Add ReputationRadar community contribution (demo replaced by link)
This commit is contained in:
Parth Verma
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"""Reusable Streamlit UI components for ReputationRadar."""
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from . import dashboard, filters, loaders, summary
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__all__ = ["dashboard", "filters", "loaders", "summary"]
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136
community-contributions/Reputation_Radar/components/dashboard.py
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136
community-contributions/Reputation_Radar/components/dashboard.py
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"""Render the ReputationRadar dashboard components."""
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from __future__ import annotations
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from typing import Dict, Optional
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import pandas as pd
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import plotly.express as px
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import streamlit as st
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SOURCE_CHIPS = {
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"reddit": "🔺 Reddit",
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"twitter": "✖️ Twitter",
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"trustpilot": "⭐ Trustpilot",
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}
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SENTIMENT_COLORS = {
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"positive": "#4caf50",
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"neutral": "#90a4ae",
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"negative": "#ef5350",
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}
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def render_overview(df: pd.DataFrame) -> None:
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"""Display charts summarising sentiment."""
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counts = (
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df["label"]
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.value_counts()
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.reindex(["positive", "neutral", "negative"], fill_value=0)
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.rename_axis("label")
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.reset_index(name="count")
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)
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pie = px.pie(
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counts,
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names="label",
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values="count",
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color="label",
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color_discrete_map=SENTIMENT_COLORS,
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title="Sentiment distribution",
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)
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pie.update_traces(textinfo="percent+label")
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ts = (
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df.set_index("timestamp")
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.groupby([pd.Grouper(freq="D"), "label"])
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.size()
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.reset_index(name="count")
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)
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if not ts.empty:
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ts_plot = px.line(
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ts,
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x="timestamp",
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y="count",
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color="label",
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color_discrete_map=SENTIMENT_COLORS,
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markers=True,
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title="Mentions over time",
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)
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else:
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ts_plot = None
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col1, col2 = st.columns(2)
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with col1:
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st.plotly_chart(pie, use_container_width=True)
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with col2:
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if ts_plot is not None:
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st.plotly_chart(ts_plot, use_container_width=True)
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else:
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st.info("Not enough data for a time-series. Try widening the date range.", icon="📆")
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def render_top_comments(df: pd.DataFrame) -> None:
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"""Show representative comments per sentiment."""
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st.subheader("Representative Mentions")
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cols = st.columns(3)
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for idx, sentiment in enumerate(["positive", "neutral", "negative"]):
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subset = (
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df[df["label"] == sentiment]
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.sort_values("confidence", ascending=False)
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.head(5)
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)
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with cols[idx]:
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st.caption(sentiment.capitalize())
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if subset.empty:
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st.write("No items yet.")
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continue
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for _, row in subset.iterrows():
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chip = SOURCE_CHIPS.get(row["source"], row["source"])
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author = row.get("author") or "Unknown"
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timestamp = row["timestamp"].strftime("%Y-%m-%d %H:%M")
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label = f"{chip} · {author} · {timestamp}"
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if row.get("url"):
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st.markdown(f"- [{label}]({row['url']})")
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else:
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st.markdown(f"- {label}")
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def render_source_explorer(df: pd.DataFrame) -> None:
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"""Interactive tabular explorer with pagination and filters."""
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with st.expander("Source Explorer", expanded=False):
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search_term = st.text_input("Search mentions", key="explorer_search")
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selected_source = st.selectbox("Source filter", options=["All"] + list(SOURCE_CHIPS.values()))
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min_conf = st.slider("Minimum confidence", min_value=0.0, max_value=1.0, value=0.0, step=0.1)
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filtered = df.copy()
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if search_term:
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filtered = filtered[filtered["text"].str.contains(search_term, case=False, na=False)]
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if selected_source != "All":
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source_key = _reverse_lookup(selected_source)
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if source_key:
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filtered = filtered[filtered["source"] == source_key]
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filtered = filtered[filtered["confidence"] >= min_conf]
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if filtered.empty:
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st.info("No results found. Try widening the date range or removing filters.", icon="🪄")
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return
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page_size = 10
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total_pages = max(1, (len(filtered) + page_size - 1) // page_size)
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page = st.number_input("Page", min_value=1, max_value=total_pages, value=1)
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start = (page - 1) * page_size
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end = start + page_size
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explorer_df = filtered.iloc[start:end].copy()
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explorer_df["source"] = explorer_df["source"].map(SOURCE_CHIPS).fillna(explorer_df["source"])
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explorer_df["timestamp"] = explorer_df["timestamp"].dt.strftime("%Y-%m-%d %H:%M")
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explorer_df = explorer_df[["timestamp", "source", "author", "label", "confidence", "text", "url"]]
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st.dataframe(explorer_df, use_container_width=True, hide_index=True)
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def _reverse_lookup(value: str) -> Optional[str]:
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for key, chip in SOURCE_CHIPS.items():
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if chip == value:
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return key
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return None
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128
community-contributions/Reputation_Radar/components/filters.py
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128
community-contributions/Reputation_Radar/components/filters.py
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"""Sidebar filters and configuration controls."""
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from __future__ import annotations
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from typing import Dict, Optional, Tuple
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import streamlit as st
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DATE_RANGE_LABELS = {
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"24h": "Last 24 hours",
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"7d": "Last 7 days",
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"30d": "Last 30 days",
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}
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SUPPORTED_LANGUAGES = {
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"en": "English",
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"es": "Spanish",
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"de": "German",
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"fr": "French",
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}
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def _store_secret(key: str, value: str) -> None:
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"""Persist sensitive values in session state only."""
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if value:
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st.session_state.setdefault("secrets", {})
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st.session_state["secrets"][key] = value
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def _get_secret(key: str, default: str = "") -> str:
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return st.session_state.get("secrets", {}).get(key, default)
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def render_sidebar(env_defaults: Dict[str, Optional[str]], openai_notices: Tuple[str, ...]) -> Dict[str, object]:
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"""Render all sidebar controls and return configuration."""
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with st.sidebar:
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st.header("Tune Your Radar", anchor=False)
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brand = st.text_input("Brand Name*", value=st.session_state.get("brand_input", ""))
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if brand:
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st.session_state["brand_input"] = brand
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date_range = st.selectbox(
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"Date Range",
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options=list(DATE_RANGE_LABELS.keys()),
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format_func=lambda key: DATE_RANGE_LABELS[key],
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index=1,
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)
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min_reddit_upvotes = st.number_input(
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"Minimum Reddit upvotes",
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min_value=0,
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value=st.session_state.get("min_reddit_upvotes", 4),
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)
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st.session_state["min_reddit_upvotes"] = min_reddit_upvotes
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min_twitter_likes = st.number_input(
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"Minimum X likes",
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min_value=0,
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value=st.session_state.get("min_twitter_likes", 100),
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)
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st.session_state["min_twitter_likes"] = min_twitter_likes
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language = st.selectbox(
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"Language",
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options=list(SUPPORTED_LANGUAGES.keys()),
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format_func=lambda key: SUPPORTED_LANGUAGES[key],
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index=0,
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)
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st.markdown("### Sources")
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reddit_enabled = st.toggle("🔺 Reddit", value=st.session_state.get("reddit_enabled", True))
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twitter_enabled = st.toggle("✖️ Twitter", value=st.session_state.get("twitter_enabled", True))
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trustpilot_enabled = st.toggle("⭐ Trustpilot", value=st.session_state.get("trustpilot_enabled", True))
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st.session_state["reddit_enabled"] = reddit_enabled
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st.session_state["twitter_enabled"] = twitter_enabled
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st.session_state["trustpilot_enabled"] = trustpilot_enabled
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st.markdown("### API Keys")
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openai_key_default = env_defaults.get("OPENAI_API_KEY") or _get_secret("OPENAI_API_KEY")
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openai_key = st.text_input("OpenAI API Key", value=openai_key_default or "", type="password", help="Stored only in this session.")
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_store_secret("OPENAI_API_KEY", openai_key.strip())
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reddit_client_id = st.text_input("Reddit Client ID", value=env_defaults.get("REDDIT_CLIENT_ID") or _get_secret("REDDIT_CLIENT_ID"), type="password")
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reddit_client_secret = st.text_input("Reddit Client Secret", value=env_defaults.get("REDDIT_CLIENT_SECRET") or _get_secret("REDDIT_CLIENT_SECRET"), type="password")
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reddit_user_agent = st.text_input("Reddit User Agent", value=env_defaults.get("REDDIT_USER_AGENT") or _get_secret("REDDIT_USER_AGENT"))
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twitter_bearer_token = st.text_input("Twitter Bearer Token", value=env_defaults.get("TWITTER_BEARER_TOKEN") or _get_secret("TWITTER_BEARER_TOKEN"), type="password")
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_store_secret("REDDIT_CLIENT_ID", reddit_client_id.strip())
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_store_secret("REDDIT_CLIENT_SECRET", reddit_client_secret.strip())
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_store_secret("REDDIT_USER_AGENT", reddit_user_agent.strip())
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_store_secret("TWITTER_BEARER_TOKEN", twitter_bearer_token.strip())
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if openai_notices:
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for notice in openai_notices:
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st.info(notice)
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with st.expander("Advanced Options", expanded=False):
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reddit_limit = st.slider("Reddit results", min_value=10, max_value=100, value=st.session_state.get("reddit_limit", 40), step=5)
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twitter_limit = st.slider("Twitter results", min_value=10, max_value=100, value=st.session_state.get("twitter_limit", 40), step=5)
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trustpilot_limit = st.slider("Trustpilot results", min_value=10, max_value=60, value=st.session_state.get("trustpilot_limit", 30), step=5)
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llm_batch_size = st.slider("OpenAI batch size", min_value=5, max_value=20, value=st.session_state.get("llm_batch_size", 20), step=5)
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st.session_state["reddit_limit"] = reddit_limit
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st.session_state["twitter_limit"] = twitter_limit
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st.session_state["trustpilot_limit"] = trustpilot_limit
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st.session_state["llm_batch_size"] = llm_batch_size
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return {
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"brand": brand.strip(),
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"date_range": date_range,
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"min_reddit_upvotes": min_reddit_upvotes,
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"min_twitter_likes": min_twitter_likes,
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"language": language,
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"sources": {
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"reddit": reddit_enabled,
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"twitter": twitter_enabled,
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"trustpilot": trustpilot_enabled,
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},
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"limits": {
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"reddit": reddit_limit,
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"twitter": twitter_limit,
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"trustpilot": trustpilot_limit,
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},
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"batch_size": llm_batch_size,
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"credentials": {
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"openai": openai_key.strip(),
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"reddit": {
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"client_id": reddit_client_id.strip(),
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"client_secret": reddit_client_secret.strip(),
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"user_agent": reddit_user_agent.strip(),
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},
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"twitter": twitter_bearer_token.strip(),
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},
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}
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"""Loading indicators and status helpers."""
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from __future__ import annotations
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from contextlib import contextmanager
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from typing import Iterator
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import streamlit as st
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@contextmanager
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def source_status(label: str) -> Iterator[st.delta_generator.DeltaGenerator]:
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"""Context manager that yields a status widget for source fetching."""
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status = st.status(label, expanded=True)
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try:
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yield status
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status.update(label=f"{label} ✅", state="complete")
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except Exception as exc: # noqa: BLE001
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status.update(label=f"{label} ⚠️ {exc}", state="error")
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raise
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def show_empty_state(message: str) -> None:
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"""Render a friendly empty-state callout."""
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st.info(message, icon="🔎")
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"""Executive summary display components."""
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from __future__ import annotations
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from typing import Dict, Optional
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import streamlit as st
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def render_summary(summary: Optional[Dict[str, str]]) -> None:
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"""Render executive summary card."""
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st.subheader("Executive Summary", anchor=False)
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if not summary:
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st.warning("Executive summary disabled. Provide an OpenAI API key to unlock this section.", icon="🤖")
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return
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st.markdown(
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"""
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<div style="padding:1rem;border:1px solid #eee;border-radius:0.75rem;background-color:#f9fafb;">
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""",
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unsafe_allow_html=True,
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)
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st.markdown(summary.get("raw", ""))
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st.markdown("</div>", unsafe_allow_html=True)
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