Upload app.py

app.py

@@ -0,0 +1,863 @@
+import os
+import re
+import json
+import time
+import traceback
+from pathlib import Path
+from typing import Dict, Any, List, Tuple
+
+import pandas as pd
+import gradio as gr
+import papermill as pm
+import plotly.graph_objects as go
+
+# Optional LLM (HuggingFace Inference API)
+try:
+    from huggingface_hub import InferenceClient
+except Exception:
+    InferenceClient = None
+
+# =========================================================
+# CONFIG
+# =========================================================
+
+BASE_DIR = Path(__file__).resolve().parent
+
+NB1 = os.environ.get("NB1", "datacreation.ipynb").strip()
+NB2 = os.environ.get("NB2", "pythonanalysis.ipynb").strip()
+
+RUNS_DIR = BASE_DIR / "runs"
+ART_DIR  = BASE_DIR / "artifacts"
+PY_FIG_DIR = ART_DIR / "py" / "figures"
+PY_TAB_DIR = ART_DIR / "py" / "tables"
+
+PAPERMILL_TIMEOUT = int(os.environ.get("PAPERMILL_TIMEOUT", "1800"))
+MAX_PREVIEW_ROWS  = int(os.environ.get("MAX_FILE_PREVIEW_ROWS", "50"))
+MAX_LOG_CHARS     = int(os.environ.get("MAX_LOG_CHARS", "8000"))
+
+HF_API_KEY    = os.environ.get("HF_API_KEY", "").strip()
+MODEL_NAME    = os.environ.get("MODEL_NAME", "deepseek-ai/DeepSeek-R1").strip()
+HF_PROVIDER   = os.environ.get("HF_PROVIDER", "novita").strip()
+N8N_WEBHOOK_URL = os.environ.get("N8N_WEBHOOK_URL", "").strip()
+
+LLM_ENABLED = bool(HF_API_KEY) and InferenceClient is not None
+llm_client  = (
+    InferenceClient(provider=HF_PROVIDER, api_key=HF_API_KEY)
+    if LLM_ENABLED else None
+)
+
+# =========================================================
+# HELPERS
+# =========================================================
+
+def ensure_dirs():
+    for p in [RUNS_DIR, ART_DIR, PY_FIG_DIR, PY_TAB_DIR]:
+        p.mkdir(parents=True, exist_ok=True)
+
+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 []
+    return sorted(p.name for p in dir_path.iterdir()
+                  if p.is_file() and p.suffix.lower() in exts)
+
+def _read_csv(path: Path) -> pd.DataFrame:
+    return pd.read_csv(path, nrows=MAX_PREVIEW_ROWS)
+
+def _read_json(path: Path):
+    with path.open(encoding="utf-8") as f:
+        return json.load(f)
+
+def artifacts_index() -> Dict[str, Any]:
+    return {
+        "python": {
+            "figures": _ls(PY_FIG_DIR, (".png", ".jpg", ".jpeg")),
+            "tables":  _ls(PY_TAB_DIR, (".csv", ".json")),
+        },
+    }
+
+# =========================================================
+# PIPELINE RUNNERS
+# =========================================================
+
+def run_notebook(nb_name: str) -> str:
+    ensure_dirs()
+    nb_in = BASE_DIR / nb_name
+    if not nb_in.exists():
+        return f"ERROR: {nb_name} not found."
+    nb_out = RUNS_DIR / f"run_{stamp()}_{nb_name}"
+    pm.execute_notebook(
+        input_path=str(nb_in),
+        output_path=str(nb_out),
+        cwd=str(BASE_DIR),
+        log_output=True,
+        progress_bar=False,
+        request_save_on_cell_execute=True,
+        execution_timeout=PAPERMILL_TIMEOUT,
+    )
+    return f"Executed {nb_name}"
+
+
+def run_datacreation() -> str:
+    try:
+        log = run_notebook(NB1)
+        csvs = [f.name for f in BASE_DIR.glob("*.csv")]
+        return f"OK  {log}\n\nCSVs now in /app:\n" + "\n".join(f"  - {c}" for c in sorted(csvs))
+    except Exception as e:
+        return f"FAILED  {e}\n\n{traceback.format_exc()[-2000:]}"
+
+
+def run_pythonanalysis() -> str:
+    try:
+        log = run_notebook(NB2)
+        idx  = artifacts_index()
+        figs = idx["python"]["figures"]
+        tabs = idx["python"]["tables"]
+        return (
+            f"OK  {log}\n\n"
+            f"Figures: {', '.join(figs) or '(none)'}\n"
+            f"Tables:  {', '.join(tabs) or '(none)'}"
+        )
+    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 (real data + synthetic enrichment)")
+    logs.append("=" * 50)
+    logs.append(run_datacreation())
+    logs.append("")
+    logs.append("=" * 50)
+    logs.append("STEP 2/2: Python Analysis (sentiment, dashboard, decisions)")
+    logs.append("=" * 50)
+    logs.append(run_pythonanalysis())
+    return "\n".join(logs)
+
+
+# =========================================================
+# GALLERY LOADERS
+# =========================================================
+
+def _load_all_figures() -> List[Tuple[str, str]]:
+    items = []
+    for p in sorted(PY_FIG_DIR.glob("*.png")):
+        items.append((str(p), p.stem.replace("_", " ").title()))
+    return items
+
+
+def _load_table_safe(path: Path) -> pd.DataFrame:
+    try:
+        if path.suffix == ".json":
+            obj = _read_json(path)
+            if isinstance(obj, dict):
+                return pd.DataFrame([obj])
+            return pd.DataFrame(obj)
+        return _read_csv(path)
+    except Exception as e:
+        return pd.DataFrame([{"error": str(e)}])
+
+
+def refresh_gallery():
+    figures = _load_all_figures()
+    idx     = artifacts_index()
+    table_choices = list(idx["python"]["tables"])
+    default_df    = pd.DataFrame()
+    if table_choices:
+        default_df = _load_table_safe(PY_TAB_DIR / table_choices[0])
+    return (
+        figures if figures else [],
+        gr.update(choices=table_choices,
+                  value=table_choices[0] if table_choices else None),
+        default_df,
+    )
+
+
+def on_table_select(choice: str):
+    if not choice:
+        return pd.DataFrame([{"hint": "Select a table above."}])
+    path = PY_TAB_DIR / choice
+    if not path.exists():
+        return pd.DataFrame([{"error": f"File not found: {choice}"}])
+    return _load_table_safe(path)
+
+
+# =========================================================
+# KPI LOADER
+# =========================================================
+
+def load_kpis() -> Dict[str, Any]:
+    # Check both the tables folder and the root directory
+    for candidate in [
+        PY_TAB_DIR / "kpis.json",
+        BASE_DIR / "kpis.json",
+    ]:
+        if candidate.exists():
+            try:
+                return _read_json(candidate)
+            except Exception:
+                pass
+    return {}
+
+
+# =========================================================
+# KPI CARDS
+# =========================================================
+
+def render_kpi_cards() -> str:
+    kpis = load_kpis()
+    if not kpis:
+        return (
+            '<div style="background:rgba(255,255,255,.65);backdrop-filter:blur(16px);'
+            '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 KPI data yet</div>'
+            '<div style="color:#9d8fc4;font-size:12px;">Run the pipeline or upload kpis.json to populate these cards.</div>'
+            '</div>'
+        )
+
+    def card(icon, label, value, colour):
+        return (
+            f'<div style="background:rgba(255,255,255,.72);backdrop-filter:blur(16px);'
+            f'border-radius:20px;padding:18px 14px 16px;text-align:center;'
+            f'border:1.5px solid rgba(255,255,255,.8);'
+            f'box-shadow:0 4px 16px rgba(124,92,191,.08);border-top:3px solid {colour};">'
+            f'<div style="font-size:26px;margin-bottom:7px;line-height:1;">{icon}</div>'
+            f'<div style="color:#9d8fc4;font-size:9.5px;text-transform:uppercase;'
+            f'letter-spacing:1.8px;margin-bottom:7px;font-weight:800;">{label}</div>'
+            f'<div style="color:#2d1f4e;font-size:16px;font-weight:800;">{value}</div>'
+            f'</div>'
+        )
+
+    # Map our food-review KPI keys to icons/labels/colours
+    kpi_config = [
+        ("total_reviews",       "🧾", "Total Reviews",   "#a48de8"),
+        ("real_reviews",        "📦", "Real Reviews",    "#7aa6f8"),
+        ("synthetic_reviews",   "🤖", "Synthetic",       "#6ee7c7"),
+        ("unique_products",     "🛒", "Unique Products", "#3dcba8"),
+        ("avg_rating",          "⭐", "Avg Rating",      "#e8a230"),
+        ("pct_positive",        "😊", "% Positive",      "#2ec4a0"),
+        ("pct_negative",        "😞", "% Negative",      "#e8537a"),
+        ("avg_sentiment_score", "📈", "Avg Sentiment",   "#5e8fef"),
+    ]
+
+    html = (
+        '<div style="display:grid;grid-template-columns:repeat(auto-fit,minmax(130px,1fr));'
+        'gap:12px;margin-bottom:24px;">'
+    )
+    shown = set()
+    for key, icon, label, colour in kpi_config:
+        val = kpis.get(key)
+        if val is None:
+            continue
+        shown.add(key)
+        if isinstance(val, float):
+            display_val = f"{val:.2f}"
+        elif isinstance(val, int) and val > 999:
+            display_val = f"{val:,}"
+        else:
+            display_val = str(val)
+        html += card(icon, label, display_val, colour)
+
+    # Any extra keys not in config
+    for key, val in kpis.items():
+        if key not in shown:
+            label = key.replace("_", " ").title()
+            display_val = f"{val:,.0f}" if isinstance(val, (int, float)) and val > 100 else str(val)
+            html += card("📊", label, display_val, "#8fa8f8")
+
+    html += "</div>"
+    return html
+
+
+# =========================================================
+# INTERACTIVE PLOTLY CHARTS — Food Reviews
+# =========================================================
+
+CHART_PALETTE = [
+    "#7c5cbf", "#2ec4a0", "#e8537a", "#e8a230",
+    "#5e8fef", "#c45ea8", "#3dbacc", "#a0522d",
+    "#6aaa3a", "#d46060",
+]
+
+
+def _styled_layout(**kwargs) -> dict:
+    defaults = dict(
+        template="plotly_white",
+        paper_bgcolor="rgba(255,255,255,0.95)",
+        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",
+        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)"),
+        )],
+    )
+    return fig
+
+
+def build_sales_chart() -> go.Figure:
+    """Rating & Sentiment overview — reads df_dashboard.csv."""
+    # Try both locations: artifacts/py/tables/ and root
+    for candidate in [PY_TAB_DIR / "df_dashboard.csv", BASE_DIR / "df_dashboard.csv"]:
+        if candidate.exists():
+            path = candidate
+            break
+    else:
+        return _empty_chart("Rating & Sentiment Overview — run the pipeline first")
+
+    try:
+        df = pd.read_csv(path)
+    except Exception as e:
+        return _empty_chart(f"Error reading df_dashboard.csv: {e}")
+
+    if "sentiment_label" not in df.columns:
+        return _empty_chart("sentiment_label column not found in df_dashboard.csv")
+
+    fig = go.Figure()
+
+    # Bar: number of reviews per sentiment
+    if "n_reviews" in df.columns:
+        colors = []
+        for s in df["sentiment_label"]:
+            sl = str(s).lower()
+            if sl == "positive":   colors.append("#2ec4a0")
+            elif sl == "negative": colors.append("#e8537a")
+            else:                  colors.append("#5e8fef")
+
+        fig.add_trace(go.Bar(
+            x=df["sentiment_label"],
+            y=df["n_reviews"],
+            name="Number of Reviews",
+            marker_color=colors,
+            hovertemplate="<b>%{x}</b><br>Reviews: %{y}<extra></extra>",
+        ))
+
+    # Line: avg rating per sentiment on secondary axis
+    if "avg_rating" in df.columns:
+        fig.add_trace(go.Scatter(
+            x=df["sentiment_label"],
+            y=df["avg_rating"],
+            name="Avg Rating",
+            mode="lines+markers",
+            line=dict(color="#7c5cbf", width=3),
+            marker=dict(size=10),
+            yaxis="y2",
+            hovertemplate="<b>%{x}</b><br>Avg Rating: %{y:.2f}⭐<extra></extra>",
+        ))
+
+    fig.update_layout(
+        **_styled_layout(
+            height=420,
+            title=dict(text="Reviews & Avg Rating by Sentiment"),
+            yaxis=dict(title="Number of Reviews"),
+            yaxis2=dict(
+                title="Avg Star Rating",
+                overlaying="y", side="right",
+                range=[0, 5.5], showgrid=False,
+            ),
+            barmode="group",
+        )
+    )
+    return fig
+
+
+def build_sentiment_chart() -> go.Figure:
+    """Sentiment pie chart — reads df_dashboard.csv."""
+    for candidate in [PY_TAB_DIR / "df_dashboard.csv", BASE_DIR / "df_dashboard.csv"]:
+        if candidate.exists():
+            path = candidate
+            break
+    else:
+        return _empty_chart("Sentiment Distribution — run the pipeline first")
+
+    try:
+        df = pd.read_csv(path)
+    except Exception as e:
+        return _empty_chart(f"Error reading df_dashboard.csv: {e}")
+
+    if "sentiment_label" not in df.columns:
+        return _empty_chart("sentiment_label column not found in df_dashboard.csv")
+
+    color_map = {
+        "positive": "#2ec4a0",
+        "neutral":  "#5e8fef",
+        "negative": "#e8537a",
+    }
+    colors = [
+        color_map.get(str(s).lower(), "#888")
+        for s in df["sentiment_label"]
+    ]
+
+    metric_col = (
+        "n_reviews" if "n_reviews" in df.columns
+        else df.select_dtypes("number").columns[0]
+    )
+
+    fig = go.Figure(go.Pie(
+        labels=df["sentiment_label"],
+        values=df[metric_col],
+        marker=dict(colors=colors, line=dict(color="white", width=2)),
+        textinfo="label+percent",
+        hovertemplate="<b>%{label}</b><br>Reviews: %{value}<br>Share: %{percent}<extra></extra>",
+        hole=0.35,
+    ))
+
+    fig.update_layout(
+        **_styled_layout(
+            height=420,
+            title=dict(text="Sentiment Distribution"),
+        )
+    )
+    return fig
+
+
+def build_top_sellers_chart() -> go.Figure:
+    """Top products bar chart — reads product_performance.csv."""
+    for candidate in [PY_TAB_DIR / "product_performance.csv", BASE_DIR / "product_performance.csv"]:
+        if candidate.exists():
+            path = candidate
+            break
+    else:
+        return _empty_chart("Top Products — run the pipeline first")
+
+    try:
+        df = pd.read_csv(path)
+    except Exception as e:
+        return _empty_chart(f"Error reading product_performance.csv: {e}")
+
+    # Find name column and rating column
+    name_col = next(
+        (c for c in df.columns if "name" in c.lower() or "product" in c.lower()),
+        df.columns[0],
+    )
+    val_col = next(
+        (c for c in df.columns if "rating" in c.lower()),
+        df.select_dtypes("number").columns[0]
+        if len(df.select_dtypes("number").columns) > 0
+        else df.columns[1],
+    )
+
+    df = df.dropna(subset=[name_col, val_col])
+    df = df.sort_values(val_col, ascending=True).tail(10)
+
+    # Color by positive_ratio if available, else fixed palette
+    if "positive_ratio" in df.columns:
+        bar_colors = [
+            f"rgba({int(46 + x*150)},{int(196 - x*50)},{int(160 + x*30)},0.85)"
+            for x in df["positive_ratio"].fillna(0.5)
+        ]
+    else:
+        bar_colors = CHART_PALETTE[: len(df)]
+
+    hover = (
+        "<b>%{y}</b><br>"
+        + val_col.replace("_", " ").title()
+        + ": %{x:.2f}<extra></extra>"
+    )
+    if "n_reviews" in df.columns:
+        hover = (
+            "<b>%{y}</b><br>"
+            + val_col.replace("_", " ").title()
+            + ": %{x:.2f}<br>Reviews: "
+            + df["n_reviews"].astype(str)
+            + "<extra></extra>"
+        )
+        hover = "<b>%{y}</b><br>Avg Rating: %{x:.2f}<extra></extra>"
+
+    fig = go.Figure(go.Bar(
+        y=df[name_col],
+        x=df[val_col],
+        orientation="h",
+        marker_color=bar_colors,
+        hovertemplate=hover,
+    ))
+
+    fig.update_layout(
+        **_styled_layout(
+            height=max(380, len(df) * 50),
+            title=dict(text="Products Ranked by Average Rating"),
+            showlegend=False,
+        )
+    )
+    fig.update_xaxes(title="Average Star Rating", range=[0, 5.5])
+    fig.update_yaxes(autorange="reversed")
+    return fig
+
+
+def refresh_dashboard():
+    return (
+        render_kpi_cards(),
+        build_sales_chart(),
+        build_sentiment_chart(),
+        build_top_sellers_chart(),
+    )
+
+
+# =========================================================
+# AI DASHBOARD
+# =========================================================
+
+DASHBOARD_SYSTEM = """You are an AI dashboard assistant for a food e-commerce analytics app.
+The user asks questions about Amazon food product reviews analysed with sentiment analysis.
+AVAILABLE ARTIFACTS (only reference ones that exist):
+{artifacts_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:
+   {{"show": "figure"|"table"|"none", "scope": "python", "filename": "...", "chart": "sales"|"sentiment"|"top_sellers"|""}}
+RULES:
+- sentiment / reviews / positive / negative → chart: "sentiment"
+- rating / score / overview / trend        → chart: "sales"
+- top / best / product / popular / rank    → chart: "top_sellers"
+- churn / risk / decision / pricing        → show table: "business_decisions.csv"
+- dashboard / summary / kpi                → show table: "df_dashboard.csv"
+- pain points / complaints / negative reviews → show table: "top_negative_reviews.csv"
+Keep answers concise (2-4 sentences) then the JSON block.
+"""
+
+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:
+        try:
+            return json.loads(m.group(1))
+        except json.JSONDecodeError:
+            pass
+    m = FALLBACK_JSON_RE.search(text)
+    if m:
+        try:
+            return json.loads(m.group(0))
+        except json.JSONDecodeError:
+            pass
+    return {"show": "none"}
+
+
+def _clean_response(text: str) -> str:
+    return JSON_BLOCK_RE.sub("", text).strip()
+
+
+def _n8n_call(msg: str) -> Tuple[str, Dict]:
+    import requests as req
+    try:
+        resp = req.post(N8N_WEBHOOK_URL, json={"question": msg}, timeout=20)
+        data = resp.json()
+        answer = data.get("answer", "No response from n8n workflow.")
+        chart  = data.get("chart", "none")
+        if chart and chart != "none":
+            return answer, {"show": "figure", "chart": chart}
+        return answer, {"show": "none"}
+    except Exception as e:
+        return f"n8n error: {e}. Falling back to keyword matching.", None
+
+
+def _keyword_fallback(msg: str, idx: Dict, kpis: Dict) -> Tuple[str, Dict]:
+    """Keyword matcher for food review data."""
+    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.",
+            {"show": "none"},
+        )
+
+    # Build a short KPI summary string
+    kpi_text = ""
+    if kpis:
+        parts = []
+        if "total_reviews"   in kpis: parts.append(f"**{kpis['total_reviews']:,}** total reviews")
+        if "unique_products" in kpis: parts.append(f"**{kpis['unique_products']}** unique products")
+        if "avg_rating"      in kpis: parts.append(f"avg rating **{kpis['avg_rating']}⭐**")
+        if "pct_positive"    in kpis: parts.append(f"**{kpis['pct_positive']}%** positive reviews")
+        if parts:
+            kpi_text = "Quick summary: " + ", ".join(parts) + "."
+
+    if any(w in msg_lower for w in ["sentiment", "positive", "negative", "distribution", "review"]):
+        return (
+            f"Here is the sentiment distribution across food reviews. {kpi_text}",
+            {"show": "figure", "chart": "sentiment"},
+        )
+
+    if any(w in msg_lower for w in ["top", "best", "product", "popular", "rank", "seller"]):
+        return (
+            f"Here are the top products ranked by average rating. {kpi_text}",
+            {"show": "figure", "chart": "top_sellers"},
+        )
+
+    if any(w in msg_lower for w in ["rating", "score", "star", "overview", "trend", "monthly"]):
+        return (
+            f"Here is the rating and sentiment overview. {kpi_text}",
+            {"show": "figure", "chart": "sales"},
+        )
+
+    if any(w in msg_lower for w in ["churn", "risk", "decision", "pricing", "action"]):
+        return (
+            f"Here are the business decisions per product. {kpi_text}",
+            {"show": "table", "scope": "python", "filename": "business_decisions.csv"},
+        )
+
+    if any(w in msg_lower for w in ["pain", "complaint", "problem", "issue", "worst"]):
+        return (
+            f"Here are the most helpful negative reviews. {kpi_text}",
+            {"show": "table", "scope": "python", "filename": "top_negative_reviews.csv"},
+        )
+
+    if any(w in msg_lower for w in ["dashboard", "summary", "kpi", "overview", "data"]):
+        return (
+            f"Dashboard overview. {kpi_text}\n\n"
+            "Ask me about: **sentiment distribution**, **product ratings**, "
+            "**top products**, **churn risk**, or **business decisions**.",
+            {"show": "table", "scope": "python", "filename": "df_dashboard.csv"},
+        )
+
+    # Default
+    return (
+        f"I can help you explore the food review data. {kpi_text}\n\n"
+        "Try asking about: **sentiment distribution**, **top products**, "
+        "**product ratings**, **churn risk**, or **business decisions**.",
+        {"show": "figure", "chart": "sentiment"},
+    )
+
+
+def ai_chat(user_msg: str, history: list):
+    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:
+            reply_fb, directive = _keyword_fallback(user_msg, idx, kpis)
+            reply += "\n\n" + reply_fb
+    elif not LLM_ENABLED:
+        reply, directive = _keyword_fallback(user_msg, idx, kpis)
+    else:
+        system = DASHBOARD_SYSTEM.format(
+            artifacts_json=json.dumps(idx, indent=2),
+            kpis_json=(json.dumps(kpis, indent=2)
+                       if kpis else "(no KPIs yet — run the pipeline first)"),
+        )
+        msgs = [{"role": "system", "content": system}]
+        for entry in (history or [])[-6:]:
+            msgs.append(entry)
+        msgs.append({"role": "user", "content": user_msg})
+        try:
+            r = llm_client.chat_completion(
+                model=MODEL_NAME, messages=msgs,
+                temperature=0.3, max_tokens=600, stream=False,
+            )
+            raw = (
+                r["choices"][0]["message"]["content"]
+                if isinstance(r, dict)
+                else r.choices[0].message.content
+            )
+            directive = _parse_display_directive(raw)
+            reply     = _clean_response(raw)
+        except Exception as e:
+            reply = f"LLM error: {e}. Falling back to keyword matching."
+            reply_fb, directive = _keyword_fallback(user_msg, idx, kpis)
+            reply += "\n\n" + reply_fb
+
+    # Resolve directive → chart or table
+    chart_out = None
+    tab_out   = None
+    show      = directive.get("show", "none")
+    fname     = directive.get("filename", "")
+    chart_name = directive.get("chart", "")
+
+    chart_builders = {
+        "sales":       build_sales_chart,
+        "sentiment":   build_sentiment_chart,
+        "top_sellers": build_top_sellers_chart,
+    }
+
+    if chart_name and chart_name in chart_builders:
+        chart_out = chart_builders[chart_name]()
+    elif show == "figure" and fname:
+        if "sentiment" in fname:
+            chart_out = build_sentiment_chart()
+        elif "product" in fname or "seller" in fname or "top" in fname:
+            chart_out = build_top_sellers_chart()
+        else:
+            chart_out = build_sales_chart()
+
+    if show == "table" and fname:
+        # Try tables folder first, then root
+        for fp in [PY_TAB_DIR / fname, BASE_DIR / fname]:
+            if fp.exists():
+                tab_out = _load_table_safe(fp)
+                break
+        if tab_out is None:
+            reply += f"\n\n*(Could not find table: {fname})*"
+
+    new_history = (history or []) + [
+        {"role": "user",      "content": user_msg},
+        {"role": "assistant", "content": reply},
+    ]
+    return new_history, "", chart_out, tab_out
+
+
+# =========================================================
+# UI
+# =========================================================
+
+ensure_dirs()
+
+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:
+
+    gr.Markdown(
+        "# SE21 App Template\n"
+        "*E-Commerce Food Review Intelligence Dashboard*",
+        elem_id="escp_title",
+    )
+
+    # ── TAB 1 — Pipeline Runner ───────────────────────────────────
+    with gr.Tab("Pipeline Runner"):
+        gr.Markdown(
+            "Run the notebooks to generate data and analysis artifacts. "
+            "If you have already uploaded the CSV files, you can skip Step 1 "
+            "and go straight to the Dashboard tab."
+        )
+        with gr.Row():
+            with gr.Column(scale=1):
+                btn_nb1 = gr.Button("Step 1: Data Creation", variant="secondary")
+            with gr.Column(scale=1):
+                btn_nb2 = gr.Button("Step 2: Python Analysis", variant="secondary")
+        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,
+        )
+
+        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 ─────────────────────────────────────────
+    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="Rating & Sentiment Overview")
+        chart_sentiment = gr.Plot(label="Sentiment Distribution")
+        chart_top       = gr.Plot(label="Products by Avg Rating")
+
+        gr.Markdown("#### Static Figures (from notebooks)")
+        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)
+
+        def _on_refresh():
+            kpi, c1, c2, c3 = refresh_dashboard()
+            figs, dd, df    = refresh_gallery()
+            return kpi, c1, c2, c3, figs, dd, df
+
+        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],
+        )
+
+    # ── 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**. Set `N8N_WEBHOOK_URL` to connect "
+                 "your n8n workflow, or set `HF_API_KEY` for direct LLM access."
+        )
+        gr.Markdown(
+            "### Ask questions about your food review data\n\n"
+            f"Type a question and the system picks 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)
+                user_input = gr.Textbox(
+                    label="Ask about your data",
+                    placeholder=(
+                        "e.g. Show sentiment distribution / "
+                        "Which products have the best ratings? / "
+                        "What are the main customer complaints?"
+                    ),
+                    lines=1,
+                )
+                gr.Examples(
+                    examples=[
+                        "Show me the sentiment distribution",
+                        "Which products have the best ratings?",
+                        "What are the top products?",
+                        "Show the business decisions",
+                        "What do negative reviews say?",
+                        "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)
+
+        user_input.submit(
+            ai_chat,
+            inputs=[user_input, chatbot],
+            outputs=[chatbot, user_input, ai_figure, ai_table],
+        )
+
+
+demo.launch(css=load_css(), allowed_paths=[str(BASE_DIR)])