Update charts.py

charts.py

@@ -76,9 +76,9 @@ def misinfo_gauge(score: float, label: str) -> go.Figure:
 #  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"]]
+    """Donut chart: Positively Engagement / Negatively Engagement / Neutral breakdown."""
+    labels  = ["Positively Engagement", "Neutral", "Negatively Engagement"]
+    values  = [summary["POSITIVELY ENGAGEMENT"], summary["NEUTRAL"], summary["NEGATIVELY ENGAGEMENT"]]
     colors  = [GREEN, TEXT_DIM, RED]
 
     fig = go.Figure(go.Pie(
@@ -94,7 +94,7 @@ def sentiment_donut(summary: Dict) -> go.Figure:
 
     # Centre annotation
     avg = summary.get("avg_compound", 0)
-    overall = "😊 Positive" if avg > 0.05 else ("😟 Negative" if avg < -0.05 else "😐 Mixed")
+    overall = "😊 Positively Engagement" if avg > 0.05 else ("😟 Negatively Engagement" 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,
@@ -453,11 +453,11 @@ def sentiment_timeline(comments_df: pd.DataFrame, sentiments: List[Dict]) -> go.
     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["color"]    = df["label"].map({"POSITIVELY ENGAGEMENT": GREEN, "NEGATIVELY ENGAGEMENT": RED, "NEUTRAL": AMBER})
     df["text_short"] = df["text"].str[:80] + "…"
 
     fig = go.Figure()
-    for lbl, clr in [("POSITIVE", GREEN), ("NEGATIVE", RED), ("NEUTRAL", AMBER)]:
+    for lbl, clr in [("POSITIVELY ENGAGEMENT", GREEN), ("NEGATIVELY ENGAGEMENT", RED), ("NEUTRAL", AMBER)]:
         sub = df[df["label"] == lbl]
         if sub.empty:
             continue
@@ -488,13 +488,13 @@ def sentiment_timeline(comments_df: pd.DataFrame, sentiments: List[Dict]) -> go.
     return fig
 
 
-#  Positive vs Negative Keyword Comparison ─
+#  Positively Engagement vs Negatively Engagement 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."""
+    """Diverging bar chart: Positive keywords right, negative left."""
     if not pos_kw and not neg_kw:
         return _empty_fig("Sentiment Keywords")
 
@@ -508,7 +508,7 @@ def keyword_comparison(
         pw, pv = zip(*pos_kw)
         max_p = max(pv) or 1
         fig.add_trace(go.Bar(
-            name="Positive",
+            name="Positively Engagement",
             y=list(pw),
             x=[v/max_p*100 for v in pv],
             orientation="h",
@@ -520,7 +520,7 @@ def keyword_comparison(
         nw, nv = zip(*neg_kw)
         max_n = max(nv) or 1
         fig.add_trace(go.Bar(
-            name="Negative",
+            name="Negatively Engagement",
             y=list(nw),
             x=[-v/max_n*100 for v in nv],
             orientation="h",
@@ -533,7 +533,7 @@ def keyword_comparison(
         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,
+        xaxis=dict(title="← Negatively Engagement  |  Positively Engagement →", gridcolor=BORDER, zeroline=True,
                    zerolinecolor=BORDER, zerolinewidth=2),
         yaxis=dict(tickfont=dict(size=10)),
         legend=dict(orientation="h", y=1.1),