Update app.py

app.py

@@ -197,13 +197,34 @@ def on_table_select(choice: str):
 # =========================================================
 
 def load_kpis() -> Dict[str, Any]:
-    for candidate in [PY_TAB_DIR / "kpis.json", PY_FIG_DIR / "kpis.json"]:
-        if candidate.exists():
-            try:
-                return _read_json(candidate)
-            except Exception:
-                pass
-    return {}
+    try:
+        summary_path = BASE_DIR / "menu_item_summary.csv"
+        sales_path = BASE_DIR / "monthly_sales.csv"
+        final_path = BASE_DIR / "final_menu_recommendations.csv"
+
+        if not summary_path.exists() or not sales_path.exists():
+            return {}
+
+        summary_df = pd.read_csv(summary_path)
+        sales_df = pd.read_csv(sales_path)
+
+        kpis = {
+            "n_menu_items": int(summary_df["menu_item"].nunique()) if "menu_item" in summary_df.columns else 0,
+            "n_months": int(sales_df["month"].nunique()) if "month" in sales_df.columns else 0,
+            "total_orders": float(sales_df["orders"].sum()) if "orders" in sales_df.columns else 0,
+            "total_revenue": float(sales_df["revenue"].sum()) if "revenue" in sales_df.columns else 0,
+        }
+
+        if final_path.exists():
+            final_df = pd.read_csv(final_path)
+            if "recommended_action" in final_df.columns:
+                counts = final_df["recommended_action"].value_counts().to_dict()
+                for k, v in counts.items():
+                    kpis[f"action_{str(k).lower()}"] = int(v)
+
+        return kpis
+    except Exception:
+        return {}
 
 
 # =========================================================
@@ -465,10 +486,10 @@ def render_kpi_cards() -> str:
         </div>"""
 
     kpi_config = [
-        ("n_titles",         "📚", "Book Titles",  "#a48de8"),
-        ("n_months",         "📅", "Time Periods", "#7aa6f8"),
-        ("total_units_sold", "📦", "Units Sold",   "#6ee7c7"),
-        ("total_revenue",    "💰", "Revenue",      "#3dcba8"),
+    ("n_menu_items", "🍝", "Menu Items", "#a48de8"),
+    ("n_months", "📅", "Months", "#7aa6f8"),
+    ("total_orders", "🧾", "Total Orders", "#6ee7c7"),
+    ("total_revenue", "💰", "Revenue", "#3dcba8"),
     ]
 
     html = (
@@ -532,74 +553,100 @@ def _empty_chart(title: str) -> go.Figure:
 
 
 def build_sales_chart() -> go.Figure:
-    path = PY_TAB_DIR / "df_dashboard.csv"
+    path = BASE_DIR / "monthly_sales.csv"
     if not path.exists():
         return _empty_chart("Sales Trends — run the pipeline first")
+
     df = pd.read_csv(path)
-    date_col = next((c for c in df.columns if "month" in c.lower() or "date" in c.lower()), None)
-    val_cols = [c for c in df.columns if c != date_col and df[c].dtype in ("float64", "int64")]
-    if not date_col or not val_cols:
-        return _empty_chart("Could not auto-detect columns in df_dashboard.csv")
-    df[date_col] = pd.to_datetime(df[date_col], errors="coerce")
+    if "month" not in df.columns or "revenue" not in df.columns:
+        return _empty_chart("monthly_sales.csv is missing required columns")
+
+    monthly = df.groupby("month", as_index=False)["revenue"].sum()
+    monthly["month"] = pd.to_datetime(monthly["month"], errors="coerce")
+
     fig = go.Figure()
-    for i, col in enumerate(val_cols):
-        fig.add_trace(go.Scatter(
-            x=df[date_col], y=df[col], name=col.replace("_", " ").title(),
-            mode="lines+markers", line=dict(color=CHART_PALETTE[i % len(CHART_PALETTE)], width=2),
-            marker=dict(size=4),
-            hovertemplate=f"<b>{col.replace('_',' ').title()}</b><br>%{{x|%b %Y}}: %{{y:,.0f}}<extra></extra>",
-        ))
-    fig.update_layout(**_styled_layout(height=450, hovermode="x unified",
-                                        title=dict(text="Monthly Overview")))
+    fig.add_trace(go.Scatter(
+        x=monthly["month"],
+        y=monthly["revenue"],
+        mode="lines+markers",
+        name="Revenue",
+        line=dict(color=CHART_PALETTE[0], width=3),
+        marker=dict(size=6),
+        hovertemplate="<b>Revenue</b><br>%{x|%b %Y}: %{y:,.2f}<extra></extra>",
+    ))
+
+    fig.update_layout(**_styled_layout(
+        height=450,
+        hovermode="x unified",
+        title=dict(text="Monthly Revenue Trend"),
+    ))
     fig.update_xaxes(gridcolor="rgba(124,92,191,0.15)", showgrid=True)
-    fig.update_yaxes(gridcolor="rgba(124,92,191,0.15)", showgrid=True)
+    fig.update_yaxes(title="Revenue", gridcolor="rgba(124,92,191,0.15)", showgrid=True)
     return fig
 
 
 def build_sentiment_chart() -> go.Figure:
-    path = PY_TAB_DIR / "sentiment_counts_sampled.csv"
+    path = BASE_DIR / "reviews.csv"
     if not path.exists():
         return _empty_chart("Sentiment Distribution — run the pipeline first")
+
     df = pd.read_csv(path)
-    title_col = df.columns[0]
-    sent_cols = [c for c in ["negative", "neutral", "positive"] if c in df.columns]
-    if not sent_cols:
-        return _empty_chart("No sentiment columns found in CSV")
-    colors = {"negative": "#e8537a", "neutral": "#5e8fef", "positive": "#2ec4a0"}
-    fig = go.Figure()
-    for col in sent_cols:
-        fig.add_trace(go.Bar(
-            name=col.title(), y=df[title_col], x=df[col],
-            orientation="h", marker_color=colors.get(col, "#888"),
-            hovertemplate=f"<b>{col.title()}</b>: %{{x}}<extra></extra>",
-        ))
+    if "rating" not in df.columns:
+        return _empty_chart("reviews.csv is missing the rating column")
+
+    def label_sentiment(x):
+        if x >= 4:
+            return "Positive"
+        elif x == 3:
+            return "Neutral"
+        return "Negative"
+
+    df["sentiment"] = df["rating"].apply(label_sentiment)
+    counts = df["sentiment"].value_counts().reindex(["Negative", "Neutral", "Positive"], fill_value=0)
+
+    fig = go.Figure(go.Bar(
+        x=counts.index,
+        y=counts.values,
+        marker_color=["#e8537a", "#5e8fef", "#2ec4a0"],
+        hovertemplate="<b>%{x}</b><br>Count: %{y}<extra></extra>",
+    ))
+
     fig.update_layout(**_styled_layout(
-        height=max(400, len(df) * 28), barmode="stack",
-        title=dict(text="Sentiment Distribution by Book"),
+        height=420,
+        title=dict(text="Review Sentiment Distribution"),
+        showlegend=False,
     ))
-    fig.update_xaxes(title="Number of Reviews")
-    fig.update_yaxes(autorange="reversed")
+    fig.update_xaxes(title="Sentiment")
+    fig.update_yaxes(title="Number of Reviews")
     return fig
 
 
 def build_top_sellers_chart() -> go.Figure:
-    path = PY_TAB_DIR / "top_titles_by_units_sold.csv"
+    path = BASE_DIR / "final_menu_recommendations.csv"
     if not path.exists():
-        return _empty_chart("Top Sellers — run the pipeline first")
-    df = pd.read_csv(path).head(15)
-    title_col = next((c for c in df.columns if "title" in c.lower()), df.columns[0])
-    val_col = next((c for c in df.columns if "unit" in c.lower() or "sold" in c.lower()), df.columns[-1])
+        return _empty_chart("Top Menu Items — run the pipeline first")
+
+    df = pd.read_csv(path)
+    if "menu_item" not in df.columns or "total_orders" not in df.columns:
+        return _empty_chart("final_menu_recommendations.csv is missing required columns")
+
+    df = df.sort_values("total_orders", ascending=False).head(10)
+
     fig = go.Figure(go.Bar(
-        y=df[title_col], x=df[val_col], orientation="h",
-        marker=dict(color=df[val_col], colorscale=[[0, "#c5b4f0"], [1, "#7c5cbf"]]),
-        hovertemplate="<b>%{y}</b><br>Units: %{x:,.0f}<extra></extra>",
+        y=df["menu_item"],
+        x=df["total_orders"],
+        orientation="h",
+        marker=dict(color=df["total_orders"], colorscale=[[0, "#c5b4f0"], [1, "#7c5cbf"]]),
+        hovertemplate="<b>%{y}</b><br>Total Orders: %{x}<extra></extra>",
     ))
+
     fig.update_layout(**_styled_layout(
         height=max(400, len(df) * 30),
-        title=dict(text="Top Selling Titles"), showlegend=False,
+        title=dict(text="Top Menu Items by Orders"),
+        showlegend=False,
     ))
     fig.update_yaxes(autorange="reversed")
-    fig.update_xaxes(title="Total Units Sold")
+    fig.update_xaxes(title="Total Orders")
     return fig