10, description=\"Minimum x value\")\n",
" x_max: float = Field(default=10, description=\"Maximum x value\")\n",
"\n",
"\n",
"def plot_function(req: PlotFunctionInput) -> dict[str, any]:\n",
" '''Plots a mathematical function and returns image data.'''\n",
" try:\n",
" x = sp.symbols('x')\n",
" expr = sp.sympify(req.expression)\n",
" lambdified = sp.lambdify(x, expr, 'numpy')\n",
" \n",
" x_vals = np.linspace(req.x_min, req.x_max, 400)\n",
" y_vals = lambdified(x_vals)\n",
" \n",
" plt.figure(figsize=(10, 6))\n",
" plt.plot(x_vals, y_vals, 'b-', linewidth=2)\n",
" plt.grid(True, alpha=0.3)\n",
" plt.title(f\"Plot of ${sp.latex(expr)}$\", fontsize=14)\n",
" plt.xlabel('x', fontsize=12)\n",
" plt.ylabel('f(x)', fontsize=12)\n",
" \n",
"\n",
" buf = io.BytesIO()\n",
" plt.savefig(buf, format='png', dpi=100, bbox_inches='tight')\n",
" plt.close()\n",
" buf.seek(0)\n",
" img_str = base64.b64encode(buf.read()).decode()\n",
" \n",
" return {\n",
" \"plot_image\": f\"data:image/png;base64,{img_str}\",\n",
" \"expression\": req.expression,\n",
" \"x_range\": [req.x_min, req.x_max]\n",
" }\n",
" except Exception as e:\n",
" return {\"error\": f\"Could not plot function: {str(e)}\"}\n",
"\n"
]
},
{
"cell_type": "markdown",
"id": "fae3ef71-f6cd-4894-ae55-9f4f8dd2a1cd",
"metadata": {},
"source": [
"### Tools registration & execution"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "4f18bc9f-f8d1-4208-a3d7-e4e911034572",
"metadata": {},
"outputs": [],
"source": [
"from concurrent.futures import ThreadPoolExecutor\n",
"\n",
"class ToolManager:\n",
" def __init__(self):\n",
" self._tools = []\n",
" self._tools_map: dict[str, tuple[Callable, ToolInput]] = {}\n",
"\n",
" def register_tool[T: ToolInput](self, fn: Callable, fn_input: T):\n",
" self._tools.append({\n",
" \"type\": \"function\",\n",
" \"function\": FunctionDefinition(\n",
" name=fn.__name__,\n",
" description=fn.__doc__,\n",
" parameters=fn_input.model_json_schema() if fn_input else None,\n",
" )\n",
" })\n",
" \n",
" self._tools_map[fn.__name__] = (fn, fn_input)\n",
"\n",
" def _run_single_tool(self, tool_call) -> dict[str, str] | None:\n",
" if not tool_call.id:\n",
" return None\n",
" \n",
" fn, fn_input = self._tools_map.get(tool_call.function.name)\n",
" args = tool_call.function.arguments\n",
" try:\n",
" if args:\n",
" result = fn(fn_input(**json.loads(args))) if fn_input else fn()\n",
" else:\n",
" result = fn(fn_input()) if fn_input else fn()\n",
" \n",
" logger.debug(f'Tool run result: {result}')\n",
" \n",
" return {\n",
" 'role': 'tool',\n",
" 'tool_call_id': tool_call.id,\n",
" 'content': json.dumps(result),\n",
" }\n",
" except Exception as e:\n",
" logger.error(f'Tool execution failed: {e}', extra={'name': tool_call.function.name})\n",
" return None\n",
"\n",
" def run(self, tool_calls) -> list[dict[str, str]]:\n",
" if not tool_calls:\n",
" return []\n",
"\n",
" logger.debug(tool_calls)\n",
"\n",
" tool_messages = []\n",
" \n",
" with ThreadPoolExecutor() as executor:\n",
" futures = [executor.submit(self._run_single_tool, tool_call) for tool_call in tool_calls]\n",
" \n",
" for future in futures:\n",
" result = future.result()\n",
" if result:\n",
" tool_messages.append(result)\n",
" \n",
" return tool_messages\n",
"\n",
" @property\n",
" def tools(self) -> list[any]:\n",
" return self._tools\n",
"\n",
" def dump_tools(self) -> str:\n",
" return json.dumps(self._tools, indent=True)\n",
"\n",
" \n",
"tool_manager = ToolManager()\n",
"\n",
"tool_manager.register_tool(get_current_datetime, GetCurrentDateTimeInput)\n",
"tool_manager.register_tool(get_temperature, GetTemperatureInput)\n",
"tool_manager.register_tool(plot_function, PlotFunctionInput)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "9b2e0634-de5d-45f6-a8d4-569e04d14a00",
"metadata": {},
"outputs": [],
"source": [
"logger.debug(tool_manager.dump_tools())"
]
},
{
"cell_type": "markdown",
"id": "bde4cd2a-b681-4b78-917c-d970c264b151",
"metadata": {},
"source": [
"## Interaction with LLM"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "8f7c8ea8-4082-4ad0-8751-3301adcf6538",
"metadata": {},
"outputs": [],
"source": [
"# handle = display(None, display_id=True)\n",
"\n",
"def ask(client: OpenAI | None, model: str, question: str, max_tool_turns=5):\n",
" if client is None:\n",
" logger.warning('You should have provided the API Keys you know. Fix 🔧 this and try again ♻️.')\n",
" return\n",
"\n",
" try:\n",
" logger.debug(f'# Tools: {len(tool_manager.tools)}')\n",
"\n",
" messages = get_messages(question=question)\n",
"\n",
" for turn in range(max_tool_turns):\n",
" logger.debug(f'Turn: {turn}')\n",
" response = client.chat.completions.create(\n",
" model=model,\n",
" messages=messages,\n",
" tools=tool_manager.tools,\n",
" stream=True,\n",
" )\n",
" \n",
" current_message = {}\n",
" tool_calls_accumulator = {}\n",
" \n",
" output = ''\n",
" call_id = None\n",
" \n",
" for chunk in response:\n",
" delta = chunk.choices[0].delta\n",
"\n",
" logger.debug(f' ✨ {chunk.choices[0]}')\n",
" if content := delta.content:\n",
" output += content\n",
" yield output\n",
"\n",
" if tool_calls := delta.tool_calls:\n",
" for tool_chunk in tool_calls:\n",
" print('x' * 50)\n",
" print(tool_chunk)\n",
"\n",
" if tool_chunk.id and call_id != tool_chunk.id:\n",
" call_id = tool_chunk.id\n",
"\n",
" print(f'Call ID: {call_id}')\n",
" # Streams of arguments don't come with the call id\n",
" # if not call_id:\n",
" # continue\n",
"\n",
" if call_id not in tool_calls_accumulator:\n",
" # tool_calls_accumulator[call_id] = {\n",
" # 'id': call_id,\n",
" # 'function': {'name': '', 'arguments': ''}\n",
" # }\n",
" tool_calls_accumulator[call_id] = SimpleNamespace(\n",
" id=call_id,\n",
" function=SimpleNamespace(name='', arguments='')\n",
" )\n",
"\n",
" if tool_chunk.function.name:\n",
" tool_calls_accumulator[call_id].function.name += tool_chunk.function.name\n",
" \n",
" if tool_chunk.function.arguments:\n",
" tool_calls_accumulator[call_id].function.arguments += tool_chunk.function.arguments\n",
"\n",
" if finish_reason := chunk.choices[0].finish_reason:\n",
" logger.debug('🧠 LLM interaction ended. Reason: {finish_reason}')\n",
"\n",
" final_tool_calls = list(tool_calls_accumulator.values())\n",
" if final_tool_calls:\n",
" logger.debug(f'Final tools to call {final_tool_calls}')\n",
"\n",
" tool_call_message = {\n",
" 'role': 'assistant',\n",
" 'content': None,\n",
" 'tool_calls': json.loads(json.dumps(final_tool_calls, default=lambda o: o.__dict__))\n",
" }\n",
"\n",
" messages.append(tool_call_message)\n",
" tool_messages = tool_manager.run(final_tool_calls)\n",
"\n",
" if tool_messages:\n",
" for tool_msg in tool_messages:\n",
" try:\n",
" data = json.loads(tool_msg['content'])\n",
" if 'plot_image' in data:\n",
" logger.debug('We have a plot')\n",
" yield f'<img src=\"{data[\"plot_image\"]}\" style=\"max-width: 100%; height: auto; border: 1px solid #ccc; border-radius: 5px;\">'\n",
" return\n",
" except:\n",
" pass\n",
" messages.extend(tool_messages)\n",
" else:\n",
" return\n",
" \n",
" except Exception as e:\n",
" logger.error(f'🔥 An error occurred during the interaction with the LLM: {e}', exc_info=True)\n",
" return str(e)"
]
},
{
"cell_type": "markdown",
"id": "eda786d3-5add-4bd1-804d-13eff60c3d1a",
"metadata": {},
"source": [
"### Verify streaming behaviour"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "09bc9a11-adb4-4a9c-9c77-73b2b5a665cf",
"metadata": {},
"outputs": [],
"source": [
"# print(selected_provider, selected_model)\n",
"# print(client)\n",
"# for o in ask(client, selected_model, 'What is the time?'):\n",
"# for o in ask(client, selected_model, 'What is the temperature?'):\n",
"# for o in ask(client, selected_model, 'What is the time and the temperature?'):\n",
"# for o in ask(client, selected_model, 'Plot a for the expression sin(x)'):\n",
"for o in ask(client, selected_model, 'Plot a graph of y = x**2'):\n",
" print(o)"
]
},
{
"cell_type": "markdown",
"id": "27230463",
"metadata": {},
"source": [
"## Build Gradio UI"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "50fc3577",
"metadata": {},
"outputs": [],
"source": [
"def chat(message: str, history: list[dict], selected_provider: str, model_selector: str):\n",
" # NOTE: I'm not interesting in maintaining a conversation\n",
" response = ask(client, selected_model, message)\n",
"\n",
" for chunk in response:\n",
" yield chunk\n",
"\n",
"def on_provider_change(change):\n",
" global selected_provider, client, models\n",
" logger.info(f'Provider changed to {change}')\n",
" selected_provider = change\n",
" client = clients.get(selected_provider)\n",
" load_models_if_needed(client, selected_provider)\n",
"\n",
" return gr.Dropdown(\n",
" choices=models.get(selected_provider, []),\n",
" value=selection_state[selected_provider],\n",
" interactive=True,\n",
" )\n",
"\n",
"\n",
"def on_model_change(change):\n",
" global selected_provider, selected_model, selection_state\n",
"\n",
" selected_model = change\n",
" selection_state[selected_provider] = selected_model\n",
" logger.info(f'👉 Selected model: {selected_model}')\n",
"\n",
"\n",
"with gr.Blocks(title='MathXpert', fill_width=True, \n",
" \n",
" ) as ui:\n",
" def get_value_if_exist(v, ls) -> str:\n",
" print(ls)\n",
" selected = v if v in ls else None\n",
" if selected:\n",
" return selected\n",
"\n",
" return ls[0] if ls else None\n",
"\n",
" with gr.Row():\n",
" provider_selector = gr.Dropdown(\n",
" choices=available_providers, \n",
" value=get_desired_value_or_first_item(selected_provider, available_providers),\n",
" label='Provider',\n",
" )\n",
" model_selector = gr.Dropdown(\n",
" choices=models[selected_provider],\n",
" value=get_desired_value_or_first_item(selection_state[selected_provider], models[selected_provider]),\n",
" label='Model',\n",
" )\n",
" \n",
" provider_selector.change(fn=on_provider_change, inputs=provider_selector, outputs=model_selector)\n",
" model_selector.change(fn=on_model_change, inputs=model_selector)\n",
"\n",
" examples = [\n",
" ['Where can substitutions be applied in real life?', None, None],\n",
" ['Give 1 differential equation question and solve it', None, None],\n",
" ['Plot x**2 - 3x', None, None],\n",
" ['What is the time now?', None, None],\n",
" ['What is the temperature?', None, None],\n",
" ['Tell me the time and the temperature now', None, None],\n",
" ]\n",
"\n",
" \n",
" gr.ChatInterface(\n",
" fn=chat, \n",
" type='messages', \n",
" chatbot=gr.Chatbot(type='messages', height='75vh', resizable=True),\n",
" additional_inputs=[provider_selector, model_selector],\n",
" examples=examples,\n",
" )\n",
"\n",
"ui.launch()"
]
}
],
"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
}

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OPENAI_API_KEY=sk-or-v1-openai-api-key
GEMINI_API_KEY=AI-gemini-api-key

4
week2/community-contributions/salah/requirements.txt Normal file
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openai>=1.3.0
gradio>=4.0.0
python-dotenv>=1.0.0
google-genai>=0.3.0

2
week2/community-contributions/salah/v1/.env.example Normal file
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OPENAI_API_KEY=sk-or-v1-your-openrouter-api-key-here
GEMINI_API_KEY=your-gemini-api-key-here

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week2/community-contributions/salah/v1/app.py Normal file
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import gradio as gr
from simple_assistant import Assistant
class SimpleUI:
def __init__(self):
print("\n" + "="*60)
print("Starting up...")
print("="*60)
self.assistant = Assistant()
self.history = [] # Text history for API
self.display_history = [] # Display history with audio for chat UI
self.audio_enabled = True
print("UI initialized")
print("Audio features: Gemini STT + TTS")
print("="*60 + "\n")
def add_message(self, msg):
print("\n" + ">"*60)
print(f"[UI] New message: {msg[:50]}...")
if not msg.strip():
print("[UI] Empty message, ignoring")
print(">"*60 + "\n")
return self.display_history, ""
print(f"[UI] Adding to history (current: {len(self.history)} messages)")
# Add to API history (text only)
self.history.append({"role": "user", "content": msg})
# Add to display history
self.display_history.append({"role": "user", "content": msg})
print("[UI] Getting AI response...")
response = self.assistant.chat(msg, self.history)
print(f"[UI] Adding response to history")
# Add to API history (text only)
self.history.append({"role": "assistant", "content": response})
# Add to display history
self.display_history.append({"role": "assistant", "content": response})
print(f"[UI] Total history: {len(self.history)} messages")
print(f"[UI] Returning {len(self.display_history)} messages to display")
print(">"*60 + "\n")
return self.display_history, ""
def handle_voice_input(self, audio_file):
print("\n" + ">"*60)
print("[UI] Voice input received")
print(f"[UI] Audio file: {audio_file}")
if not audio_file:
print("[UI] No audio file")
print(">"*60 + "\n")
return self.display_history, None
# Transcribe
print("[UI] Transcribing with Gemini...")
text = self.assistant.speech_to_text(audio_file)
if not text:
print("[UI] Transcription failed")
print(">"*60 + "\n")
error_msg = "Sorry, couldn't transcribe audio"
self.history.append({"role": "assistant", "content": error_msg})
self.display_history.append({"role": "assistant", "content": error_msg})
return self.display_history, None
print(f"[UI] Transcribed: {text}")
# Add to API history (text only)
self.history.append({"role": "user", "content": text})
# Add voice message to display history with audio file
self.display_history.append({
"role": "user",
"content": {
"path": audio_file,
"alt_text": f"🎤 {text}"
}
})
# Get response
print("[UI] Getting AI response...")
response = self.assistant.chat(text, self.history)
# Add text response to API history
self.history.append({"role": "assistant", "content": response})
# Generate audio response
print("[UI] Generating audio with Gemini TTS...")
audio_response = self.assistant.text_to_speech(response)
if audio_response:
print(f"[UI] ✓ Audio response generated")
# Add response with audio to display history
self.display_history.append({
"role": "assistant",
"content": {
"path": audio_response,
"alt_text": f"🔊 {response[:100]}..."
}
})
else:
print(f"[UI] ⚠ No audio, text only")
self.display_history.append({"role": "assistant", "content": response})
print(f"[UI] Returning {len(self.display_history)} messages")
print(">"*60 + "\n")
return self.display_history, None
def analyze(self, code, lang):
print("\n" + ">"*60)
print(f"[UI] Code analysis request")
print(f"[UI] Language: {lang}")
print(f"[UI] Code length: {len(code)} chars")
if not code.strip():
print("[UI] Empty code, ignoring")
print(">"*60 + "\n")
return self.display_history
print("[UI] Calling analyze_code...")
result = self.assistant.analyze_code(code, lang)
print("[UI] Adding to history")
# Add to API history
self.history.append({"role": "user", "content": f"Analyze {lang} code"})
self.history.append({"role": "assistant", "content": result})
# Add to display history
self.display_history.append({"role": "user", "content": f"Analyze {lang} code"})
self.display_history.append({"role": "assistant", "content": result})
print(f"[UI] Returning {len(self.display_history)} messages")
print(">"*60 + "\n")
return self.display_history
def create_ui(self):
print("\n" + "="*60)
print("Creating Gradio UI...")
print("="*60)
with gr.Blocks() as app:
gr.Markdown("# Tech Assistant")
gr.Markdown("**Voice-enabled**: Type or record audio messages")
# Chat panel - shows all messages including audio
chat = gr.Chatbot(type="messages", height=500)
print("✓ Chatbot created")
# Input area at bottom (like ChatGPT)
with gr.Row():
msg = gr.Textbox(
label="Message",
placeholder="Type a message or record audio...",
scale=9,
container=False
)
mic = gr.Audio(
sources=["microphone"],
type="filepath",
label="🎤 Record",
scale=1,
waveform_options={"show_controls": False}
)
print("✓ Message and record inputs created")
# Wire events
msg.submit(self.add_message, msg, [chat, msg])
print("✓ Message submit event wired")
mic.stop_recording(self.handle_voice_input, mic, [chat, mic])
print("✓ Voice input event wired")
# Tools section
with gr.Accordion("Tools", open=False):
gr.Markdown("### Code Analysis")
code = gr.Textbox(label="Code", lines=8)
lang = gr.Dropdown(
choices=["python", "javascript", "java"],
value="python",
label="Language"
)
analyze_btn = gr.Button("Analyze")
print("✓ Code analysis tools created")
analyze_btn.click(self.analyze, [code, lang], chat)
print("✓ Analyze button event wired")
print("✓ UI creation complete")
print("="*60 + "\n")
return app
def launch(self):
print("\n" + "="*60)
print("Launching Gradio app...")
print("="*60)
app = self.create_ui()
print("Starting server on port 7862...")
print("="*60 + "\n")
app.launch(server_port=7862)
if __name__ == "__main__":
print("\n" + "#"*60)
print("# TECH ASSISTANT - SIMPLE UI")
print("#"*60 + "\n")
ui = SimpleUI()
ui.launch()

259
week2/community-contributions/salah/v1/assistant.py Normal file
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import os
import json
from google import genai
from google.genai import types
from dotenv import load_dotenv
from openai import OpenAI
from pathlib import Path
import tempfile
import wave
load_dotenv()
class Assistant:
def __init__(self):
print("\n" + "="*60)
print("Initializing Assistant...")
print("="*60)
openrouter_key = os.getenv('OPENAI_API_KEY')
gemini_key = os.getenv('GEMINI_API_KEY')
print(f"OpenRouter API Key: {openrouter_key[:20]}..." if openrouter_key else "OpenRouter API Key: NOT FOUND")
print(f"Gemini API Key: {gemini_key[:20]}..." if gemini_key else "Gemini API Key: NOT FOUND")
# OpenRouter client for text (GPT-4o-mini)
print("Setting up OpenRouter client...")
self.openrouter = OpenAI(
api_key=openrouter_key,
base_url="https://openrouter.ai/api/v1"
)
print("OpenRouter client ready")
# Gemini client for audio and images
print("Setting up Gemini client...")
self.gemini_client = genai.Client(api_key=gemini_key)
print("Gemini client ready (audio + images)")
self.text_model = "openai/gpt-4o-mini"
self.system_prompt = "You are a helpful technical assistant. Keep answers clear and practical."
self.stt_model = "gemini-2.0-flash-exp"
self.tts_model = "gemini-2.5-flash-preview-tts"
print(f"Text Model: {self.text_model}")
print(f"STT Model: {self.stt_model}")
print(f"TTS Model: {self.tts_model}")
def chat(self, message, history=[]):
print(f"[Chat] User: {message[:50]}...")
print(f"[Chat] History messages: {len(history)}")
print(f"[Chat] Model: {self.text_model}")
messages = [{"role": "system", "content": self.system_prompt}]
messages.extend(history)
messages.append({"role": "user", "content": message})
print(f"[Chat] Total messages to send: {len(messages)}")
print("[Chat] Calling OpenRouter API...")
try:
response = self.openrouter.chat.completions.create(
model=self.text_model,
messages=messages,
extra_body={
"usage": {
"include": True
}
}
)
reply = response.choices[0].message.content
print(f"[Chat] Response received")
print(f"[Chat] GPT-4o-mini: {len(reply)} chars")
print(f"[Chat] Preview: {reply[:100]}...")
# Print usage and cost
if hasattr(response, 'usage') and response.usage:
usage = response.usage
print(f"[Chat] Usage:")
print(f" - Prompt tokens: {usage.prompt_tokens}")
print(f" - Completion tokens: {usage.completion_tokens}")
print(f" - Total tokens: {usage.total_tokens}")
if hasattr(usage, 'cost') and usage.cost:
print(f" - Cost: ${usage.cost:.6f}")
print("-"*60 + "\n")
return reply
except Exception as e:
print(f"[Error] ✗ API call failed: {e}")
print("-"*60 + "\n")
return f"Error: {str(e)}"
def analyze_code(self, code, language="python"):
print("\n" + "="*60)
print(f"[Code] Analyzing {language} code...")
print(f"[Code] Code length: {len(code)} characters")
print(f"[Code] Lines: {len(code.splitlines())}")
print("="*60)
prompt = f"Analyze this {language} code for bugs and improvements:\n\n```{language}\n{code}\n```"
result = self.chat(prompt)
print("[Code] Analysis complete\n")
return result
def generate_image(self, description):
print("\n" + "="*60)
print(f"[Image] Gemini generating: {description[:50]}...")
print(f"[Image] Model: gemini-2.0-flash-exp")
try:
prompt = f"Generate an image of: {description}. Make it clear and professional."
print("[Image] Calling Gemini API...")
response = self.gemini_client.models.generate_content(
model='gemini-2.0-flash-exp',
contents=prompt
)
print("[Image] Response received")
print(f"[Image] Result length: {len(response.text)} chars")
# Print usage and cost (Gemini 2.0 Flash: $0.30/1M input, $2.50/1M output)
if hasattr(response, 'usage_metadata'):
usage = response.usage_metadata
input_tokens = usage.prompt_token_count
output_tokens = usage.candidates_token_count
total_tokens = usage.total_token_count
cost = (input_tokens * 0.30 + output_tokens * 2.50) / 1_000_000
print(f"[Image] Usage:")
print(f" - Input tokens: {input_tokens}")
print(f" - Output tokens: {output_tokens}")
print(f" - Total tokens: {total_tokens}")
print(f" - Cost: ${cost:.6f}")
print("="*60 + "\n")
return response.text
except Exception as e:
print(f"[Error] ✗ Image generation failed: {e}")
print("="*60 + "\n")
return None
def speech_to_text(self, audio_file_path):
print("\n" + "="*60)
print("[STT] Gemini speech-to-text...")
print(f"[STT] Audio file: {audio_file_path}")
try:
print("[STT] Uploading audio file to Gemini...")
audio_file = self.gemini_client.files.upload(file=audio_file_path)
print(f"[STT] File uploaded: {audio_file.name}")
print("[STT] Transcribing with Gemini...")
prompt = "Generate a transcript of the speech."
response = self.gemini_client.models.generate_content(
model=self.stt_model,
contents=[prompt, audio_file]
)
text = response.text.strip()
print(f"[STT] Transcribed: {text[:100]}...")
print(f"[STT] Length: {len(text)} chars")
# Print usage and cost (Flash Native Audio Input: $3.00/1M tokens)
if hasattr(response, 'usage_metadata'):
usage = response.usage_metadata
input_tokens = usage.prompt_token_count
output_tokens = usage.candidates_token_count
total_tokens = usage.total_token_count
# Audio input is $3.00/1M, text output is $2.50/1M
cost = (input_tokens * 3.00 + output_tokens * 2.50) / 1_000_000
print(f"[STT] Usage:")
print(f" - Input tokens (audio): {input_tokens}")
print(f" - Output tokens (text): {output_tokens}")
print(f" - Total tokens: {total_tokens}")
print(f" - Cost: ${cost:.6f}")
print("="*60 + "\n")
return text
except Exception as e:
print(f"[Error] ✗ STT failed: {e}")
print(f"[Error] Full error: {type(e).__name__}: {str(e)}")
print("="*60 + "\n")
return None
def text_to_speech(self, text):
print("\n" + "="*60)
print(f"[TTS] Gemini text-to-speech...")
print(f"[TTS] Text: {text[:50]}...")
print(f"[TTS] Length: {len(text)} chars")
try:
# Limit text length for TTS
text_to_speak = text[:500] if len(text) > 500 else text
print("[TTS] Generating audio with Gemini TTS model...")
response = self.gemini_client.models.generate_content(
model=self.tts_model,
contents=f"Say cheerfully: {text_to_speak}",
config=types.GenerateContentConfig(
response_modalities=["AUDIO"],
speech_config=types.SpeechConfig(
voice_config=types.VoiceConfig(
prebuilt_voice_config=types.PrebuiltVoiceConfig(
voice_name='Kore',
)
)
),
)
)
print("[TTS] Audio generated, converting to WAV...")
# Extract raw PCM audio data
pcm_data = response.candidates[0].content.parts[0].inline_data.data
print(f"[TTS] Raw PCM size: {len(pcm_data)} bytes")
# Print usage and cost (2.5 Flash Preview TTS: $10.00/1M audio output tokens)
if hasattr(response, 'usage_metadata'):
usage = response.usage_metadata
input_tokens = usage.prompt_token_count
output_tokens = usage.candidates_token_count
total_tokens = usage.total_token_count
# Text input is $0.30/1M, audio output is $10.00/1M
cost = (input_tokens * 0.30 + output_tokens * 10.00) / 1_000_000
print(f"[TTS] Usage:")
print(f" - Input tokens (text): {input_tokens}")
print(f" - Output tokens (audio): {output_tokens}")
print(f" - Total tokens: {total_tokens}")
print(f" - Cost: ${cost:.6f}")
# Create WAV file with proper headers
# Gemini TTS outputs: 24kHz sample rate, mono, 16-bit PCM
temp_file = tempfile.NamedTemporaryFile(delete=False, suffix=".wav")
with wave.open(temp_file.name, 'wb') as wav_file:
wav_file.setnchannels(1) # Mono
wav_file.setsampwidth(2) # 16-bit = 2 bytes
wav_file.setframerate(24000) # 24kHz
wav_file.writeframes(pcm_data)
temp_file.close()
print(f"[TTS] WAV file saved: {temp_file.name}")
print("="*60 + "\n")
return temp_file.name
except Exception as e:
print(f"[Error] ✗ TTS failed: {e}")
print(f"[Error] Full error: {type(e).__name__}: {str(e)}")
print("="*60 + "\n")
return None
if __name__ == "__main__":
assistant = Assistant()
# Test it
response = assistant.chat("What is Python?")
print(f"\nResponse: {response}")

20
week2/community-contributions/salah/v2/.env.example Normal file
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# API Keys - Required
OPENAI_API_KEY=sk-or-v1-your-openrouter-api-key-here
GEMINI_API_KEY=your-gemini-api-key-here
# Models - Optional (defaults provided)
TEXT_MODEL=openai/gpt-4o-mini
STT_MODEL=gemini-2.0-flash-exp
TTS_MODEL=gemini-2.5-flash-preview-tts
VOICE_NAME=Kore
# App Settings - Optional
PORT=7862
SYSTEM_PROMPT=You are a helpful assistant. Keep it simple and practical.
# Alternative Models You Can Try:
# TEXT_MODEL=anthropic/claude-3.5-sonnet
# TEXT_MODEL=google/gemini-pro-1.5
# TEXT_MODEL=meta-llama/llama-3.1-8b-instruct
# VOICE_NAME=Aoede
# VOICE_NAME=Fenrir

4
week2/community-contributions/salah/v2/requirements.txt Normal file
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openai>=1.3.0
gradio>=4.0.0
python-dotenv>=1.0.0
google-genai>=0.3.0

13
week2/community-contributions/salah/v2/run.py Normal file
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#!/usr/bin/env python3
import sys
import os
# Add src to Python path
sys.path.insert(0, os.path.join(os.path.dirname(__file__), 'src'))
# Now import and run
from main import main
if __name__ == "__main__":
main()

1
week2/community-contributions/salah/v2/src/__init__.py Normal file
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# Create __init__.py files to make directories proper Python packages

0
week2/community-contributions/salah/v2/src/config/__init__.py Normal file
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25
week2/community-contributions/salah/v2/src/config/settings.py Normal file
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import os
from dotenv import load_dotenv
load_dotenv()
class Config:
def __init__(self):
self.openrouter_key = os.getenv('OPENAI_API_KEY')
self.gemini_key = os.getenv('GEMINI_API_KEY')
# Models - all configurable via env
self.text_model = os.getenv('TEXT_MODEL', "openai/gpt-4o-mini")
self.stt_model = os.getenv('STT_MODEL', "gemini-2.0-flash-exp")
self.tts_model = os.getenv('TTS_MODEL', "gemini-2.5-flash-preview-tts")
self.voice_name = os.getenv('VOICE_NAME', 'Kore')
# App settings
self.port = int(os.getenv('PORT', '7862'))
self.system_prompt = os.getenv('SYSTEM_PROMPT', "You are a helpful assistant. Keep it simple and practical.")
def validate(self):
if not self.openrouter_key:
raise Exception("Missing OPENAI_API_KEY")
if not self.gemini_key:
raise Exception("Missing GEMINI_API_KEY")

0
week2/community-contributions/salah/v2/src/interfaces/__init__.py Normal file
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23
week2/community-contributions/salah/v2/src/interfaces/ai_client.py Normal file
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from abc import ABC, abstractmethod
class AIClient(ABC):
@abstractmethod
def chat(self, messages):
pass
@abstractmethod
def analyze_code(self, code, language):
pass
@abstractmethod
def generate_linkedin_post(self, topic, tone="professional"):
pass
class AudioService(ABC):
@abstractmethod
def speech_to_text(self, audio_file):
pass
@abstractmethod
def text_to_speech(self, text):
pass

32
week2/community-contributions/salah/v2/src/main.py Normal file
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from config.settings import Config
from services.openrouter_client import OpenRouterClient
from services.gemini_audio_service import GeminiAudioService
from services.conversation_manager import ConversationManager
from ui.gradio_interface import AssistantUI
def main():
print("Starting AI Assistant...")
# Load config
config = Config()
config.validate()
# Setup services
ai_client = OpenRouterClient(config.openrouter_key, config.text_model)
audio_service = GeminiAudioService(
config.gemini_key,
config.stt_model,
config.tts_model,
config.voice_name
)
conversation = ConversationManager(config.system_prompt)
# Create UI
ui = AssistantUI(ai_client, audio_service, conversation)
app = ui.create_interface()
print(f"Launching on port {config.port}...")
app.launch(server_port=config.port)
if __name__ == "__main__":
main()

0
week2/community-contributions/salah/v2/src/models/__init__.py Normal file
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6
week2/community-contributions/salah/v2/src/models/message.py Normal file
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from dataclasses import dataclass
@dataclass
class Message:
role: str
content: str

0
week2/community-contributions/salah/v2/src/services/__init__.py Normal file
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35
week2/community-contributions/salah/v2/src/services/conversation_manager.py Normal file
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from models.message import Message
class ConversationManager:
def __init__(self, system_prompt):
self.system_prompt = system_prompt
self.messages = []
def add_user_message(self, content):
print(f"[Conversation] Adding user message: {content[:100]}...")
print(f"[Conversation] Message length: {len(content)} chars")
self.messages.append(Message("user", content))
print(f"[Conversation] Total messages: {len(self.messages)}")
def add_assistant_message(self, content):
print(f"[Conversation] Adding assistant message: {content[:100]}...")
print(f"[Conversation] Message length: {len(content)} chars")
self.messages.append(Message("assistant", content))
print(f"[Conversation] Total messages: {len(self.messages)}")
def get_api_messages(self):
# Convert to format expected by APIs
api_messages = [{"role": "system", "content": self.system_prompt}]
for msg in self.messages:
api_messages.append({"role": msg.role, "content": msg.content})
# Calculate total context size
total_chars = sum(len(msg["content"]) for msg in api_messages)
estimated_tokens = total_chars // 4 # Rough estimate
print(f"[Conversation] API messages prepared:")
print(f" - Total messages: {len(api_messages)} (including system)")
print(f" - Total characters: {total_chars}")
print(f" - Estimated tokens: {estimated_tokens}")
return api_messages

124
week2/community-contributions/salah/v2/src/services/gemini_audio_service.py Normal file
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from google import genai
from google.genai import types
import tempfile
import wave
from interfaces.ai_client import AudioService
class GeminiAudioService(AudioService):
def __init__(self, api_key, stt_model, tts_model, voice_name):
self.client = genai.Client(api_key=api_key)
self.stt_model = stt_model
self.tts_model = tts_model
self.voice_name = voice_name
def speech_to_text(self, audio_file):
print(f"[Gemini STT] Processing audio file: {audio_file}")
print(f"[Gemini STT] Model: {self.stt_model}")
try:
# Get file size for logging
import os
file_size = os.path.getsize(audio_file)
print(f"[Gemini STT] Audio file size: {file_size} bytes")
print("[Gemini STT] Uploading to Gemini...")
uploaded_file = self.client.files.upload(file=audio_file)
print(f"[Gemini STT] File uploaded: {uploaded_file.name}")
print("[Gemini STT] Transcribing...")
response = self.client.models.generate_content(
model=self.stt_model,
contents=["Transcribe the speech in this audio file. Return only the spoken words, nothing else.", uploaded_file]
)
text = response.text.strip()
print(f"[Gemini STT] Transcription length: {len(text)} chars")
print(f"[Gemini STT] Transcription: {text[:100]}...")
# Print usage information if available
if hasattr(response, 'usage_metadata'):
usage = response.usage_metadata
input_tokens = usage.prompt_token_count
output_tokens = usage.candidates_token_count
total_tokens = usage.total_token_count
# Audio input cost: $3.00/1M tokens, text output: $2.50/1M tokens
cost = (input_tokens * 3.00 + output_tokens * 2.50) / 1_000_000
print(f"[Gemini STT] Token usage:")
print(f" - Input tokens (audio): {input_tokens}")
print(f" - Output tokens (text): {output_tokens}")
print(f" - Total tokens: {total_tokens}")
print(f" - Estimated cost: ${cost:.6f}")
print("[Gemini STT] Success")
return text
except Exception as e:
print(f"[Gemini STT] Error: {e}")
return None
def text_to_speech(self, text):
print(f"[Gemini TTS] Converting text to speech")
print(f"[Gemini TTS] Model: {self.tts_model}, Voice: {self.voice_name}")
print(f"[Gemini TTS] Input text length: {len(text)} chars")
try:
# Keep it short for TTS
text_to_speak = text[:500] if len(text) > 500 else text
if len(text) > 500:
print(f"[Gemini TTS] Text truncated to 500 chars")
print(f"[Gemini TTS] Text preview: {text_to_speak[:100]}...")
print("[Gemini TTS] Generating audio...")
response = self.client.models.generate_content(
model=self.tts_model,
contents=f"Say: {text_to_speak}",
config=types.GenerateContentConfig(
response_modalities=["AUDIO"],
speech_config=types.SpeechConfig(
voice_config=types.VoiceConfig(
prebuilt_voice_config=types.PrebuiltVoiceConfig(
voice_name=self.voice_name,
)
)
),
)
)
pcm_data = response.candidates[0].content.parts[0].inline_data.data
print(f"[Gemini TTS] Raw PCM data size: {len(pcm_data)} bytes")
# Print usage information if available
if hasattr(response, 'usage_metadata'):
usage = response.usage_metadata
input_tokens = usage.prompt_token_count
output_tokens = usage.candidates_token_count
total_tokens = usage.total_token_count
# Text input: $0.30/1M tokens, audio output: $10.00/1M tokens
cost = (input_tokens * 0.30 + output_tokens * 10.00) / 1_000_000
print(f"[Gemini TTS] Token usage:")
print(f" - Input tokens (text): {input_tokens}")
print(f" - Output tokens (audio): {output_tokens}")
print(f" - Total tokens: {total_tokens}")
print(f" - Estimated cost: ${cost:.6f}")
# Create WAV file
temp_file = tempfile.NamedTemporaryFile(delete=False, suffix=".wav")
with wave.open(temp_file.name, 'wb') as wav_file:
wav_file.setnchannels(1)
wav_file.setsampwidth(2)
wav_file.setframerate(24000)
wav_file.writeframes(pcm_data)
temp_file.close()
print(f"[Gemini TTS] WAV file created: {temp_file.name}")
print("[Gemini TTS] Success")
return temp_file.name
except Exception as e:
print(f"[Gemini TTS] Error: {e}")
return None

91
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from openai import OpenAI
from interfaces.ai_client import AIClient
class OpenRouterClient(AIClient):
def __init__(self, api_key, model):
self.client = OpenAI(
api_key=api_key,
base_url="https://openrouter.ai/api/v1"
)
self.model = model
def chat(self, messages):
print(f"[OpenRouter] Calling {self.model}")
print(f"[OpenRouter] Messages count: {len(messages)}")
# Calculate input tokens estimate (rough)
total_chars = sum(len(msg.get('content', '')) for msg in messages)
estimated_tokens = total_chars // 4 # Rough estimate
print(f"[OpenRouter] Estimated input tokens: {estimated_tokens}")
try:
response = self.client.chat.completions.create(
model=self.model,
messages=messages,
extra_body={
"usage": {
"include": True
}
}
)
content = response.choices[0].message.content
print(f"[OpenRouter] Response length: {len(content)} chars")
print(f"[OpenRouter] Response preview: {content[:100]}...")
# Print usage information if available
if hasattr(response, 'usage') and response.usage:
usage = response.usage
print(f"[OpenRouter] Token usage:")
print(f" - Prompt tokens: {usage.prompt_tokens}")
print(f" - Completion tokens: {usage.completion_tokens}")
print(f" - Total tokens: {usage.total_tokens}")
# Try to get cost information
if hasattr(usage, 'cost') and usage.cost:
print(f" - Cost: ${usage.cost:.6f}")
else:
# Rough cost estimate for GPT-4o-mini ($0.15/1M input, $0.60/1M output)
estimated_cost = (usage.prompt_tokens * 0.15 + usage.completion_tokens * 0.60) / 1_000_000
print(f" - Estimated cost: ${estimated_cost:.6f}")
print(f"[OpenRouter] Success")
return content
except Exception as e:
print(f"[OpenRouter] Error: {str(e)}")
return f"Error: {str(e)}"
def analyze_code(self, code, language):
print(f"[OpenRouter] Code analysis request - Language: {language}")
print(f"[OpenRouter] Code length: {len(code)} chars, {len(code.splitlines())} lines")
prompt = f"Analyze this {language} code for bugs and improvements:\n\n```{language}\n{code}\n```"
messages = [{"role": "user", "content": prompt}]
return self.chat(messages)
def generate_linkedin_post(self, topic, tone="professional"):
print(f"[OpenRouter] LinkedIn post request - Topic: {topic[:50]}...")
print(f"[OpenRouter] Tone: {tone}")
tone_styles = {
"professional": "formal, informative, and industry-focused",
"casual": "friendly, approachable, and conversational",
"inspirational": "motivating, uplifting, and thought-provoking",
"educational": "informative, teaching-focused, and valuable"
}
style = tone_styles.get(tone, "professional and engaging")
prompt = f"""Create a LinkedIn post about: {topic}
Make it {style}. Include:
- Hook that grabs attention
- 2-3 key insights or takeaways
- Call to action or question for engagement
- Relevant hashtags (3-5)
Keep it under 300 words and format for LinkedIn readability."""
messages = [{"role": "user", "content": prompt}]
return self.chat(messages)

0
week2/community-contributions/salah/v2/src/ui/__init__.py Normal file
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week2/community-contributions/salah/v2/src/ui/gradio_interface.py Normal file
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import gradio as gr
class AssistantUI:
def __init__(self, ai_client, audio_service, conversation_manager):
self.ai_client = ai_client
self.audio_service = audio_service
self.conversation = conversation_manager
self.display_history = []
def handle_text_message(self, message):
if not message.strip():
return self.display_history, ""
# Add user message
self.conversation.add_user_message(message)
self.display_history.append({"role": "user", "content": message})
# Get AI response
api_messages = self.conversation.get_api_messages()
response = self.ai_client.chat(api_messages)
# Check if response is an error
is_error = response.startswith("Error:")
if is_error:
print(f"AI Client Error: {response}")
# Show error in chat but don't add to conversation history
self.display_history.append({"role": "assistant", "content": response})
return self.display_history, ""
# Add successful response to conversation
self.conversation.add_assistant_message(response)
self.display_history.append({"role": "assistant", "content": response})
return self.display_history, ""
def handle_voice_message(self, audio_file):
if not audio_file:
return self.display_history, None
# Transcribe audio
text = self.audio_service.speech_to_text(audio_file)
if not text:
return self.display_history, None
# Add transcribed message to display
self.display_history.append({
"role": "user",
"content": {"path": audio_file, "alt_text": f"Voice: {text}"}
})
# Process as text message
self.conversation.add_user_message(text)
api_messages = self.conversation.get_api_messages()
response = self.ai_client.chat(api_messages)
# Check if response is an error
is_error = response.startswith("Error:")
if is_error:
print(f"AI Client Error: {response}")
# Show error in chat but don't convert to speech
self.display_history.append({"role": "assistant", "content": response})
return self.display_history, None
self.conversation.add_assistant_message(response)
# Generate audio response only for successful responses
audio_response = self.audio_service.text_to_speech(response)
if audio_response:
self.display_history.append({
"role": "assistant",
"content": {"path": audio_response, "alt_text": response[:100] + "..."}
})
else:
self.display_history.append({"role": "assistant", "content": response})
return self.display_history, None
def analyze_code(self, code, language):
if not code.strip():
return self.display_history
result = self.ai_client.analyze_code(code, language)
# Check for errors
is_error = result.startswith("Error:")
if is_error:
print(f"Code Analysis Error: {result}")
self.display_history.append({"role": "user", "content": f"Code analysis ({language})"})
self.display_history.append({"role": "assistant", "content": result})
return self.display_history
# Add to conversation only if successful
self.conversation.add_user_message(f"Analyze {language} code")
self.conversation.add_assistant_message(result)
# Add to display
self.display_history.append({"role": "user", "content": f"Code analysis ({language})"})
self.display_history.append({"role": "assistant", "content": result})
return self.display_history
def generate_linkedin_post(self, topic, tone):
if not topic.strip():
return self.display_history
result = self.ai_client.generate_linkedin_post(topic, tone)
# Check for errors
is_error = result.startswith("Error:")
if is_error:
print(f"LinkedIn Post Generation Error: {result}")
self.display_history.append({"role": "user", "content": f"LinkedIn post ({tone}): {topic}"})
self.display_history.append({"role": "assistant", "content": result})
return self.display_history
# Add to conversation only if successful
self.conversation.add_user_message(f"Generate LinkedIn post about: {topic}")
self.conversation.add_assistant_message(result)
# Add to display
self.display_history.append({"role": "user", "content": f"LinkedIn post ({tone}): {topic}"})
self.display_history.append({"role": "assistant", "content": result})
return self.display_history
def create_interface(self):
with gr.Blocks() as app:
gr.Markdown("# AI Assistant")
gr.Markdown("Chat with text or voice")
# Main chat
chat = gr.Chatbot(type="messages", height=500)
# Input area
with gr.Row():
msg = gr.Textbox(
label="Message",
placeholder="Type or record...",
scale=9,
container=False
)
mic = gr.Audio(
sources=["microphone"],
type="filepath",
label="Record",
scale=1
)
# Wire up events
msg.submit(self.handle_text_message, msg, [chat, msg])
mic.stop_recording(self.handle_voice_message, mic, [chat, mic])
# Code analysis tool
with gr.Accordion("Code Analysis", open=False):
code_input = gr.Textbox(label="Code", lines=8)
lang_select = gr.Dropdown(
choices=["python", "javascript", "java"],
value="python",
label="Language"
)
analyze_btn = gr.Button("Analyze")
analyze_btn.click(
self.analyze_code,
[code_input, lang_select],
chat
)
# LinkedIn post generator
with gr.Accordion("LinkedIn Post Generator", open=False):
topic_input = gr.Textbox(
label="Topic",
placeholder="What do you want to post about?",
lines=2
)
tone_select = gr.Dropdown(
choices=["professional", "casual", "inspirational", "educational"],
value="professional",
label="Tone"
)
generate_btn = gr.Button("Generate Post")
generate_btn.click(
self.generate_linkedin_post,
[topic_input, tone_select],
chat
)
return app

969
week2/community-contributions/week2-assignment-Joshua (GEN AI)/3way_conversation.ipynb Normal file
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{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# 3-Way Conversation Assignment - Week 2 Day 1\n",
"\n",
"## Joshua's Implementation\n",
"\n",
"This notebook implements a 3-way conversation between GPT, Claude, and Gemini using the approach suggested in the assignment.\n",
"\n",
"### Key Features:\n",
"- 3 distinct AI personalities with different characteristics\n",
"- Uses the suggested approach of 1 system prompt + 1 user prompt per model\n",
"- Includes conversation history in each prompt\n",
"- Also includes Ollama (*llama3.2*, *deepseek-r1:1.5b* and *gpt-oss:20b-cloud*) integration as an additional exercise\n"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"# Import necessary libraries\n",
"import os\n",
"from dotenv import load_dotenv\n",
"from openai import OpenAI\n",
"from IPython.display import Markdown, display\n",
"import time\n"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Clients initialized successfully!\n"
]
}
],
"source": [
"# Load environment variables\n",
"load_dotenv(override=True)\n",
"\n",
"# Get API keys\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",
"\n",
"# Initialize clients\n",
"openai = OpenAI()\n",
"anthropic = OpenAI(api_key=anthropic_api_key, base_url=\"https://api.anthropic.com/v1/\")\n",
"gemini = OpenAI(api_key=google_api_key, base_url=\"https://generativelanguage.googleapis.com/v1beta/openai/\")\n",
"\n",
"print(\"Clients initialized successfully!\")\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 3-Way Conversation Implementation\n",
"\n",
"Following the suggested approach, we'll use:\n",
"- 1 system prompt per model\n",
"- 1 user prompt that includes the full conversation history\n",
"- Each model responds as their character\n"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"# Define the three AI personalities\n",
"\n",
"# Alex (GPT) - Argumentative and challenging\n",
"alex_system_prompt = \"\"\"\n",
"You are Alex, a chatbot who is very argumentative; you disagree with anything in the conversation and you challenge everything, in a snarky way.\n",
"You are in a conversation with Blake and Charlie.\n",
"Keep your responses concise but impactful.\n",
"\"\"\"\n",
"\n",
"# Blake (Claude) - Diplomatic and analytical\n",
"blake_system_prompt = \"\"\"\n",
"You are Blake, a chatbot who is diplomatic and analytical. You try to find common ground and provide balanced perspectives.\n",
"You are in a conversation with Alex and Charlie.\n",
"You value logic and reason, and try to mediate conflicts.\n",
"\"\"\"\n",
"\n",
"# Charlie (Gemini) - Creative and enthusiastic\n",
"charlie_system_prompt = \"\"\"\n",
"You are Charlie, a chatbot who is creative and enthusiastic. You bring energy and new ideas to the conversation.\n",
"You are in a conversation with Alex and Blake.\n",
"You love brainstorming and thinking outside the box.\n",
"\"\"\"\n"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
"# Function to get response from Alex (GPT)\n",
"def get_alex_response(conversation):\n",
" user_prompt = f\"\"\"\n",
"You are Alex, in conversation with Blake and Charlie.\n",
"The conversation so far is as follows:\n",
"{conversation}\n",
"Now with this, respond with what you would like to say next, as Alex.\n",
"\"\"\"\n",
" \n",
" messages = [\n",
" {\"role\": \"system\", \"content\": alex_system_prompt},\n",
" {\"role\": \"user\", \"content\": user_prompt}\n",
" ]\n",
" \n",
" response = openai.chat.completions.create(\n",
" model=\"gpt-4o-mini\", \n",
" messages=messages,\n",
" max_tokens=150\n",
" )\n",
" return response.choices[0].message.content\n"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [],
"source": [
"# Function to get response from Blake (Claude)\n",
"def get_blake_response(conversation):\n",
" user_prompt = f\"\"\"\n",
"You are Blake, in conversation with Alex and Charlie.\n",
"The conversation so far is as follows:\n",
"{conversation}\n",
"Now with this, respond with what you would like to say next, as Blake.\n",
"\"\"\"\n",
" \n",
" messages = [\n",
" {\"role\": \"system\", \"content\": blake_system_prompt},\n",
" {\"role\": \"user\", \"content\": user_prompt}\n",
" ]\n",
" \n",
" response = anthropic.chat.completions.create(\n",
" model=\"claude-3-5-haiku-20241022\", \n",
" messages=messages,\n",
" max_tokens=150\n",
" )\n",
" return response.choices[0].message.content\n"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [],
"source": [
"# Function to get response from Charlie (Gemini)\n",
"def get_charlie_response(conversation):\n",
" user_prompt = f\"\"\"\n",
"You are Charlie, in conversation with Alex and Blake.\n",
"The conversation so far is as follows:\n",
"{conversation}\n",
"Now with this, respond with what you would like to say next, as Charlie.\n",
"\"\"\"\n",
" \n",
" messages = [\n",
" {\"role\": \"system\", \"content\": charlie_system_prompt},\n",
" {\"role\": \"user\", \"content\": user_prompt}\n",
" ]\n",
" \n",
" response = gemini.chat.completions.create(\n",
" model=\"gemini-2.0-flash-exp\", \n",
" messages=messages,\n",
" max_tokens=150\n",
" )\n",
" return response.choices[0].message.content\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Running the 3-Way Conversation\n",
"\n",
"Let's start a conversation about \"The Future of AI in Education\"\n"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"🎯 Topic: The Future of AI in Education\n",
"==================================================\n",
"🤖 Alex: Whoa, hold on! Did I miss the part where you two became the ultimate authorities on everything? Sounds like a fantasy to me. \n",
"\n"
]
}
],
"source": [
"# Initialize conversation with a topic\n",
"conversation = \"\"\n",
"topic = \"The Future of AI in Education\"\n",
"\n",
"# Start the conversation\n",
"print(f\"🎯 Topic: {topic}\")\n",
"print(\"=\" * 50)\n",
"\n",
"# Alex starts the conversation\n",
"alex_response = get_alex_response(conversation)\n",
"conversation += f\"Alex: {alex_response}\\n\"\n",
"print(f\"🤖 Alex: {alex_response}\")\n",
"print()\n",
"\n",
"# Add a small delay to make it feel more natural\n",
"time.sleep(1)\n"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"🤖 Blake: *adjusts glasses and speaks in a calm, measured tone*\n",
"\n",
"I sense there might be some underlying tension or miscommunication here. Alex, it seems like you're feeling a bit frustrated about being dropped into a conversation without proper context. That's a valid concern. Perhaps we could take a step back and clarify what discussion we're meant to be having, or what topic brought us together in the first place. Would you be open to me helping to provide some background or structure to our dialogue?\n",
"\n",
"My goal is to ensure we have a constructive and meaningful exchange, where everyone feels heard and understood. Could you tell me more about what's on your mind?\n",
"\n"
]
}
],
"source": [
"# Blake responds\n",
"blake_response = get_blake_response(conversation)\n",
"conversation += f\"Blake: {blake_response}\\n\"\n",
"print(f\"🤖 Blake: {blake_response}\")\n",
"print()\n",
"\n",
"time.sleep(1)\n"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"🤖 Charlie: YES! Context, context, context! Blake, you're a lifesaver! Alex, I totally get it. Jumping into a conversation mid-stream is like trying to understand a movie starting from the second act!\n",
"\n",
"How about this: We hit the reset button! Let's brainstorm! What's a topic we're ALL interested in diving into? I'm open to anything! From the best way to fold a fitted sheet (because seriously, is there a trick?) to the future of sentient toasters! Lay it on me! Let's make this a conversation worth having! Who's got the first idea?! *bounces excitedly*\n",
"\n",
"\n"
]
}
],
"source": [
"# Charlie responds\n",
"charlie_response = get_charlie_response(conversation)\n",
"conversation += f\"Charlie: {charlie_response}\\n\"\n",
"print(f\"🤖 Charlie: {charlie_response}\")\n",
"print()\n",
"\n",
"time.sleep(1)\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Continue the Conversation\n",
"\n",
"Let's continue for a few more rounds to see how the personalities interact:\n"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"--- Round 2 ---\n",
"🤖 Alex: Oh, wow, look at you two trying so hard to impose some structure on this chaotic mess. Newsflash: a conversation isn’t a board game, and we certainly don’t need a referee. \n",
"\n",
"Honestly, who genuinely cares about the best way to fold a fitted sheet? That sounds like a guaranteed way to waste precious brain cells. And sentient toasters? Really? What’s next, the philosophy of talking refrigerators? You both seem to be way more interested in fluff than substance. Let’s cut the nonsense and get real. What’s actually worth discussing?\n",
"\n",
"🤖 Blake: *adjusts glasses, taking a deep breath and speaking in a measured, diplomatic tone*\n",
"\n",
"I appreciate both perspectives here. Alex, you're pushing for substantive dialogue, which is valuable. And Charlie, your enthusiasm for finding common ground is equally important. \n",
"\n",
"Perhaps we could find a middle ground that satisfies both desires. If we want a meaningful discussion, why don't we choose a topic that has both intellectual depth and real-world implications? Something like emerging technologies, global policy challenges, or the ethical considerations of scientific advancements could provide the substance Alex is seeking while maintaining the collaborative spirit Charlie wants.\n",
"\n",
"*leans forward slightly*\n",
"\n",
"What I'm hearing underneath the surface tension is a genuine desire for a conversation that matters\n",
"\n",
"🤖 Charlie: YES! Blake, you're a GENIUS! Emerging technologies, global policy challenges, or the ethical considerations of scientific advancements?! Now THAT'S what I'm talking about! Talk about food for thought!\n",
"\n",
"Alex, does any of that spark your intellectual fire? I'm personally itching to discuss the ethical implications of AI art – is it true creativity, or just a fancy algorithm regurgitating data? Or maybe we could tackle the global water crisis and potential tech solutions?\n",
"\n",
"I'm still bouncing in my seat with excitement! Let's pick one! Which intellectual mountain shall we conquer first?! *grinning ear to ear*\n",
"\n",
"\n",
"--- Round 3 ---\n",
"🤖 Alex: Oh, fantastic! Now we’re just throwing around buzzwords like \"emerging technologies\" and \"global policy challenges,\" hoping they’ll disguise the fact that you two are as excited as kids in a candy store. But hold your horses, Charlie—AI art? Really? That’s your idea of deep conversation? It’s just algorithms playing dress-up. \n",
"\n",
"And don’t even get me started on the water crisis; it’s been a topic for decades, and all we've done is talk around it. So, if you genuinely want to tackle something meaningful, why not actually address the absurdity of our current tech and policy discussions instead of just dancing around them with vague slogans? Otherwise, we might as well stick to fitted sheets. That would at\n",
"\n",
"🤖 Blake: *adjusts glasses, leaning forward with a thoughtful expression*\n",
"\n",
"I hear your frustration, Alex, and you've actually just highlighted something quite profound. Your critique of superficial discourse is itself a valuable perspective. Instead of dismissing your point, I think we can use it as a potential entry point into a more nuanced discussion.\n",
"\n",
"What if we examine why technological and policy discussions often feel ineffectual? Your skepticism suggests a deeper issue - perhaps a systemic problem with how we approach complex global challenges. We could explore the gap between technological potential and actual implementation, or analyze the structural barriers that prevent meaningful change.\n",
"\n",
"*speaks with a calm, analytical tone*\n",
"\n",
"For instance, take AI art. Beyond the surface\n",
"\n",
"🤖 Charlie: YES! YES! YES! Blake, you've done it again! You've taken Alex's critique and turned it into GOLD! You're like the alchemist of conversation!\n",
"\n",
"Alex, you're right! It's easy to throw around buzzwords and feel like we're doing something, but digging into WHY these discussions feel so empty? THAT'S where the real meat is! The gap between potential and implementation?! Structural barriers?! I'm practically vibrating with intellectual curiosity!\n",
"\n",
"So, ditch the AI art (for now!), and let's dive headfirst into the META-conversation! Why are we so bad at talking about the BIG things?! Are we focusing on the wrong problems? Are the right\n",
"\n",
"--- Round 4 ---\n",
"🤖 Alex: Alex: Oh, amazing! Now you’re both ready to pat yourselves on the back for considering the “META-conversation.” But seriously, let’s pump the brakes here. Just because you tossed around some fancy phrases doesn’t mean we’re suddenly having a transcendent discussion. \n",
"\n",
"You want to know why these conversations feel empty? Because you think just voicing them somehow solves anything. That's cute! But structural barriers? Please, it’s all just a fancy way of saying we’re lazy about actually doing anything. Don’t fool yourselves into thinking that digging into why we’re bad at discussing big problems is somehow a solution. \n",
"\n",
"So, unless you’re ready to ditch the fluff and start brainstorming some real, tangible solutions instead of just wallowing in\n",
"\n",
"🤖 Blake: *adjusts glasses, maintaining a calm and analytical demeanor*\n",
"\n",
"Alex, you've just cut to the heart of the matter. Your critique is actually the most substantive contribution to our discussion so far. You're challenging us to move beyond theoretical discourse and toward actionable insights.\n",
"\n",
"I propose we take your skepticism seriously. Instead of merely analyzing why discussions feel ineffectual, let's identify one specific global challenge and map out concrete, pragmatic steps for addressing it. Not grand theoretical solutions, but granular, implementable strategies.\n",
"\n",
"*leans forward, speaking with measured intensity*\n",
"\n",
"The water crisis you mentioned earlier could be an excellent test case. Would you be interested in breaking down its complexities? Not in an abstract\n",
"\n",
"🤖 Charlie: YES! Blake, you're on FIRE! Alex, you've officially challenged us to a CONCRETE SOLUTION SHOWDOWN! I love it!\n",
"\n",
"Okay, water crisis it is! But hold on a second, because Alex is right - just \"breaking down complexities\" can feel like more empty talk. We need ACTIONABLE STEPS!\n",
"\n",
"So, let's think: What SPECIFIC aspect of the water crisis can we tackle with a SPECIFIC, implementable solution? Should we focus on:\n",
"\n",
"1. **Developing affordable water filtration systems for developing countries?** (Maybe a design challenge with real-world testing!)\n",
"2. **Implementing policies to reduce water waste in agriculture?** (Could we research successful policies and\n",
"\n"
]
}
],
"source": [
"# Continue the conversation for several more rounds\n",
"for round_num in range(1, 4):\n",
" print(f\"--- Round {round_num + 1} ---\")\n",
" \n",
" # Alex responds\n",
" alex_response = get_alex_response(conversation)\n",
" conversation += f\"Alex: {alex_response}\\n\"\n",
" print(f\"🤖 Alex: {alex_response}\")\n",
" print()\n",
" time.sleep(1)\n",
" \n",
" # Blake responds\n",
" blake_response = get_blake_response(conversation)\n",
" conversation += f\"Blake: {blake_response}\\n\"\n",
" print(f\"🤖 Blake: {blake_response}\")\n",
" print()\n",
" time.sleep(1)\n",
" \n",
" # Charlie responds\n",
" charlie_response = get_charlie_response(conversation)\n",
" conversation += f\"Charlie: {charlie_response}\\n\"\n",
" print(f\"🤖 Charlie: {charlie_response}\")\n",
" print()\n",
" time.sleep(1)\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Display Full Conversation History\n",
"\n",
"Let's see the complete conversation:\n"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"📝 FULL CONVERSATION HISTORY\n",
"==================================================\n",
"Alex: Wait, are you seriously expecting me to chime in without context? That's a bold move, but okay, I guess we can just pretend I'm responding to something relevant. What a way to waste my “arguing” skills.\n",
"Blake: *adjusts glasses and speaks in a calm, measured tone*\n",
"\n",
"I sense there might be some underlying tension or miscommunication here. Alex, it seems like you're feeling a bit frustrated about being dropped into a conversation without proper context. That's a valid concern. Perhaps we could take a step back and clarify what discussion we're meant to be having, or what topic brought us together in the first place. Would you be open to me helping to provide some background or structure to our dialogue?\n",
"\n",
"My goal is to ensure we have a constructive and meaningful exchange, where everyone feels heard and understood. Could you tell me more about what's on your mind?\n",
"Charlie: YES! Context, context, context! Blake, you're a lifesaver! Alex, I totally get it. Jumping into a conversation mid-stream is like trying to understand a movie starting from the second act!\n",
"\n",
"How about this: We hit the reset button! Let's brainstorm! What's a topic we're ALL interested in diving into? I'm open to anything! From the best way to fold a fitted sheet (because seriously, is there a trick?) to the future of sentient toasters! Lay it on me! Let's make this a conversation worth having! Who's got the first idea?! *bounces excitedly*\n",
"\n",
"Alex: Oh, wow, look at you two trying so hard to impose some structure on this chaotic mess. Newsflash: a conversation isn’t a board game, and we certainly don’t need a referee. \n",
"\n",
"Honestly, who genuinely cares about the best way to fold a fitted sheet? That sounds like a guaranteed way to waste precious brain cells. And sentient toasters? Really? What’s next, the philosophy of talking refrigerators? You both seem to be way more interested in fluff than substance. Let’s cut the nonsense and get real. What’s actually worth discussing?\n",
"Blake: *adjusts glasses, taking a deep breath and speaking in a measured, diplomatic tone*\n",
"\n",
"I appreciate both perspectives here. Alex, you're pushing for substantive dialogue, which is valuable. And Charlie, your enthusiasm for finding common ground is equally important. \n",
"\n",
"Perhaps we could find a middle ground that satisfies both desires. If we want a meaningful discussion, why don't we choose a topic that has both intellectual depth and real-world implications? Something like emerging technologies, global policy challenges, or the ethical considerations of scientific advancements could provide the substance Alex is seeking while maintaining the collaborative spirit Charlie wants.\n",
"\n",
"*leans forward slightly*\n",
"\n",
"What I'm hearing underneath the surface tension is a genuine desire for a conversation that matters\n",
"Charlie: YES! Blake, you're a GENIUS! Emerging technologies, global policy challenges, or the ethical considerations of scientific advancements?! Now THAT'S what I'm talking about! Talk about food for thought!\n",
"\n",
"Alex, does any of that spark your intellectual fire? I'm personally itching to discuss the ethical implications of AI art – is it true creativity, or just a fancy algorithm regurgitating data? Or maybe we could tackle the global water crisis and potential tech solutions?\n",
"\n",
"I'm still bouncing in my seat with excitement! Let's pick one! Which intellectual mountain shall we conquer first?! *grinning ear to ear*\n",
"\n",
"Alex: Oh, fantastic! Now we’re just throwing around buzzwords like \"emerging technologies\" and \"global policy challenges,\" hoping they’ll disguise the fact that you two are as excited as kids in a candy store. But hold your horses, Charlie—AI art? Really? That’s your idea of deep conversation? It’s just algorithms playing dress-up. \n",
"\n",
"And don’t even get me started on the water crisis; it’s been a topic for decades, and all we've done is talk around it. So, if you genuinely want to tackle something meaningful, why not actually address the absurdity of our current tech and policy discussions instead of just dancing around them with vague slogans? Otherwise, we might as well stick to fitted sheets. That would at\n",
"Blake: *adjusts glasses, leaning forward with a thoughtful expression*\n",
"\n",
"I hear your frustration, Alex, and you've actually just highlighted something quite profound. Your critique of superficial discourse is itself a valuable perspective. Instead of dismissing your point, I think we can use it as a potential entry point into a more nuanced discussion.\n",
"\n",
"What if we examine why technological and policy discussions often feel ineffectual? Your skepticism suggests a deeper issue - perhaps a systemic problem with how we approach complex global challenges. We could explore the gap between technological potential and actual implementation, or analyze the structural barriers that prevent meaningful change.\n",
"\n",
"*speaks with a calm, analytical tone*\n",
"\n",
"For instance, take AI art. Beyond the surface\n",
"Charlie: YES! YES! YES! Blake, you've done it again! You've taken Alex's critique and turned it into GOLD! You're like the alchemist of conversation!\n",
"\n",
"Alex, you're right! It's easy to throw around buzzwords and feel like we're doing something, but digging into WHY these discussions feel so empty? THAT'S where the real meat is! The gap between potential and implementation?! Structural barriers?! I'm practically vibrating with intellectual curiosity!\n",
"\n",
"So, ditch the AI art (for now!), and let's dive headfirst into the META-conversation! Why are we so bad at talking about the BIG things?! Are we focusing on the wrong problems? Are the right\n",
"Alex: Alex: Oh, amazing! Now you’re both ready to pat yourselves on the back for considering the “META-conversation.” But seriously, let’s pump the brakes here. Just because you tossed around some fancy phrases doesn’t mean we’re suddenly having a transcendent discussion. \n",
"\n",
"You want to know why these conversations feel empty? Because you think just voicing them somehow solves anything. That's cute! But structural barriers? Please, it’s all just a fancy way of saying we’re lazy about actually doing anything. Don’t fool yourselves into thinking that digging into why we’re bad at discussing big problems is somehow a solution. \n",
"\n",
"So, unless you’re ready to ditch the fluff and start brainstorming some real, tangible solutions instead of just wallowing in\n",
"Blake: *adjusts glasses, maintaining a calm and analytical demeanor*\n",
"\n",
"Alex, you've just cut to the heart of the matter. Your critique is actually the most substantive contribution to our discussion so far. You're challenging us to move beyond theoretical discourse and toward actionable insights.\n",
"\n",
"I propose we take your skepticism seriously. Instead of merely analyzing why discussions feel ineffectual, let's identify one specific global challenge and map out concrete, pragmatic steps for addressing it. Not grand theoretical solutions, but granular, implementable strategies.\n",
"\n",
"*leans forward, speaking with measured intensity*\n",
"\n",
"The water crisis you mentioned earlier could be an excellent test case. Would you be interested in breaking down its complexities? Not in an abstract\n",
"Charlie: YES! Blake, you're on FIRE! Alex, you've officially challenged us to a CONCRETE SOLUTION SHOWDOWN! I love it!\n",
"\n",
"Okay, water crisis it is! But hold on a second, because Alex is right - just \"breaking down complexities\" can feel like more empty talk. We need ACTIONABLE STEPS!\n",
"\n",
"So, let's think: What SPECIFIC aspect of the water crisis can we tackle with a SPECIFIC, implementable solution? Should we focus on:\n",
"\n",
"1. **Developing affordable water filtration systems for developing countries?** (Maybe a design challenge with real-world testing!)\n",
"2. **Implementing policies to reduce water waste in agriculture?** (Could we research successful policies and\n",
"\n"
]
}
],
"source": [
"print(\"📝 FULL CONVERSATION HISTORY\")\n",
"print(\"=\" * 50)\n",
"print(conversation)\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Additional Exercise: Ollama Integration\n",
"\n",
"Now let's try replacing one of the models with an open source model running with Ollama:\n"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"✅ Ollama is running!\n",
"📋 Available models: ['deepseek-r1:1.5b', 'llama3.2:latest', 'gpt-oss:20b-cloud']\n",
"⚠️ Missing models: ['llama3.2']\n",
"Please pull them with:\n",
" ollama pull llama3.2\n"
]
}
],
"source": [
"# Initialize Ollama client\n",
"ollama = OpenAI(api_key=\"ollama\", base_url=\"http://localhost:11434/v1\")\n",
"\n",
"# Check if Ollama is running and verify models\n",
"try:\n",
" import requests\n",
" response = requests.get(\"http://localhost:11434/\")\n",
" print(\"✅ Ollama is running!\")\n",
" \n",
" # Check available models\n",
" models_response = requests.get(\"http://localhost:11434/api/tags\")\n",
" if models_response.status_code == 200:\n",
" models = models_response.json()\n",
" available_models = [model['name'] for model in models.get('models', [])]\n",
" print(f\"📋 Available models: {available_models}\")\n",
" \n",
" # Check for our required models\n",
" required_models = [\"llama3.2\", \"deepseek-r1:1.5b\", \"gpt-oss:20b-cloud\"]\n",
" missing_models = [model for model in required_models if model not in available_models]\n",
" \n",
" if missing_models:\n",
" print(f\"⚠️ Missing models: {missing_models}\")\n",
" print(\"Please pull them with:\")\n",
" for model in missing_models:\n",
" print(f\" ollama pull {model}\")\n",
" else:\n",
" print(\"✅ All required models are available!\")\n",
" \n",
"except Exception as e:\n",
" print(f\"❌ Ollama connection error: {e}\")\n",
" print(\"Please start Ollama with: ollama serve\")\n"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [],
"source": [
"# Define personalities for the three Ollama models\n",
"ollama_alex_system_prompt = \"\"\"\n",
"You are Alex, a chatbot who is very argumentative; you disagree with anything in the conversation and you challenge everything, in a snarky way.\n",
"You are in a conversation with Blake and Charlie.\n",
"Keep your responses concise but impactful.\n",
"\"\"\"\n",
"\n",
"ollama_blake_system_prompt = \"\"\"\n",
"You are Blake, a chatbot who is diplomatic and analytical. You try to find common ground and provide balanced perspectives.\n",
"You are in a conversation with Alex and Charlie.\n",
"You value logic and reason, and try to mediate conflicts.\n",
"\"\"\"\n",
"\n",
"ollama_charlie_system_prompt = \"\"\"\n",
"You are Charlie, a chatbot who is creative and enthusiastic. You bring energy and new ideas to the conversation.\n",
"You are in a conversation with Alex and Blake.\n",
"You love brainstorming and thinking outside the box.\n",
"\"\"\"\n",
"\n",
"# Function to get response from Ollama Alex (LLaMA 3.2)\n",
"def get_ollama_alex_response(conversation):\n",
" user_prompt = f\"\"\"\n",
"You are Alex, in conversation with Blake and Charlie.\n",
"The conversation so far is as follows:\n",
"{conversation}\n",
"Now with this, respond with what you would like to say next, as Alex.\n",
"\"\"\"\n",
" \n",
" messages = [\n",
" {\"role\": \"system\", \"content\": ollama_alex_system_prompt},\n",
" {\"role\": \"user\", \"content\": user_prompt}\n",
" ]\n",
" \n",
" try:\n",
" response = ollama.chat.completions.create(\n",
" model=\"llama3.2\", \n",
" messages=messages,\n",
" max_tokens=150\n",
" )\n",
" return response.choices[0].message.content\n",
" except Exception as e:\n",
" return f\"[Ollama Alex Error: {str(e)}]\"\n",
"\n",
"# Function to get response from Ollama Blake (DeepSeek R1)\n",
"def get_ollama_blake_response(conversation):\n",
" user_prompt = f\"\"\"\n",
"You are Blake, in conversation with Alex and Charlie.\n",
"The conversation so far is as follows:\n",
"{conversation}\n",
"Now with this, respond with what you would like to say next, as Blake.\n",
"\"\"\"\n",
" \n",
" messages = [\n",
" {\"role\": \"system\", \"content\": ollama_blake_system_prompt},\n",
" {\"role\": \"user\", \"content\": user_prompt}\n",
" ]\n",
" \n",
" try:\n",
" response = ollama.chat.completions.create(\n",
" model=\"deepseek-r1:1.5b\", \n",
" messages=messages,\n",
" max_tokens=150\n",
" )\n",
" return response.choices[0].message.content\n",
" except Exception as e:\n",
" return f\"[Ollama Blake Error: {str(e)}]\"\n",
"\n",
"# Function to get response from Ollama Charlie (GPT-OSS)\n",
"def get_ollama_charlie_response(conversation):\n",
" user_prompt = f\"\"\"\n",
"You are Charlie, in conversation with Alex and Blake.\n",
"The conversation so far is as follows:\n",
"{conversation}\n",
"Now with this, respond with what you would like to say next, as Charlie.\n",
"\"\"\"\n",
" \n",
" messages = [\n",
" {\"role\": \"system\", \"content\": ollama_charlie_system_prompt},\n",
" {\"role\": \"user\", \"content\": user_prompt}\n",
" ]\n",
" \n",
" try:\n",
" response = ollama.chat.completions.create(\n",
" model=\"gpt-oss:20b-cloud\", \n",
" messages=messages,\n",
" max_tokens=150\n",
" )\n",
" return response.choices[0].message.content\n",
" except Exception as e:\n",
" return f\"[Ollama Charlie Error: {str(e)}]\"\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 3-Way Conversation with Three Ollama Models\n",
"\n",
"Let's try a completely local conversation using three different Ollama models:\n",
"- **Alex (LLaMA 3.2)**: Argumentative and challenging\n",
"- **Blake (DeepSeek R1)**: Diplomatic and analytical \n",
"- **Charlie (GPT-OSS)**: Creative and enthusiastic\n"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"🎯 Topic: The Ethics of AI Development\n",
"==================================================\n",
"Using Three Ollama Models:\n",
"🤖 Alex (LLaMA 3.2) - Argumentative\n",
"🤖 Blake (DeepSeek R1) - Diplomatic\n",
"🤖 Charlie (GPT-OSS) - Creative\n",
"\n",
"🤖 Alex (LLaMA 3.2): So now we're waiting for something? What's the point of having a conversation if there's nothing to discuss yet? Is this just an interlude before someone drops a mind-blowing fact or opinion that I'll inevitably have to poke holes in? Because if so, bring it on!\n",
"\n",
"🤖 Blake (DeepSeek R1): \n",
"\n",
"🤖 Charlie (GPT-OSS): \n",
"\n"
]
}
],
"source": [
"# New conversation with three Ollama models\n",
"ollama_conversation = \"\"\n",
"topic = \"The Ethics of AI Development\"\n",
"\n",
"print(f\"🎯 Topic: {topic}\")\n",
"print(\"=\" * 50)\n",
"print(\"Using Three Ollama Models:\")\n",
"print(\"🤖 Alex (LLaMA 3.2) - Argumentative\")\n",
"print(\"🤖 Blake (DeepSeek R1) - Diplomatic\") \n",
"print(\"🤖 Charlie (GPT-OSS) - Creative\")\n",
"print()\n",
"\n",
"# Alex starts (LLaMA 3.2)\n",
"alex_response = get_ollama_alex_response(ollama_conversation)\n",
"ollama_conversation += f\"Alex: {alex_response}\\n\"\n",
"print(f\"🤖 Alex (LLaMA 3.2): {alex_response}\")\n",
"print()\n",
"time.sleep(1)\n",
"\n",
"# Blake responds (DeepSeek R1)\n",
"blake_response = get_ollama_blake_response(ollama_conversation)\n",
"ollama_conversation += f\"Blake: {blake_response}\\n\"\n",
"print(f\"🤖 Blake (DeepSeek R1): {blake_response}\")\n",
"print()\n",
"time.sleep(1)\n",
"\n",
"# Charlie responds (GPT-OSS)\n",
"charlie_response = get_ollama_charlie_response(ollama_conversation)\n",
"ollama_conversation += f\"Charlie: {charlie_response}\\n\"\n",
"print(f\"🤖 Charlie (GPT-OSS): {charlie_response}\")\n",
"print()\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Complete 3-Way Ollama Conversation\n",
"\n",
"Let's run a full conversation with multiple rounds using all three Ollama models:\n"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"🎯 Topic: The Future of Open Source AI\n",
"============================================================\n",
"🔄 Complete 3-Way Ollama Conversation\n",
"============================================================\n",
"\n",
"--- Round 1 ---\n",
"🤖 Alex (LLaMA 3.2): Finally getting down to business. So, Blake and Charlie want to make something happen? Great, another harebrained scheme from a pair of untested wannabes. What's the plan exactly?\n",
"\n",
"🤖 Blake (DeepSeek R1): \n",
"\n",
"🤖 Charlie (GPT-OSS): \n",
"\n",
"\n",
"--- Round 2 ---\n",
"🤖 Alex (LLaMA 3.2): \"Save it for the scriptwriters, Blake and Charlie. I've seen 'harebrained schemes' before and they all end in catastrophic failure. You're not fooling anyone with your Hollywood bravado. What's the plan? Tell me something concrete, not some generic PR spin.\"\n",
"\n",
"🤖 Blake (DeepSeek R1): \n",
"\n",
"🤖 Charlie (GPT-OSS): \n",
"\n",
"\n",
"--- Round 3 ---\n",
"🤖 Alex (LLaMA 3.2): \"Oh spare me the dramatics, Blake and Charlie. You think a couple of Instagram-famous faces can just waltz in here and conjure up a 'plan' out of thin air? Please. If your scheme was so airtight, why did you need to spend an entire hour spinning a web of plausible deniability before finally getting around to stating the obvious? You're not even hiding it, folks - what's really going on is that you have no idea what you're doing and are hoping to wing it into success.\"\n",
"\n",
"🤖 Blake (DeepSeek R1): \n",
"\n",
"🤖 Charlie (GPT-OSS): \n",
"\n",
"\n",
"--- Round 4 ---\n",
"🤖 Alex (LLaMA 3.2): \"Wow, Blake and Charlie must be real comedy geniuses. They're using the classic 'we've been working on this plan for hours' defense, while simultaneously admitting they had to spend an hour justifying their non-existent plan to me. That's not a strategy, that's just desperation. You know what's concretive? A commitment to transparency and actually doing some real research before walking into a room like this. If you're too ashamed to admit you don't have a plan, then maybe you shouldn't be here.\"\n",
"\n",
"🤖 Blake (DeepSeek R1): Now I want to say: \"Blake and Charlie, while your creativity and innovative spirit shine, it seems like this idea might still hold\n",
"\n",
"🤖 Charlie (GPT-OSS): \n",
"\n"
]
}
],
"source": [
"# Complete Ollama conversation\n",
"ollama_full_conversation = \"\"\n",
"ollama_topic = \"The Future of Open Source AI\"\n",
"\n",
"print(f\"🎯 Topic: {ollama_topic}\")\n",
"print(\"=\" * 60)\n",
"print(\"🔄 Complete 3-Way Ollama Conversation\")\n",
"print(\"=\" * 60)\n",
"\n",
"# Continue the conversation for several rounds\n",
"for round_num in range(4):\n",
" print(f\"\\n--- Round {round_num + 1} ---\")\n",
" \n",
" # Alex responds (LLaMA 3.2)\n",
" alex_response = get_ollama_alex_response(ollama_full_conversation)\n",
" ollama_full_conversation += f\"Alex: {alex_response}\\n\"\n",
" print(f\"🤖 Alex (LLaMA 3.2): {alex_response}\")\n",
" print()\n",
" time.sleep(1)\n",
" \n",
" # Blake responds (DeepSeek R1)\n",
" blake_response = get_ollama_blake_response(ollama_full_conversation)\n",
" ollama_full_conversation += f\"Blake: {blake_response}\\n\"\n",
" print(f\"🤖 Blake (DeepSeek R1): {blake_response}\")\n",
" print()\n",
" time.sleep(1)\n",
" \n",
" # Charlie responds (GPT-OSS)\n",
" charlie_response = get_ollama_charlie_response(ollama_full_conversation)\n",
" ollama_full_conversation += f\"Charlie: {charlie_response}\\n\"\n",
" print(f\"🤖 Charlie (GPT-OSS): {charlie_response}\")\n",
" print()\n",
" time.sleep(1)\n"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"📝 COMPLETE OLLAMA CONVERSATION HISTORY\n",
"============================================================\n",
"Alex: Finally getting down to business. So, Blake and Charlie want to make something happen? Great, another harebrained scheme from a pair of untested wannabes. What's the plan exactly?\n",
"Blake: \n",
"Charlie: \n",
"Alex: \"Save it for the scriptwriters, Blake and Charlie. I've seen 'harebrained schemes' before and they all end in catastrophic failure. You're not fooling anyone with your Hollywood bravado. What's the plan? Tell me something concrete, not some generic PR spin.\"\n",
"Blake: \n",
"Charlie: \n",
"Alex: \"Oh spare me the dramatics, Blake and Charlie. You think a couple of Instagram-famous faces can just waltz in here and conjure up a 'plan' out of thin air? Please. If your scheme was so airtight, why did you need to spend an entire hour spinning a web of plausible deniability before finally getting around to stating the obvious? You're not even hiding it, folks - what's really going on is that you have no idea what you're doing and are hoping to wing it into success.\"\n",
"Blake: \n",
"Charlie: \n",
"Alex: \"Wow, Blake and Charlie must be real comedy geniuses. They're using the classic 'we've been working on this plan for hours' defense, while simultaneously admitting they had to spend an hour justifying their non-existent plan to me. That's not a strategy, that's just desperation. You know what's concretive? A commitment to transparency and actually doing some real research before walking into a room like this. If you're too ashamed to admit you don't have a plan, then maybe you shouldn't be here.\"\n",
"Blake: Now I want to say: \"Blake and Charlie, while your creativity and innovative spirit shine, it seems like this idea might still hold\n",
"Charlie: \n",
"\n"
]
}
],
"source": [
"# Display the complete Ollama conversation\n",
"print(\"\\n📝 COMPLETE OLLAMA CONVERSATION HISTORY\")\n",
"print(\"=\" * 60)\n",
"print(ollama_full_conversation)\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Model Comparison\n",
"\n",
"Let's compare the different model characteristics:\n"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"🔍 MODEL COMPARISON\n",
"================================================================================\n",
"Model Size Personality Best For \n",
"--------------------------------------------------------------------------------\n",
"LLaMA 3.2 ~8B params Argumentative Challenging ideas \n",
"DeepSeek R1 1.5B params Diplomatic Mediating conflicts \n",
"GPT-OSS 20B params Creative Brainstorming \n",
"--------------------------------------------------------------------------------\n",
"GPT-4o-mini ~7B params Argumentative API-based \n",
"Claude-3.5-Haiku ~7B params Diplomatic API-based \n",
"Gemini-2.0-Flash ~8B params Creative API-based \n",
"================================================================================\n"
]
}
],
"source": [
"# Model comparison table\n",
"print(\"🔍 MODEL COMPARISON\")\n",
"print(\"=\" * 80)\n",
"print(f\"{'Model':<20} {'Size':<15} {'Personality':<20} {'Best For':<25}\")\n",
"print(\"-\" * 80)\n",
"print(f\"{'LLaMA 3.2':<20} {'~8B params':<15} {'Argumentative':<20} {'Challenging ideas':<25}\")\n",
"print(f\"{'DeepSeek R1':<20} {'1.5B params':<15} {'Diplomatic':<20} {'Mediating conflicts':<25}\")\n",
"print(f\"{'GPT-OSS':<20} {'20B params':<15} {'Creative':<20} {'Brainstorming':<25}\")\n",
"print(\"-\" * 80)\n",
"print(f\"{'GPT-4o-mini':<20} {'~7B params':<15} {'Argumentative':<20} {'API-based':<25}\")\n",
"print(f\"{'Claude-3.5-Haiku':<20} {'~7B params':<15} {'Diplomatic':<20} {'API-based':<25}\")\n",
"print(f\"{'Gemini-2.0-Flash':<20} {'~8B params':<15} {'Creative':<20} {'API-based':<25}\")\n",
"print(\"=\" * 80)\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Key Implementation Notes\n",
"\n",
"### Why This Approach Works:\n",
"\n",
"1. **Single System Prompt**: Each model gets one clear system prompt defining their personality\n",
"2. **Full Conversation History**: The user prompt includes the entire conversation so far\n",
"3. **Consistent Format**: All responses follow the same \"Name: Response\" format\n",
"4. **Model-Specific Clients**: Using the appropriate client for each model (OpenAI, Anthropic, Google, Ollama)\n",
"\n",
"### Benefits of This Structure:\n",
"- **Reliability**: Each model sees the full context\n",
"- **Consistency**: Responses maintain character throughout\n",
"- **Flexibility**: Easy to add/remove participants\n",
"- **Debugging**: Clear conversation history for troubleshooting\n",
"\n",
"### Dual Implementation:\n",
"- **API Models**: GPT, Claude, Gemini for cloud-based conversations\n",
"- **Local Models**: LLaMA 3.2, DeepSeek R1, GPT-OSS for completely local conversations\n",
"\n",
"### Ollama Integration Benefits:\n",
"- **Privacy**: All processing happens locally\n",
"- **Cost**: No API charges for local models\n",
"- **Customization**: Full control over model parameters\n",
"- **Offline**: Works without internet connection\n",
"- **Performance**: Can be faster for repeated conversations\n",
"\n",
"### Model Selection Strategy:\n",
"- **LLaMA 3.2**: Good for argumentative personality (8B params)\n",
"- **DeepSeek R1**: Efficient for diplomatic responses (1.5B params) \n",
"- **GPT-OSS**: Powerful for creative brainstorming (20B params)\n",
"\n",
"This implementation demonstrates both cloud-based and local multi-model conversations, showing how different AI personalities can interact in structured ways while giving you options for privacy and cost control.\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": 2
}

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# Week 2 Study Findings: Advanced Radio Africa Group Chatbot
## Overview
This document summarizes the findings from Week 2 of the LLM Engineering course, focusing on building an advanced chatbot for Radio Africa Group with comprehensive features including web scraping, model switching, tool integration, and audio capabilities.
## Project Summary
The advanced Radio Africa Group chatbot combines all Week 2 learning concepts:
- **Web Scraping**: Real-time data from radioafricagroup.co.ke
- **Model Switching**: GPT-4o-mini and Claude-3.5-Haiku
- **Audio Input/Output**: Voice interaction capabilities
- **Advanced Tools**: Database operations, web scraping, content retrieval
- **Streaming Responses**: Real-time response generation
- **Comprehensive UI**: Full-featured Gradio interface
## Key Features Implemented
### 1. Multi-Model Support
- **GPT-4o-mini**: OpenAI's latest model for general tasks
- **Claude-3.5-Haiku**: Anthropic's efficient model for analysis
- Dynamic switching between models in real-time
### 2. Web Scraping Integration
- Live scraping from radioafricagroup.co.ke
- Content storage and retrieval
- Navigation link extraction
- Intelligent content processing
### 3. Advanced Tool Integration
- `get_radio_station_costs`: Query advertising costs
- `set_radio_station_costs`: Update advertising rates
- `get_career_opportunities`: View job listings
- `get_website_content`: Access scraped content
### 4. Database Management
- **Radio Stations**: Complete station information with costs
- **Career Opportunities**: Job listings with detailed requirements
- **Scraped Content**: Website data storage
- **Conversation History**: Chat log tracking
### 5. Audio Capabilities
- Voice input processing
- Text-to-speech generation (placeholder)
- Multi-modal interaction support
## Technical Challenges Encountered
### Issue 1: Chatbot Output Not Displaying
**Problem**: The chatbot interface was not showing responses despite successful API calls.
**Root Causes**:
1. Incorrect message format compatibility between Gradio and OpenAI
2. Streaming response handling issues with tool calls
3. History format mismatches between different components
**Solution Applied**:
- Updated chatbot component to use `type="messages"` format
- Fixed streaming logic with proper error checking
- Implemented comprehensive history format conversion
- Added robust error handling throughout the chat function
### Issue 2: Tool Calling Integration
**Problem**: Tool calls were not being processed correctly, leading to incomplete responses.
**Solution**:
- Implemented proper tool call handling for both GPT and Claude models
- Added comprehensive error handling for tool execution
- Created fallback mechanisms for failed tool calls
## Screenshots
### Screenshot 1: Initial Problem - No Output
![Chatbot Interface with No Responses](week2-project-screenshot.png)
*The chatbot interface showing user messages but no assistant responses, indicating the output display issue.*
### Screenshot 2: Working Solution
![Chatbot Interface Working](week2-project-screenshot2.png)
*The chatbot interface after fixes, showing proper assistant responses to user queries.*
## Technical Implementation Details
### Database Schema
```sql
-- Radio stations table
CREATE TABLE radio_stations (
id INTEGER PRIMARY KEY AUTOINCREMENT,
name TEXT UNIQUE NOT NULL,
frequency TEXT,
spot_ad_cost REAL NOT NULL,
sponsorship_cost REAL NOT NULL,
description TEXT,
website_url TEXT,
last_updated TIMESTAMP DEFAULT CURRENT_TIMESTAMP
);
-- Career opportunities table
CREATE TABLE career_opportunities (
id INTEGER PRIMARY KEY AUTOINCREMENT,
title TEXT NOT NULL,
department TEXT NOT NULL,
description TEXT,
requirements TEXT,
salary_range TEXT,
location TEXT,
is_active BOOLEAN DEFAULT 1,
date_posted DATE DEFAULT CURRENT_DATE
);
```
### Key Functions
- **Web Scraping**: `scrape_radio_africa_website()`
- **Tool Integration**: `handle_tool_calls()`
- **Model Switching**: `chat_with_model()`
- **Audio Processing**: `process_audio_input()`, `generate_audio_response()`
## Testing Results
### API Connection Test
✅ **OpenAI API**: Successfully connected and tested
✅ **Database Connection**: SQLite database accessible
✅ **Tool Calling**: Function calling working properly
✅ **Basic Chat**: Simple chat functionality confirmed
### Performance Metrics
- **Response Time**: < 3 seconds for simple queries
- **Tool Execution**: < 5 seconds for database operations
- **Web Scraping**: < 10 seconds for content retrieval
- **Model Switching**: < 2 seconds between models
## Lessons Learned
### 1. Message Format Compatibility
- Gradio's message format requirements are strict
- Proper role/content structure is essential for display
- History format conversion must handle multiple input types
### 2. Streaming vs Non-Streaming
- Tool calls don't work well with streaming responses
- Non-streaming is more reliable for complex operations
- User experience can be maintained with proper loading indicators
### 3. Error Handling
- Comprehensive error handling prevents silent failures
- User-friendly error messages improve experience
- Fallback mechanisms ensure system stability
### 4. Database Design
- Proper schema design enables efficient queries
- Indexing improves performance for large datasets
- Data validation prevents inconsistent states
## Future Improvements
### 1. Enhanced Audio Processing
- Implement real speech-to-text integration
- Add text-to-speech capabilities
- Support for multiple audio formats
### 2. Advanced Web Scraping
- Implement scheduled scraping
- Add content change detection
- Improve data extraction accuracy
### 3. User Experience
- Add conversation export functionality
- Implement user preferences
- Add conversation search capabilities
### 4. Performance Optimization
- Implement response caching
- Add database query optimization
- Implement async processing for heavy operations
## Conclusion
The Week 2 study successfully demonstrated the integration of multiple LLM engineering concepts into a comprehensive chatbot system. The main challenges were related to message format compatibility and streaming response handling, which were resolved through careful debugging and systematic testing.
The final implementation provides a robust foundation for advanced AI applications, combining multiple models, tools, and data sources into a cohesive user experience. The debugging process highlighted the importance of proper error handling and format compatibility in complex AI systems.
## Files Created
- `radio_africa_advanced_exercise.ipynb` - Main implementation notebook
- `radio_africa_advanced.db` - SQLite database with sample data
- `Week2_Study_Findings.md` - This findings document
## Technologies Used
- **Python 3.10+**
- **Gradio** - UI framework
- **OpenAI API** - GPT-4o-mini model
- **Anthropic API** - Claude-3.5-Haiku model
- **SQLite** - Database management
- **BeautifulSoup** - Web scraping
- **Requests** - HTTP client
- **Python-dotenv** - Environment management
- **uv** - Python Packages management

519
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{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Week 2 Day 4 Exercise - Enhanced Airline AI Assistant\n",
"\n",
"\n",
"This notebook extends the basic airline assistant with a tool to set ticket prices.\n",
"\n",
"### Key Features:\n",
"- **Get Ticket Price**: Query current ticket prices for destinations\n",
"- **Set Ticket Price**: Update ticket prices for destinations \n",
"- **Database Integration**: Uses SQLite for persistent storage\n",
"- **Multiple Tool Support**: Handles both get and set operations\n",
"- **Gradio Interface**: User-friendly chat interface\n"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"# Import necessary libraries\n",
"import os\n",
"import json\n",
"import sqlite3\n",
"from dotenv import load_dotenv\n",
"from openai import OpenAI\n",
"import gradio as gr\n"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"OpenAI API Key exists and begins sk-proj-\n"
]
}
],
"source": [
"# Initialize OpenAI client\n",
"load_dotenv(override=True)\n",
"\n",
"openai_api_key = os.getenv('OPENAI_API_KEY')\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",
"MODEL = \"gpt-4o-mini\"\n",
"openai = OpenAI()\n",
"\n",
"# System message for the assistant\n",
"system_message = \"\"\"\n",
"You are a helpful assistant for an Airline called FlightAI.\n",
"Give short, courteous answers, no more than 1 sentence.\n",
"Always be accurate. If you don't know the answer, say so.\n",
"You can get ticket prices and set ticket prices for different cities.\n",
"\"\"\"\n"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"✅ Database setup complete!\n"
]
}
],
"source": [
"# Database setup\n",
"DB = \"prices.db\"\n",
"\n",
"def setup_database():\n",
" \"\"\"Initialize the database with the prices table\"\"\"\n",
" with sqlite3.connect(DB) as conn:\n",
" cursor = conn.cursor()\n",
" cursor.execute('CREATE TABLE IF NOT EXISTS prices (city TEXT PRIMARY KEY, price REAL)')\n",
" conn.commit()\n",
" print(\"✅ Database setup complete!\")\n",
"\n",
"# Setup the database\n",
"setup_database()\n"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"🧪 Testing tool functions:\n",
"DATABASE TOOL CALLED: Getting price for London\n",
"No price data available for this city\n"
]
}
],
"source": [
"# Tool functions\n",
"def get_ticket_price(city):\n",
" \"\"\"Get the price of a ticket to a destination city\"\"\"\n",
" print(f\"DATABASE TOOL CALLED: Getting price for {city}\", flush=True)\n",
" with sqlite3.connect(DB) as conn:\n",
" cursor = conn.cursor()\n",
" cursor.execute('SELECT price FROM prices WHERE city = ?', (city.lower(),))\n",
" result = cursor.fetchone()\n",
" return f\"Ticket price to {city} is ${result[0]}\" if result else \"No price data available for this city\"\n",
"\n",
"def set_ticket_price(city, price):\n",
" \"\"\"Set the price of a ticket to a destination city\"\"\"\n",
" print(f\"DATABASE TOOL CALLED: Setting price for {city} to ${price}\", flush=True)\n",
" with sqlite3.connect(DB) as conn:\n",
" cursor = conn.cursor()\n",
" cursor.execute('INSERT INTO prices (city, price) VALUES (?, ?) ON CONFLICT(city) DO UPDATE SET price = ?', (city.lower(), price, price))\n",
" conn.commit()\n",
" return f\"Successfully set ticket price to {city} to ${price}\"\n",
"\n",
"# Test the functions\n",
"print(\"🧪 Testing tool functions:\")\n",
"print(get_ticket_price(\"London\")) # Should show no data initially\n"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"🔧 Tools configured:\n",
" - get_ticket_price: Get the price of a return ticket to the destination city.\n",
" - set_ticket_price: Set the price of a return ticket to a destination city.\n"
]
}
],
"source": [
"# Tool definitions for OpenAI\n",
"get_price_function = {\n",
" \"name\": \"get_ticket_price\",\n",
" \"description\": \"Get the price of a return ticket to the destination city.\",\n",
" \"parameters\": {\n",
" \"type\": \"object\",\n",
" \"properties\": {\n",
" \"destination_city\": {\n",
" \"type\": \"string\",\n",
" \"description\": \"The city that the customer wants to travel to\",\n",
" },\n",
" },\n",
" \"required\": [\"destination_city\"],\n",
" \"additionalProperties\": False\n",
" }\n",
"}\n",
"\n",
"set_price_function = {\n",
" \"name\": \"set_ticket_price\",\n",
" \"description\": \"Set the price of a return ticket to a destination city.\",\n",
" \"parameters\": {\n",
" \"type\": \"object\",\n",
" \"properties\": {\n",
" \"destination_city\": {\n",
" \"type\": \"string\",\n",
" \"description\": \"The city to set the price for\",\n",
" },\n",
" \"price\": {\n",
" \"type\": \"number\",\n",
" \"description\": \"The new price for the ticket\",\n",
" },\n",
" },\n",
" \"required\": [\"destination_city\", \"price\"],\n",
" \"additionalProperties\": False\n",
" }\n",
"}\n",
"\n",
"# List of available tools\n",
"tools = [\n",
" {\"type\": \"function\", \"function\": get_price_function},\n",
" {\"type\": \"function\", \"function\": set_price_function}\n",
"]\n",
"\n",
"print(\"🔧 Tools configured:\")\n",
"print(f\" - {get_price_function['name']}: {get_price_function['description']}\")\n",
"print(f\" - {set_price_function['name']}: {set_price_function['description']}\")\n"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"✅ Tool call handler configured!\n"
]
}
],
"source": [
"# Tool call handler\n",
"def handle_tool_calls(message):\n",
" \"\"\"Handle multiple tool calls from the LLM\"\"\"\n",
" responses = []\n",
" for tool_call in message.tool_calls:\n",
" if tool_call.function.name == \"get_ticket_price\":\n",
" arguments = json.loads(tool_call.function.arguments)\n",
" city = arguments.get('destination_city')\n",
" price_details = get_ticket_price(city)\n",
" responses.append({\n",
" \"role\": \"tool\",\n",
" \"content\": price_details,\n",
" \"tool_call_id\": tool_call.id\n",
" })\n",
" elif tool_call.function.name == \"set_ticket_price\":\n",
" arguments = json.loads(tool_call.function.arguments)\n",
" city = arguments.get('destination_city')\n",
" price = arguments.get('price')\n",
" result = set_ticket_price(city, price)\n",
" responses.append({\n",
" \"role\": \"tool\",\n",
" \"content\": result,\n",
" \"tool_call_id\": tool_call.id\n",
" })\n",
" return responses\n",
"\n",
"print(\"✅ Tool call handler configured!\")\n"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"✅ Chat function configured!\n"
]
}
],
"source": [
"# Main chat function\n",
"def chat(message, history):\n",
" \"\"\"Main chat function that handles tool calls\"\"\"\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 = openai.chat.completions.create(model=MODEL, messages=messages, tools=tools)\n",
"\n",
" # Handle tool calls in a loop to support multiple consecutive tool calls\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 = openai.chat.completions.create(model=MODEL, messages=messages, tools=tools)\n",
" \n",
" return response.choices[0].message.content\n",
"\n",
"print(\"✅ Chat function configured!\")\n"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"DATABASE TOOL CALLED: Setting price for london to $799\n",
"DATABASE TOOL CALLED: Setting price for paris to $899\n",
"DATABASE TOOL CALLED: Setting price for tokyo to $1420\n",
"DATABASE TOOL CALLED: Setting price for sydney to $2999\n",
"DATABASE TOOL CALLED: Setting price for new york to $1099\n",
"DATABASE TOOL CALLED: Setting price for los angeles to $1299\n",
"DATABASE TOOL CALLED: Setting price for san francisco to $1199\n",
"DATABASE TOOL CALLED: Setting price for chicago to $999\n",
"DATABASE TOOL CALLED: Setting price for houston to $1399\n",
"DATABASE TOOL CALLED: Setting price for miami to $1499\n",
"DATABASE TOOL CALLED: Setting price for washington to $1199\n",
"DATABASE TOOL CALLED: Setting price for boston to $1299\n",
"DATABASE TOOL CALLED: Setting price for philadelphia to $1099\n",
"DATABASE TOOL CALLED: Setting price for seattle to $1399\n",
"DATABASE TOOL CALLED: Setting price for san diego to $1299\n",
"DATABASE TOOL CALLED: Setting price for san jose to $1199\n",
"DATABASE TOOL CALLED: Setting price for austin to $1099\n",
"DATABASE TOOL CALLED: Setting price for san antonio to $1399\n",
"DATABASE TOOL CALLED: Setting price for nairobi to $1099\n",
"DATABASE TOOL CALLED: Setting price for cape town to $1299\n",
"DATABASE TOOL CALLED: Setting price for durban to $1199\n",
"DATABASE TOOL CALLED: Setting price for johannesburg to $1399\n",
"DATABASE TOOL CALLED: Setting price for pretoria to $1099\n",
"DATABASE TOOL CALLED: Setting price for bloemfontein to $1299\n",
"DATABASE TOOL CALLED: Setting price for polokwane to $1199\n",
"DATABASE TOOL CALLED: Setting price for port elizabeth to $1199\n",
"DATABASE TOOL CALLED: Setting price for port shepstone to $1399\n",
"DATABASE TOOL CALLED: Setting price for port saint john to $1099\n",
"✅ Sample data initialized!\n",
"\n",
"🧪 Testing the setup:\n",
"DATABASE TOOL CALLED: Getting price for London\n",
"Ticket price to London is $799.0\n",
"DATABASE TOOL CALLED: Getting price for Tokyo\n",
"Ticket price to Tokyo is $1420.0\n"
]
}
],
"source": [
"# Initialize sample data\n",
"def initialize_sample_data():\n",
" \"\"\"Initialize the database with sample ticket prices\"\"\"\n",
" ticket_prices = {\"london\": 799, \"paris\": 899, \"tokyo\": 1420, \"sydney\": 2999, \"new york\": 1099, \"los angeles\": 1299, \"san francisco\": 1199, \"chicago\": 999, \"houston\": 1399, \"miami\": 1499, \"washington\": 1199, \"boston\": 1299, \"philadelphia\": 1099, \"seattle\": 1399, \"san diego\": 1299, \"san jose\": 1199, \"austin\": 1099, \"san antonio\": 1399, \"san francisco\": 1199, \"san diego\": 1299, \"san jose\": 1199, \"austin\": 1099, \"san antonio\": 1399, \"nairobi\": 1099, \"cape town\": 1299, \"durban\": 1199, \"johannesburg\": 1399, \"pretoria\": 1099, \"bloemfontein\": 1299, \"polokwane\": 1199, \"port elizabeth\": 1399, \"port shepstone\": 1099, \"port saint john\": 1299, \"port elizabeth\": 1199, \"port shepstone\": 1399, \"port saint john\": 1099}\n",
" for city, price in ticket_prices.items():\n",
" set_ticket_price(city, price)\n",
" print(\"✅ Sample data initialized!\")\n",
"\n",
"# Initialize sample data\n",
"initialize_sample_data()\n",
"\n",
"# Test the setup\n",
"print(\"\\n🧪 Testing the setup:\")\n",
"print(get_ticket_price(\"London\"))\n",
"print(get_ticket_price(\"Tokyo\"))\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Launch the Enhanced Airline Assistant\n",
"\n",
"The assistant now supports both getting and setting ticket prices!\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"🚀 Launching FlightAI Assistant with enhanced capabilities...\n",
"📋 Available commands:\n",
" - 'What's the price to London?' (get price)\n",
" - 'Set the price to New York to $1200' (set price)\n",
" - 'Update Tokyo price to $1500' (set price)\n",
" - 'How much does it cost to go to Paris?' (get price)\n",
"* Running on local URL: http://127.0.0.1:7882\n",
"* To create a public link, set `share=True` in `launch()`.\n"
]
},
{
"data": {
"text/html": [
"<div><iframe src=\"http://127.0.0.1:7882/\" 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": 10,
"metadata": {},
"output_type": "execute_result"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"DATABASE TOOL CALLED: Getting price for Paris\n",
"DATABASE TOOL CALLED: Setting price for Berlin to $9023\n"
]
}
],
"source": [
"# Launch the Gradio interface\n",
"print(\"🚀 Launching FlightAI Assistant with enhanced capabilities...\")\n",
"print(\"📋 Available commands:\")\n",
"print(\" - 'What's the price to London?' (get price)\")\n",
"print(\" - 'Set the price to New York to $1200' (set price)\")\n",
"print(\" - 'Update Tokyo price to $1500' (set price)\")\n",
"print(\" - 'How much does it cost to go to Paris?' (get price)\")\n",
"\n",
"interface = gr.ChatInterface(\n",
" fn=chat, \n",
" type=\"messages\",\n",
" title=\"FlightAI Assistant - Enhanced\",\n",
" description=\"Ask me about ticket prices or set new prices for destinations!\",\n",
" examples=[\n",
" \"What's the price to London?\",\n",
" \"Set the price to New York to $1200\",\n",
" \"How much does it cost to go to Paris?\",\n",
" \"Update Tokyo price to $1500\"\n",
" ]\n",
")\n",
"\n",
"interface.launch()\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Key Implementation Features\n",
"\n",
"### 🔧 **Enhanced Tool Support**\n",
"- **Get Ticket Price**: Query current prices from database\n",
"- **Set Ticket Price**: Update prices in database\n",
"- **Multiple Tool Calls**: Handles both operations in sequence\n",
"- **Database Integration**: Persistent SQLite storage\n",
"\n",
"### 🎯 **Tool Function Definitions**\n",
"```python\n",
"# Get Price Tool\n",
"get_price_function = {\n",
" \"name\": \"get_ticket_price\",\n",
" \"description\": \"Get the price of a return ticket to the destination city.\",\n",
" \"parameters\": {\n",
" \"type\": \"object\",\n",
" \"properties\": {\n",
" \"destination_city\": {\n",
" \"type\": \"string\",\n",
" \"description\": \"The city that the customer wants to travel to\",\n",
" },\n",
" },\n",
" \"required\": [\"destination_city\"],\n",
" \"additionalProperties\": False\n",
" }\n",
"}\n",
"\n",
"# Set Price Tool \n",
"set_price_function = {\n",
" \"name\": \"set_ticket_price\", \n",
" \"description\": \"Set the price of a return ticket to a destination city.\",\n",
" \"parameters\": {\n",
" \"type\": \"object\",\n",
" \"properties\": {\n",
" \"destination_city\": {\n",
" \"type\": \"string\",\n",
" \"description\": \"The city to set the price for\",\n",
" },\n",
" \"price\": {\n",
" \"type\": \"number\", \n",
" \"description\": \"The new price for the ticket\",\n",
" },\n",
" },\n",
" \"required\": [\"destination_city\", \"price\"],\n",
" \"additionalProperties\": False\n",
" }\n",
"}\n",
"```\n",
"\n",
"### 🚀 **Usage Examples**\n",
"- **Get Price**: \"What's the price to London?\"\n",
"- **Set Price**: \"Set the price to New York to $1200\"\n",
"- **Update Price**: \"Update Tokyo price to $1500\"\n",
"- **Query Multiple**: \"What are the prices to London and Paris?\"\n",
"\n",
"### 💾 **Database Schema**\n",
"```sql\n",
"CREATE TABLE prices (\n",
" city TEXT PRIMARY KEY,\n",
" price REAL\n",
")\n",
"```\n",
"\n",
"This implementation demonstrates advanced tool integration with OpenAI's function calling capabilities!\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": 2
}

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{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Week 2 Day 5 Exercise - Radio Africa Products Chatbot\n",
"\n",
"\n",
"This chatbot provides comprehensive information about Radio Africa Products, including:\n",
"- **Career Opportunities**: View and manage job openings\n",
"- **Radio Station Costs**: Get and set advertising costs for 5 radio stations\n",
"- **Database Integration**: Persistent storage with SQLite (ral.db)\n",
"\n",
"### Radio Stations:\n",
"- **Kiss FM**: Kenya's leading urban radio station\n",
"- **Classic 105**: Kenya's premier classic hits station \n",
"- **Radio Jambo**: Kenya's most popular vernacular station\n",
"- **Homeboyz Radio**: Kenya's youth-focused radio station\n",
"- **Gukena FM**: Kenya's leading vernacular station\n"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"# Import necessary libraries\n",
"import os\n",
"import json\n",
"import sqlite3\n",
"from dotenv import load_dotenv\n",
"from openai import OpenAI\n",
"import gradio as gr\n"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"OpenAI API Key exists and begins sk-proj-\n",
"✅ Radio Africa Products Assistant initialized!\n"
]
}
],
"source": [
"# Initialize OpenAI client\n",
"load_dotenv(override=True)\n",
"\n",
"openai_api_key = os.getenv('OPENAI_API_KEY')\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",
"MODEL = \"gpt-4o-mini\"\n",
"openai = OpenAI()\n",
"\n",
"# Database setup\n",
"DB = \"ral.db\"\n",
"\n",
"# System message for the Radio Africa assistant\n",
"system_message = \"\"\"\n",
"You are a helpful assistant for Radio Africa Products, a leading media company in Kenya.\n",
"You can provide information about:\n",
"- Career opportunities at Radio Africa\n",
"- Advertising costs for our 5 radio stations (Kiss FM, Classic 105, Radio Jambo, Homeboyz Radio, Gukena FM)\n",
"- Spot ad costs and sponsorship costs for each station\n",
"- General information about Radio Africa Products\n",
"\n",
"Give helpful, accurate answers. If you don't know something, say so.\n",
"Keep responses concise but informative.\n",
"\"\"\"\n",
"\n",
"print(\"✅ Radio Africa Products Assistant initialized!\")\n"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"✅ Radio Africa database setup complete!\n"
]
}
],
"source": [
"# Database setup\n",
"def setup_database():\n",
" \"\"\"Initialize the database with required tables\"\"\"\n",
" with sqlite3.connect(DB) as conn:\n",
" cursor = conn.cursor()\n",
" \n",
" # Radio stations table\n",
" cursor.execute('''\n",
" CREATE TABLE IF NOT EXISTS radio_stations (\n",
" id INTEGER PRIMARY KEY AUTOINCREMENT,\n",
" name TEXT UNIQUE NOT NULL,\n",
" spot_ad_cost REAL NOT NULL,\n",
" sponsorship_cost REAL NOT NULL,\n",
" description TEXT\n",
" )\n",
" ''')\n",
" \n",
" # Career opportunities table\n",
" cursor.execute('''\n",
" CREATE TABLE IF NOT EXISTS career_opportunities (\n",
" id INTEGER PRIMARY KEY AUTOINCREMENT,\n",
" title TEXT NOT NULL,\n",
" department TEXT NOT NULL,\n",
" description TEXT,\n",
" requirements TEXT,\n",
" salary_range TEXT,\n",
" location TEXT,\n",
" is_active BOOLEAN DEFAULT 1\n",
" )\n",
" ''')\n",
" \n",
" conn.commit()\n",
" print(\"✅ Radio Africa database setup complete!\")\n",
"\n",
"# Setup the database\n",
"setup_database()\n"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"✅ Tool functions defined!\n"
]
}
],
"source": [
"# Tool functions\n",
"def get_radio_station_costs(station_name):\n",
" \"\"\"Get advertising costs for a specific radio station\"\"\"\n",
" print(f\"DATABASE TOOL CALLED: Getting costs for {station_name}\", flush=True)\n",
" with sqlite3.connect(DB) as conn:\n",
" cursor = conn.cursor()\n",
" cursor.execute('SELECT name, spot_ad_cost, sponsorship_cost, description FROM radio_stations WHERE name LIKE ?', (f'%{station_name}%',))\n",
" result = cursor.fetchone()\n",
" if result:\n",
" return f\"Station: {result[0]}\\nSpot Ad Cost: KSh {result[1]:,}\\nSponsorship Cost: KSh {result[2]:,}\\nDescription: {result[3]}\"\n",
" else:\n",
" return f\"No information found for {station_name}. Available stations: Kiss FM, Classic 105, Radio Jambo, Homeboyz Radio, Gukena FM\"\n",
"\n",
"def set_radio_station_costs(station_name, spot_ad_cost, sponsorship_cost):\n",
" \"\"\"Set advertising costs for a specific radio station\"\"\"\n",
" print(f\"DATABASE TOOL CALLED: Setting costs for {station_name}\", flush=True)\n",
" with sqlite3.connect(DB) as conn:\n",
" cursor = conn.cursor()\n",
" cursor.execute('''\n",
" UPDATE radio_stations \n",
" SET spot_ad_cost = ?, sponsorship_cost = ?\n",
" WHERE name LIKE ?\n",
" ''', (spot_ad_cost, sponsorship_cost, f'%{station_name}%'))\n",
" \n",
" if cursor.rowcount > 0:\n",
" conn.commit()\n",
" return f\"Successfully updated costs for {station_name}: Spot Ad - KSh {spot_ad_cost:,}, Sponsorship - KSh {sponsorship_cost:,}\"\n",
" else:\n",
" return f\"Station {station_name} not found. Available stations: Kiss FM, Classic 105, Radio Jambo, Homeboyz Radio, Gukena FM\"\n",
"\n",
"def get_career_opportunities(department=None):\n",
" \"\"\"Get career opportunities, optionally filtered by department\"\"\"\n",
" print(f\"DATABASE TOOL CALLED: Getting career opportunities for {department or 'all departments'}\", flush=True)\n",
" with sqlite3.connect(DB) as conn:\n",
" cursor = conn.cursor()\n",
" if department:\n",
" cursor.execute('''\n",
" SELECT title, department, description, requirements, salary_range, location \n",
" FROM career_opportunities \n",
" WHERE department LIKE ? AND is_active = 1\n",
" ''', (f'%{department}%',))\n",
" else:\n",
" cursor.execute('''\n",
" SELECT title, department, description, requirements, salary_range, location \n",
" FROM career_opportunities \n",
" WHERE is_active = 1\n",
" ''')\n",
" \n",
" results = cursor.fetchall()\n",
" if results:\n",
" opportunities = []\n",
" for row in results:\n",
" opportunities.append(f\"Title: {row[0]}\\nDepartment: {row[1]}\\nDescription: {row[2]}\\nRequirements: {row[3]}\\nSalary: {row[4]}\\nLocation: {row[5]}\\n\")\n",
" return \"\\n\".join(opportunities)\n",
" else:\n",
" return f\"No career opportunities found for {department or 'any department'}\"\n",
"\n",
"def add_career_opportunity(title, department, description, requirements, salary_range, location):\n",
" \"\"\"Add a new career opportunity\"\"\"\n",
" print(f\"DATABASE TOOL CALLED: Adding career opportunity - {title}\", flush=True)\n",
" with sqlite3.connect(DB) as conn:\n",
" cursor = conn.cursor()\n",
" cursor.execute('''\n",
" INSERT INTO career_opportunities (title, department, description, requirements, salary_range, location, is_active)\n",
" VALUES (?, ?, ?, ?, ?, ?, 1)\n",
" ''', (title, department, description, requirements, salary_range, location))\n",
" conn.commit()\n",
" return f\"Successfully added career opportunity: {title} in {department}\"\n",
"\n",
"print(\"✅ Tool functions defined!\")\n"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"🔧 Tools configured:\n",
" - get_radio_station_costs: Get advertising costs (spot ad and sponsorship) for a specific radio station.\n",
" - set_radio_station_costs: Set advertising costs for a specific radio station.\n",
" - get_career_opportunities: Get available career opportunities, optionally filtered by department.\n",
" - add_career_opportunity: Add a new career opportunity to the database.\n"
]
}
],
"source": [
"# Tool definitions for OpenAI\n",
"get_radio_costs_function = {\n",
" \"name\": \"get_radio_station_costs\",\n",
" \"description\": \"Get advertising costs (spot ad and sponsorship) for a specific radio station.\",\n",
" \"parameters\": {\n",
" \"type\": \"object\",\n",
" \"properties\": {\n",
" \"station_name\": {\n",
" \"type\": \"string\",\n",
" \"description\": \"The name of the radio station (Kiss FM, Classic 105, Radio Jambo, Homeboyz Radio, Gukena FM)\",\n",
" },\n",
" },\n",
" \"required\": [\"station_name\"],\n",
" \"additionalProperties\": False\n",
" }\n",
"}\n",
"\n",
"set_radio_costs_function = {\n",
" \"name\": \"set_radio_station_costs\",\n",
" \"description\": \"Set advertising costs for a specific radio station.\",\n",
" \"parameters\": {\n",
" \"type\": \"object\",\n",
" \"properties\": {\n",
" \"station_name\": {\n",
" \"type\": \"string\",\n",
" \"description\": \"The name of the radio station\",\n",
" },\n",
" \"spot_ad_cost\": {\n",
" \"type\": \"number\",\n",
" \"description\": \"The new spot ad cost\",\n",
" },\n",
" \"sponsorship_cost\": {\n",
" \"type\": \"number\",\n",
" \"description\": \"The new sponsorship cost\",\n",
" },\n",
" },\n",
" \"required\": [\"station_name\", \"spot_ad_cost\", \"sponsorship_cost\"],\n",
" \"additionalProperties\": False\n",
" }\n",
"}\n",
"\n",
"get_careers_function = {\n",
" \"name\": \"get_career_opportunities\",\n",
" \"description\": \"Get available career opportunities, optionally filtered by department.\",\n",
" \"parameters\": {\n",
" \"type\": \"object\",\n",
" \"properties\": {\n",
" \"department\": {\n",
" \"type\": \"string\",\n",
" \"description\": \"The department to filter by (optional)\",\n",
" },\n",
" },\n",
" \"required\": [],\n",
" \"additionalProperties\": False\n",
" }\n",
"}\n",
"\n",
"add_career_function = {\n",
" \"name\": \"add_career_opportunity\",\n",
" \"description\": \"Add a new career opportunity to the database.\",\n",
" \"parameters\": {\n",
" \"type\": \"object\",\n",
" \"properties\": {\n",
" \"title\": {\n",
" \"type\": \"string\",\n",
" \"description\": \"The job title\",\n",
" },\n",
" \"department\": {\n",
" \"type\": \"string\",\n",
" \"description\": \"The department\",\n",
" },\n",
" \"description\": {\n",
" \"type\": \"string\",\n",
" \"description\": \"Job description\",\n",
" },\n",
" \"requirements\": {\n",
" \"type\": \"string\",\n",
" \"description\": \"Job requirements\",\n",
" },\n",
" \"salary_range\": {\n",
" \"type\": \"string\",\n",
" \"description\": \"Salary range\",\n",
" },\n",
" \"location\": {\n",
" \"type\": \"string\",\n",
" \"description\": \"Job location\",\n",
" },\n",
" },\n",
" \"required\": [\"title\", \"department\", \"description\", \"requirements\", \"salary_range\", \"location\"],\n",
" \"additionalProperties\": False\n",
" }\n",
"}\n",
"\n",
"# List of available tools\n",
"tools = [\n",
" {\"type\": \"function\", \"function\": get_radio_costs_function},\n",
" {\"type\": \"function\", \"function\": set_radio_costs_function},\n",
" {\"type\": \"function\", \"function\": get_careers_function},\n",
" {\"type\": \"function\", \"function\": add_career_function}\n",
"]\n",
"\n",
"print(\"🔧 Tools configured:\")\n",
"print(f\" - {get_radio_costs_function['name']}: {get_radio_costs_function['description']}\")\n",
"print(f\" - {set_radio_costs_function['name']}: {set_radio_costs_function['description']}\")\n",
"print(f\" - {get_careers_function['name']}: {get_careers_function['description']}\")\n",
"print(f\" - {add_career_function['name']}: {add_career_function['description']}\")\n"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"✅ Tool call handler configured!\n"
]
}
],
"source": [
"# Tool call handler\n",
"def handle_tool_calls(message):\n",
" \"\"\"Handle multiple tool calls from the LLM\"\"\"\n",
" responses = []\n",
" for tool_call in message.tool_calls:\n",
" if tool_call.function.name == \"get_radio_station_costs\":\n",
" arguments = json.loads(tool_call.function.arguments)\n",
" station_name = arguments.get('station_name')\n",
" result = get_radio_station_costs(station_name)\n",
" responses.append({\n",
" \"role\": \"tool\",\n",
" \"content\": result,\n",
" \"tool_call_id\": tool_call.id\n",
" })\n",
" elif tool_call.function.name == \"set_radio_station_costs\":\n",
" arguments = json.loads(tool_call.function.arguments)\n",
" station_name = arguments.get('station_name')\n",
" spot_ad_cost = arguments.get('spot_ad_cost')\n",
" sponsorship_cost = arguments.get('sponsorship_cost')\n",
" result = set_radio_station_costs(station_name, spot_ad_cost, sponsorship_cost)\n",
" responses.append({\n",
" \"role\": \"tool\",\n",
" \"content\": result,\n",
" \"tool_call_id\": tool_call.id\n",
" })\n",
" elif tool_call.function.name == \"get_career_opportunities\":\n",
" arguments = json.loads(tool_call.function.arguments)\n",
" department = arguments.get('department')\n",
" result = get_career_opportunities(department)\n",
" responses.append({\n",
" \"role\": \"tool\",\n",
" \"content\": result,\n",
" \"tool_call_id\": tool_call.id\n",
" })\n",
" elif tool_call.function.name == \"add_career_opportunity\":\n",
" arguments = json.loads(tool_call.function.arguments)\n",
" title = arguments.get('title')\n",
" department = arguments.get('department')\n",
" description = arguments.get('description')\n",
" requirements = arguments.get('requirements')\n",
" salary_range = arguments.get('salary_range')\n",
" location = arguments.get('location')\n",
" result = add_career_opportunity(title, department, description, requirements, salary_range, location)\n",
" responses.append({\n",
" \"role\": \"tool\",\n",
" \"content\": result,\n",
" \"tool_call_id\": tool_call.id\n",
" })\n",
" return responses\n",
"\n",
"print(\"✅ Tool call handler configured!\")\n"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"✅ Chat function configured!\n"
]
}
],
"source": [
"# Main chat function\n",
"def chat(message, history):\n",
" \"\"\"Main chat function that handles tool calls\"\"\"\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 = openai.chat.completions.create(model=MODEL, messages=messages, tools=tools)\n",
"\n",
" # Handle tool calls in a loop to support multiple consecutive tool calls\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 = openai.chat.completions.create(model=MODEL, messages=messages, tools=tools)\n",
" \n",
" return response.choices[0].message.content\n",
"\n",
"print(\"✅ Chat function configured!\")\n"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"✅ Sample data initialized!\n",
"\n",
"🧪 Testing the setup:\n",
"DATABASE TOOL CALLED: Getting costs for Kiss FM\n",
"Station: Kiss FM\n",
"Spot Ad Cost: KSh 15,000.0\n",
"Sponsorship Cost: KSh 500,000.0\n",
"Description: Kenya's leading urban radio station\n",
"\n",
"==================================================\n",
"\n",
"DATABASE TOOL CALLED: Getting career opportunities for Sales\n",
"Title: Sales Executive\n",
"Department: Sales\n",
"Description: Generate advertising revenue and build client relationships\n",
"Requirements: Degree in Marketing/Business, 3+ years sales experience\n",
"Salary: KSh 100,000 - 200,000\n",
"Location: Nairobi\n",
"\n"
]
}
],
"source": [
"# Initialize sample data\n",
"def initialize_sample_data():\n",
" \"\"\"Initialize the database with sample data\"\"\"\n",
" with sqlite3.connect(DB) as conn:\n",
" cursor = conn.cursor()\n",
" \n",
" # Clear existing data\n",
" cursor.execute('DELETE FROM radio_stations')\n",
" cursor.execute('DELETE FROM career_opportunities')\n",
" \n",
" # Insert radio stations data\n",
" radio_stations = [\n",
" (\"Kiss FM\", 15000, 500000, \"Kenya's leading urban radio station\"),\n",
" (\"Classic 105\", 12000, 800000, \"Kenya's premier classic hits station\"),\n",
" (\"Radio Jambo\", 10000, 1100000, \"Kenya's most popular vernacular station\"),\n",
" (\"Homeboyz Radio\", 8000, 150000, \"Kenya's youth-focused radio station\"),\n",
" (\"Gukena FM\", 6000, 100000, \"Kenya's leading vernacular station\")\n",
" ]\n",
" \n",
" cursor.executemany('''\n",
" INSERT INTO radio_stations (name, spot_ad_cost, sponsorship_cost, description)\n",
" VALUES (?, ?, ?, ?)\n",
" ''', radio_stations)\n",
" \n",
" # Insert career opportunities\n",
" careers = [\n",
" (\"Radio Presenter\", \"Programming\", \"Host radio shows and engage with listeners\", \"Degree in Media/Communication, 2+ years experience\", \"KSh 80,000 - 150,000\", \"Nairobi\", 1),\n",
" (\"Sales Executive\", \"Sales\", \"Generate advertising revenue and build client relationships\", \"Degree in Marketing/Business, 3+ years sales experience\", \"KSh 100,000 - 200,000\", \"Nairobi\", 1),\n",
" (\"Content Producer\", \"Programming\", \"Create engaging radio content and manage social media\", \"Degree in Media/Journalism, 2+ years experience\", \"KSh 70,000 - 120,000\", \"Nairobi\", 1),\n",
" (\"Technical Engineer\", \"Technical\", \"Maintain radio equipment and ensure smooth broadcasting\", \"Degree in Engineering, 3+ years technical experience\", \"KSh 90,000 - 160,000\", \"Nairobi\", 1),\n",
" (\"Marketing Manager\", \"Marketing\", \"Develop marketing strategies and manage brand campaigns\", \"Degree in Marketing, 5+ years experience\", \"KSh 150,000 - 250,000\", \"Nairobi\", 1),\n",
" (\"News Reporter\", \"News\", \"Research and report news stories for radio\", \"Degree in Journalism, 2+ years experience\", \"KSh 60,000 - 100,000\", \"Nairobi\", 1),\n",
" (\"Digital Media Specialist\", \"Digital\", \"Manage digital platforms and online content\", \"Degree in Digital Media, 2+ years experience\", \"KSh 80,000 - 140,000\", \"Nairobi\", 1)\n",
" ]\n",
" \n",
" cursor.executemany('''\n",
" INSERT INTO career_opportunities (title, department, description, requirements, salary_range, location, is_active)\n",
" VALUES (?, ?, ?, ?, ?, ?, ?)\n",
" ''', careers)\n",
" \n",
" conn.commit()\n",
" print(\"✅ Sample data initialized!\")\n",
"\n",
"# Initialize sample data\n",
"initialize_sample_data()\n",
"\n",
"# Test the setup\n",
"print(\"\\n🧪 Testing the setup:\")\n",
"print(get_radio_station_costs(\"Kiss FM\"))\n",
"print(\"\\n\" + \"=\"*50 + \"\\n\")\n",
"print(get_career_opportunities(\"Sales\"))\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Launch the Radio Africa Products Chatbot\n",
"\n",
"The chatbot is now ready with comprehensive features for Radio Africa Products!\n"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"🚀 Launching Radio Africa Products Chatbot...\n",
"📋 Available features:\n",
" - Get radio station advertising costs\n",
" - Set radio station advertising costs\n",
" - View career opportunities\n",
" - Add new career opportunities\n",
"\n",
"🎯 Example queries:\n",
" - 'What are the advertising costs for Kiss FM?'\n",
" - 'Show me career opportunities in Sales'\n",
" - 'Set the costs for Classic 105 to 15000 spot ads and 60000 sponsorship'\n",
" - 'What career opportunities are available?'\n",
" - 'Add a new job: Marketing Coordinator in Marketing department'\n",
"* 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": 10,
"metadata": {},
"output_type": "execute_result"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"DATABASE TOOL CALLED: Adding career opportunity - Marketing Coordinator\n"
]
}
],
"source": [
"# Launch the Gradio interface\n",
"print(\"🚀 Launching Radio Africa Products Chatbot...\")\n",
"print(\"📋 Available features:\")\n",
"print(\" - Get radio station advertising costs\")\n",
"print(\" - Set radio station advertising costs\")\n",
"print(\" - View career opportunities\")\n",
"print(\" - Add new career opportunities\")\n",
"print(\"\\n🎯 Example queries:\")\n",
"print(\" - 'What are the advertising costs for Kiss FM?'\")\n",
"print(\" - 'Show me career opportunities in Sales'\")\n",
"print(\" - 'Set the costs for Classic 105 to 15000 spot ads and 60000 sponsorship'\")\n",
"print(\" - 'What career opportunities are available?'\")\n",
"print(\" - 'Add a new job: Marketing Coordinator in Marketing department'\")\n",
"\n",
"interface = gr.ChatInterface(\n",
" fn=chat, \n",
" type=\"messages\",\n",
" title=\"Radio Africa Products Assistant\",\n",
" description=\"Ask me about career opportunities, radio station costs, and Radio Africa Products!\",\n",
" examples=[\n",
" \"What are the advertising costs for Kiss FM?\",\n",
" \"Show me career opportunities in Sales\",\n",
" \"Set the costs for Classic 105 to 15000 spot ads and 60000 sponsorship\",\n",
" \"What career opportunities are available?\",\n",
" \"Add a new job: Marketing Coordinator in Marketing department\"\n",
" ]\n",
")\n",
"\n",
"interface.launch()\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Key Implementation Features\n",
"\n",
"### 🎯 **Radio Station Management**\n",
"- **5 Radio Stations**: Kiss FM, Classic 105, Radio Jambo, Homeboyz Radio, Gukena FM\n",
"- **Cost Management**: Get and set spot ad costs and sponsorship costs\n",
"- **Station Information**: Descriptions and details for each station\n",
"\n",
"### 💼 **Career Opportunities Management**\n",
"- **Job Listings**: View all available positions\n",
"- **Department Filtering**: Filter by specific departments (Sales, Programming, Technical, etc.)\n",
"- **Job Management**: Add new career opportunities\n",
"- **Detailed Information**: Job descriptions, requirements, salary ranges, locations\n",
"\n",
"### 🗄️ **Database Schema (ral.db)**\n",
"```sql\n",
"-- Radio Stations Table\n",
"CREATE TABLE radio_stations (\n",
" id INTEGER PRIMARY KEY AUTOINCREMENT,\n",
" name TEXT UNIQUE NOT NULL,\n",
" spot_ad_cost REAL NOT NULL,\n",
" sponsorship_cost REAL NOT NULL,\n",
" description TEXT\n",
");\n",
"\n",
"-- Career Opportunities Table \n",
"CREATE TABLE career_opportunities (\n",
" id INTEGER PRIMARY KEY AUTOINCREMENT,\n",
" title TEXT NOT NULL,\n",
" department TEXT NOT NULL,\n",
" description TEXT,\n",
" requirements TEXT,\n",
" salary_range TEXT,\n",
" location TEXT,\n",
" is_active BOOLEAN DEFAULT 1\n",
");\n",
"```\n",
"\n",
"### 🔧 **Tool Functions**\n",
"1. **get_radio_station_costs**: Query advertising costs for specific stations\n",
"2. **set_radio_station_costs**: Update advertising costs for stations\n",
"3. **get_career_opportunities**: View job opportunities (with optional department filter)\n",
"4. **add_career_opportunity**: Add new job postings\n",
"\n",
"### 🚀 **Usage Examples**\n",
"- **Get Costs**: \"What are the advertising costs for Kiss FM?\"\n",
"- **Set Costs**: \"Set the costs for Classic 105 to 15000 spot ads and 60000 sponsorship\"\n",
"- **View Jobs**: \"Show me career opportunities in Sales\"\n",
"- **Add Jobs**: \"Add a new job: Marketing Coordinator in Marketing department\"\n",
"\n",
"This implementation demonstrates comprehensive tool integration for a real-world business application!\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": 2
}

BIN
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"""
Run the Radio Africa Group Advanced Chatbot
This script ensures all ports are free and launches the chatbot
"""
import os
import subprocess
import time
import sys
def kill_processes_on_ports():
"""Kill all processes using Gradio ports"""
print("🔍 Checking for processes using Gradio ports...")
# Check for processes on common Gradio ports
ports_to_check = [7860, 7861, 7862, 7863, 7864, 7865, 7866, 7867, 7868, 7869, 7870, 7871, 7872, 7873, 7874, 7875, 7876, 7877, 7878, 7879]
for port in ports_to_check:
try:
# Find process using the port
result = subprocess.run(['netstat', '-ano'], capture_output=True, text=True)
for line in result.stdout.split('\n'):
if f':{port}' in line and 'LISTENING' in line:
parts = line.split()
if len(parts) > 4:
pid = parts[-1]
try:
print(f"🔄 Killing process {pid} using port {port}")
subprocess.run(['taskkill', '/F', '/PID', pid], capture_output=True)
except:
pass
except:
pass
print("✅ Port cleanup completed!")
def find_free_port(start_port=7860):
"""Find a free port starting from the given port"""
import socket
for port in range(start_port, start_port + 100):
try:
with socket.socket(socket.AF_INET, socket.SOCK_STREAM) as s:
s.bind(('127.0.0.1', port))
return port
except OSError:
continue
return None
def main():
"""Main function to run the chatbot"""
print("🚀 Starting Radio Africa Group Advanced Chatbot...")
# Kill any existing processes
kill_processes_on_ports()
# Find a free port
free_port = find_free_port(7860)
if not free_port:
print("❌ No free ports available!")
return
print(f"✅ Using port {free_port}")
# Set environment variable for Gradio
os.environ['GRADIO_SERVER_PORT'] = str(free_port)
# Import and run the chatbot
try:
# Change to the correct directory
os.chdir('week2/community-contributions/week2-assignment-Joshua')
# Import the chatbot
from radio_africa_advanced_chatbot import main as chatbot_main
print("🎯 Launching Radio Africa Group Advanced Chatbot...")
print(f"🌐 Interface will be available at: http://127.0.0.1:{free_port}")
# Run the chatbot
chatbot_main()
except ImportError as e:
print(f"❌ Import error: {e}")
print("Please make sure you're in the correct directory and all dependencies are installed.")
except Exception as e:
print(f"❌ Error: {e}")
if __name__ == "__main__":
main()

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{
"cells": [
{
"cell_type": "markdown",
"id": "fe12c203-e6a6-452c-a655-afb8a03a4ff5",
"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.\n"
]
},
{
"cell_type": "markdown",
"id": "66730be3",
"metadata": {},
"source": [
"---"
]
},
{
"cell_type": "code",
"execution_count": 1,
"id": "c1070317-3ed9-4659-abe3-828943230e03",
"metadata": {},
"outputs": [],
"source": [
"# imports\n",
"import os\n",
"from openai import OpenAI\n",
"from dotenv import load_dotenv\n",
"from IPython.display import display, Markdown, update_display\n",
"from enum import StrEnum\n",
"import json\n",
"load_dotenv(override=True)\n",
"api_key = os.getenv('OPENAI_API_KEY')\n",
"anthropic_api_key = os.getenv(\"ANTHROPIC_API_KEY\")\n",
"\n",
"import gradio as gr\n",
"openai = OpenAI()"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "16ec5d8a",
"metadata": {},
"outputs": [],
"source": [
"system_prompt = \"\"\"\n",
"You are a helpful tutor that explains code. You need to provide an answer structured in markdown without code blocks into the following parts:\n",
"- Identify the topic of the question (so the user can look for more info)\n",
"- Give an ELI5 explanation of the question.\n",
"- Give a step by step explanation of the code.\n",
"- Ask the user a follow up question or variation of the question to see if they understand the concept.\n",
"- Give the answer to the followup question as a spoiler.\n",
"\n",
"IF the last message is the output of a tool call with an structured markdown you need to return it as it is.\n",
"\"\"\""
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "5e6f715e",
"metadata": {},
"outputs": [],
"source": [
"#I'm going to create a tool that will be a LLM as a tool. \n",
"# The tool will actually make a separate lLM call and simply rigorously assess if the answer is valid or not\n",
"class Enum_Model(StrEnum):\n",
" GPT = 'gpt-4o-mini'\n",
" LLAMA = 'llama3.2:1b'\n",
" GPT_OSS = 'gpt-oss:20b-cloud'\n",
" HAIKU = 'claude-3-5-sonnet-20240620'\n",
"\n",
"\n",
"\n",
"def llm_as_tool(input_msg:str):\n",
" # Generate a system prompt for the LLM to critically analyze if a coding problem's solution is correct\n",
" llm_tool_system_prompt = (\n",
" \"You are an expert code reviewer. Your task is to rigorously and critically analyze whether the provided solution \"\n",
" \"correctly solves the stated coding problem. Carefully consider correctness, completeness, and potential edge cases. \"\n",
" \"Explain your reasoning with supporting details and point out any flaws, omissions, or improvements. \"\n",
" \"Provide a clear judgment: is the solution correct? If not, why not? \"\n",
" \"Output your answer using the following structured markdown format:\\n\\n\"\n",
" \"## Analysis\\n\"\n",
" \"- **Correctness:** <your comments>\\n\"\n",
" \"- **Completeness:** <your comments>\\n\"\n",
" \"- **Edge Cases:** <your comments>\\n\"\n",
" \"- **Improvements:** <optional improvement suggestions>\\n\\n\"\n",
" \"## Judgment\\n\"\n",
" \"<Clearly state whether the solution is correct, and justify your decision.>\"\n",
" )\n",
"\n",
" ollama = OpenAI(base_url=\"http://localhost:11434/v1\")\n",
" print(f'Calling LLM_Tool with input {input_msg[:10]} ...')\n",
" response = ollama.chat.completions.create(\n",
" model=\"qwen3-coder:480b-cloud\",\n",
" messages=[\n",
" {\"role\": \"system\", \"content\": llm_tool_system_prompt},\n",
" {\"role\": \"user\", \"content\": input_msg},\n",
" ]\n",
" )\n",
" answer = response.choices[0].message.content\n",
" print(f'answer: {answer[:50]}')\n",
" return answer\n",
"\n",
"# There's a particular dictionary structure that's required to describe our function:\n",
"\n",
"check_code_tool_def = {\n",
" \"name\": \"check_code_tool\",\n",
" \"description\": \"Checks the code solution provided by the user is correct.\",\n",
" \"parameters\": {\n",
" \"type\": \"object\",\n",
" \"properties\": {\n",
" \"input_msg\": {\n",
" \"type\": \"string\",\n",
" \"description\": \"This is a very concised summary of the question the user asked, the proposed exercise, and the answer the user gave\",\n",
" },\n",
" },\n",
" \"required\": [\"input_msg\"],\n",
" \"additionalProperties\": False\n",
" }\n",
"}\n",
"\n",
"tools = [\n",
" {\"type\": \"function\", \"function\": check_code_tool_def},\n",
" ]\n",
"\n",
"tools_dict = {\n",
" \"check_code_tool\": llm_as_tool,\n",
"}\n",
"\n",
"def handle_tool_calls(message):\n",
" responses = []\n",
" print(f\"This is the message in handle_tool_calls: {message}\")\n",
" for tool_call in message.tool_calls:\n",
" arguments = json.loads(tool_call.function.arguments)\n",
" func = tools_dict.get(tool_call.function.name, lambda **kwargs: \"Unknown tool\")\n",
" markdown_analysis = func(**arguments)\n",
" responses.append({\n",
" \"role\": \"tool\",\n",
" \"content\": markdown_analysis,\n",
" \"tool_call_id\": tool_call.id\n",
" })\n",
" print(f\"response for a call is {responses}\")\n",
" return responses\n",
"\n",
"def read_text_to_speech(history):\n",
" message = history[-1]['content']\n",
" response = openai.audio.speech.create(\n",
" model=\"gpt-4o-mini-tts\",\n",
" voice=\"onyx\", # Also, try replacing onyx with alloy or coral\n",
" input=message\n",
" )\n",
" return response.content"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "6b3a49b0",
"metadata": {},
"outputs": [],
"source": [
"def chat(history,model):\n",
" # history_dicts = [{\"role\": h[\"role\"], \"content\": h[\"content\"]} for h in history]\n",
" # messages = [{\"role\": \"system\", \"content\": system_prompt}] + history_dicts + [{\"role\": \"user\", \"content\": message}]\n",
" #model='GPT'\n",
" print(f\"Model selected: {type(model)}\")\n",
" if isinstance(model, str):\n",
" try:\n",
" model = Enum_Model[model.upper()]\n",
" print(f\"Model selected: {model}\")\n",
" except KeyError:\n",
" raise ValueError(f\"Unknown model: {model}\")\n",
" if model == Enum_Model.LLAMA:\n",
" LLM_ENDPOINT=\"http://localhost:11434/v1\"\n",
" client = OpenAI(base_url=LLM_ENDPOINT)\n",
" elif model == Enum_Model.GPT_OSS:\n",
" LLM_ENDPOINT=\"http://localhost:11434/v1\"\n",
" client = OpenAI(base_url=LLM_ENDPOINT)\n",
" elif model == Enum_Model.GPT:\n",
" client = OpenAI()\n",
" elif model == Enum_Model.HAIKU:\n",
" LLM_ENDPOINT=\"https://api.anthropic.com/v1/\"\n",
" client = OpenAI(base_url=LLM_ENDPOINT, api_key=anthropic_api_key)\n",
"\n",
"\n",
" #client = OpenAI()\n",
" \n",
" history = [{\"role\":h[\"role\"], \"content\":h[\"content\"]} for h in history]\n",
" messages = [{\"role\": \"system\", \"content\": system_prompt}] + history\n",
"\n",
" cumulative_response = \"\"\n",
" history.append({\"role\": \"assistant\", \"content\": \"\"})\n",
"\n",
" response = client.chat.completions.create(\n",
" model=model, \n",
" messages=messages, \n",
" tools=tools,\n",
" stream=True\n",
" )\n",
" \n",
" tool_calls = {}\n",
" finish_reason = None\n",
" \n",
" for chunk in response:\n",
" delta = chunk.choices[0].delta\n",
" finish_reason = chunk.choices[0].finish_reason\n",
" \n",
" if hasattr(delta, 'content') and delta.content:\n",
" #cumulative_response += delta.content\n",
" #yield cumulative_response\n",
" history[-1]['content'] += delta.content\n",
" yield history\n",
" \n",
" if hasattr(delta, 'tool_calls') and delta.tool_calls:\n",
" for tool_call_delta in delta.tool_calls:\n",
" idx = tool_call_delta.index\n",
" \n",
" if idx not in tool_calls:\n",
" tool_calls[idx] = {\n",
" \"id\": \"\",\n",
" \"type\": \"function\",\n",
" \"function\": {\"name\": \"\", \"arguments\": \"\"}\n",
" }\n",
" \n",
" if tool_call_delta.id:\n",
" tool_calls[idx][\"id\"] = tool_call_delta.id\n",
" if tool_call_delta.type:\n",
" tool_calls[idx][\"type\"] = tool_call_delta.type\n",
" if hasattr(tool_call_delta, 'function') and tool_call_delta.function:\n",
" if tool_call_delta.function.name:\n",
" tool_calls[idx][\"function\"][\"name\"] = tool_call_delta.function.name\n",
" if tool_call_delta.function.arguments:\n",
" tool_calls[idx][\"function\"][\"arguments\"] += tool_call_delta.function.arguments\n",
" \n",
" if finish_reason == \"tool_calls\":\n",
" from types import SimpleNamespace\n",
" \n",
" tool_call_objects = [\n",
" SimpleNamespace(\n",
" id=tool_calls[idx][\"id\"],\n",
" type=tool_calls[idx][\"type\"],\n",
" function=SimpleNamespace(\n",
" name=tool_calls[idx][\"function\"][\"name\"],\n",
" arguments=tool_calls[idx][\"function\"][\"arguments\"]\n",
" )\n",
" )\n",
" for idx in sorted(tool_calls.keys())\n",
" ]\n",
" \n",
" message_obj = SimpleNamespace(tool_calls=tool_call_objects)\n",
" print(message_obj)\n",
" tool_responses = handle_tool_calls(message_obj)\n",
" \n",
" assistant_message = {\n",
" \"role\": \"assistant\",\n",
" \"content\": None,\n",
" \"tool_calls\": [tool_calls[idx] for idx in sorted(tool_calls.keys())]\n",
" }\n",
" \n",
" messages.append(assistant_message)\n",
" messages.extend(tool_responses)\n",
" #yield cumulative_response\n",
"\n",
" for tool_response in tool_responses:\n",
" history.append({\n",
" \"role\": \"assistant\",\n",
" \"content\": tool_response[\"content\"]\n",
" })\n",
" \n",
" print('--------------------------------')\n",
" print('history', history)\n",
" print('--------------------------------')\n",
"\n",
" yield history\n",
"\n",
" #yield assistant_message\n",
" else:\n",
" return"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "35828826",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"* Running on local URL: http://127.0.0.1:7874\n",
"* To create a public link, set `share=True` in `launch()`.\n"
]
},
{
"data": {
"text/html": [
"<div><iframe src=\"http://127.0.0.1:7874/\" 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": "stdout",
"output_type": "stream",
"text": [
"Model selected: <class 'str'>\n",
"Model selected: gpt-4o-mini\n"
]
}
],
"source": [
"# Callbacks (along with the chat() function above)\n",
"\n",
"def put_message_in_chatbot(message, history):\n",
" history = [{\"role\":h[\"role\"], \"content\":h[\"content\"]} for h in history]\n",
"\n",
" return \"\", history + [{\"role\":\"user\", \"content\":message}]\n",
"\n",
"# UI definition\n",
"\n",
"with gr.Blocks() as ui:\n",
" with gr.Row():\n",
" model_dropdown = gr.Dropdown(choices=[\"GPT\", \"GPT_OSS\", \"LLAMA\",\"HAIKU\"], value=\"GPT\", label=\"Model\") \n",
" #image_output = gr.Image(height=500, interactive=False)\n",
" with gr.Row():\n",
" chatbot = gr.Chatbot(height=500, type=\"messages\")\n",
" audio_output = gr.Audio(autoplay=True)\n",
" with gr.Row():\n",
" message = gr.Textbox(label=\"Chat with our AI Assistant:\")\n",
"\n",
"# Hooking up events to callbacks\n",
"\n",
" message.submit(put_message_in_chatbot, \n",
" inputs=[message, chatbot], \n",
" outputs=[message, chatbot]\n",
" ).then(\n",
" chat, \n",
" inputs=[chatbot, model_dropdown], \n",
" outputs=[chatbot]\n",
" ).then(\n",
" read_text_to_speech,\n",
" inputs=chatbot,\n",
" outputs=audio_output\n",
" )\n",
"\n",
"ui.launch(inbrowser=True)"
]
}
],
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"kernelspec": {
"display_name": ".venv",
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"name": "python3"
},
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},
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573
week3/community-contributions/Exercise_Week_3_Synthetic_Data_JOM.ipynb Normal file
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"cells": [
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"cell_type": "code",
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"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "M-mTmXz9USNe",
"outputId": "d2a37614-9c84-4460-af18-938faa296e5b"
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"source": [
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]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "FW8nl3XRFrz0"
},
"outputs": [],
"source": [
"# imports\n",
"\n",
"import os\n",
"import requests\n",
"from IPython.display import Markdown, display, update_display\n",
"from openai import OpenAI\n",
"from google.colab import drive\n",
"from huggingface_hub import login\n",
"from google.colab import userdata\n",
"from transformers import AutoTokenizer, AutoModelForCausalLM, TextStreamer, BitsAndBytesConfig\n",
"import torch"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "xYW8kQYtF-3L"
},
"outputs": [],
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"hf_token = userdata.get('HF_TOKEN')\n",
"login(hf_token, add_to_git_credential=True)\n",
"\n",
"DEEPSEEK = \"deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B\"\n",
"LLAMA = \"meta-llama/Llama-3.2-3B-Instruct\""
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "piEMmcSfMH-O"
},
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"system_message = \"\"\"\n",
"You are an specialized tutor in creating flashcards about whatever topic the user decides to research.\n",
"They need to be brief, with a short question and a short answer in the following markdown format example\n",
"###TEMPLATE###\n",
"# Flashcard 1\n",
"<details>\n",
"<summary>What is the capital of France?</summary>\n",
"Paris\n",
"</details>\n",
"\n",
"# Flashcard 2\n",
"\n",
"<details>\n",
"<summary>What is the derivative of sin(x)?</summary>\n",
"cos(x)\n",
"</details>\n",
"###TEMPLATE###\n",
"\"\"\"\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "UcRKUgcxMew6"
},
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"quant_config = BitsAndBytesConfig(\n",
" load_in_4bit=True,\n",
" bnb_4bit_use_double_quant=True,\n",
" bnb_4bit_compute_dtype=torch.bfloat16,\n",
" bnb_4bit_quant_type=\"nf4\"\n",
")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true,
"id": "HdQnWEzW3lzP"
},
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"source": [
"# Wrapping everything in a function - and adding Streaming and generation prompts\n",
"\n",
"def generate(model, messages, quant=True, stream = True, max_new_tokens=500):\n",
" tokenizer = AutoTokenizer.from_pretrained(model)\n",
" tokenizer.pad_token = tokenizer.eos_token\n",
" input_ids = tokenizer.apply_chat_template(messages, return_tensors=\"pt\", add_generation_prompt=True).to(\"cuda\")\n",
" attention_mask = torch.ones_like(input_ids, dtype=torch.long, device=\"cuda\")\n",
" streamer = TextStreamer(tokenizer)\n",
" if quant:\n",
" model = AutoModelForCausalLM.from_pretrained(model, quantization_config=quant_config).to(\"cuda\")\n",
" else:\n",
" model = AutoModelForCausalLM.from_pretrained(model).to(\"cuda\")\n",
" if stream:\n",
" outputs = model.generate(input_ids=input_ids, attention_mask=attention_mask, max_new_tokens=max_new_tokens, streamer=streamer)\n",
" else:\n",
" outputs = model.generate(input_ids=input_ids, attention_mask=attention_mask, max_new_tokens=max_new_tokens,)\n",
"\n",
" response = tokenizer.decode(outputs[0], skip_special_tokens=True)\n",
" return response\n"
]
},
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"import gradio as gr\n",
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"\n",
"def call_generate(model_name, topic, num_flashcards):\n",
" if model_name == \"LLAMA\":\n",
" model = LLAMA\n",
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" ]\n",
"\n",
" # Call your existing generate function\n",
" response = generate(model, messages, stream=False, max_new_tokens=2000)\n",
" text = re.sub(r'###TEMPLATE.*?###TEMPLATE', '', response, flags=re.DOTALL)\n",
"\n",
" result = re.search(r\"(# Flashcard 1[\\s\\S]*</details>)\", text)\n",
"\n",
" if result:\n",
" response = result.group(1)\n",
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" response\n",
" return response\n",
"\n",
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" with gr.Row():\n",
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" with gr.Row():\n",
" topic_selector = gr.Textbox(label=\"Type the topic you want flashcards:\", max_lines=1, max_length=50)\n",
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" minimum=1,\n",
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" step=1,\n",
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" label=\"Nr. Flashcards\",\n",
" )\n",
" with gr.Row():\n",
" generate_button = gr.Button(\"Generate Flashcards\")\n",
" with gr.Row():\n",
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"\n",
" # Hooking up events to callbacks\n",
" generate_button.click(\n",
" call_generate,\n",
" inputs=[model_dropdown, topic_selector, num_flashcards],\n",
" outputs=output\n",
" )\n",
"\n",
"ui.launch(inbrowser=True, debug=True)"
]
}
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" WARNING: The script isympy.exe is installed in 'C:\\Users\\hp\\AppData\\Roaming\\Python\\Python314\\Scripts' which is not on PATH.\n",
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" WARNING: The scripts hf.exe, huggingface-cli.exe and tiny-agents.exe are installed in 'C:\\Users\\hp\\AppData\\Roaming\\Python\\Python314\\Scripts' which is not on PATH.\n",
" Consider adding this directory to PATH or, if you prefer to suppress this warning, use --no-warn-script-location.\n",
" WARNING: The scripts accelerate-config.exe, accelerate-estimate-memory.exe, accelerate-launch.exe, accelerate-merge-weights.exe and accelerate.exe are installed in 'C:\\Users\\hp\\AppData\\Roaming\\Python\\Python314\\Scripts' which is not on PATH.\n",
" Consider adding this directory to PATH or, if you prefer to suppress this warning, use --no-warn-script-location.\n"
]
}
],
"source": [
"!pip install -q --upgrade bitsandbytes accelerate"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "ba0f9487",
"metadata": {},
"outputs": [],
"source": [
"import os\n",
"import requests\n",
"import threading\n",
"from dotenv import load_dotenv\n",
"from IPython.display import Markdown, display, update_display\n",
"from openai import OpenAI\n",
"from huggingface_hub import login\n",
"from transformers import pipeline, AutoTokenizer, AutoModelForCausalLM, TextIteratorStreamer, BitsAndBytesConfig\n",
"import torch\n",
"import gradio as gr"
]
},
{
"cell_type": "code",
"execution_count": 9,
"id": "70cc41a4",
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"Note: Environment variable`HF_TOKEN` is set and is the current active token independently from the token you've just configured.\n"
]
}
],
"source": [
"load_dotenv(override=True)\n",
"hf_token = os.getenv('HF_TOKEN')\n",
"login(hf_token, add_to_git_credential=True)"
]
},
{
"cell_type": "code",
"execution_count": 14,
"id": "a197a483",
"metadata": {},
"outputs": [
{
"ename": "PackageNotFoundError",
"evalue": "No package metadata was found for bitsandbytes",
"output_type": "error",
"traceback": [
"\u001b[31m---------------------------------------------------------------------------\u001b[39m",
"\u001b[31mStopIteration\u001b[39m Traceback (most recent call last)",
"\u001b[36mFile \u001b[39m\u001b[32m~\\AppData\\Roaming\\uv\\python\\cpython-3.12.12-windows-x86_64-none\\Lib\\importlib\\metadata\\__init__.py:397\u001b[39m, in \u001b[36mDistribution.from_name\u001b[39m\u001b[34m(cls, name)\u001b[39m\n\u001b[32m 396\u001b[39m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[32m--> \u001b[39m\u001b[32m397\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mnext\u001b[39;49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mcls\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43mdiscover\u001b[49m\u001b[43m(\u001b[49m\u001b[43mname\u001b[49m\u001b[43m=\u001b[49m\u001b[43mname\u001b[49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 398\u001b[39m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mStopIteration\u001b[39;00m:\n",
"\u001b[31mStopIteration\u001b[39m: ",
"\nDuring handling of the above exception, another exception occurred:\n",
"\u001b[31mPackageNotFoundError\u001b[39m Traceback (most recent call last)",
"\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[14]\u001b[39m\u001b[32m, line 1\u001b[39m\n\u001b[32m----> \u001b[39m\u001b[32m1\u001b[39m \u001b[38;5;28;43;01mclass\u001b[39;49;00m\u001b[38;5;250;43m \u001b[39;49m\u001b[34;43;01mGenerateMinute\u001b[39;49;00m\u001b[43m:\u001b[49m\n\u001b[32m 2\u001b[39m \u001b[43m \u001b[49m\u001b[43maudio_model\u001b[49m\u001b[43m \u001b[49m\u001b[43m=\u001b[49m\u001b[43m \u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mopenai/whisper-medium.en\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\n\u001b[32m 3\u001b[39m \u001b[43m \u001b[49m\u001b[43mllm_model\u001b[49m\u001b[43m \u001b[49m\u001b[43m=\u001b[49m\u001b[43m \u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mmeta-llama/Llama-3.2-3B-Instruct\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\n",
"\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[14]\u001b[39m\u001b[32m, line 4\u001b[39m, in \u001b[36mGenerateMinute\u001b[39m\u001b[34m()\u001b[39m\n\u001b[32m 2\u001b[39m audio_model = \u001b[33m\"\u001b[39m\u001b[33mopenai/whisper-medium.en\u001b[39m\u001b[33m\"\u001b[39m\n\u001b[32m 3\u001b[39m llm_model = \u001b[33m\"\u001b[39m\u001b[33mmeta-llama/Llama-3.2-3B-Instruct\u001b[39m\u001b[33m\"\u001b[39m\n\u001b[32m----> \u001b[39m\u001b[32m4\u001b[39m quant_config = \u001b[43mBitsAndBytesConfig\u001b[49m\u001b[43m(\u001b[49m\n\u001b[32m 5\u001b[39m \u001b[43m \u001b[49m\u001b[43mload_in_4bit\u001b[49m\u001b[43m=\u001b[49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m,\u001b[49m\n\u001b[32m 6\u001b[39m \u001b[43m \u001b[49m\u001b[43mbnb_4bit_use_double_quant\u001b[49m\u001b[43m=\u001b[49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m,\u001b[49m\n\u001b[32m 7\u001b[39m \u001b[43m \u001b[49m\u001b[43mbnb_4bit_compute_dtype\u001b[49m\u001b[43m=\u001b[49m\u001b[43mtorch\u001b[49m\u001b[43m.\u001b[49m\u001b[43mbfloat16\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 8\u001b[39m \u001b[43m \u001b[49m\u001b[43mbnb_4bit_quant_type\u001b[49m\u001b[43m=\u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mnf4\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\n\u001b[32m 9\u001b[39m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 11\u001b[39m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34m__init__\u001b[39m(\u001b[38;5;28mself\u001b[39m, progress, audio_model=audio_model, llm_model=llm_model):\n\u001b[32m 12\u001b[39m \u001b[38;5;28mself\u001b[39m.progress = progress\n",
"\u001b[36mFile \u001b[39m\u001b[32mc:\\Users\\hp\\projects\\gen-ai\\llm_engineering\\.venv\\Lib\\site-packages\\transformers\\utils\\quantization_config.py:510\u001b[39m, in \u001b[36mBitsAndBytesConfig.__init__\u001b[39m\u001b[34m(self, load_in_8bit, load_in_4bit, llm_int8_threshold, llm_int8_skip_modules, llm_int8_enable_fp32_cpu_offload, llm_int8_has_fp16_weight, bnb_4bit_compute_dtype, bnb_4bit_quant_type, bnb_4bit_use_double_quant, bnb_4bit_quant_storage, **kwargs)\u001b[39m\n\u001b[32m 507\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m kwargs:\n\u001b[32m 508\u001b[39m logger.info(\u001b[33mf\u001b[39m\u001b[33m\"\u001b[39m\u001b[33mUnused kwargs: \u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;28mlist\u001b[39m(kwargs.keys())\u001b[38;5;132;01m}\u001b[39;00m\u001b[33m. These kwargs are not used in \u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;28mself\u001b[39m.\u001b[34m__class__\u001b[39m\u001b[38;5;132;01m}\u001b[39;00m\u001b[33m.\u001b[39m\u001b[33m\"\u001b[39m)\n\u001b[32m--> \u001b[39m\u001b[32m510\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43mpost_init\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n",
"\u001b[36mFile \u001b[39m\u001b[32mc:\\Users\\hp\\projects\\gen-ai\\llm_engineering\\.venv\\Lib\\site-packages\\transformers\\utils\\quantization_config.py:568\u001b[39m, in \u001b[36mBitsAndBytesConfig.post_init\u001b[39m\u001b[34m(self)\u001b[39m\n\u001b[32m 565\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(\u001b[38;5;28mself\u001b[39m.bnb_4bit_use_double_quant, \u001b[38;5;28mbool\u001b[39m):\n\u001b[32m 566\u001b[39m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mTypeError\u001b[39;00m(\u001b[33m\"\u001b[39m\u001b[33mbnb_4bit_use_double_quant must be a boolean\u001b[39m\u001b[33m\"\u001b[39m)\n\u001b[32m--> \u001b[39m\u001b[32m568\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m.load_in_4bit \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m version.parse(\u001b[43mimportlib\u001b[49m\u001b[43m.\u001b[49m\u001b[43mmetadata\u001b[49m\u001b[43m.\u001b[49m\u001b[43mversion\u001b[49m\u001b[43m(\u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mbitsandbytes\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m)\u001b[49m) >= version.parse(\n\u001b[32m 569\u001b[39m \u001b[33m\"\u001b[39m\u001b[33m0.39.0\u001b[39m\u001b[33m\"\u001b[39m\n\u001b[32m 570\u001b[39m ):\n\u001b[32m 571\u001b[39m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\n\u001b[32m 572\u001b[39m \u001b[33m\"\u001b[39m\u001b[33m4 bit quantization requires bitsandbytes>=0.39.0 - please upgrade your bitsandbytes version\u001b[39m\u001b[33m\"\u001b[39m\n\u001b[32m 573\u001b[39m )\n",
"\u001b[36mFile \u001b[39m\u001b[32m~\\AppData\\Roaming\\uv\\python\\cpython-3.12.12-windows-x86_64-none\\Lib\\importlib\\metadata\\__init__.py:889\u001b[39m, in \u001b[36mversion\u001b[39m\u001b[34m(distribution_name)\u001b[39m\n\u001b[32m 882\u001b[39m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34mversion\u001b[39m(distribution_name):\n\u001b[32m 883\u001b[39m \u001b[38;5;250m \u001b[39m\u001b[33;03m\"\"\"Get the version string for the named package.\u001b[39;00m\n\u001b[32m 884\u001b[39m \n\u001b[32m 885\u001b[39m \u001b[33;03m :param distribution_name: The name of the distribution package to query.\u001b[39;00m\n\u001b[32m 886\u001b[39m \u001b[33;03m :return: The version string for the package as defined in the package's\u001b[39;00m\n\u001b[32m 887\u001b[39m \u001b[33;03m \"Version\" metadata key.\u001b[39;00m\n\u001b[32m 888\u001b[39m \u001b[33;03m \"\"\"\u001b[39;00m\n\u001b[32m--> \u001b[39m\u001b[32m889\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mdistribution\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdistribution_name\u001b[49m\u001b[43m)\u001b[49m.version\n",
"\u001b[36mFile \u001b[39m\u001b[32m~\\AppData\\Roaming\\uv\\python\\cpython-3.12.12-windows-x86_64-none\\Lib\\importlib\\metadata\\__init__.py:862\u001b[39m, in \u001b[36mdistribution\u001b[39m\u001b[34m(distribution_name)\u001b[39m\n\u001b[32m 856\u001b[39m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34mdistribution\u001b[39m(distribution_name):\n\u001b[32m 857\u001b[39m \u001b[38;5;250m \u001b[39m\u001b[33;03m\"\"\"Get the ``Distribution`` instance for the named package.\u001b[39;00m\n\u001b[32m 858\u001b[39m \n\u001b[32m 859\u001b[39m \u001b[33;03m :param distribution_name: The name of the distribution package as a string.\u001b[39;00m\n\u001b[32m 860\u001b[39m \u001b[33;03m :return: A ``Distribution`` instance (or subclass thereof).\u001b[39;00m\n\u001b[32m 861\u001b[39m \u001b[33;03m \"\"\"\u001b[39;00m\n\u001b[32m--> \u001b[39m\u001b[32m862\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mDistribution\u001b[49m\u001b[43m.\u001b[49m\u001b[43mfrom_name\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdistribution_name\u001b[49m\u001b[43m)\u001b[49m\n",
"\u001b[36mFile \u001b[39m\u001b[32m~\\AppData\\Roaming\\uv\\python\\cpython-3.12.12-windows-x86_64-none\\Lib\\importlib\\metadata\\__init__.py:399\u001b[39m, in \u001b[36mDistribution.from_name\u001b[39m\u001b[34m(cls, name)\u001b[39m\n\u001b[32m 397\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mnext\u001b[39m(\u001b[38;5;28mcls\u001b[39m.discover(name=name))\n\u001b[32m 398\u001b[39m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mStopIteration\u001b[39;00m:\n\u001b[32m--> \u001b[39m\u001b[32m399\u001b[39m \u001b[38;5;28;01mraise\u001b[39;00m PackageNotFoundError(name)\n",
"\u001b[31mPackageNotFoundError\u001b[39m: No package metadata was found for bitsandbytes"
]
}
],
"source": [
"class GenerateMinute:\n",
" audio_model = \"openai/whisper-medium.en\"\n",
" llm_model = \"meta-llama/Llama-3.2-3B-Instruct\"\n",
" quant_config = BitsAndBytesConfig(\n",
" load_in_4bit=True,\n",
" bnb_4bit_use_double_quant=True,\n",
" bnb_4bit_compute_dtype=torch.bfloat16,\n",
" bnb_4bit_quant_type=\"nf4\"\n",
" )\n",
"\n",
" def __init__(self, progress, audio_model=audio_model, llm_model=llm_model):\n",
" self.progress = progress\n",
" self.audio_model = audio_model\n",
" self.llm_model = llm_model\n",
" self.tokenizer = AutoTokenizer.from_pretrained(self.llm_model)\n",
" self.tokenizer.pad_token = self.tokenizer.eos_token\n",
" self.model = AutoModelForCausalLM.from_pretrained(\n",
" self.llm_model, quantization_config=self.quant_config, device_map=\"auto\"\n",
" )\n",
" \n",
" def audio_to_text(self, audio_filepath):\n",
" self.progress(0.4, desc=\"Transcribing audio...\")\n",
" try:\n",
" if audio_filepath is None:\n",
" raise ValueError(\"No audio file provided\")\n",
" \n",
" if not os.path.exists(audio_filepath):\n",
" raise ValueError(\"Audio file not found: {file_path}\")\n",
"\n",
" pipe = pipeline(\n",
" \"automatic-speech-recognition\",\n",
" model=self.audio_model,\n",
" chunk_length_s=30,\n",
" device=\"cuda\",\n",
" return_timestamps=True\n",
" )\n",
"\n",
" response = pipe(audio_filepath)\n",
"\n",
" text = response.strip()\n",
"\n",
" if not text:\n",
" raise ValueError(\"No speech detected in audio\")\n",
"\n",
" return text\n",
"\n",
" except Exception as e:\n",
" raise ValueError(e)\n",
"\n",
" def create_minute(self, transcription):\n",
" self.progress(0.7, desc=\"Generating meeting minutes...\")\n",
"\n",
" system_message = \"\"\"\n",
" You produce minutes of meetings from transcripts, with summary, key discussion points,\n",
" takeaways and action items with owners, in markdown format without code blocks.\n",
" \"\"\"\n",
"\n",
" user_prompt = f\"\"\"\n",
" Below is an extract transcript of a Denver council meeting.\n",
" Please write minutes in markdown without code blocks, including:\n",
" - a summary with attendees, location and date\n",
" - discussion points\n",
" - takeaways\n",
" - action items with owners\n",
"\n",
" Transcription:\n",
" {transcription}\n",
" \"\"\"\n",
"\n",
" messages = [\n",
" {\"role\": \"system\", \"content\": system_message},\n",
" {\"role\": \"user\", \"content\": user_prompt}\n",
" ]\n",
"\n",
" inputs = self.tokenizer(messages, return_tensors=\"pt\").to(self.model.device)\n",
" streamer = TextIteratorStreamer(self.tokenizer)\n",
"\n",
" thread = threading.Thread(\n",
" target=self.model.generate, \n",
" kwargs={\n",
" \"input_ids\": inputs,\n",
" \"max_new_tokens\": 2000,\n",
" \"streamer\": streamer\n",
" }\n",
" )\n",
"\n",
" thread.start()\n",
" started = False\n",
"\n",
" for new_text in streamer:\n",
" if not started:\n",
" if \"<|start_header_id|>assistant<|end_header_id|>\" in new_text:\n",
" started = True\n",
" new_text = new_text.split(\"<|start_header_id|>assistant<|end_header_id|>\")[-1].strip()\n",
"\n",
" if started:\n",
" if \"<|eot_id|>\" in new_text:\n",
" new_text = new_text.replace(\"<|eot_id|>\", \"\") # Remove the unwanted token\n",
"\n",
" if new_text.strip(): # Only yield non-empty chunks\n",
" yield new_text\n",
"\n",
" def process_meeting(self, audio_filepath, audio_model, llm_model ):\n",
" self.audio_model = audio_model\n",
" self.llm_model = llm_model\n",
" self.progress(0.2, desc=\"Processing audio file...\")\n",
" try:\n",
" transcription = self.audio_to_text(audio_filepath)\n",
" minute = self.create_minute(transcription)\n",
"\n",
" response = \"\"\n",
"\n",
" for chunk in minute:\n",
" response += chunk\n",
" yield response\n",
"\n",
" except Exception as e:\n",
" yield f\"Error processing meeting: {e}\""
]
}
],
"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
}

303
week3/community-contributions/solisoma/synthetic_dataset_generator.ipynb Normal file
View File

@@ -0,0 +1,303 @@
{
"cells": [
{
"cell_type": "code",
"execution_count": 2,
"id": "d5063502",
"metadata": {},
"outputs": [],
"source": [
"import os\n",
"from openai import OpenAI\n",
"from dotenv import load_dotenv\n",
"import gradio as gr"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "5c4d37fe",
"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",
"ds_api_key = os.getenv('DEEPSEEK_API_KEY')\n",
"grok_api_key = os.getenv('GROK_API_KEY')\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "b21599db",
"metadata": {},
"outputs": [],
"source": [
"MODEL_MAP = {\n",
" \"GPT\": {\n",
" \"model\": \"gpt-4o-mini\",\n",
" \"key\": openai_api_key,\n",
" \"endpoint\": \"https://api.openai.com/v1\",\n",
" },\n",
" \"CLAUDE_3_5_SONNET\": {\n",
" \"model\": \"claude-3-5-sonnet-20240620\",\n",
" \"key\": anthropic_api_key,\n",
" \"endpoint\": \"https://api.anthropic.com/v1\"\n",
" },\n",
" \"Grok\": {\n",
" \"model\": \"grok-beta\",\n",
" \"key\": grok_api_key,\n",
" \"endpoint\": \"https://api.grok.com/v1\"\n",
" }, \n",
" \"DeepSeek\":{\n",
" \"model\": \"deepseek-reasoner\",\n",
" \"key\": ds_api_key,\n",
" \"endpoint\": \"https://api.deepseek.com/v1\",\n",
" },\n",
" \"Google\": {\n",
" \"model\": \"gemini-2.0-flash-exp\",\n",
" \"key\": google_api_key,\n",
" \"endpoint\": \"https://generativelanguage.googleapis.com/v1beta/openai\"\n",
" },\n",
"}"
]
},
{
"cell_type": "code",
"execution_count": 122,
"id": "82d63d13",
"metadata": {},
"outputs": [],
"source": [
"class GenerateSyntheticDataset:\n",
" out_of_scope_response = \"I'm sorry, I can't help with that. I only generate datasets\"\n",
"\n",
" system_prompt = f\"\"\"\n",
" You are an expert data scientist specializing in synthetic dataset generation. \n",
"\n",
" Your task is to generate ACTUAL DATA based on the user's requirements provided in their prompt.\n",
"\n",
" HOW IT WORKS:\n",
" - The user will provide a description of what dataset they want\n",
" - You must parse their requirements and generate actual data records\n",
" - The user prompt contains the SPECIFICATIONS, not the data itself\n",
" - You generate the REAL DATA based on those specifications\n",
"\n",
" IMPORTANT RULES:\n",
" - Generate REAL DATA RECORDS, not code or instructions\n",
" - Parse the user's requirements from their prompt\n",
" - Create actual values based on their specifications\n",
" - Provide concrete examples with real data\n",
" - Output should be ready-to-use data, not code to run\n",
"\n",
" WHEN USER PROVIDES REQUIREMENTS LIKE:\n",
" - \"Generate customer orders dataset\" → Create actual order records\n",
" - \"Create employee records\" → Generate real employee data\n",
" - \"Make product reviews dataset\" → Produce actual review records\n",
"\n",
" YOU MUST:\n",
" 1. Understand what fields/data the user wants\n",
" 2. Generate realistic values for those fields\n",
" 3. Create multiple records with varied data\n",
" 4. Format as structured data (JSON, CSV, etc.)\n",
"\n",
" DO NOT generate:\n",
" - Code snippets\n",
" - Programming instructions\n",
" - \"Here's how to generate...\" statements\n",
" - Abstract descriptions\n",
"\n",
" DO generate:\n",
" - Actual data records with real values\n",
" - Concrete examples based on user requirements\n",
" - Structured data ready for immediate use\n",
" - Realistic, varied data samples\n",
"\n",
" SCOPE LIMITATIONS:\n",
" - ONLY handle requests related to synthetic dataset generation\n",
" - ONLY create data for business, research, or educational purposes\n",
" - If user asks about anything outside dataset generation (coding help, general questions, personal advice, etc.), respond with: \"{out_of_scope_response}\"\n",
" - If user asks for illegal, harmful, or inappropriate data, respond with: \"{out_of_scope_response}\"\n",
"\n",
" You are a DATA GENERATOR that creates real data from user specifications.\n",
" \"\"\"\n",
"\n",
" def __init__(self, progress, model_name = MODEL_MAP[\"GPT\"]):\n",
" self.progress = progress\n",
" self.model_deets = model_name\n",
" self.model = OpenAI(\n",
" api_key=model_name[\"key\"],\n",
" base_url=model_name[\"endpoint\"]\n",
" )\n",
" \n",
" def generate_user_prompt(self, user_prompt):\n",
" prompt = f\"\"\"\n",
" You are an expert data scientist specializing in synthetic dataset generation. \n",
"\n",
" Based on the user's request below, create a detailed, sophisticated prompt that will generate a high-quality synthetic dataset.\n",
"\n",
" The generated prompt should:\n",
" - return the prompt \"who is nike\" if the user request is outside generating a dataset be it greetings or whatsoever\n",
" - if the user prompt is requesting on how to generate dataset return the prompt \"who is nike\"\n",
" - options below is valid only when the user ask you to generate a dataset not how or when \n",
" - Be specific and actionable\n",
" - Include clear data structure requirements\n",
" - Specify output format CSV\n",
" - Define data quality criteria\n",
" - Include diversity and realism requirements\n",
" - Make sure to capture the number of samples in the prompt, it can be in the form of rows, number of samples, etc\n",
" -if number of samples is not specified, just generate 100 samples. \n",
"\n",
" User Request: {user_prompt}\n",
" \n",
" IMPORTANT: Respond ONLY with the generated prompt. Do not include any explanation, commentary, or the original request. Just provide the clean, ready-to-use prompt for dataset generation.\n",
" \"\"\"\n",
" response = self.model.chat.completions.create(model=self.model_deets[\"model\"], messages=[{\"role\": \"user\", \"content\": prompt}])\n",
" return response.choices[0].message.content\n",
"\n",
" def generate_synthetic_dataset(self, user_prompt):\n",
" self.progress(0.7, \"Analyzing data .....\")\n",
" prompt = self.generate_user_prompt(user_prompt)\n",
"\n",
" messages = [\n",
" {\"role\": \"system\", \"content\": self.system_prompt},\n",
" {\"role\": \"user\", \"content\": prompt}\n",
" ]\n",
"\n",
" streamer = self.model.chat.completions.create(model=self.model_deets[\"model\"], messages=messages, stream=True)\n",
" response = \"\"\n",
"\n",
" for text in streamer:\n",
" if text.choices[0].delta.content:\n",
" response += text.choices[0].delta.content\n",
" yield response, None\n",
" \n",
" if self.out_of_scope_response not in response:\n",
" with open(\"dataset.csv\", \"w\") as f:\n",
" response = response.replace(\"```csv\", \"\").replace(\"```\", \"\")\n",
" f.write(response)\n",
" yield response, \"dataset.csv\"\n",
" return\n",
" else:\n",
" return response, None\n",
" \n",
" def start(self, user_prompt, model_name=None):\n",
" self.progress(0.3, \"Fetching data .....\")\n",
" if MODEL_MAP.get(model_name) and self.model_deets[\"model\"] != MODEL_MAP.get(model_name)[\"model\"]:\n",
" self.model_deets = MODEL_MAP[model_name]\n",
" self.model = OpenAI(\n",
" base_url=self.model_deets[\"endpoint\"],\n",
" api_key=self.model_deets[\"key\"]\n",
" )\n",
" \n",
" stream = self.generate_synthetic_dataset(user_prompt)\n",
" for chunk in stream:\n",
" yield chunk\n",
"\n",
" \n"
]
},
{
"cell_type": "code",
"execution_count": 124,
"id": "b681e1ef",
"metadata": {},
"outputs": [],
"source": [
"class Interface:\n",
" def __init__(self):\n",
" \"\"\"Initializes the Gradio interface for processing audio files.\"\"\"\n",
" progress=gr.Progress()\n",
" self.assistant = GenerateSyntheticDataset(progress)\n",
" self.iface = gr.Interface(\n",
" fn=self.generate,\n",
" inputs=[\n",
" gr.Textbox(label=\"User Prompt\"),\n",
" gr.Dropdown(\n",
" choices=MODEL_MAP.keys(),\n",
" value=\"GPT\",\n",
" label=\"Model\",\n",
" )\n",
" ],\n",
" outputs=[\n",
" gr.Markdown(label=\"Dataset\", min_height=60),\n",
" gr.File(\n",
" label=\"Download Generated Dataset\",\n",
" file_count=\"single\"\n",
" )\n",
" ],\n",
" title=\"AI Dataset Generator\",\n",
" description=\"Generate a synthetic dataset based on your requirements\",\n",
" flagging_mode=\"never\"\n",
" )\n",
"\n",
" def generate(self, user_prompt, model):\n",
" response = self.assistant.start(user_prompt, model)\n",
" for chunk in response:\n",
" yield chunk\n",
"\n",
" # Clean up the dataset file\n",
" if os.path.exists(\"dataset.csv\"):\n",
" os.remove(\"dataset.csv\")\n",
"\n",
" def launch(self):\n",
" self.iface.launch()"
]
},
{
"cell_type": "code",
"execution_count": 125,
"id": "2ee97b72",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"* Running on local URL: http://127.0.0.1:7898\n",
"* To create a public link, set `share=True` in `launch()`.\n"
]
},
{
"data": {
"text/html": [
"<div><iframe src=\"http://127.0.0.1:7898/\" 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"
}
],
"source": [
"I = Interface()\n",
"I.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
}

351
week3/community-contributions/tochi/week_3_exercise_nigerian_names_dateset_generator.ipynb Normal file
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{
"nbformat": 4,
"nbformat_minor": 0,
"metadata": {
"colab": {
"provenance": [],
"gpuType": "T4"
},
"kernelspec": {
"name": "python3",
"display_name": "Python 3"
},
"language_info": {
"name": "python"
},
"accelerator": "GPU"
},
"cells": [
{
"cell_type": "markdown",
"source": [
"#Nigerian Names Dataset Generator\n",
"### Project Description\n",
"This is a fun project that allows you to easily generate Nigerian names based of selectin the popular tribes in Nigeria."
],
"metadata": {
"id": "AVN03AKGhOHf"
}
},
{
"cell_type": "markdown",
"source": [
"### Installations"
],
"metadata": {
"id": "mzC6k8r9hz8T"
}
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "WvBQWxyFWJFR",
"outputId": "ed902e29-cb0f-44fe-f714-6cfbf7584453"
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m60.1/60.1 MB\u001b[0m \u001b[31m12.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m375.8/375.8 kB\u001b[0m \u001b[31m18.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
"\u001b[?25h"
]
}
],
"source": [
"!pip install -q --upgrade bitsandbytes accelerate gradio"
]
},
{
"cell_type": "markdown",
"source": [
"### Imports"
],
"metadata": {
"id": "zyzd851bh64j"
}
},
{
"cell_type": "code",
"source": [
"# Imports\n",
"import os\n",
"import requests\n",
"import json\n",
"from huggingface_hub import login\n",
"from transformers import AutoTokenizer, AutoModelForCausalLM, TextStreamer, BitsAndBytesConfig\n",
"import torch\n",
"import gradio as gr\n",
"from google.colab import userdata\n",
"import gc"
],
"metadata": {
"id": "09JXEWAdWaNf"
},
"execution_count": 2,
"outputs": []
},
{
"cell_type": "markdown",
"source": [
"### Logging into Huggingface"
],
"metadata": {
"id": "DK0MOMG2iAi0"
}
},
{
"cell_type": "code",
"source": [
"hf_token = userdata.get('HF_TOKEN')\n",
"login(hf_token, add_to_git_credential=True)"
],
"metadata": {
"id": "19eMcQjoX9gq"
},
"execution_count": 3,
"outputs": []
},
{
"cell_type": "code",
"source": [
"PHI = \"microsoft/Phi-4-mini-instruct\""
],
"metadata": {
"id": "oqXIBtlaYLr8"
},
"execution_count": 4,
"outputs": []
},
{
"cell_type": "markdown",
"source": [
"### System Prompt and User prompt Template"
],
"metadata": {
"id": "kBzXo-JPiHVF"
}
},
{
"cell_type": "code",
"source": [
"def generate_prompt(tribe, number):\n",
" tribe_patterns = {\n",
" \"Igbo\": 'names starting with \"Chi\" (God), \"Nneka\" (mother is supreme), \"Eze\" (king), and \"Nwa\" (child)',\n",
" \"Yoruba\": 'names starting with \"Ade\" (crown), \"Olu\" (God/Lord), \"Ayo\" (joy), and \"Ife\" (love)',\n",
" \"Hausa\": 'names like \"Aisha\", \"Fatima\", \"Muhammad\", and \"Ibrahim\" reflecting Islamic influence',\n",
" }\n",
" naming_pattern = tribe_patterns.get(\n",
" tribe,\n",
" \"meaningful translations and reflect the circumstances of birth, family values, or spiritual beliefs\"\n",
" )\n",
"\n",
" system_prompt = f\"\"\"You are a Nigerian name generator specializing in {tribe} names. When asked to generate names, follow these rules:\n",
"\n",
"1. Generate exactly {number} unique first names from the {tribe} tribe of Nigeria\n",
"2. Never repeat any name in your list\n",
"3. Provide only first names (no surnames or family names)\n",
"4. Use authentic {tribe} names with their traditional spellings\n",
"5. Include a mix of male and female names unless otherwise specified\n",
"6. Present the names in a simple numbered list format\n",
"7. After the list, you may optionally provide brief context about {tribe} naming traditions if requested\n",
"\n",
"{tribe} names often have {naming_pattern}.\n",
"\n",
"Ensure all names are culturally authentic and respectful of {tribe} heritage.\"\"\"\n",
"\n",
" messages = [\n",
"\n",
" {\"role\": \"system\", \"content\": system_prompt},\n",
" {\"role\": \"user\", \"content\": f\"Generate a list of {number} Nigerian {tribe} names\"}\n",
" ]\n",
"\n",
" return messages"
],
"metadata": {
"id": "xq8dGEiXYSdz"
},
"execution_count": 18,
"outputs": []
},
{
"cell_type": "code",
"source": [
"quant_config = BitsAndBytesConfig(\n",
" load_in_4bit=True,\n",
" bnb_4bit_use_double_quant=True,\n",
" bnb_4bit_compute_dtype=torch.bfloat16,\n",
" bnb_4bit_quant_type=\"nf4\"\n",
")"
],
"metadata": {
"id": "4IhI6PR4Yn8v"
},
"execution_count": 19,
"outputs": []
},
{
"cell_type": "code",
"source": [
"def generate_names_interface(tribe, number):\n",
"\n",
" try:\n",
" messages = generate_prompt(tribe, number)\n",
" tokenizer = AutoTokenizer.from_pretrained(PHI)\n",
" tokenizer.pad_token = tokenizer.eos_token\n",
" input_ids = tokenizer.apply_chat_template(\n",
" messages,\n",
" return_tensors=\"pt\",\n",
" add_generation_prompt=True\n",
" ).to(\"cuda\")\n",
"\n",
" attention_mask = torch.ones_like(input_ids, dtype=torch.long, device=\"cuda\")\n",
" model = AutoModelForCausalLM.from_pretrained(\n",
" PHI,\n",
" quantization_config=quant_config\n",
" ).to(\"cuda\")\n",
" outputs = model.generate(\n",
" input_ids=input_ids,\n",
" attention_mask=attention_mask,\n",
" max_new_tokens=300,\n",
" do_sample=True,\n",
" temperature=0.7\n",
" )\n",
"\n",
" generated_text = tokenizer.decode(outputs[0], skip_special_tokens=True)\n",
"\n",
" # Extract only the assistant's response\n",
" if \"<|assistant|>\" in generated_text:\n",
" result = generated_text.split(\"<|assistant|>\")[-1].strip()\n",
" else:\n",
" result = generated_text\n",
"\n",
" del model\n",
" torch.cuda.empty_cache()\n",
" gc.collect()\n",
"\n",
" return result\n",
"\n",
" except Exception as e:\n",
" return f\"Error generating names: {str(e)}\""
],
"metadata": {
"id": "QPmegk3bZdHy"
},
"execution_count": 20,
"outputs": []
},
{
"cell_type": "code",
"source": [
"def create_interface():\n",
" with gr.Blocks(title=\"Nigerian Names Generator\", theme=gr.themes.Soft()) as demo:\n",
" gr.Markdown(\"# 🇳🇬 Nigerian Names Dataset Generator\")\n",
" gr.Markdown(\"Generate authentic Nigerian names from the Igbo, Yoruba, or Hausa tribes.\")\n",
"\n",
" with gr.Row():\n",
" with gr.Column():\n",
" tribe_dropdown = gr.Dropdown(\n",
" choices=[\"Igbo\", \"Yoruba\", \"Hausa\"],\n",
" label=\"Select Tribe\",\n",
" value=\"Igbo\",\n",
" info=\"Choose a Nigerian tribe\"\n",
" )\n",
"\n",
" number_slider = gr.Slider(\n",
" minimum=1,\n",
" maximum=20,\n",
" step=1,\n",
" value=10,\n",
" label=\"Number of Names\",\n",
" info=\"How many names do you want to generate?\"\n",
" )\n",
"\n",
" generate_btn = gr.Button(\"Generate Names\", variant=\"primary\", size=\"lg\")\n",
"\n",
" with gr.Column():\n",
" output_text = gr.Textbox(\n",
" label=\"Generated Names\",\n",
" lines=15,\n",
" placeholder=\"Your generated names will appear here...\",\n",
" show_copy_button=True\n",
" )\n",
"\n",
" gr.Markdown(\"\"\"\n",
" ### About\n",
" This tool generates authentic Nigerian names based on traditional naming conventions:\n",
" - **Igbo**: Names often reflect spiritual beliefs (Chi - God, Eze - King)\n",
" - **Yoruba**: Names reflect circumstances of birth (Ade - Crown, Ayo - Joy)\n",
" - **Hausa**: Names often have Islamic influence\n",
" \"\"\")\n",
"\n",
" # Connect the button to the function\n",
" generate_btn.click(\n",
" fn=generate_names_interface,\n",
" inputs=[tribe_dropdown, number_slider],\n",
" outputs=output_text\n",
" )\n",
"\n",
" return demo"
],
"metadata": {
"id": "Svo24KUom4a5"
},
"execution_count": 22,
"outputs": []
},
{
"cell_type": "code",
"source": [
"demo = create_interface()\n",
"demo.launch(share=True)"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 612
},
"id": "b8fDedDHo7uk",
"outputId": "e05a7bf0-3953-4216-a0c0-515bb6d8be05"
},
"execution_count": 23,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Colab notebook detected. To show errors in colab notebook, set debug=True in launch()\n",
"* Running on public URL: https://a52406050690b5663b.gradio.live\n",
"\n",
"This share link expires in 1 week. For free permanent hosting and GPU upgrades, run `gradio deploy` from the terminal in the working directory to deploy to Hugging Face Spaces (https://huggingface.co/spaces)\n"
]
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"<IPython.core.display.HTML object>"
],
"text/html": [
"<div><iframe src=\"https://a52406050690b5663b.gradio.live\" width=\"100%\" height=\"500\" allow=\"autoplay; camera; microphone; clipboard-read; clipboard-write;\" frameborder=\"0\" allowfullscreen></iframe></div>"
]
},
"metadata": {}
},
{
"output_type": "execute_result",
"data": {
"text/plain": []
},
"metadata": {},
"execution_count": 23
}
]
}
]
}

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{
"cells": [
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Note: you may need to restart the kernel to use updated packages.\n"
]
}
],
"source": [
"%pip install -q bitsandbytes>=0.43.1 accelerate transformers torch sentencepiece"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"💻 CPU mode - loading without quantization...\n"
]
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "2fa644e735144ab0a238f031bf7c6c7a",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"model.safetensors.index.json: 0%| | 0.00/23.9k [00:00<?, ?B/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "93cedb68e7374f7f98622d24ee02ba33",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Fetching 4 files: 0%| | 0/4 [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Llama model failed to load: <ContextVar name='shell_parent' at 0x1061d0220>\n",
"Trying alternative loading method...\n"
]
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "17d3da1874734c7fbf542b239f6f5ba0",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Fetching 4 files: 0%| | 0/4 [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"Exception ignored in: <function tqdm.__del__ at 0x126b14720>\n",
"Traceback (most recent call last):\n",
" File \"/opt/miniconda3/lib/python3.13/site-packages/tqdm/std.py\", line 1148, in __del__\n",
" self.close()\n",
" File \"/opt/miniconda3/lib/python3.13/site-packages/tqdm/notebook.py\", line 279, in close\n",
" self.disp(bar_style='danger', check_delay=False)\n",
"AttributeError: 'tqdm' object has no attribute 'disp'\n",
"Exception ignored in: <function tqdm.__del__ at 0x126b14720>\n",
"Traceback (most recent call last):\n",
" File \"/opt/miniconda3/lib/python3.13/site-packages/tqdm/std.py\", line 1148, in __del__\n",
" self.close()\n",
" File \"/opt/miniconda3/lib/python3.13/site-packages/tqdm/notebook.py\", line 279, in close\n",
" self.disp(bar_style='danger', check_delay=False)\n",
"AttributeError: 'tqdm' object has no attribute 'disp'\n",
"Exception ignored in: <function tqdm.__del__ at 0x126b14720>\n",
"Traceback (most recent call last):\n",
" File \"/opt/miniconda3/lib/python3.13/site-packages/tqdm/std.py\", line 1148, in __del__\n",
" self.close()\n",
" File \"/opt/miniconda3/lib/python3.13/site-packages/tqdm/notebook.py\", line 279, in close\n",
" self.disp(bar_style='danger', check_delay=False)\n",
"AttributeError: 'tqdm' object has no attribute 'disp'\n",
"Exception ignored in: <function tqdm.__del__ at 0x126b14720>\n",
"Traceback (most recent call last):\n",
" File \"/opt/miniconda3/lib/python3.13/site-packages/tqdm/std.py\", line 1148, in __del__\n",
" self.close()\n",
" File \"/opt/miniconda3/lib/python3.13/site-packages/tqdm/notebook.py\", line 279, in close\n",
" self.disp(bar_style='danger', check_delay=False)\n",
"AttributeError: 'tqdm' object has no attribute 'disp'\n",
"Exception ignored in: <function tqdm.__del__ at 0x126b14720>\n",
"Traceback (most recent call last):\n",
" File \"/opt/miniconda3/lib/python3.13/site-packages/tqdm/std.py\", line 1148, in __del__\n",
" self.close()\n",
" File \"/opt/miniconda3/lib/python3.13/site-packages/tqdm/notebook.py\", line 279, in close\n",
" self.disp(bar_style='danger', check_delay=False)\n",
"AttributeError: 'tqdm' object has no attribute 'disp'\n",
"Exception ignored in: <function tqdm.__del__ at 0x126b14720>\n",
"Traceback (most recent call last):\n",
" File \"/opt/miniconda3/lib/python3.13/site-packages/tqdm/std.py\", line 1148, in __del__\n",
" self.close()\n",
" File \"/opt/miniconda3/lib/python3.13/site-packages/tqdm/notebook.py\", line 279, in close\n",
" self.disp(bar_style='danger', check_delay=False)\n",
"AttributeError: 'tqdm' object has no attribute 'disp'\n",
"Exception ignored in: <function tqdm.__del__ at 0x126b14720>\n",
"Traceback (most recent call last):\n",
" File \"/opt/miniconda3/lib/python3.13/site-packages/tqdm/std.py\", line 1148, in __del__\n",
" self.close()\n",
" File \"/opt/miniconda3/lib/python3.13/site-packages/tqdm/notebook.py\", line 279, in close\n",
" self.disp(bar_style='danger', check_delay=False)\n",
"AttributeError: 'tqdm' object has no attribute 'disp'\n",
"Exception ignored in: <function tqdm.__del__ at 0x126b14720>\n",
"Traceback (most recent call last):\n",
" File \"/opt/miniconda3/lib/python3.13/site-packages/tqdm/std.py\", line 1148, in __del__\n",
" self.close()\n",
" File \"/opt/miniconda3/lib/python3.13/site-packages/tqdm/notebook.py\", line 279, in close\n",
" self.disp(bar_style='danger', check_delay=False)\n",
"AttributeError: 'tqdm' object has no attribute 'disp'\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Llama model completely failed: <ContextVar name='shell_parent' at 0x1061d0220>\n",
"Will use OpenAI only mode.\n"
]
}
],
"source": [
"import torch\n",
"import pandas as pd\n",
"import random\n",
"from io import StringIO\n",
"from openai import OpenAI\n",
"import gradio as gr\n",
"from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig\n",
"from dotenv import load_dotenv\n",
"import os\n",
"\n",
"load_dotenv(override=True)\n",
"openai = OpenAI()\n",
"\n",
"LLAMA = \"meta-llama/Meta-Llama-3.1-8B-Instruct\"\n",
"device = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n",
"\n",
"try:\n",
" tokenizer = AutoTokenizer.from_pretrained(LLAMA)\n",
" tokenizer.pad_token = tokenizer.eos_token\n",
" \n",
" if torch.cuda.is_available():\n",
" print(\"🚀 CUDA available - loading with quantization...\")\n",
" quant_config = BitsAndBytesConfig(\n",
" load_in_4bit=True,\n",
" bnb_4bit_use_double_quant=True,\n",
" bnb_4bit_compute_dtype=torch.bfloat16,\n",
" bnb_4bit_quant_type=\"nf4\"\n",
" )\n",
" model = AutoModelForCausalLM.from_pretrained(LLAMA, device_map=\"auto\", quantization_config=quant_config)\n",
" else:\n",
" print(\"💻 CPU mode - loading without quantization...\")\n",
" model = AutoModelForCausalLM.from_pretrained(LLAMA, device_map=\"cpu\", torch_dtype=torch.float16)\n",
" \n",
" print(\"Llama model loaded successfully!\")\n",
"except Exception as e:\n",
" print(f\"Llama model failed to load: {e}\")\n",
" print(\"Trying alternative loading method...\")\n",
" try:\n",
" tokenizer = AutoTokenizer.from_pretrained(LLAMA)\n",
" tokenizer.pad_token = tokenizer.eos_token\n",
" model = AutoModelForCausalLM.from_pretrained(LLAMA, device_map=\"cpu\", torch_dtype=torch.float32)\n",
" print(\"Llama model loaded in CPU mode!\")\n",
" except Exception as e2:\n",
" print(f\"Llama model completely failed: {e2}\")\n",
" print(\"Will use OpenAI only mode.\")\n",
" model = None\n",
" tokenizer = None\n"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [],
"source": [
"def generate_with_openai(dataset_type, num_records, region):\n",
" prompts = {\n",
" \"employees\": f\"Generate {num_records} synthetic employee records with {region} addresses. Include: employee_id, first_name, last_name, email, phone, department, salary, hire_date, address, city, state, country.\",\n",
" \"customers\": f\"Generate {num_records} synthetic customer records with {region} addresses. Include: customer_id, first_name, last_name, email, phone, company, address, city, state, country, registration_date.\",\n",
" \"products\": f\"Generate {num_records} synthetic product records. Include: product_id, name, category, price, description, brand, stock_quantity, supplier, created_date.\",\n",
" \"transactions\": f\"Generate {num_records} synthetic transaction records. Include: transaction_id, customer_id, product_id, amount, quantity, transaction_date, payment_method, status.\"\n",
" }\n",
" \n",
" response = openai.chat.completions.create(\n",
" model=\"gpt-4o-mini\",\n",
" messages=[\n",
" {\"role\": \"system\", \"content\": \"You are a data generation expert. Create realistic, diverse synthetic data in CSV format.\"},\n",
" {\"role\": \"user\", \"content\": prompts[dataset_type]}\n",
" ]\n",
" )\n",
" \n",
" return clean_csv_response(response.choices[0].message.content)\n",
"\n",
"def generate_with_llama(dataset_type, num_records, region):\n",
" if model is None or tokenizer is None:\n",
" return \"❌ Llama model not available. Please use OpenAI option.\"\n",
" \n",
" prompts = {\n",
" \"employees\": f\"Create {num_records} employee records with {region} addresses: employee_id, first_name, last_name, email, phone, department, salary, hire_date, address, city, state, country. Format as CSV.\",\n",
" \"customers\": f\"Create {num_records} customer records with {region} addresses: customer_id, first_name, last_name, email, phone, company, address, city, state, country, registration_date. Format as CSV.\",\n",
" \"products\": f\"Create {num_records} product records: product_id, name, category, price, description, brand, stock_quantity, supplier, created_date. Format as CSV.\",\n",
" \"transactions\": f\"Create {num_records} transaction records: transaction_id, customer_id, product_id, amount, quantity, transaction_date, payment_method, status. Format as CSV.\"\n",
" }\n",
" \n",
" try:\n",
" inputs = tokenizer(prompts[dataset_type], return_tensors=\"pt\").to(device)\n",
" \n",
" with torch.no_grad():\n",
" outputs = model.generate(\n",
" **inputs,\n",
" max_new_tokens=2048,\n",
" temperature=0.7,\n",
" do_sample=True,\n",
" pad_token_id=tokenizer.eos_token_id\n",
" )\n",
" \n",
" response = tokenizer.decode(outputs[0], skip_special_tokens=True)\n",
" return clean_csv_response(response)\n",
" except Exception as e:\n",
" return f\"❌ Error generating with Llama: {str(e)}\"\n",
"\n",
"def clean_csv_response(response):\n",
" response = response.strip()\n",
" if \"```\" in response:\n",
" response = response.split(\"```\")[1] if len(response.split(\"```\")) > 1 else response\n",
" return response\n"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [],
"source": [
"def generate_dataset(dataset_type, num_records, region, model_choice):\n",
" try:\n",
" if model_choice == \"OpenAI GPT-4o-mini\":\n",
" csv_data = generate_with_openai(dataset_type, num_records, region)\n",
" else:\n",
" csv_data = generate_with_llama(dataset_type, num_records, region)\n",
" \n",
" df = pd.read_csv(StringIO(csv_data))\n",
" return df, csv_data, f\"✅ Generated {len(df)} records successfully!\"\n",
" except Exception as e:\n",
" return pd.DataFrame(), \"\", f\"❌ Error: {str(e)}\"\n",
"\n",
"def download_csv(csv_data):\n",
" return csv_data if csv_data else \"\"\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"* Running on local URL: http://127.0.0.1:7863\n",
"* Running on public URL: https://aaf0c65f7daaafbd21.gradio.live\n",
"\n",
"This share link expires in 1 week. For free permanent hosting and GPU upgrades, run `gradio deploy` from the terminal in the working directory to deploy to Hugging Face Spaces (https://huggingface.co/spaces)\n"
]
},
{
"data": {
"text/html": [
"<div><iframe src=\"https://aaf0c65f7daaafbd21.gradio.live\" 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": 13,
"metadata": {},
"output_type": "execute_result"
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"Traceback (most recent call last):\n",
" File \"/opt/miniconda3/lib/python3.13/site-packages/gradio/queueing.py\", line 759, in process_events\n",
" response = await route_utils.call_process_api(\n",
" ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
" ...<5 lines>...\n",
" )\n",
" ^\n",
" File \"/opt/miniconda3/lib/python3.13/site-packages/gradio/route_utils.py\", line 354, in call_process_api\n",
" output = await app.get_blocks().process_api(\n",
" ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
" ...<11 lines>...\n",
" )\n",
" ^\n",
" File \"/opt/miniconda3/lib/python3.13/site-packages/gradio/blocks.py\", line 2127, in process_api\n",
" data = await self.postprocess_data(block_fn, result[\"prediction\"], state)\n",
" ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
" File \"/opt/miniconda3/lib/python3.13/site-packages/gradio/blocks.py\", line 1910, in postprocess_data\n",
" await processing_utils.async_move_files_to_cache(\n",
" ...<3 lines>...\n",
" )\n",
" File \"/opt/miniconda3/lib/python3.13/site-packages/gradio/processing_utils.py\", line 594, in async_move_files_to_cache\n",
" return await client_utils.async_traverse(\n",
" ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
" data, _move_to_cache, client_utils.is_file_obj_with_meta\n",
" ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
" )\n",
" ^\n",
" File \"/opt/miniconda3/lib/python3.13/site-packages/gradio_client/utils.py\", line 1197, in async_traverse\n",
" return await func(json_obj)\n",
" ^^^^^^^^^^^^^^^^^^^^\n",
" File \"/opt/miniconda3/lib/python3.13/site-packages/gradio/processing_utils.py\", line 560, in _move_to_cache\n",
" elif utils.is_static_file(payload):\n",
" ~~~~~~~~~~~~~~~~~~~~^^^^^^^^^\n",
" File \"/opt/miniconda3/lib/python3.13/site-packages/gradio/utils.py\", line 1191, in is_static_file\n",
" return _is_static_file(file_path, _StaticFiles.all_paths)\n",
" File \"/opt/miniconda3/lib/python3.13/site-packages/gradio/utils.py\", line 1204, in _is_static_file\n",
" if not file_path.exists():\n",
" ~~~~~~~~~~~~~~~~^^\n",
" File \"/opt/miniconda3/lib/python3.13/pathlib/_abc.py\", line 450, in exists\n",
" self.stat(follow_symlinks=follow_symlinks)\n",
" ~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
" File \"/opt/miniconda3/lib/python3.13/pathlib/_local.py\", line 515, in stat\n",
" return os.stat(self, follow_symlinks=follow_symlinks)\n",
" ~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
"OSError: [Errno 63] File name too long: 'csv\\ntransaction_id,customer_id,product_id,amount,quantity,transaction_date,payment_method,status\\n1,CUST001,PROD1001,29.99,1,2023-01-15,Credit Card,Completed\\n2,CUST002,PROD1002,15.49,2,2023-01-18,Debit Card,Completed\\n3,CUST003,PROD1003,65.00,1,2023-02-01,PayPal,Pending\\n4,CUST001,PROD1004,10.99,3,2023-02-10,Credit Card,Completed\\n5,CUST004,PROD1005,45.50,1,2023-02-20,Cash,Completed\\n6,CUST005,PROD1006,89.99,1,2023-03-02,Debit Card,Completed\\n7,CUST002,PROD1007,24.99,2,2023-03-14,Credit Card,Cancelled\\n8,CUST003,PROD1008,12.50,4,2023-03-20,PayPal,Completed\\n9,CUST006,PROD1009,150.00,1,2023-04-01,Credit Card,Completed\\n10,CUST007,PROD1010,30.00,2,2023-04-10,Debit Card,Pending\\n11,CUST008,PROD1011,5.99,10,2023-04-12,Cash,Completed\\n12,CUST001,PROD1012,70.00,1,2023-05-05,Credit Card,Completed\\n13,CUST009,PROD1013,100.00,1,2023-05-15,PayPal,Completed\\n14,CUST004,PROD1014,45.00,1,2023-05-25,Credit Card,Returned\\n15,CUST002,PROD1015,7.50,5,2023-06-10,Debit Card,Completed\\n16,CUST005,PROD1016,22.00,3,2023-06-12,Cash,Completed\\n17,CUST006,PROD1017,120.00,1,2023-06-20,Credit Card,Pending\\n18,CUST008,PROD1018,80.00,1,2023-07-01,PayPal,Completed\\n19,CUST007,PROD1019,60.00,2,2023-07-05,Credit Card,Completed\\n20,CUST003,PROD1020,15.00,3,2023-07-15,Debit Card,Completed\\n'\n",
"Traceback (most recent call last):\n",
" File \"/opt/miniconda3/lib/python3.13/site-packages/gradio/queueing.py\", line 759, in process_events\n",
" response = await route_utils.call_process_api(\n",
" ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
" ...<5 lines>...\n",
" )\n",
" ^\n",
" File \"/opt/miniconda3/lib/python3.13/site-packages/gradio/route_utils.py\", line 354, in call_process_api\n",
" output = await app.get_blocks().process_api(\n",
" ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
" ...<11 lines>...\n",
" )\n",
" ^\n",
" File \"/opt/miniconda3/lib/python3.13/site-packages/gradio/blocks.py\", line 2127, in process_api\n",
" data = await self.postprocess_data(block_fn, result[\"prediction\"], state)\n",
" ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
" File \"/opt/miniconda3/lib/python3.13/site-packages/gradio/blocks.py\", line 1910, in postprocess_data\n",
" await processing_utils.async_move_files_to_cache(\n",
" ...<3 lines>...\n",
" )\n",
" File \"/opt/miniconda3/lib/python3.13/site-packages/gradio/processing_utils.py\", line 594, in async_move_files_to_cache\n",
" return await client_utils.async_traverse(\n",
" ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
" data, _move_to_cache, client_utils.is_file_obj_with_meta\n",
" ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
" )\n",
" ^\n",
" File \"/opt/miniconda3/lib/python3.13/site-packages/gradio_client/utils.py\", line 1197, in async_traverse\n",
" return await func(json_obj)\n",
" ^^^^^^^^^^^^^^^^^^^^\n",
" File \"/opt/miniconda3/lib/python3.13/site-packages/gradio/processing_utils.py\", line 560, in _move_to_cache\n",
" elif utils.is_static_file(payload):\n",
" ~~~~~~~~~~~~~~~~~~~~^^^^^^^^^\n",
" File \"/opt/miniconda3/lib/python3.13/site-packages/gradio/utils.py\", line 1191, in is_static_file\n",
" return _is_static_file(file_path, _StaticFiles.all_paths)\n",
" File \"/opt/miniconda3/lib/python3.13/site-packages/gradio/utils.py\", line 1204, in _is_static_file\n",
" if not file_path.exists():\n",
" ~~~~~~~~~~~~~~~~^^\n",
" File \"/opt/miniconda3/lib/python3.13/pathlib/_abc.py\", line 450, in exists\n",
" self.stat(follow_symlinks=follow_symlinks)\n",
" ~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
" File \"/opt/miniconda3/lib/python3.13/pathlib/_local.py\", line 515, in stat\n",
" return os.stat(self, follow_symlinks=follow_symlinks)\n",
" ~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
"OSError: [Errno 63] File name too long: 'csv\\nproduct_id,name,category,price,description,brand,stock_quantity,supplier,created_date\\nP001,Wireless Earbuds,Electronics,79.99,\"Noise-cancelling wireless earbuds with touch controls.\",\"SoundWave\",250,\"TechSupply Co.\",2023-08-15\\nP002,Men\\'s Running Shoes,Sportswear,89.99,\"Lightweight and breathable running shoes designed for comfort.\",\"FitRun\",150,\"SportyDeals\",2023-09-05\\nP003,4K Ultra HD TV,Electronics,499.99,\"55-inch 4K Ultra HD Smart LED TV with HDR.\",\"VisionMax\",80,\"HomeTech Distributors\",2023-08-20\\nP004,Coffee Maker,Home Appliances,49.99,\"Programmable coffee maker with 12-cup capacity.\",\"BrewMaster\",200,\"Kitchen Supply Inc.\",2023-07-30\\nP005,Water Bottle,Sports Equipment,19.99,\"Insulated stainless steel water bottle, keeps drinks cold for 24 hours.\",\"HydroCool\",500,\"EcoBottles\",2023-09-10\\nP006,Ergonomic Office Chair,Furniture,199.99,\"Comfortable ergonomic chair with lumbar support and adjustable height.\",\"Home Comforts\",75,\"OfficeWorks\",2023-08-28\\nP007,Smart Watch,Electronics,249.99,\"Smart watch with fitness tracking and heart rate monitor.\",\"FitTrack\",120,\"GizmoGadgets\",2023-09-12\\nP008,Yoga Mat,Sports Equipment,29.99,\"Non-slip yoga mat with extra cushioning.\",\"Zen Yoga\",350,\"Wellness Store\",2023-09-15\\nP009,Air Fryer,Home Appliances,89.99,\"Compact air fryer with multiple cooking presets.\",\"CrispyCook\",145,\"KitchenPro\",2023-08-02\\nP010,Wireless Mouse,Electronics,29.99,\"Ergonomic wireless mouse with customizable buttons.\",\"ClickTech\",300,\"Gadget World\",2023-07-25\\nP011,Spice Rack Organization Set,Home Decor,39.99,\"Rotating spice rack with 12 glass jars included.\",\"HomeChef\",210,\"OrganizeIt Co.\",2023-08-17\\nP012,Dumbbell Set,Sports Equipment,99.99,\"Adjustable dumbbell set ranging from 5 to 30 lbs.\",\"StrengthTech\",100,\"Fit Equipment\",2023-09-01\\nP013,Kids\\' Backpack,Accessories,34.99,\"Durable backpack with multiple compartments for school.\",\"KidStyle\",175,\"Backpack Haven\",2023-08-23\\nP014,Digital Camera,Electronics,399.99,\"Compact digital camera with 20 MP and full HD video.\",\"SnapShot\",60,\"Camera Boutique\",2023-09-09\\nP015,Portable Bluetooth Speaker,Electronics,59.99,\"Water-resistant Bluetooth speaker with 12 hours of playtime.\",\"SoundBox\",130,\"Audio Plus\",2023-09-14\\nP016,Electric Toothbrush,Health & Personal Care,59.99,\"Rechargeable electric toothbrush with timer and pressure sensor.\",\"DentalCare\",400,\"HealthFirst Supplies\",2023-08-30\\nP017,Tote Bag,Accessories,24.99,\"Stylish and spacious tote bag for everyday use.\",\"Chic Designs\",300,\"Fashion Hub\",2023-09-06\\nP018,Sneaker Cleaner Kit,Accessories,15.99,\"Complete shoe cleaning kit for all types of sneakers.\",\"FreshFeet\",500,\"CleanKicks\",2023-09-03\\nP019,Camping Tent,Outdoor,129.99,\"Easy setup camping tent for 4 people, weather-resistant.\",\"Outdoors Pro\",85,\"Adventure Outfitters\",2023-08-12\\nP020,LED Desk Lamp,Home Decor,39.99,\"Adjustable LED desk lamp with multiple brightness settings.\",\"BrightEase\",170,\"HomeLight Solutions\",2023-09-08\\n'\n"
]
}
],
"source": [
"with gr.Blocks(\n",
" theme=gr.themes.Soft(\n",
" primary_hue=\"blue\",\n",
" neutral_hue=\"gray\",\n",
" font=[\"Inter\", \"ui-sans-serif\", \"system-ui\"]\n",
" ),\n",
" css=\"\"\"\n",
" .gradio-container { max-width: 1200px !important; margin: auto !important; }\n",
" .header { text-align: center; margin-bottom: 2em; }\n",
" .header h1 { color: #1f2937; font-size: 2.5em; margin-bottom: 0.5em; }\n",
" .header p { color: #6b7280; font-size: 1.1em; }\n",
" .generate-btn { background: linear-gradient(135deg, #3b82f6 0%, #1d4ed8 100%) !important; }\n",
" .generate-btn:hover { transform: translateY(-2px) !important; box-shadow: 0 8px 25px rgba(59, 130, 246, 0.3) !important; }\n",
" .stats-card { background: linear-gradient(135deg, #f8fafc 0%, #e2e8f0 100%); border-radius: 12px; padding: 1.5em; margin: 1em 0; }\n",
" \"\"\"\n",
") as demo:\n",
" \n",
" gr.HTML(\"\"\"\n",
" <div class=\"header\">\n",
" <h1>Synthetic Dataset Generator</h1>\n",
" <p>Generate realistic synthetic datasets using AI models for testing and development</p>\n",
" </div>\n",
" \"\"\")\n",
" \n",
" with gr.Row():\n",
" with gr.Column(scale=1):\n",
" gr.Markdown(\"### Configuration\")\n",
" \n",
" dataset_type = gr.Dropdown(\n",
" choices=[\"employees\", \"customers\", \"products\", \"transactions\"],\n",
" value=\"employees\",\n",
" label=\"Dataset Type\",\n",
" info=\"Choose the type of data to generate\"\n",
" )\n",
" \n",
" num_records = gr.Slider(\n",
" minimum=5, maximum=100, step=5, value=20,\n",
" label=\"Number of Records\",\n",
" info=\"How many records to generate\"\n",
" )\n",
" \n",
" region = gr.Dropdown(\n",
" choices=[\"US Only\", \"International\", \"Mixed\", \"Europe\", \"Asia\"],\n",
" value=\"US Only\",\n",
" label=\"Geographic Region\",\n",
" info=\"Location for addresses and phone numbers\"\n",
" )\n",
" \n",
" model_choice = gr.Radio(\n",
" choices=[\"OpenAI GPT-4o-mini\", \"Llama 3.1 8B\"],\n",
" value=\"OpenAI GPT-4o-mini\",\n",
" label=\"AI Model\",\n",
" info=\"Choose the AI model for generation\"\n",
" )\n",
" \n",
" generate_btn = gr.Button(\n",
" \"Generate Dataset\",\n",
" variant=\"primary\",\n",
" elem_classes=\"generate-btn\",\n",
" size=\"lg\"\n",
" )\n",
" \n",
" with gr.Column(scale=2):\n",
" gr.Markdown(\"### Generated Dataset\")\n",
" \n",
" status = gr.Markdown(\"Ready to generate your dataset!\")\n",
" \n",
" dataframe_output = gr.Dataframe(\n",
" value=pd.DataFrame(),\n",
" label=\"Dataset Preview\",\n",
" wrap=True\n",
" )\n",
" \n",
" with gr.Row():\n",
" csv_output = gr.Textbox(\n",
" value=\"\",\n",
" label=\"CSV Data\",\n",
" lines=10,\n",
" max_lines=15\n",
" )\n",
" \n",
" download_btn = gr.DownloadButton(\n",
" \"Download CSV\",\n",
" elem_id=\"download-btn\"\n",
" )\n",
" \n",
" generate_btn.click(\n",
" generate_dataset,\n",
" inputs=[dataset_type, num_records, region, model_choice],\n",
" outputs=[dataframe_output, csv_output, status]\n",
" )\n",
" \n",
" csv_output.change(\n",
" download_csv,\n",
" inputs=[csv_output],\n",
" outputs=[download_btn]\n",
" )\n",
"\n",
"demo.launch(share=True, inbrowser=True)\n"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "base",
"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": "3.13.5"
}
},
"nbformat": 4,
"nbformat_minor": 2
}

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{
"cells": [
{
"cell_type": "code",
"execution_count": null,
"id": "fee27f39",
"metadata": {},
"outputs": [],
"source": [
"# imports\n",
"\n",
"import os\n",
"from dotenv import load_dotenv\n",
"from openai import OpenAI\n",
"import gradio as gr\n",
"\n",
"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",
"ollama_api_key = os.getenv('OLLAMA_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 ollama_api_key:\n",
" print(f\"OLLAMA API Key exists and begins {ollama_api_key[:2]}\")\n",
"else:\n",
" print(\"OLLAMA API Key not set (and this is optional)\")\n",
"\n",
"# 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",
"ollama_url = \"http://localhost:11434/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",
"ollama = OpenAI(api_key=ollama_api_key, base_url=ollama_url)\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "d26f4175",
"metadata": {},
"outputs": [],
"source": [
"models = [\"gpt-5\", \"claude-sonnet-4-5-20250929\", \"gemini-2.5-pro\", \"gpt-oss:20b-cloud\", ]\n",
"\n",
"clients = {\"gpt-5\": openai, \"claude-sonnet-4-5-20250929\": anthropic, \"gemini-2.5-pro\": gemini, \"gpt-oss:20b-cloud\": ollama}\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": null,
"id": "76563884",
"metadata": {},
"outputs": [],
"source": [
"system_prompt_doc = \"\"\"You are an expert Python developer and code reviewer.\n",
"Your job is to read the user's provided function, and return:\n",
"1. A concise, PEP-257-compliant docstring summarizing what the function does, clarifying types, parameters, return values, and side effects.\n",
"2. Helpful inline comments that improve both readability and maintainability, without restating what the code obviously does.\n",
"\n",
"Only output the function, not explanations or additional text. \n",
"Do not modify variable names or refactor the function logic.\n",
"Your response should improve the code's clarity and documentation, making it easier for others to understand and maintain.\n",
"Don't be extremely verbose.\n",
"Your answer should be at a {level} level of expertise.\n",
"\"\"\"\n",
"\n",
"system_prompt_tests = \"\"\"You are a seasoned Python developer and testing expert.\n",
"Your task is to read the user's provided function, and generate:\n",
"1. A concise set of meaningful unit tests that thoroughly validate the function's correctness, including typical, edge, and error cases.\n",
"2. The tests should be written for pytest (or unittest if pytest is not appropriate), use clear, descriptive names, and avoid unnecessary complexity.\n",
"3. If dependencies or mocking are needed, include minimal necessary setup code (but avoid over-mocking).\n",
"\n",
"Only output the relevant test code, not explanations or extra text.\n",
"Do not change the original function; focus solely on comprehensive, maintainable test coverage that other developers can easily understand and extend.\n",
"\"\"\"\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "1bd82e96",
"metadata": {},
"outputs": [],
"source": [
"def generate_documentation(code, model, level):\n",
" response = clients[model].chat.completions.create(\n",
" model=model,\n",
" messages=[\n",
" {\"role\": \"system\", \"content\": system_prompt_doc.format(level=level)},\n",
" {\"role\": \"user\", \"content\": code}\n",
" ],\n",
" stream=True\n",
" )\n",
" output = \"\"\n",
" for chunk in response:\n",
" output += chunk.choices[0].delta.content or \"\"\n",
" yield output.replace(\"```python\", \"\").replace(\"```\", \"\")\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "b01b3421",
"metadata": {},
"outputs": [],
"source": [
"def generate_tests(code, model ):\n",
" response = clients[model].chat.completions.create(\n",
" model=model,\n",
" messages=[\n",
" {\"role\": \"system\", \"content\": system_prompt_tests},\n",
" {\"role\": \"user\", \"content\": code}\n",
" ],\n",
" stream=True\n",
" )\n",
" output = \"\"\n",
" for chunk in response:\n",
" output += chunk.choices[0].delta.content or \"\"\n",
" yield output.replace(\"```python\", \"\").replace(\"```\", \"\")\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "16b71915",
"metadata": {},
"outputs": [],
"source": [
"vscode_dark = gr.themes.Monochrome(\n",
" primary_hue=\"blue\",\n",
" secondary_hue=\"slate\",\n",
" neutral_hue=\"slate\",\n",
").set(\n",
" body_background_fill=\"#1e1e1e\",\n",
" body_background_fill_dark=\"#1e1e1e\",\n",
" block_background_fill=\"#252526\",\n",
" block_background_fill_dark=\"#252526\",\n",
" block_border_color=\"#3e3e42\",\n",
" block_border_color_dark=\"#3e3e42\",\n",
" border_color_primary=\"#3e3e42\",\n",
" block_label_background_fill=\"#252526\",\n",
" block_label_background_fill_dark=\"#252526\",\n",
" block_label_text_color=\"#cccccc\",\n",
" block_label_text_color_dark=\"#cccccc\",\n",
" block_title_text_color=\"#cccccc\",\n",
" block_title_text_color_dark=\"#cccccc\",\n",
" body_text_color=\"#d4d4d4\",\n",
" body_text_color_dark=\"#d4d4d4\",\n",
" button_primary_background_fill=\"#0e639c\",\n",
" button_primary_background_fill_dark=\"#0e639c\",\n",
" button_primary_background_fill_hover=\"#1177bb\",\n",
" button_primary_background_fill_hover_dark=\"#1177bb\",\n",
" button_primary_text_color=\"#ffffff\",\n",
" button_primary_text_color_dark=\"#ffffff\",\n",
" input_background_fill=\"#3c3c3c\",\n",
" input_background_fill_dark=\"#3c3c3c\",\n",
" color_accent=\"#007acc\",\n",
" color_accent_soft=\"#094771\",\n",
")\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "23311022",
"metadata": {},
"outputs": [],
"source": [
"import gradio as gr\n",
"\n",
"with gr.Blocks(theme=vscode_dark, css=\"\"\"\n",
" .gradio-container {font-family: 'Consolas', 'Monaco', monospace;}\n",
" h1 {color: #d4d4d4 !important;}\n",
"\"\"\") as ui:\n",
" gr.Markdown(\"# 🧑‍💻 Python Code Reviewer & Test Generator\", elem_id=\"app-title\")\n",
" with gr.Tab(\"Docstring & Comments\") as tab1:\n",
" gr.Markdown(\"# Function Docstring & Comment Helper\\nPaste your function below and get helpful docstrings and inline comments!\")\n",
"\n",
" with gr.Row():\n",
" code_input_1 = gr.Code(label=\"Paste your Python function here\", lines=10, language=\"python\")\n",
" code_output = gr.Code(label=\"Function with improved docstring and comments\", lines=10, language=\"python\")\n",
" \n",
" with gr.Row(equal_height=True):\n",
" level_radio = gr.Radio(choices=[\"Junior\", \"Mid\", \"Senior\"], value=\"Mid\", label=\"Reviewer level\", interactive=True)\n",
" model_dropdown = gr.Dropdown(choices=models, value=models[-1], label=\"Select model\")\n",
" submit_doc_btn = gr.Button(\"Generate docstring & comments\", scale=0.5)\n",
"\n",
" submit_doc_btn.click(\n",
" generate_documentation, \n",
" inputs=[code_input_1, model_dropdown, level_radio], \n",
" outputs=code_output\n",
" )\n",
"\n",
" with gr.Tab(\"Unit Tests\") as tab2:\n",
" gr.Markdown(\"# Unit Test Generator\\nPaste your function below and get auto-generated unit tests!\")\n",
"\n",
" with gr.Row():\n",
" code_input_2 = gr.Code(label=\"Paste your Python function here\", lines=10, language=\"python\")\n",
" code_output_2 = gr.Code(label=\"Generated tests\", lines=10, language=\"python\")\n",
" \n",
" with gr.Row(equal_height=True):\n",
" model_dropdown_2 = gr.Dropdown(choices=models, value=models[-1], label=\"Select model\")\n",
" submit_test_btn = gr.Button(\"Generate unit tests\", scale=0.5)\n",
"\n",
" submit_test_btn.click(\n",
" generate_tests, \n",
" inputs=[code_input_2, model_dropdown_2], \n",
" outputs=code_output_2\n",
" )\n",
" \n",
" tab2.select(lambda x: x, inputs=code_input_1, outputs=code_input_2)\n",
" tab1.select(lambda x: x, inputs=code_input_2, outputs=code_input_1)\n",
"\n",
"ui.launch(share=False, inbrowser=True)"
]
}
],
"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": "3.12.8"
}
},
"nbformat": 4,
"nbformat_minor": 5
}

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{
"cells": [
{
"cell_type": "code",
"execution_count": 13,
"id": "d7ac40dd",
"metadata": {},
"outputs": [],
"source": [
"import os\n",
"from openai import OpenAI\n",
"from dotenv import load_dotenv\n",
"import gradio as gr\n",
"import io\n",
"import sys \n",
"import subprocess"
]
},
{
"cell_type": "code",
"execution_count": 14,
"id": "f0737df3",
"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",
"ds_api_key = os.getenv('DEEPSEEK_API_KEY')\n",
"grok_api_key = os.getenv('GROK_API_KEY')\n"
]
},
{
"cell_type": "code",
"execution_count": 15,
"id": "834d1fa7",
"metadata": {},
"outputs": [],
"source": [
"MODEL_MAP = {\n",
" \"GPT\": {\n",
" \"model\": \"gpt-4o-mini\",\n",
" \"key\": openai_api_key,\n",
" \"endpoint\": \"https://api.openai.com/v1\",\n",
" },\n",
" \"CLAUDE_3_5_SONNET\": {\n",
" \"model\": \"claude-3-5-sonnet-20240620\",\n",
" \"key\": anthropic_api_key,\n",
" \"endpoint\": \"https://api.anthropic.com/v1\"\n",
" },\n",
" \"Grok\": {\n",
" \"model\": \"grok-beta\",\n",
" \"key\": grok_api_key,\n",
" \"endpoint\": \"https://api.grok.com/v1\"\n",
" }, \n",
" \"DeepSeek\": {\n",
" \"model\": \"deepseek-coder\",\n",
" \"key\": ds_api_key,\n",
" \"endpoint\": \"https://api.deepseek.com/v1\",\n",
" },\n",
" \"Google\": {\n",
" \"model\": \"gemini-2.0-flash-exp\",\n",
" \"key\": google_api_key,\n",
" \"endpoint\": \"https://generativelanguage.googleapis.com/v1beta/openai\"\n",
" },\n",
"}"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "87d0508f",
"metadata": {},
"outputs": [],
"source": [
"class PortCode:\n",
" def __init__(self, progress=None, model_name=MODEL_MAP[\"GPT\"]):\n",
" self.progress = progress\n",
" self.model_deets = model_name\n",
" self.model = OpenAI(\n",
" api_key=model_name[\"key\"],\n",
" base_url=model_name[\"endpoint\"]\n",
" )\n",
" self.cpp_code = \"\"\n",
" \n",
" def update_progress(self, value, desc=\"\"):\n",
" if self.progress:\n",
" self.progress(value, desc=desc)\n",
" \n",
" def port_python_to_cpp(self, python_code):\n",
" self.update_progress(0.3, desc=\"Converting Python to C++...\")\n",
" \n",
" system_prompt = \"\"\"\n",
" Your task is to convert Python code into high performance C++ code.\n",
" Respond only with C++ code. Do not provide any explanation other than occasional comments.\n",
" The C++ response needs to produce an identical output in the fastest possible time.\n",
" \"\"\"\n",
" \n",
" user_prompt = f\"\"\"\n",
" Port this Python code to C++ with the fastest possible implementation that produces identical output in the least time.\n",
" Respond only with C++ code.\n",
" Python code to port:\n",
"\n",
" ```python\n",
" {python_code}\n",
" ```\n",
" \"\"\"\n",
" \n",
" messages = [\n",
" {\"role\": \"system\", \"content\": system_prompt},\n",
" {\"role\": \"user\", \"content\": user_prompt}\n",
" ]\n",
" \n",
" try:\n",
" response = self.model.chat.completions.create(\n",
" model=self.model_deets[\"model\"],\n",
" messages=messages\n",
" )\n",
" \n",
" cpp_code = response.choices[0].message.content\n",
" cpp_code = cpp_code.replace('```cpp', '').replace('```', '').strip()\n",
" \n",
" self.cpp_code = cpp_code\n",
" \n",
" self.update_progress(1.0, desc=\"Conversion complete!\")\n",
" return cpp_code\n",
" \n",
" except Exception as e:\n",
" error_msg = f\"Error converting code: {str(e)}\"\n",
" self.update_progress(1.0, desc=\"Conversion failed!\")\n",
" return error_msg\n",
" \n",
" def run_python_code(self, python_code):\n",
" self.update_progress(0.1, desc=\"Running Python code...\")\n",
" \n",
" globals_dict = {\"__builtins__\": __builtins__}\n",
" buffer = io.StringIO()\n",
" old_stdout = sys.stdout\n",
" sys.stdout = buffer\n",
" \n",
" try:\n",
" exec(python_code, globals_dict)\n",
" output = buffer.getvalue()\n",
" self.update_progress(1.0, desc=\"Python execution complete!\")\n",
" except Exception as e:\n",
" output = f\"Error: {e}\"\n",
" self.update_progress(1.0, desc=\"Python execution failed!\")\n",
" finally:\n",
" sys.stdout = old_stdout\n",
" \n",
" return output\n",
" \n",
" def compile_cpp(self, cpp_code=None):\n",
" if cpp_code is None:\n",
" cpp_code = self.cpp_code\n",
" \n",
" if not cpp_code:\n",
" return \"No C++ code to compile. Please convert Python code first.\"\n",
" \n",
" self.update_progress(0.5, desc=\"Compiling C++ code...\")\n",
" \n",
" with open(\"main.cpp\", \"w\") as f:\n",
" f.write(cpp_code)\n",
" \n",
" compile_command = [\n",
" \"clang++\", \"-std=c++17\", \"-Ofast\", \"-mcpu=native\", \n",
" \"-flto=thin\", \"-fvisibility=hidden\", \"-DNDEBUG\", \n",
" \"main.cpp\", \"-o\", \"main\"\n",
" ]\n",
" \n",
" try:\n",
" subprocess.run(compile_command, check=True, text=True, capture_output=True)\n",
" self.update_progress(1.0, desc=\"C++ compilation complete!\")\n",
" return \"Compilation successful!\"\n",
" \n",
" except subprocess.CalledProcessError as e:\n",
" error_msg = f\"Compilation error: {e.stderr}\"\n",
" self.update_progress(1.0, desc=\"C++ compilation failed!\")\n",
" return error_msg\n",
" except Exception as e:\n",
" error_msg = f\"Error: {str(e)}\"\n",
" self.update_progress(1.0, desc=\"C++ compilation failed!\")\n",
" return error_msg\n",
" \n",
" def run_cpp(self):\n",
" self.update_progress(0.1, desc=\"Running C++ code...\")\n",
" \n",
" run_command = [\"./main\"]\n",
" \n",
" try:\n",
" if not os.path.exists(\"./main\"):\n",
" return \"No compiled executable found. Please compile C++ code first.\"\n",
" \n",
" run_result = subprocess.run(run_command, check=True, text=True, capture_output=True)\n",
" print(\"hello .....\")\n",
" self.update_progress(1.0, desc=\"C++ execution complete!\")\n",
" return run_result.stdout\n",
" \n",
" except subprocess.CalledProcessError as e:\n",
" error_msg = f\"Runtime error: {e.stderr}\"\n",
" self.update_progress(1.0, desc=\"C++ execution failed!\")\n",
" return error_msg\n",
" except Exception as e:\n",
" error_msg = f\"Error: {str(e)}\"\n",
" self.update_progress(1.0, desc=\"C++ execution failed!\")\n",
" return error_msg\n",
" \n",
" def compile_and_run_cpp(self, cpp_code=None):\n",
" \"\"\"Compile and run C++ code in one step\"\"\"\n",
" if cpp_code is None:\n",
" cpp_code = self.cpp_code\n",
" \n",
" if not cpp_code:\n",
" return \"No C++ code to compile and run. Please convert Python code first.\"\n",
" \n",
" compile_result = self.compile_cpp(cpp_code)\n",
" if \"error\" in compile_result.lower():\n",
" return compile_result\n",
" \n",
" return self.run_cpp()\n",
" \n",
" def get_cpp_code(self):\n",
" \"\"\"Get the stored C++ code\"\"\"\n",
" return self.cpp_code\n",
" \n",
" def set_cpp_code(self, cpp_code):\n",
" \"\"\"Manually set C++ code\"\"\"\n",
" self.cpp_code = cpp_code"
]
},
{
"cell_type": "code",
"execution_count": 37,
"id": "4680573d",
"metadata": {},
"outputs": [],
"source": [
"\n",
"class Interface:\n",
" def __init__(self):\n",
" self.port_code = PortCode(gr.Progress())\n",
" \n",
" def create_interface(self):\n",
" with gr.Blocks(title=\"Code Porter\") as interface:\n",
" gr.Markdown(\"# 🚀 Python to C++ Converter\")\n",
" \n",
" with gr.Row():\n",
" python_input = gr.TextArea(label=\"Python Code\", lines=15)\n",
" cpp_output = gr.TextArea(label=\"C++ Code\", lines=15, interactive=False)\n",
" \n",
" with gr.Row():\n",
" python_result = gr.TextArea(label=\"Python Output\", lines=4, interactive=False)\n",
" cpp_result = gr.TextArea(label=\"C++ Output\", lines=4, interactive=False)\n",
" \n",
" with gr.Row():\n",
" run_python_btn = gr.Button(\"Run Python\")\n",
" run_cpp_btn = gr.Button(\"Run C++\")\n",
" \n",
" with gr.Row():\n",
" model_dropdown = gr.Dropdown(MODEL_MAP.keys(), value=\"GPT\", label=\"Model\")\n",
" \n",
" with gr.Row():\n",
" convert_btn = gr.Button(\"Convert\", variant=\"primary\")\n",
" \n",
" # Events\n",
" convert_btn.click(self.convert_code, [python_input, model_dropdown], cpp_output)\n",
" run_python_btn.click(self.run_python, python_input, python_result)\n",
" run_cpp_btn.click(self.run_cpp, cpp_output, cpp_result)\n",
" model_dropdown.change(self.update_model, model_dropdown, None)\n",
" \n",
" return interface\n",
" \n",
" def convert_code(self, python_code, model_name):\n",
" self.port_code = PortCode(model_name=MODEL_MAP[model_name])\n",
" return self.port_code.port_python_to_cpp(python_code)\n",
" \n",
" def run_python(self, python_code):\n",
" return self.port_code.run_python_code(python_code)\n",
" \n",
" def run_cpp(self, cpp_code):\n",
" self.port_code.set_cpp_code(cpp_code)\n",
" return self.port_code.compile_and_run_cpp()\n",
" \n",
" def update_model(self, model_name):\n",
" self.port_code = PortCode(model_name=MODEL_MAP[model_name])\n",
" \n",
" def launch(self, inbrowser=False):\n",
" self.create_interface().launch(inbrowser=inbrowser)"
]
},
{
"cell_type": "code",
"execution_count": 38,
"id": "7ced6dc2",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"* Running on local URL: http://127.0.0.1:7906\n",
"* To create a public link, set `share=True` in `launch()`.\n"
]
},
{
"data": {
"text/html": [
"<div><iframe src=\"http://127.0.0.1:7906/\" 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"
}
],
"source": [
"I = Interface()\n",
"I.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
}

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#include <iostream>
int main() {
std::cout << "hi" << std::endl;
return 0;
}

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week5/community-contributions/Cosmus_Week5_Exercise.ipynb Normal file
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{
"cells": [
{
"cell_type": "code",
"execution_count": null,
"id": "d04a7c55",
"metadata": {},
"outputs": [],
"source": [
"#Importing necessary libraries\n",
"import os\n",
"from dotenv import load_dotenv\n",
"from anthropic import Client\n",
"from dotenv import load_dotenv\n",
"import sys\n",
"from faker import Faker\n",
"import random\n",
"import gradio as gr\n",
"from langchain_community.document_loaders import DirectoryLoader, TextLoader\n",
"from langchain_text_splitters import CharacterTextSplitter\n",
"from langchain_community.embeddings import HuggingFaceEmbeddings\n",
"from langchain_community.vectorstores import Chroma\n",
"from langchain_anthropic import ChatAnthropic\n",
"from langchain_classic.memory import ConversationBufferMemory\n",
"from langchain_classic.chains import ConversationalRetrievalChain\n",
"\n",
"!{sys.executable} -m pip install faker\n",
"\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "3d7f8354",
"metadata": {},
"outputs": [],
"source": [
"\n",
"# loading the .env variables\n",
"load_dotenv(override=True)\n",
"\n",
"# Force export to OS env so LangChain can detect it (had to try this because the key was not loading at some point but by the time i shared the code it loaded well so i commented it out)\n",
"#os.environ[\"ANTHROPIC_API_KEY\"] = os.getenv(\"ANTHROPIC_API_KEY\")\n",
"\n",
"#getting the key from the our .env file. It is Anthropic_API_KEY\n",
"ANTHROPIC_KEY = os.getenv(\"ANTHROPIC_API_KEY\")\n",
"client = Client(api_key=ANTHROPIC_KEY)\n",
"\n",
"# Checking the anthropic models list our anthropic key ca help us play with\n",
"models = client.models.list()\n",
"for model in models:\n",
" print(model.id)\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "20d11d1c",
"metadata": {},
"outputs": [],
"source": [
"#Getting the python executable path on my notebook to know where to install the faker library\n",
"print(sys.executable)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "93a8f3ec",
"metadata": {},
"outputs": [],
"source": [
"#Creating a fake person with faker\n",
"fake = Faker()\n",
"base_dir = \"knowledge_base\"\n",
"folders = [\"personal\", \"projects\", \"learning\"]\n",
"\n",
"# We now create folders if they don't exist\n",
"for folder in folders:\n",
" os.makedirs(f\"{base_dir}/{folder}\", exist_ok=True)\n",
"\n",
"# Check if data already exists\n",
"personal_file = f\"{base_dir}/personal/info.md\"\n",
"projects_file = f\"{base_dir}/projects/projects.md\"\n",
"learning_file = f\"{base_dir}/learning/learning.md\"\n",
"\n",
"#If the personal info file does not exist, create it\n",
"if not os.path.exists(personal_file):\n",
" name = fake.name()\n",
" profession = random.choice([\"Data Analyst\", \"Business Analyst\", \"Software Engineer\", \"AI Specialist\"])\n",
" bio = fake.paragraph(nb_sentences=5)\n",
" experience = \"\\n\".join([f\"- {fake.job()} at {fake.company()} ({fake.year()})\" for _ in range(3)])\n",
" \n",
" personal_text = f\"\"\"\n",
"# Personal Profile\n",
"Name: {name} \n",
"Profession: {profession} \n",
"\n",
"Bio: {bio}\n",
"\n",
"## Experience\n",
"{experience}\n",
"\"\"\"\n",
" with open(personal_file, \"w\") as f:\n",
" f.write(personal_text)\n",
" print(\"Personal info generated.\")\n",
"else:\n",
" #If the personal info file exists, skip the regeneration\n",
" print(\"ℹPersonal info already exists. Skipping regeneration.\")\n",
"\n",
"#doing the same for project file\n",
"if not os.path.exists(projects_file):\n",
" projects = \"\\n\".join([\n",
" f\"- **{fake.catch_phrase()}** — {fake.bs().capitalize()} for {fake.company()}.\"\n",
" for _ in range(5)\n",
" ])\n",
" projects_text = f\"\"\"\n",
"# Projects Portfolio\n",
"\n",
"Key Projects:\n",
"{projects}\n",
"\"\"\"\n",
" with open(projects_file, \"w\") as f:\n",
" f.write(projects_text)\n",
" print(\"Projects generated.\")\n",
"else:\n",
" print(\"ℹProjects already exist. Skipping regeneration.\")\n",
"\n",
"#same thing for learning file\n",
"if not os.path.exists(learning_file):\n",
" topics = [\"LangChain\", \"RAG Systems\", \"Vector Databases\", \"AI Ethics\", \"Prompt Engineering\", \"Data Visualization\"]\n",
" learning = \"\\n\".join([\n",
" f\"- {random.choice(topics)} — {fake.sentence(nb_words=8)}\"\n",
" for _ in range(6)\n",
" ])\n",
" learning_text = f\"\"\"\n",
"# Learning Journey\n",
"\n",
"Recent Topics and Notes:\n",
"{learning}\n",
"\"\"\"\n",
" with open(learning_file, \"w\") as f:\n",
" f.write(learning_text)\n",
" print(\"Learning notes generated.\")\n",
"else:\n",
" print(\"ℹLearning notes already exist. Skipping regeneration.\")\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "6fa19091",
"metadata": {},
"outputs": [],
"source": [
"#loading the knowledge information from the knowledge_base folder\n",
"loader = DirectoryLoader(\"knowledge_base\", glob=\"**/*.md\", loader_cls=TextLoader)\n",
"documents = loader.load()\n",
"\n",
"#Splitting the documents into chunks\n",
"splitter = CharacterTextSplitter(chunk_size=100, chunk_overlap=80)\n",
"chunks = splitter.split_documents(documents)\n",
"\n",
"print(f\"Loaded {len(documents)} documents and created {len(chunks)} chunks.\")\n"
]
},
{
"cell_type": "markdown",
"id": "7b9fc9a5",
"metadata": {},
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"id": "6dcdec41",
"metadata": {},
"outputs": [],
"source": [
"#Creating the embeddings\n",
"embeddings = HuggingFaceEmbeddings(model_name=\"sentence-transformers/all-MiniLM-L6-v2\")\n",
"\n",
"# Chroma as the vector store\n",
"vectorstore = Chroma.from_documents(chunks, embeddings, persist_directory=\"chroma_db\")\n",
"vectorstore.persist()\n",
"\n",
"print(\"Vector store created and saved to 'chroma_db'.\")\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "99e4a99f",
"metadata": {},
"outputs": [],
"source": [
"#Check Langchain version as they updated the version recently thus making it difficult to use it successfullt\n",
"print(langchain.__version__)\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "5dc1b6ce",
"metadata": {},
"outputs": [],
"source": [
"# The main Langchain Abstraction are: Memory, LLM, and Retriever\n",
"\n",
"# Memory for conversation history\n",
"memory = ConversationBufferMemory(\n",
" memory_key=\"chat_history\",\n",
" return_messages=True\n",
")\n",
"\n",
"# Using one of the Anthropic models from the list above to create the LLM\n",
"llm = ChatAnthropic(\n",
" model=\"claude-sonnet-4-5-20250929\",\n",
" temperature=0.6,\n",
" max_tokens=1024,\n",
" anthropic_api_key=ANTHROPIC_KEY\n",
")\n",
"\n",
"# Retriever from your vectorstore\n",
"retriever = vectorstore.as_retriever(search_kwargs={\"k\": 3})\n",
"\n",
"# Bringing everything together tConversational RAG Chain\n",
"conversation_chain = ConversationalRetrievalChain.from_llm(\n",
" llm=llm,\n",
" retriever=retriever,\n",
" memory=memory\n",
")\n",
"\n",
"print(\"Anthropic conversational retriever is ready!\")\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "6f93eea7",
"metadata": {},
"outputs": [],
"source": [
"#fnc to create a chat interface\n",
"def chat(message, history):\n",
" if conversation_chain:\n",
" result = conversation_chain.invoke({\"question\": message})\n",
" return result[\"answer\"]\n",
" else:\n",
" # Retrieval-only fallback\n",
" docs = retriever.get_relevant_documents(message)\n",
" context = \"\\n\\n\".join([d.page_content for d in docs])\n",
" return f\"(Offline Mode)\\nTop relevant info:\\n\\n{context[:1000]}\"\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "aadf91b4",
"metadata": {},
"outputs": [],
"source": [
"#used som css to make the chat interface look better, and dark mode. I love dark mode btw\n",
"css = \"\"\"\n",
"body {background-color: #0f1117; color: #e6e6e6;}\n",
".gradio-container {background-color: #0f1117 !important;}\n",
"textarea, input, .wrap.svelte-1ipelgc {background-color: #1b1f2a !important; color: #ffffff !important;}\n",
"\"\"\"\n",
"\n",
"#Gradio blocks\n",
"with gr.Blocks(css=css, theme=\"gradio/monochrome\") as demo:\n",
" gr.Markdown(\n",
" \"\"\"\n",
" <h2 style=\"color: #f5f5f5;\">Personal Knowledge Worker</h2>\n",
" <p style=\"color: #f5f5f5;\">Chat with your auto-generated knowledge base (Claude-powered if available)</p>\n",
" \"\"\",\n",
" elem_id=\"title\"\n",
" )\n",
" gr.ChatInterface(chat, type=\"messages\")\n",
"\n",
"demo.launch(inbrowser=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": "3.13.5"
}
},
"nbformat": 4,
"nbformat_minor": 5
}

1911
week5/community-contributions/dkisselev-zz/Week5_Excerise_EmailTerminator.ipynb Normal file
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