Create app.py

app.py

@@ -0,0 +1,753 @@
+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 (web scraping + synthetic data)")
+    logs.append("=" * 50)
+    logs.append(run_datacreation())
+    logs.append("")
+    logs.append("=" * 50)
+    logs.append("STEP 2/2: Python Analysis (sentiment, ARIMA, dashboard)")
+    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()))
+    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():
+    """Called when user clicks Refresh on Gallery tab."""
+    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]:
+    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 {}
+
+
+# =========================================================
+# AI DASHBOARD -- LLM picks what to display
+# =========================================================
+
+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.
+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. 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.
+"""
+
+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:
+    """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)."""
+    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 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:
+            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 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,
+    }
+
+    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 show == "table" and fname:
+        fp = PY_TAB_DIR / fname
+        if fp.exists():
+            tab_out = _load_table_safe(fp)
+        else:
+            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
+
+
+def _keyword_fallback(msg: str, idx: Dict, kpis: Dict) -> Tuple[str, Dict]:
+    """Simple keyword matcher when LLM is unavailable."""
+    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"},
+        )
+
+    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."
+        )
+
+    if any(w in msg_lower for w in ["trend", "sales trend", "monthly sale"]):
+        return (
+            f"Here are the sales trends. {kpi_text}",
+            {"show": "figure", "chart": "sales"},
+        )
+
+    if any(w in msg_lower for w in ["sentiment", "review", "positive", "negative"]):
+        return (
+            f"Here is the sentiment distribution across sampled book titles. {kpi_text}",
+            {"show": "figure", "chart": "sentiment"},
+        )
+
+    if any(w in msg_lower for w in ["arima", "forecast", "predict"]):
+        return (
+            f"Here are the sales trends and forecasts. {kpi_text}",
+            {"show": "figure", "chart": "sales"},
+        )
+
+    if any(w in msg_lower for w in ["top", "best sell", "popular", "rank"]):
+        return (
+            f"Here are the top-selling titles by units sold. {kpi_text}",
+            {"show": "table", "scope": "python", "filename": "top_titles_by_units_sold.csv"},
+        )
+
+    if any(w in msg_lower for w in ["price", "pricing", "decision"]):
+        return (
+            f"Here are the pricing decisions. {kpi_text}",
+            {"show": "table", "scope": "python", "filename": "pricing_decisions.csv"},
+        )
+
+    if any(w in msg_lower for w in ["dashboard", "overview", "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"},
+        )
+
+    # 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**.",
+        {"show": "none"},
+    )
+
+
+# =========================================================
+# KPI CARDS (BubbleBusters style)
+# =========================================================
+
+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 data yet</div>'
+            '<div style="color:#9d8fc4;font-size:12px;">'
+            'Run the pipeline 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);
+                    border-radius:20px;padding:18px 14px 16px;text-align:center;
+                    border:1.5px solid rgba(255,255,255,.8);
+                    box-shadow:0 4px 16px rgba(124,92,191,.08);
+                    border-top:3px solid {colour};">
+            <div style="font-size:26px;margin-bottom:7px;line-height:1;">{icon}</div>
+            <div style="color:#9d8fc4;font-size:9.5px;text-transform:uppercase;
+                        letter-spacing:1.8px;margin-bottom:7px;font-weight:800;">{label}</div>
+            <div style="color:#2d1f4e;font-size:16px;font-weight:800;">{value}</div>
+        </div>"""
+
+    kpi_config = [
+        ("n_titles",         "📚", "Book Titles",  "#a48de8"),
+        ("n_months",         "📅", "Time Periods", "#7aa6f8"),
+        ("total_units_sold", "📦", "Units Sold",   "#6ee7c7"),
+        ("total_revenue",    "💰", "Revenue",      "#3dcba8"),
+    ]
+
+    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:
+            continue
+        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)
+# =========================================================
+
+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:
+    path = PY_TAB_DIR / "df_dashboard.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")
+    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
+
+
+def build_sentiment_chart() -> go.Figure:
+    path = PY_TAB_DIR / "sentiment_counts_sampled.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>",
+        ))
+    fig.update_layout(**_styled_layout(
+        height=max(400, len(df) * 28), barmode="stack",
+        title=dict(text="Sentiment Distribution by Book"),
+    ))
+    fig.update_xaxes(title="Number of Reviews")
+    fig.update_yaxes(autorange="reversed")
+    return fig
+
+
+def build_top_sellers_chart() -> go.Figure:
+    path = PY_TAB_DIR / "top_titles_by_units_sold.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>",
+    ))
+    fig.update_layout(**_styled_layout(
+        height=max(400, len(df) * 30),
+        title=dict(text="Top Selling Titles"), showlegend=False,
+    ))
+    fig.update_yaxes(autorange="reversed")
+    fig.update_xaxes(title="Total Units Sold")
+    return fig
+
+
+def refresh_dashboard():
+    return render_kpi_cards(), build_sales_chart(), build_sentiment_chart(), build_top_sellers_chart()
+
+
+# =========================================================
+# 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"
+        "*This is an app template for SE21 students*",
+        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")
+            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 (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")
+
+        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**. Upgrade options: "
+                 "set `N8N_WEBHOOK_URL` to connect your n8n workflow, "
+                 "or set `HF_API_KEY` for direct LLM access."
+        )
+        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}"
+        )
+
+        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 me sales trends / What are the top sellers? / Sentiment analysis",
+                    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?",
+                        "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)])