Create charts.py

charts.py

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+"""
+charts.py — All Plotly chart builders. Pure functions, no Streamlit imports.
+"""
+
+from typing import Dict, List, Tuple, Optional
+import plotly.graph_objects as go
+import plotly.express as px
+import pandas as pd
+import numpy as np
+
+# ── Shared theme ───────────────────────────────────────────────────────────────
+DARK_BG     = "#0d0f14"
+CARD_BG     = "#13161e"
+BORDER      = "#1e2330"
+TEXT_MAIN   = "#e8eaf0"
+TEXT_DIM    = "#5a6070"
+CYAN        = "#00d4ff"
+GREEN       = "#00e5a0"
+RED         = "#ff4757"
+AMBER       = "#ffb347"
+PURPLE      = "#b388ff"
+BLUE        = "#4a8eff"
+
+PLOTLY_LAYOUT = dict(
+    paper_bgcolor="rgba(0,0,0,0)",
+    plot_bgcolor="rgba(0,0,0,0)",
+    font=dict(family="'DM Mono', monospace", color=TEXT_MAIN, size=12),
+    margin=dict(l=20, r=20, t=40, b=20),
+)
+
+
+# ── Misinformation Gauge ──────────────────────────────────────────────────────
+
+def misinfo_gauge(score: float, label: str) -> go.Figure:
+    """Gauge chart for misinformation confidence score (0–1)."""
+    pct = score * 100
+    if score < 0.35:
+        bar_color = GREEN
+    elif score < 0.65:
+        bar_color = AMBER
+    else:
+        bar_color = RED
+
+    fig = go.Figure(go.Indicator(
+        mode="gauge+number+delta",
+        value=pct,
+        number={"suffix": "%", "font": {"size": 32, "color": bar_color, "family": "'DM Mono', monospace"}},
+        delta={"reference": 50, "increasing": {"color": RED}, "decreasing": {"color": GREEN}},
+        title={"text": label, "font": {"size": 13, "color": TEXT_DIM}},
+        gauge={
+            "axis": {
+                "range": [0, 100],
+                "tickwidth": 1,
+                "tickcolor": BORDER,
+                "tickfont": {"color": TEXT_DIM, "size": 10},
+            },
+            "bar": {"color": bar_color, "thickness": 0.3},
+            "bgcolor": CARD_BG,
+            "borderwidth": 0,
+            "steps": [
+                {"range": [0, 35],  "color": "#0d1f18"},
+                {"range": [35, 65], "color": "#1f1a0d"},
+                {"range": [65, 100],"color": "#1f0d0d"},
+            ],
+            "threshold": {
+                "line": {"color": TEXT_MAIN, "width": 2},
+                "thickness": 0.75,
+                "value": pct,
+            },
+        },
+    ))
+    fig.update_layout(**PLOTLY_LAYOUT, height=260)
+    return fig
+
+
+# ── Sentiment Donut ───────────────────────────────────────────────────────────
+
+def sentiment_donut(summary: Dict) -> go.Figure:
+    """Donut chart: Positive / Negative / Neutral breakdown."""
+    labels  = ["Positive", "Neutral", "Negative"]
+    values  = [summary["POSITIVE"], summary["NEUTRAL"], summary["NEGATIVE"]]
+    colors  = [GREEN, TEXT_DIM, RED]
+
+    fig = go.Figure(go.Pie(
+        labels=labels,
+        values=values,
+        hole=0.62,
+        marker=dict(colors=colors, line=dict(color=DARK_BG, width=3)),
+        textinfo="label+percent",
+        textfont=dict(family="'DM Mono', monospace", size=11, color=TEXT_MAIN),
+        hovertemplate="<b>%{label}</b><br>%{value} comments (%{percent})<extra></extra>",
+        rotation=90,
+    ))
+
+    # Centre annotation
+    avg = summary.get("avg_compound", 0)
+    overall = "😊 Positive" if avg > 0.05 else ("😟 Negative" if avg < -0.05 else "😐 Mixed")
+    fig.add_annotation(
+        text=f"<b>{overall}</b><br><span style='font-size:11px;color:{TEXT_DIM}'>{summary['total']} comments</span>",
+        x=0.5, y=0.5,
+        showarrow=False,
+        font=dict(size=13, color=TEXT_MAIN, family="'DM Mono', monospace"),
+        align="center",
+    )
+    fig.update_layout(**PLOTLY_LAYOUT, height=300,
+                      legend=dict(orientation="h", y=-0.08, font=dict(size=11)))
+    return fig
+
+
+# ── Keyword Bar Chart ─────────────────────────────────────────────────────────
+
+def keyword_bar(
+    keywords: List[Tuple[str, float]],
+    title: str = "Top Keywords",
+    color: str = CYAN,
+) -> go.Figure:
+    if not keywords:
+        return _empty_fig(title)
+
+    words, weights = zip(*keywords[:15])
+    # Normalize to 0-100
+    max_w = max(weights) or 1
+    norm = [w / max_w * 100 for w in weights]
+
+    fig = go.Figure(go.Bar(
+        x=norm,
+        y=words,
+        orientation="h",
+        marker=dict(
+            color=norm,
+            colorscale=[[0, f"{color}33"], [1, color]],
+            line=dict(width=0),
+        ),
+        text=[f"{w:.0f}" for w in weights],
+        textposition="inside",
+        textfont=dict(size=10, color=DARK_BG),
+        hovertemplate="<b>%{y}</b><br>Weight: %{text}<extra></extra>",
+    ))
+    fig.update_layout(
+        **PLOTLY_LAYOUT,
+        title=dict(text=title, font=dict(size=13, color=TEXT_DIM), x=0),
+        height=380,
+        yaxis=dict(autorange="reversed", tickfont=dict(size=11), gridcolor=BORDER),
+        xaxis=dict(showticklabels=False, gridcolor=BORDER),
+        bargap=0.35,
+    )
+    return fig
+
+
+# ── Stream Trust Bars ─────────────────────────────────────────────────────────
+
+def stream_trust_bars(stream_details: Dict) -> go.Figure:
+    """Horizontal bar chart for per-stream misinfo scores."""
+    labels = list(stream_details.keys())
+    values = [round(v * 100, 1) for v in stream_details.values()]
+    colors = [RED if v > 50 else (AMBER if v > 30 else GREEN) for v in values]
+
+    fig = go.Figure(go.Bar(
+        x=values,
+        y=[l.replace("_", " ").title() for l in labels],
+        orientation="h",
+        marker=dict(color=colors, line=dict(width=0)),
+        text=[f"{v}%" for v in values],
+        textposition="outside",
+        textfont=dict(size=11, color=TEXT_MAIN),
+        hovertemplate="<b>%{y}</b><br>Score: %{x}%<extra></extra>",
+    ))
+    fig.update_layout(
+        **PLOTLY_LAYOUT,
+        title=dict(text="Per-Stream Analysis", font=dict(size=13, color=TEXT_DIM), x=0),
+        height=220,
+        xaxis=dict(range=[0, 110], showticklabels=False, gridcolor=BORDER),
+        yaxis=dict(tickfont=dict(size=11)),
+        bargap=0.4,
+    )
+    return fig
+
+
+# ── Comment Sentiment Timeline ────────────────────────────────────────────────
+
+def sentiment_timeline(comments_df: pd.DataFrame, sentiments: List[Dict]) -> go.Figure:
+    """Scatter: comment likes vs. sentiment compound score."""
+    if comments_df.empty:
+        return _empty_fig("Comment Sentiment Distribution")
+
+    df = comments_df.copy()
+    df["compound"] = [s.get("compound", 0) for s in sentiments]
+    df["label"]    = [s.get("label", "NEUTRAL") for s in sentiments]
+    df["color"]    = df["label"].map({"POSITIVE": GREEN, "NEGATIVE": RED, "NEUTRAL": AMBER})
+    df["text_short"] = df["text"].str[:80] + "…"
+
+    fig = go.Figure()
+    for lbl, clr in [("POSITIVE", GREEN), ("NEGATIVE", RED), ("NEUTRAL", AMBER)]:
+        sub = df[df["label"] == lbl]
+        if sub.empty:
+            continue
+        fig.add_trace(go.Scatter(
+            x=sub.index,
+            y=sub["compound"],
+            mode="markers",
+            name=lbl,
+            marker=dict(
+                size=np.clip(np.log1p(sub["likes"].fillna(0)) * 4 + 4, 4, 20),
+                color=clr,
+                opacity=0.75,
+                line=dict(width=0),
+            ),
+            text=sub["text_short"],
+            hovertemplate="<b>%{text}</b><br>Sentiment: %{y:.2f}<br>Likes: %{marker.size}<extra></extra>",
+        ))
+
+    fig.add_hline(y=0, line=dict(color=BORDER, width=1, dash="dot"))
+    fig.update_layout(
+        **PLOTLY_LAYOUT,
+        title=dict(text="Comment Sentiment (size = likes)", font=dict(size=13, color=TEXT_DIM), x=0),
+        height=320,
+        xaxis=dict(title="Comment index", gridcolor=BORDER, showgrid=False),
+        yaxis=dict(title="Compound score", gridcolor=BORDER, range=[-1.1, 1.1]),
+        legend=dict(orientation="h", y=1.12, font=dict(size=11)),
+    )
+    return fig
+
+
+# ── Positive vs Negative Keyword Comparison ───────────────────────────────────
+
+def keyword_comparison(
+    pos_kw: List[Tuple[str, float]],
+    neg_kw: List[Tuple[str, float]],
+) -> go.Figure:
+    """Diverging bar chart: positive keywords right, negative left."""
+    if not pos_kw and not neg_kw:
+        return _empty_fig("Sentiment Keywords")
+
+    top = 10
+    pos_kw = pos_kw[:top]
+    neg_kw = neg_kw[:top]
+
+    fig = go.Figure()
+
+    if pos_kw:
+        pw, pv = zip(*pos_kw)
+        max_p = max(pv) or 1
+        fig.add_trace(go.Bar(
+            name="Positive",
+            y=list(pw),
+            x=[v/max_p*100 for v in pv],
+            orientation="h",
+            marker_color=GREEN,
+            hovertemplate="<b>%{y}</b><br>Score: %{x:.1f}<extra></extra>",
+        ))
+
+    if neg_kw:
+        nw, nv = zip(*neg_kw)
+        max_n = max(nv) or 1
+        fig.add_trace(go.Bar(
+            name="Negative",
+            y=list(nw),
+            x=[-v/max_n*100 for v in nv],
+            orientation="h",
+            marker_color=RED,
+            hovertemplate="<b>%{y}</b><br>Score: %{x:.1f}<extra></extra>",
+        ))
+
+    fig.update_layout(
+        **PLOTLY_LAYOUT,
+        title=dict(text="Sentiment-Weighted Keywords", font=dict(size=13, color=TEXT_DIM), x=0),
+        height=360,
+        barmode="overlay",
+        xaxis=dict(title="← Negative  |  Positive →", gridcolor=BORDER, zeroline=True,
+                   zerolinecolor=BORDER, zerolinewidth=2),
+        yaxis=dict(tickfont=dict(size=10)),
+        legend=dict(orientation="h", y=1.1),
+    )
+    return fig
+
+
+# ── Helpers ───────────────────────────────────────────────────────────────────
+
+def _empty_fig(title: str) -> go.Figure:
+    fig = go.Figure()
+    fig.add_annotation(text="No data available", x=0.5, y=0.5, showarrow=False,
+                       font=dict(size=14, color=TEXT_DIM))
+    fig.update_layout(**PLOTLY_LAYOUT, title=dict(text=title, x=0), height=250)
+    return fig