Update app.py

app.py

@@ -10,6 +10,7 @@ import pandas as pd
 import gradio as gr
 import papermill as pm
 import plotly.graph_objects as go
+from plotly.subplots import make_subplots
 
 # Optional LLM (HuggingFace Inference API)
 try:
@@ -603,6 +604,268 @@ def build_top_sellers_chart() -> go.Figure:
     return fig
 
 
+    # ---- Data loaders (cached at import) -------------------------------------
+
+def _load_lookup_data():
+    """Load Camille's analyzed Amazon dataset + time-series, cached on first call."""
+    main_path = BASE_DIR / "final_analyzed_amazon_dataset.csv"
+    ts_path = BASE_DIR / "synthetic_historical_sales_prices.csv"
+    if not main_path.exists() or not ts_path.exists():
+        return None, None
+    df = pd.read_csv(main_path)
+    ts = pd.read_csv(ts_path)
+    ts["date"] = pd.to_datetime(ts["date"])
+    # Group rare categories under "Other" — long tail is 6 categories with 1-2 products
+    cat_counts = df["main_category"].value_counts()
+    main_cats = cat_counts[cat_counts >= 10].index.tolist()
+    df["display_category"] = df["main_category"].where(
+        df["main_category"].isin(main_cats), "Other"
+    )
+    return df, ts
+
+
+_LOOKUP_DF, _LOOKUP_TS = _load_lookup_data()
+
+
+# ---- Helpers -------------------------------------------------------------
+
+def _short_name(name, n=70):
+    s = str(name)
+    return s if len(s) <= n else s[: n - 1] + "…"
+
+
+def _lookup_categories():
+    if _LOOKUP_DF is None:
+        return ["All categories"]
+    return ["All categories"] + sorted(_LOOKUP_DF["display_category"].unique().tolist())
+
+
+def _lookup_products(category):
+    if _LOOKUP_DF is None:
+        return []
+    sub = _LOOKUP_DF if category == "All categories" else _LOOKUP_DF[_LOOKUP_DF["display_category"] == category]
+    return [f"{_short_name(r['product_name'])}  |  {r['product_id']}" for _, r in sub.iterrows()]
+
+
+def _parse_pid(choice):
+    if not choice or "|" not in choice:
+        return None
+    return choice.split("|")[-1].strip()
+
+
+# ---- Recommendation badge (matches BubbleBusters glass-morphism style) ----
+
+REC_STYLES = {
+    "increase_price": ("#2ec4a0", "↑ INCREASE PRICE",
+                       "Demand signals support a price increase"),
+    "maintain_price": ("#7c5cbf", "→ MAINTAIN PRICE",
+                       "Current pricing is well-aligned with demand"),
+    "decrease_price": ("#e8537a", "↓ DECREASE PRICE",
+                       "Demand softness suggests a price reduction"),
+}
+
+
+def _render_lookup_recommendation(rec):
+    color, label, sub = REC_STYLES.get(rec, ("#9d8fc4", "—", ""))
+    return f"""
+    <div style="background:linear-gradient(135deg,{color}f0,{color}c0);
+                color:white;padding:28px 24px;border-radius:20px;text-align:center;
+                box-shadow:0 8px 24px rgba(124,92,191,.18);
+                border:1.5px solid rgba(255,255,255,.4);
+                backdrop-filter:blur(16px);margin-bottom:20px;">
+      <div style="font-size:30px;font-weight:800;letter-spacing:1.2px;">{label}</div>
+      <div style="font-size:13px;opacity:0.92;margin-top:6px;font-weight:500;">{sub}</div>
+    </div>
+    """
+
+
+def _render_lookup_kpi_cards(row):
+    cards = [
+        ("Current price",   f"₹{row['discounted_price']:,.0f}",
+         f"List ₹{row['actual_price']:,.0f}",                                  "#a48de8"),
+        ("Competitor price", f"₹{row['competitor_price']:,.0f}",
+         f"{((row['discounted_price'] - row['competitor_price']) / row['competitor_price'] * 100):+.1f}% vs us", "#7aa6f8"),
+        ("Customer rating", f"★ {row['rating']:.1f}",
+         f"{int(row['rating_count']):,} reviews",                              "#6ee7c7"),
+        ("Monthly sales",   f"{int(row['monthly_sales']):,}",
+         f"Demand idx {row['demand_index']:.0f}",                              "#3dcba8"),
+        ("Sentiment",       f"{row['sentiment_score']:+.2f}",
+         str(row['sentiment_label']).title(),                                  "#e8a230"),
+        ("Customer segment", str(row['customer_segment']).replace('_', ' ').title(),
+         f"Return rate {row['return_rate']*100:.1f}%",                         "#c45ea8"),
+    ]
+    html = (
+        '<div style="display:grid;'
+        'grid-template-columns:repeat(auto-fit,minmax(160px,1fr));'
+        'gap:14px;margin-bottom:20px;">'
+    )
+    for label, value, sub, accent in cards:
+        html += f"""
+        <div style="background:rgba(255,255,255,.72);backdrop-filter:blur(16px);
+                    border-radius:18px;padding:16px 14px;
+                    border:1.5px solid rgba(255,255,255,.8);
+                    box-shadow:0 4px 16px rgba(124,92,191,.08);
+                    border-top:3px solid {accent};">
+          <div style="color:#9d8fc4;font-size:9.5px;font-weight:800;
+                      text-transform:uppercase;letter-spacing:1.5px;
+                      margin-bottom:6px;">{label}</div>
+          <div style="color:#2d1f4e;font-size:18px;font-weight:800;
+                      margin-bottom:3px;">{value}</div>
+          <div style="color:#9d8fc4;font-size:11px;font-weight:500;">{sub}</div>
+        </div>"""
+    html += "</div>"
+    return html
+
+
+def _render_lookup_review(row):
+    text = str(row.get("review_content", ""))[:380]
+    truncated = "…" if len(str(row.get("review_content", ""))) > 380 else ""
+    return f"""
+    <div style="background:rgba(255,255,255,.72);backdrop-filter:blur(16px);
+                border-radius:18px;padding:18px 22px;
+                border:1.5px solid rgba(255,255,255,.8);
+                border-left:4px solid #7c5cbf;
+                box-shadow:0 4px 16px rgba(124,92,191,.08);
+                margin-bottom:20px;">
+      <div style="color:#4b2d8a;font-weight:800;font-size:11px;
+                  text-transform:uppercase;letter-spacing:1.5px;
+                  margin-bottom:8px;">Customer voice</div>
+      <div style="color:#2d1f4e;font-size:13px;line-height:1.6;">{text}{truncated}</div>
+    </div>
+    """
+
+
+# ---- Charts (use existing _styled_layout + CHART_PALETTE) ----------------
+
+def build_lookup_history(product_id):
+    """18-month history: sales (left axis) + price (right axis)."""
+    if _LOOKUP_TS is None:
+        return _empty_chart("Place CSVs at Space root to enable Product Lookup")
+    sub = _LOOKUP_TS[_LOOKUP_TS["product_id"] == product_id].sort_values("date")
+    if sub.empty:
+        return _empty_chart("No history for this product")
+
+    fig = make_subplots(specs=[[{"secondary_y": True}]])
+    fig.add_trace(
+        go.Scatter(
+            x=sub["date"], y=sub["historical_sales"], name="Sales (units)",
+            mode="lines+markers", line=dict(color="#7c5cbf", width=2.5),
+            marker=dict(size=5),
+            hovertemplate="<b>Sales</b>: %{y:,.0f}<br>%{x|%b %Y}<extra></extra>",
+        ),
+        secondary_y=False,
+    )
+    fig.add_trace(
+        go.Scatter(
+            x=sub["date"], y=sub["historical_price"], name="Price (₹)",
+            mode="lines+markers", line=dict(color="#e8a230", width=2, dash="dot"),
+            marker=dict(size=4),
+            hovertemplate="<b>Price</b>: ₹%{y:,.0f}<br>%{x|%b %Y}<extra></extra>",
+        ),
+        secondary_y=True,
+    )
+    fig.update_layout(**_styled_layout(
+        height=400, hovermode="x unified",
+        title=dict(text="18-month history: sales and price"),
+    ))
+    fig.update_xaxes(gridcolor="rgba(124,92,191,0.15)")
+    fig.update_yaxes(title_text="Sales (units)", secondary_y=False,
+                     gridcolor="rgba(124,92,191,0.15)")
+    fig.update_yaxes(title_text="Price (₹)", secondary_y=True, showgrid=False)
+    return fig
+
+
+def build_lookup_forecast(product_id):
+    """6-month sales projection with confidence band (trend-based)."""
+    if _LOOKUP_TS is None:
+        return _empty_chart("Place CSVs at Space root to enable Product Lookup")
+    sub = _LOOKUP_TS[_LOOKUP_TS["product_id"] == product_id].sort_values("date").copy()
+    if sub.empty or len(sub) < 6:
+        return _empty_chart("Not enough history to forecast")
+
+    recent = sub.tail(6)["historical_sales"].values
+    trend = (recent[-1] - recent[0]) / 5
+    last_val = recent[-1]
+    last_date = sub["date"].iloc[-1]
+    future_dates = pd.date_range(last_date, periods=7, freq="ME")[1:]
+    future_vals = [max(0, last_val + trend * (i + 1)) for i in range(6)]
+    std = sub["historical_sales"].tail(12).std()
+    upper = [v + std for v in future_vals]
+    lower = [max(0, v - std) for v in future_vals]
+
+    fig = go.Figure()
+    fig.add_trace(go.Scatter(
+        x=sub["date"], y=sub["historical_sales"], name="Historical",
+        mode="lines", line=dict(color="#7c5cbf", width=2.5),
+        hovertemplate="<b>Historical</b>: %{y:,.0f}<extra></extra>",
+    ))
+    fig.add_trace(go.Scatter(x=future_dates, y=upper, mode="lines",
+                             line=dict(width=0), showlegend=False, hoverinfo="skip"))
+    fig.add_trace(go.Scatter(
+        x=future_dates, y=lower, mode="lines", fill="tonexty",
+        fillcolor="rgba(46,196,160,0.18)", line=dict(width=0),
+        name="Forecast confidence", hoverinfo="skip",
+    ))
+    fig.add_trace(go.Scatter(
+        x=future_dates, y=future_vals, name="Forecast",
+        mode="lines+markers", line=dict(color="#2ec4a0", width=2.5, dash="dash"),
+        marker=dict(size=6),
+        hovertemplate="<b>Forecast</b>: %{y:,.0f}<extra></extra>",
+    ))
+    fig.update_layout(**_styled_layout(
+        height=400, hovermode="x unified",
+        title=dict(text="6-month sales forecast"),
+    ))
+    fig.update_xaxes(gridcolor="rgba(124,92,191,0.15)")
+    fig.update_yaxes(title_text="Sales (units)", gridcolor="rgba(124,92,191,0.15)")
+    return fig
+
+
+# ---- Update callback (called on dropdown changes) ------------------------
+
+def update_product_lookup(category, product_choice):
+    if _LOOKUP_DF is None:
+        msg = (
+            '<div style="background:rgba(255,255,255,.72);backdrop-filter:blur(16px);'
+            'border-radius:18px;padding:24px;text-align:center;'
+            'border:1.5px solid rgba(255,255,255,.8);'
+            'box-shadow:0 4px 16px rgba(124,92,191,.08);">'
+            '<div style="font-size:32px;margin-bottom:10px;">⚙️</div>'
+            '<div style="color:#4b2d8a;font-weight:800;margin-bottom:6px;">Data not loaded</div>'
+            '<div style="color:#9d8fc4;font-size:12px;">'
+            'Place <code>final_analyzed_amazon_dataset.csv</code> and '
+            '<code>synthetic_historical_sales_prices.csv</code> at the Space root.</div></div>'
+        )
+        return msg, "", "", _empty_chart(""), _empty_chart("")
+
+    pid = _parse_pid(product_choice)
+    if pid is None or pid not in _LOOKUP_DF["product_id"].values:
+        empty = ('<div style="padding:20px;color:#9d8fc4;text-align:center;">'
+                 'Select a product to see details</div>')
+        return empty, "", "", _empty_chart(""), _empty_chart("")
+
+    row = _LOOKUP_DF[_LOOKUP_DF["product_id"] == pid].iloc[0]
+    return (
+        _render_lookup_recommendation(row["pricing_recommendation"]),
+        _render_lookup_kpi_cards(row),
+        _render_lookup_review(row),
+        build_lookup_history(pid),
+        build_lookup_forecast(pid),
+    )
+
+
+def update_product_choices(category):
+    """When category changes, refresh the product dropdown."""
+    products = _lookup_products(category)
+    return gr.update(
+        choices=products,
+        value=products[0] if products else None,
+    )
+
+
+# ============================================================================
+
+
 def refresh_dashboard():
     return render_kpi_cards(), build_sales_chart(), build_sentiment_chart(), build_top_sellers_chart()
 
@@ -748,11 +1011,71 @@ with gr.Blocks(title="AIBDM 2026 Workshop App") as demo:
                     interactive=False,
                 )
 
-        user_input.submit(
+        user_input.submit( 
+
             ai_chat,
             inputs=[user_input, chatbot],
             outputs=[chatbot, user_input, ai_figure, ai_table],
         )
 
+                   
+
+    with gr.Tab("Product Lookup"):
+        gr.Markdown(
+            "### Single-product pricing deep dive\n"
+            "Pick any product to see its pricing recommendation, key metrics, "
+            "customer voice, 18-month history, and 6-month forecast."
+        )
+
+        with gr.Row():
+            with gr.Column(scale=1):
+                lookup_cat = gr.Dropdown(
+                    choices=_lookup_categories(),
+                    value="All categories",
+                    label="Filter by category",
+                )
+                lookup_prod = gr.Dropdown(
+                    choices=_lookup_products("All categories"),
+                    value=(
+                        _lookup_products("All categories")[0]
+                        if _lookup_products("All categories") else None
+                    ),
+                    label="Select a product",
+                )
+            with gr.Column(scale=2):
+                lookup_rec = gr.HTML()
+
+        lookup_kpis = gr.HTML()
+        lookup_review = gr.HTML()
+
+        with gr.Row():
+            lookup_history = gr.Plot()
+            lookup_forecast = gr.Plot()
+
+        lookup_cat.change(
+            fn=update_product_choices,
+            inputs=lookup_cat,
+            outputs=lookup_prod,
+        )
+        lookup_prod.change(
+            fn=update_product_lookup,
+            inputs=[lookup_cat, lookup_prod],
+            outputs=[lookup_rec, lookup_kpis, lookup_review,
+                     lookup_history, lookup_forecast],
+        )
+        demo.load(
+            fn=update_product_lookup,
+            inputs=[lookup_cat, lookup_prod],
+            outputs=[lookup_rec, lookup_kpis, lookup_review,
+                     lookup_history, lookup_forecast],
+        )
+
+
+            
+
+
+
+
+
 
 demo.launch(css=load_css(), allowed_paths=[str(BASE_DIR)])