Update app.py

app.py

@@ -58,9 +58,6 @@ def ensure_dirs():
 def stamp():
     return time.strftime("%Y%m%d-%H%M%S")
 
-def tail(text: str, n: int = MAX_LOG_CHARS) -> str:
-    return (text or "")[-n:]
-
 def _ls(dir_path: Path, exts: Tuple[str, ...]) -> List[str]:
     if not dir_path.is_dir():
         return []
@@ -102,7 +99,6 @@ def run_notebook(nb_name: str) -> str:
     )
     return f"Executed {nb_name}"
 
-
 def run_datacreation() -> str:
     try:
         log = run_notebook(NB1)
@@ -111,7 +107,6 @@ def run_datacreation() -> str:
     except Exception as e:
         return f"FAILED  {e}\n\n{traceback.format_exc()[-2000:]}"
 
-
 def run_pythonanalysis() -> str:
     try:
         log = run_notebook(NB2)
@@ -126,33 +121,29 @@ def run_pythonanalysis() -> str:
     except Exception as e:
         return f"FAILED  {e}\n\n{traceback.format_exc()[-2000:]}"
 
-
 def run_full_pipeline() -> str:
     logs = []
     logs.append("=" * 50)
-    logs.append("STEP 1/2: Data Creation (web scraping + synthetic data)")
+    logs.append("STEP 1/2: Data Creation")
     logs.append("=" * 50)
     logs.append(run_datacreation())
     logs.append("")
     logs.append("=" * 50)
-    logs.append("STEP 2/2: Python Analysis (sentiment, ARIMA, dashboard)")
+    logs.append("STEP 2/2: Python Analysis")
     logs.append("=" * 50)
     logs.append(run_pythonanalysis())
     return "\n".join(logs)
 
-
 # =========================================================
 # GALLERY LOADERS
 # =========================================================
 
 def _load_all_figures() -> List[Tuple[str, str]]:
-    """Return list of (filepath, caption) for Gallery."""
     items = []
     for p in sorted(PY_FIG_DIR.glob("*.png")):
-        items.append((str(p), p.stem.replace('_', ' ').title()))
+        items.append((str(p), p.stem.replace("_", " ").title()))
     return items
 
-
 def _load_table_safe(path: Path) -> pd.DataFrame:
     try:
         if path.suffix == ".json":
@@ -164,9 +155,7 @@ def _load_table_safe(path: Path) -> pd.DataFrame:
     except Exception as e:
         return pd.DataFrame([{"error": str(e)}])
 
-
 def refresh_gallery():
-    """Called when user clicks Refresh on Gallery tab."""
     figures = _load_all_figures()
     idx = artifacts_index()
 
@@ -182,7 +171,6 @@ def refresh_gallery():
         default_df,
     )
 
-
 def on_table_select(choice: str):
     if not choice:
         return pd.DataFrame([{"hint": "Select a table above."}])
@@ -191,57 +179,54 @@ def on_table_select(choice: str):
         return pd.DataFrame([{"error": f"File not found: {choice}"}])
     return _load_table_safe(path)
 
-
 # =========================================================
 # KPI LOADER
 # =========================================================
 
 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
+    candidate = PY_TAB_DIR / "kpis.json"
+    if candidate.exists():
+        try:
+            return _read_json(candidate)
+        except Exception:
+            pass
     return {}
 
-
 # =========================================================
-# AI DASHBOARD -- LLM picks what to display
+# AI DASHBOARD
 # =========================================================
 
-DASHBOARD_SYSTEM = """You are an AI dashboard assistant for a book-sales analytics app.
-The user asks questions or requests about their data. You have access to pre-computed
-artifacts from a Python analysis pipeline.
+DASHBOARD_SYSTEM = """You are an AI dashboard assistant for a food and nutrition analytics app.
+The user asks questions about food products, calories, sugar, fat, protein, fiber, salt,
+Nutri-Score, health labels, and nutrition trends.
+
+You have access to pre-computed artifacts from a Python analysis pipeline.
 
 AVAILABLE ARTIFACTS (only reference ones that exist):
 {artifacts_json}
 
-KPI SUMMARY: {kpis_json}
+KPI SUMMARY:
+{kpis_json}
 
 YOUR JOB:
-1. Answer the user's question conversationally using the KPIs and your knowledge of the artifacts.
-2. At the END of your response, output a JSON block (fenced with ```json ... ```) that tells
-   the dashboard which artifact to display. The JSON must have this shape:
+1. Answer the user's question conversationally using the KPIs and available artifacts.
+2. At the END of your response, output a JSON block fenced with ```json ... ```.
+3. The JSON must have this shape:
    {{"show": "figure"|"table"|"none", "scope": "python", "filename": "..."}}
 
-   - Use "show": "figure" to display a chart image.
-   - Use "show": "table" to display a CSV/JSON table.
-   - Use "show": "none" if no artifact is relevant.
-
-RULES:
-- If the user asks about sales trends or forecasting by title, show sales_trends or arima figures.
-- If the user asks about sentiment, show sentiment figure or sentiment_counts table.
-- If the user asks about forecast accuracy or ARIMA, show arima figures.
-- If the user asks about top sellers, show top_titles_by_units_sold.csv.
-- If the user asks a general data question, pick the most relevant artifact.
-- Keep your answer concise (2-4 sentences), then the JSON block.
+Rules:
+- If the user asks about calories, energy, or nutrition overview, prefer food_dashboard.csv or calorie charts.
+- If the user asks about sugar, fat, salt, protein, or fiber by health label, show the relevant table or figure.
+- If the user asks about health label distribution, show the health label figure or table.
+- If the user asks about Nutri-Score versus health label, show the comparison figure or table.
+- If the user asks for recommendations, show recommendations.csv.
+- If no artifact is relevant, return show = none.
+- Keep your answer concise.
 """
 
 JSON_BLOCK_RE = re.compile(r"```json\s*(\{.*?\})\s*```", re.DOTALL)
 FALLBACK_JSON_RE = re.compile(r"\{[^{}]*\"show\"[^{}]*\}", re.DOTALL)
 
-
 def _parse_display_directive(text: str) -> Dict[str, str]:
     m = JSON_BLOCK_RE.search(text)
     if m:
@@ -257,14 +242,10 @@ def _parse_display_directive(text: str) -> Dict[str, str]:
             pass
     return {"show": "none"}
 
-
 def _clean_response(text: str) -> str:
-    """Strip the JSON directive block from the displayed response."""
     return JSON_BLOCK_RE.sub("", text).strip()
 
-
-def _n8n_call(msg: str) -> Tuple[str, Dict]:
-    """Call the student's n8n webhook and return (reply, directive)."""
+def _n8n_call(msg: str):
     import requests as req
     try:
         resp = req.post(N8N_WEBHOOK_URL, json={"question": msg}, timeout=20)
@@ -277,16 +258,13 @@ def _n8n_call(msg: str) -> Tuple[str, Dict]:
     except Exception as e:
         return f"n8n error: {e}. Falling back to keyword matching.", None
 
-
 def ai_chat(user_msg: str, history: list):
-    """Chat function for the AI Dashboard tab."""
     if not user_msg or not user_msg.strip():
         return history, "", None, None
 
     idx = artifacts_index()
     kpis = load_kpis()
 
-    # Priority: n8n webhook > HF LLM > keyword fallback
     if N8N_WEBHOOK_URL:
         reply, directive = _n8n_call(user_msg)
         if directive is None:
@@ -324,32 +302,30 @@ def ai_chat(user_msg: str, history: list):
             reply_fb, directive = _keyword_fallback(user_msg, idx, kpis)
             reply += "\n\n" + reply_fb
 
-    # Resolve artifacts — build interactive Plotly charts when possible
     chart_out = None
     tab_out = None
     show = directive.get("show", "none")
     fname = directive.get("filename", "")
     chart_name = directive.get("chart", "")
 
-    # Interactive chart builders keyed by name
     chart_builders = {
-        "sales": build_sales_chart,
-        "sentiment": build_sentiment_chart,
-        "top_sellers": build_top_sellers_chart,
+        "calories": build_calories_chart,
+        "health_label": build_health_label_chart,
+        "nutriscore": build_nutriscore_chart,
+        "macros": build_macros_chart,
     }
 
     if chart_name and chart_name in chart_builders:
         chart_out = chart_builders[chart_name]()
     elif show == "figure" and fname:
-        # Fallback: try to match filename to a chart builder
-        if "sales_trend" in fname:
-            chart_out = build_sales_chart()
-        elif "sentiment" in fname:
-            chart_out = build_sentiment_chart()
-        elif "arima" in fname or "forecast" in fname:
-            chart_out = build_sales_chart()  # closest interactive equivalent
-        else:
-            chart_out = _empty_chart(f"No interactive chart for {fname}")
+        if "calorie" in fname or "energy" in fname:
+            chart_out = build_calories_chart()
+        elif "health_label" in fname:
+            chart_out = build_health_label_chart()
+        elif "nutriscore" in fname:
+            chart_out = build_nutriscore_chart()
+        elif "macro" in fname or "nutrition" in fname:
+            chart_out = build_macros_chart()
 
     if show == "table" and fname:
         fp = PY_TAB_DIR / fname
@@ -365,74 +341,66 @@ def ai_chat(user_msg: str, history: list):
 
     return new_history, "", chart_out, tab_out
 
-
-def _keyword_fallback(msg: str, idx: Dict, kpis: Dict) -> Tuple[str, Dict]:
-    """Simple keyword matcher when LLM is unavailable."""
+def _keyword_fallback(msg: str, idx: Dict, kpis: Dict):
     msg_lower = msg.lower()
 
     if not idx["python"]["figures"] and not idx["python"]["tables"]:
         return (
-            "No artifacts found yet. Please run the pipeline first (Tab 1), "
-            "then come back here to explore the results.",
+            "No artifacts found yet. Please run the pipeline first, then come back here.",
             {"show": "none"},
         )
 
     kpi_text = ""
     if kpis:
-        total = kpis.get("total_units_sold", 0)
         kpi_text = (
-            f"Quick summary: **{kpis.get('n_titles', '?')}** book titles across "
-            f"**{kpis.get('n_months', '?')}** months, with **{total:,.0f}** total units sold."
+            f"Quick summary: **{kpis.get('n_products', '?')}** food products, "
+            f"average calories **{kpis.get('avg_calories_per_100g', '?')} kcal/100g**, "
+            f"and **{kpis.get('healthy_count', '?')}** products labelled healthy."
         )
 
-    if any(w in msg_lower for w in ["trend", "sales trend", "monthly sale"]):
+    if any(w in msg_lower for w in ["calorie", "calories", "energy"]):
         return (
-            f"Here are the sales trends. {kpi_text}",
-            {"show": "figure", "chart": "sales"},
+            f"Here is the calorie overview for your food dataset. {kpi_text}",
+            {"show": "figure", "chart": "calories"},
         )
 
-    if any(w in msg_lower for w in ["sentiment", "review", "positive", "negative"]):
+    if any(w in msg_lower for w in ["health label", "healthy", "unhealthy", "moderate"]):
         return (
-            f"Here is the sentiment distribution across sampled book titles. {kpi_text}",
-            {"show": "figure", "chart": "sentiment"},
+            f"Here is the health label distribution. {kpi_text}",
+            {"show": "figure", "chart": "health_label"},
         )
 
-    if any(w in msg_lower for w in ["arima", "forecast", "predict"]):
+    if any(w in msg_lower for w in ["nutriscore", "nutri-score", "grade"]):
         return (
-            f"Here are the sales trends and forecasts. {kpi_text}",
-            {"show": "figure", "chart": "sales"},
+            f"Here is the Nutri-Score overview. {kpi_text}",
+            {"show": "figure", "chart": "nutriscore"},
         )
 
-    if any(w in msg_lower for w in ["top", "best sell", "popular", "rank"]):
+    if any(w in msg_lower for w in ["protein", "fat", "sugar", "salt", "fiber", "nutrition", "macros"]):
         return (
-            f"Here are the top-selling titles by units sold. {kpi_text}",
-            {"show": "table", "scope": "python", "filename": "top_titles_by_units_sold.csv"},
+            f"Here is the nutrition breakdown across health labels. {kpi_text}",
+            {"show": "figure", "chart": "macros"},
         )
 
-    if any(w in msg_lower for w in ["price", "pricing", "decision"]):
+    if any(w in msg_lower for w in ["recommendation", "recommend", "action"]):
         return (
-            f"Here are the pricing decisions. {kpi_text}",
-            {"show": "table", "scope": "python", "filename": "pricing_decisions.csv"},
+            f"Here are the recommendation actions for the products. {kpi_text}",
+            {"show": "table", "scope": "python", "filename": "recommendations.csv"},
         )
 
-    if any(w in msg_lower for w in ["dashboard", "overview", "summary", "kpi"]):
+    if any(w in msg_lower for w in ["overview", "dashboard", "summary", "kpi"]):
         return (
-            f"Dashboard overview: {kpi_text}\n\nAsk me about sales trends, sentiment, forecasts, "
-            "pricing, or top sellers to see specific visualizations.",
-            {"show": "table", "scope": "python", "filename": "df_dashboard.csv"},
+            f"Dashboard overview: {kpi_text}",
+            {"show": "table", "scope": "python", "filename": "food_dashboard.csv"},
         )
 
-    # Default
     return (
-        f"I can show you various analyses. {kpi_text}\n\n"
-        "Try asking about: **sales trends**, **sentiment**, **ARIMA forecasts**, "
-        "**pricing decisions**, **top sellers**, or **dashboard overview**.",
+        f"I can help with calories, protein, fat, sugar, salt, fiber, Nutri-Score, health labels, and recommendations. {kpi_text}",
         {"show": "none"},
     )
 
-
 # =========================================================
-# KPI CARDS (BubbleBusters style)
+# KPI CARDS
 # =========================================================
 
 def render_kpi_cards() -> str:
@@ -443,11 +411,9 @@ def render_kpi_cards() -> str:
             'border-radius:20px;padding:28px;text-align:center;'
             'border:1.5px solid rgba(255,255,255,.7);'
             'box-shadow:0 8px 32px rgba(124,92,191,.08);">'
-            '<div style="font-size:36px;margin-bottom:10px;">📊</div>'
-            '<div style="color:#a48de8;font-size:14px;'
-            'font-weight:800;margin-bottom:6px;">No data yet</div>'
-            '<div style="color:#9d8fc4;font-size:12px;">'
-            'Run the pipeline to populate these cards.</div>'
+            '<div style="font-size:36px;margin-bottom:10px;">🍽️</div>'
+            '<div style="color:#a48de8;font-size:14px;font-weight:800;margin-bottom:6px;">No data yet</div>'
+            '<div style="color:#9d8fc4;font-size:12px;">Run the pipeline to populate these cards.</div>'
             '</div>'
         )
 
@@ -465,16 +431,13 @@ 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_products", "🍎", "Products", "#a48de8"),
+        ("avg_calories_per_100g", "🔥", "Avg Calories", "#7aa6f8"),
+        ("healthy_count", "🥗", "Healthy", "#6ee7c7"),
+        ("unhealthy_count", "⚠️", "Unhealthy", "#3dcba8"),
     ]
 
-    html = (
-        '<div style="display:grid;grid-template-columns:repeat(auto-fit,minmax(140px,1fr));'
-        'gap:12px;margin-bottom:24px;">'
-    )
+    html = '<div style="display:grid;grid-template-columns:repeat(auto-fit,minmax(140px,1fr));gap:12px;margin-bottom:24px;">'
     for key, icon, label, colour in kpi_config:
         val = kpis.get(key)
         if val is None:
@@ -482,24 +445,14 @@ def render_kpi_cards() -> str:
         if isinstance(val, (int, float)) and val > 100:
             val = f"{val:,.0f}"
         html += card(icon, label, str(val), colour)
-    # Extra KPIs not in config
-    known = {k for k, *_ in kpi_config}
-    for key, val in kpis.items():
-        if key not in known:
-            label = key.replace("_", " ").title()
-            if isinstance(val, (int, float)) and val > 100:
-                val = f"{val:,.0f}"
-            html += card("📈", label, str(val), "#8fa8f8")
     html += "</div>"
     return html
 
-
 # =========================================================
-# INTERACTIVE PLOTLY CHARTS (BubbleBusters style)
+# CHARTS
 # =========================================================
 
-CHART_PALETTE = ["#7c5cbf", "#2ec4a0", "#e8537a", "#e8a230", "#5e8fef",
-                 "#c45ea8", "#3dbacc", "#a0522d", "#6aaa3a", "#d46060"]
+CHART_PALETTE = ["#7c5cbf", "#2ec4a0", "#e8537a", "#e8a230", "#5e8fef"]
 
 def _styled_layout(**kwargs) -> dict:
     defaults = dict(
@@ -508,104 +461,114 @@ def _styled_layout(**kwargs) -> dict:
         plot_bgcolor="rgba(255,255,255,0.98)",
         font=dict(family="system-ui, sans-serif", color="#2d1f4e", size=12),
         margin=dict(l=60, r=20, t=70, b=70),
-        legend=dict(
-            orientation="h", yanchor="bottom", y=1.02, xanchor="right", x=1,
-            bgcolor="rgba(255,255,255,0.92)",
-            bordercolor="rgba(124,92,191,0.35)", borderwidth=1,
-        ),
         title=dict(font=dict(size=15, color="#4b2d8a")),
     )
     defaults.update(kwargs)
     return defaults
 
-
 def _empty_chart(title: str) -> go.Figure:
     fig = go.Figure()
     fig.update_layout(
-        title=title, height=420, template="plotly_white",
+        title=title,
+        height=420,
+        template="plotly_white",
         paper_bgcolor="rgba(255,255,255,0.95)",
-        annotations=[dict(text="Run the pipeline to generate data",
-            x=0.5, y=0.5, xref="paper", yref="paper", showarrow=False,
-            font=dict(size=14, color="rgba(124,92,191,0.5)"))],
+        annotations=[dict(
+            text="Run the pipeline to generate data",
+            x=0.5, y=0.5, xref="paper", yref="paper",
+            showarrow=False,
+            font=dict(size=14, color="rgba(124,92,191,0.5)")
+        )],
     )
     return fig
 
+def build_calories_chart() -> go.Figure:
+    path = PY_TAB_DIR / "food_dashboard.csv"
+    if not path.exists():
+        return _empty_chart("Calories Overview — run the pipeline first")
+    df = pd.read_csv(path).sort_values("energy-kcal_100g", ascending=False).head(15)
+
+    fig = go.Figure(go.Bar(
+        x=df["energy-kcal_100g"],
+        y=df["product_name"],
+        orientation="h"
+    ))
+    fig.update_layout(**_styled_layout(
+        height=500,
+        title=dict(text="Top 15 Products by Calories (per 100g)")
+    ))
+    fig.update_yaxes(autorange="reversed")
+    fig.update_xaxes(title="Calories per 100g")
+    return fig
 
-def build_sales_chart() -> go.Figure:
-    path = PY_TAB_DIR / "df_dashboard.csv"
+def build_health_label_chart() -> go.Figure:
+    path = PY_TAB_DIR / "health_label_counts.csv"
     if not path.exists():
-        return _empty_chart("Sales Trends — run the pipeline first")
+        return _empty_chart("Health Label Distribution — 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")
-    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.update_xaxes(gridcolor="rgba(124,92,191,0.15)", showgrid=True)
-    fig.update_yaxes(gridcolor="rgba(124,92,191,0.15)", showgrid=True)
-    return fig
 
+    fig = go.Figure(go.Bar(
+        x=df["health_label"],
+        y=df["count"]
+    ))
+    fig.update_layout(**_styled_layout(
+        height=420,
+        title=dict(text="Health Label Distribution")
+    ))
+    fig.update_xaxes(title="Health Label")
+    fig.update_yaxes(title="Count")
+    return fig
 
-def build_sentiment_chart() -> go.Figure:
-    path = PY_TAB_DIR / "sentiment_counts_sampled.csv"
+def build_nutriscore_chart() -> go.Figure:
+    path = PY_TAB_DIR / "nutriscore_vs_health.csv"
     if not path.exists():
-        return _empty_chart("Sentiment Distribution — run the pipeline first")
+        return _empty_chart("Nutri-Score vs Health Label — 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:
+    for label in df["health_label"].unique():
+        sub = df[df["health_label"] == label]
         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>",
+            x=sub["nutriscore_grade"],
+            y=sub["count"],
+            name=label
         ))
+
     fig.update_layout(**_styled_layout(
-        height=max(400, len(df) * 28), barmode="stack",
-        title=dict(text="Sentiment Distribution by Book"),
+        height=450,
+        barmode="stack",
+        title=dict(text="Nutri-Score vs Health Label")
     ))
-    fig.update_xaxes(title="Number of Reviews")
-    fig.update_yaxes(autorange="reversed")
+    fig.update_xaxes(title="Nutri-Score Grade")
+    fig.update_yaxes(title="Count")
     return fig
 
-
-def build_top_sellers_chart() -> go.Figure:
-    path = PY_TAB_DIR / "top_titles_by_units_sold.csv"
+def build_macros_chart() -> go.Figure:
+    path = PY_TAB_DIR / "nutrition_by_health_label.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])
-    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>",
-    ))
+        return _empty_chart("Nutrition by Health Label — run the pipeline first")
+    df = pd.read_csv(path)
+
+    fig = go.Figure()
+    for col in ["sugars_100g", "fat_100g", "salt_100g", "proteins_100g", "fiber_100g"]:
+        if col in df.columns:
+            fig.add_trace(go.Bar(
+                x=df["health_label"],
+                y=df[col],
+                name=col.replace("_100g", "").replace("_", " ").title()
+            ))
+
     fig.update_layout(**_styled_layout(
-        height=max(400, len(df) * 30),
-        title=dict(text="Top Selling Titles"), showlegend=False,
+        height=450,
+        barmode="group",
+        title=dict(text="Nutrition by Health Label")
     ))
-    fig.update_yaxes(autorange="reversed")
-    fig.update_xaxes(title="Total Units Sold")
+    fig.update_xaxes(title="Health Label")
+    fig.update_yaxes(title="Average per 100g")
     return fig
 
-
 def refresh_dashboard():
-    return render_kpi_cards(), build_sales_chart(), build_sentiment_chart(), build_top_sellers_chart()
-
+    return render_kpi_cards(), build_calories_chart(), build_health_label_chart(), build_nutriscore_chart()
 
 # =========================================================
 # UI
@@ -617,21 +580,15 @@ def load_css() -> str:
     css_path = BASE_DIR / "style.css"
     return css_path.read_text(encoding="utf-8") if css_path.exists() else ""
 
-
-with gr.Blocks(title="AIBDM 2026 Workshop App") as demo:
+with gr.Blocks(title="Food Nutrition Dashboard") as demo:
 
     gr.Markdown(
         "# SE21 App Template\n"
-        "*This is an app template for SE21 students*",
+        "*Food and nutrition analytics dashboard*",
         elem_id="escp_title",
     )
 
-    # ===========================================================
-    # TAB 1 -- Pipeline Runner
-    # ===========================================================
     with gr.Tab("Pipeline Runner"):
-        gr.Markdown()
-
         with gr.Row():
             with gr.Column(scale=1):
                 btn_nb1 = gr.Button("Step 1: Data Creation", variant="secondary")
@@ -641,48 +598,27 @@ with gr.Blocks(title="AIBDM 2026 Workshop App") as demo:
         with gr.Row():
             btn_all = gr.Button("Run Full Pipeline (Both Steps)", variant="primary")
 
-        run_log = gr.Textbox(
-            label="Execution Log",
-            lines=18,
-            max_lines=30,
-            interactive=False,
-        )
+        run_log = gr.Textbox(label="Execution Log", lines=18, max_lines=30, interactive=False)
 
         btn_nb1.click(run_datacreation, outputs=[run_log])
         btn_nb2.click(run_pythonanalysis, outputs=[run_log])
         btn_all.click(run_full_pipeline, outputs=[run_log])
 
-    # ===========================================================
-    # TAB 2 -- Dashboard (KPIs + Interactive Charts + Gallery)
-    # ===========================================================
     with gr.Tab("Dashboard"):
         kpi_html = gr.HTML(value=render_kpi_cards)
-
         refresh_btn = gr.Button("Refresh Dashboard", variant="primary")
 
         gr.Markdown("#### Interactive Charts")
-        chart_sales = gr.Plot(label="Monthly Overview")
-        chart_sentiment = gr.Plot(label="Sentiment Distribution")
-        chart_top = gr.Plot(label="Top Sellers")
+        chart_calories = gr.Plot(label="Calories Overview")
+        chart_health = gr.Plot(label="Health Label Distribution")
+        chart_nutri = gr.Plot(label="Nutri-Score Comparison")
 
         gr.Markdown("#### Static Figures (from notebooks)")
-        gallery = gr.Gallery(
-            label="Generated Figures",
-            columns=2,
-            height=480,
-            object_fit="contain",
-        )
+        gallery = gr.Gallery(label="Generated Figures", columns=2, height=480, object_fit="contain")
 
         gr.Markdown("#### Data Tables")
-        table_dropdown = gr.Dropdown(
-            label="Select a table to view",
-            choices=[],
-            interactive=True,
-        )
-        table_display = gr.Dataframe(
-            label="Table Preview",
-            interactive=False,
-        )
+        table_dropdown = gr.Dropdown(label="Select a table to view", choices=[], interactive=True)
+        table_display = gr.Dataframe(label="Table Preview", interactive=False)
 
         def _on_refresh():
             kpi, c1, c2, c3 = refresh_dashboard()
@@ -691,62 +627,44 @@ with gr.Blocks(title="AIBDM 2026 Workshop App") as demo:
 
         refresh_btn.click(
             _on_refresh,
-            outputs=[kpi_html, chart_sales, chart_sentiment, chart_top,
-                     gallery, table_dropdown, table_display],
-        )
-        table_dropdown.change(
-            on_table_select,
-            inputs=[table_dropdown],
-            outputs=[table_display],
+            outputs=[kpi_html, chart_calories, chart_health, chart_nutri, gallery, table_dropdown, table_display],
         )
+        table_dropdown.change(on_table_select, inputs=[table_dropdown], outputs=[table_display])
 
-    # ===========================================================
-    # TAB 3 -- AI Dashboard
-    # ===========================================================
     with gr.Tab('"AI" Dashboard'):
         _ai_status = (
             "Connected to your **n8n workflow**." if N8N_WEBHOOK_URL
             else "**LLM active.**" if LLM_ENABLED
-            else "Using **keyword matching**. Upgrade options: "
-                 "set `N8N_WEBHOOK_URL` to connect your n8n workflow, "
-                 "or set `HF_API_KEY` for direct LLM access."
+            else "Using **keyword matching**."
         )
         gr.Markdown(
             "### Ask questions, get interactive visualisations\n\n"
-            f"Type a question and the system will pick the right interactive chart or table. {_ai_status}"
+            f"Type a question and the system will pick the right chart or table. {_ai_status}"
         )
 
         with gr.Row(equal_height=True):
             with gr.Column(scale=1):
-                chatbot = gr.Chatbot(
-                    label="Conversation",
-                    height=380,
-                )
+                chatbot = gr.Chatbot(label="Conversation", height=380)
                 user_input = gr.Textbox(
                     label="Ask about your data",
-                    placeholder="e.g. Show me sales trends / What are the top sellers? / Sentiment analysis",
+                    placeholder="e.g. Show me calories / Which foods have the most protein? / Show health label distribution",
                     lines=1,
                 )
                 gr.Examples(
                     examples=[
-                        "Show me the sales trends",
-                        "What does the sentiment look like?",
-                        "Which titles sell the most?",
-                        "Show the ARIMA forecasts",
-                        "What are the pricing decisions?",
+                        "Show me calories",
+                        "Which products are highest in sugar?",
+                        "Show health label distribution",
+                        "Compare Nutri-Score and health label",
+                        "Show me protein and fat by health label",
                         "Give me a dashboard overview",
                     ],
                     inputs=user_input,
                 )
 
             with gr.Column(scale=1):
-                ai_figure = gr.Plot(
-                    label="Interactive Chart",
-                )
-                ai_table = gr.Dataframe(
-                    label="Data Table",
-                    interactive=False,
-                )
+                ai_figure = gr.Plot(label="Interactive Chart")
+                ai_table = gr.Dataframe(label="Data Table", interactive=False)
 
         user_input.submit(
             ai_chat,
@@ -754,5 +672,4 @@ with gr.Blocks(title="AIBDM 2026 Workshop App") as demo:
             outputs=[chatbot, user_input, ai_figure, ai_table],
         )
 
-
 demo.launch(css=load_css(), allowed_paths=[str(BASE_DIR)])