Add ReputationRadar community contribution (demo replaced by link)

community-contributions/Reputation_Radar/components/dashboard.py

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