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app.py

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+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 / "figures"   # notebooks write to artifacts/figures/
+PY_TAB_DIR = ART_DIR / "tables"    # notebooks write to artifacts/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 (ART_DIR).glob("*.csv")]
+        return f"OK  {log}\n\nCSVs in /artifacts/:\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 (IMDb scrape + synthetic data)")
+    logs.append("=" * 50)
+    logs.append(run_datacreation())
+    logs.append("")
+    logs.append("=" * 50)
+    logs.append("STEP 2/2: Python Analysis (VADER, ARIMA, Random Forest)")
+    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():
+    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 streaming platform analytics app.
+The platform uses data from IMDb combined with synthetic streaming KPIs to predict whether
+each TV show should be Renewed, Cancelled, or given more Investment.
+
+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 sentiment or VADER scores, show vader_sentiment_analysis.png or figure chart "sentiment".
+- If the user asks about viewership trends, show viewership_trends_sampled.png or chart "platform_streams".
+- If the user asks about ARIMA or forecasting, show arima_forecasts.png or chart "platform_streams".
+- If the user asks about the Random Forest or model accuracy, show random_forest_results.png.
+- If the user asks about renewal decisions or genre analysis, show decision_analysis.png or table "renewal_recommendations".
+- If the user asks about platform overview or total streams, show platform_overview.png or chart "platform_streams".
+- If the user asks about feature importances, show table feature_importances.csv.
+- If the user asks about recommendations or show list, show table renewal_recommendations.csv.
+- 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:
+    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 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 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", "")
+
+    chart_builders = {
+        "platform_streams": build_platform_streams_chart,
+        "sentiment":        build_sentiment_chart,
+        "renewal":          build_renewal_chart,
+    }
+
+    if chart_name and chart_name in chart_builders:
+        chart_out = chart_builders[chart_name]()
+    elif show == "figure" and fname:
+        if "vader" in fname or "sentiment" in fname:
+            chart_out = build_sentiment_chart()
+        elif "platform_overview" in fname or "viewership" in fname or "arima" in fname:
+            chart_out = build_platform_streams_chart()
+        elif "decision" in fname or "random_forest" in fname:
+            chart_out = build_renewal_chart()
+        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]:
+    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_shows", "?")
+        renew   = kpis.get("shows_to_renew", "?")
+        cancel  = kpis.get("shows_to_cancel", "?")
+        invest  = kpis.get("shows_invest_more", "?")
+        kpi_text = (
+            f"Quick summary: **{total}** shows analysed — "
+            f"**{renew}** to renew, **{cancel}** to cancel, **{invest}** need more investment."
+        )
+
+    if any(w in msg_lower for w in ["sentiment", "vader", "review", "positive", "negative"]):
+        return (
+            f"Here is the VADER sentiment analysis by renewal decision. {kpi_text}",
+            {"show": "figure", "chart": "sentiment"},
+        )
+
+    if any(w in msg_lower for w in ["arima", "forecast", "predict", "viewership", "trend", "stream"]):
+        return (
+            f"Here are the platform viewership trends and ARIMA forecasts. {kpi_text}",
+            {"show": "figure", "chart": "platform_streams"},
+        )
+
+    if any(w in msg_lower for w in ["renew", "cancel", "decision", "genre", "random forest", "model"]):
+        return (
+            f"Here is the renewal decision breakdown. {kpi_text}",
+            {"show": "figure", "chart": "renewal"},
+        )
+
+    if any(w in msg_lower for w in ["feature", "importance", "variable"]):
+        return (
+            f"Here are the most important features from the Random Forest model. {kpi_text}",
+            {"show": "table", "scope": "python", "filename": "feature_importances.csv"},
+        )
+
+    if any(w in msg_lower for w in ["recommend", "list", "show", "top", "best", "worst"]):
+        return (
+            f"Here is the full renewal recommendations table. {kpi_text}",
+            {"show": "table", "scope": "python", "filename": "renewal_recommendations.csv"},
+        )
+
+    if any(w in msg_lower for w in ["dashboard", "overview", "summary", "kpi"]):
+        return (
+            f"Dashboard overview: {kpi_text}\n\nAsk me about sentiment, viewership trends, "
+            "ARIMA forecasts, renewal decisions, or feature importances.",
+            {"show": "figure", "chart": "renewal"},
+        )
+
+    # Default
+    return (
+        f"I can help you explore the streaming analytics. {kpi_text}\n\n"
+        "Try asking about: **sentiment analysis**, **viewership trends**, **ARIMA forecasts**, "
+        "**renewal decisions**, **feature importances**, or **show recommendations**.",
+        {"show": "none"},
+    )
+
+
+# =========================================================
+# 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 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>"""
+
+    # Ordered KPI config aligned with kpis.json from pythonanalysis notebook
+    kpi_config = [
+        ("total_shows",         "🎬", "Shows Analysed",    "#a48de8"),
+        ("shows_to_renew",      "✅", "Renew",              "#2ec4a0"),
+        ("shows_invest_more",   "📈", "Invest More",        "#e8a230"),
+        ("shows_to_cancel",     "❌", "Cancel",             "#e8537a"),
+        ("avg_imdb_rating",     "⭐", "Avg IMDb Rating",    "#7aa6f8"),
+        ("avg_platform_roi",    "💰", "Avg Platform ROI %", "#3dcba8"),
+        ("avg_completion_rate", "▶️", "Avg Completion Rate","#5e8fef"),
+        ("sentiment_alignment", "💬", "Sentiment Alignment","#c45ea8"),
+    ]
+
+    html = (
+        '<div style="display:grid;grid-template-columns:repeat(auto-fit,minmax(140px,1fr));'
+        'gap:12px;margin-bottom:24px;">'
+    )
+    known = {k for k, *_ in kpi_config}
+    for key, icon, label, colour in kpi_config:
+        val = kpis.get(key)
+        if val is None:
+            continue
+        if isinstance(val, float):
+            val = f"{val:,.3f}" if val < 10 else f"{val:,.1f}"
+        elif isinstance(val, int) and val > 999:
+            val = f"{val:,}"
+        html += card(icon, label, str(val), colour)
+    # Any extra keys not in 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
+# =========================================================
+
+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_platform_streams_chart() -> go.Figure:
+    """Monthly platform total streams from monthly_platform_totals.csv."""
+    path = ART_DIR / "monthly_platform_totals.csv"
+    if not path.exists():
+        return _empty_chart("Platform Streams — run the pipeline first")
+    df = pd.read_csv(path)
+    df["month"] = pd.to_datetime(df["month"], errors="coerce")
+    fig = go.Figure()
+    fig.add_trace(go.Scatter(
+        x=df["month"], y=df["total_streams_k"],
+        name="Monthly Streams (k)",
+        mode="lines+markers",
+        fill="tozeroy",
+        fillcolor="rgba(124,92,191,0.12)",
+        line=dict(color="#7c5cbf", width=2.5),
+        marker=dict(size=5),
+        hovertemplate="<b>%{x|%b %Y}</b>: %{y:,.0f}k streams<extra></extra>",
+    ))
+    rolling = df["total_streams_k"].rolling(3, center=True).mean()
+    fig.add_trace(go.Scatter(
+        x=df["month"], y=rolling,
+        name="3-month rolling avg",
+        mode="lines",
+        line=dict(color="#e8537a", width=2, dash="dash"),
+        hovertemplate="<b>Rolling avg</b>: %{y:,.0f}k<extra></extra>",
+    ))
+    fig.update_layout(**_styled_layout(
+        height=420, hovermode="x unified",
+        title=dict(text="Total Platform Streams Over Time"),
+    ))
+    fig.update_xaxes(gridcolor="rgba(124,92,191,0.15)", showgrid=True)
+    fig.update_yaxes(gridcolor="rgba(124,92,191,0.15)", showgrid=True, title="Streams (thousands)")
+    return fig
+
+
+def build_sentiment_chart() -> go.Figure:
+    """VADER score distribution by renewal decision from shows_final.csv."""
+    path = PY_TAB_DIR / "renewal_recommendations.csv"
+    if not path.exists():
+        return _empty_chart("Sentiment Analysis — run the pipeline first")
+    df = pd.read_csv(path)
+    if "avg_vader_score" not in df.columns or "renewal_decision" not in df.columns:
+        return _empty_chart("Expected columns not found in renewal_recommendations.csv")
+
+    decision_order  = ["Renew", "Invest More", "Cancel"]
+    decision_colors = {"Renew": "#2ec4a0", "Invest More": "#e8a230", "Cancel": "#e8537a"}
+
+    fig = go.Figure()
+    for decision in decision_order:
+        subset = df[df["renewal_decision"] == decision]["avg_vader_score"].dropna()
+        if subset.empty:
+            continue
+        fig.add_trace(go.Box(
+            y=subset,
+            name=decision,
+            marker_color=decision_colors.get(decision, "#888"),
+            boxmean="sd",
+            hovertemplate=f"<b>{decision}</b><br>VADER: %{{y:.3f}}<extra></extra>",
+        ))
+    fig.update_layout(**_styled_layout(
+        height=440,
+        title=dict(text="VADER Sentiment Score by Renewal Decision"),
+    ))
+    fig.update_yaxes(title="Avg VADER Compound Score", gridcolor="rgba(124,92,191,0.15)")
+    return fig
+
+
+def build_renewal_chart() -> go.Figure:
+    """Renewal decision breakdown from renewal_recommendations.csv."""
+    path = PY_TAB_DIR / "renewal_recommendations.csv"
+    if not path.exists():
+        return _empty_chart("Renewal Decisions — run the pipeline first")
+    df = pd.read_csv(path)
+    if "renewal_decision" not in df.columns:
+        return _empty_chart("renewal_decision column not found")
+
+    counts = df["renewal_decision"].value_counts().reindex(["Renew", "Invest More", "Cancel"], fill_value=0)
+    colors = ["#2ec4a0", "#e8a230", "#e8537a"]
+
+    fig = go.Figure(go.Bar(
+        x=counts.index,
+        y=counts.values,
+        marker_color=colors,
+        text=counts.values,
+        textposition="outside",
+        hovertemplate="<b>%{x}</b>: %{y} shows<extra></extra>",
+    ))
+    fig.update_layout(**_styled_layout(
+        height=400, showlegend=False,
+        title=dict(text="Renewal Decision Breakdown"),
+    ))
+    fig.update_yaxes(title="Number of Shows", gridcolor="rgba(124,92,191,0.15)")
+    return fig
+
+
+def refresh_dashboard():
+    return (
+        render_kpi_cards(),
+        build_platform_streams_chart(),
+        build_sentiment_chart(),
+        build_renewal_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="Streaming Cancellation Risk Predictor") as demo:
+
+    gr.Markdown(
+        "# 🎬 Streaming Platform — Cancellation Risk Predictor\n"
+        "*Group 36 · ESCP · AIBDM 2026*",
+        elem_id="escp_title",
+    )
+
+    # ===========================================================
+    # TAB 1 — Pipeline Runner
+    # ===========================================================
+    with gr.Tab("Pipeline Runner"):
+        gr.Markdown(
+            "Run the notebooks to collect IMDb data, generate synthetic streaming KPIs, "
+            "and train the cancellation-risk model."
+        )
+
+        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_streams   = gr.Plot(label="Platform Streams Over Time")
+        chart_sentiment = gr.Plot(label="Sentiment by Renewal Decision")
+        chart_renewal   = gr.Plot(label="Renewal Decision Breakdown")
+
+        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_streams, chart_sentiment, chart_renewal,
+                     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 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 analysis / Which shows should be cancelled? / ARIMA forecasts",
+                    lines=1,
+                )
+                gr.Examples(
+                    examples=[
+                        "Show me the platform viewership trends",
+                        "What does the sentiment analysis show?",
+                        "Which shows should be cancelled?",
+                        "Show the ARIMA forecasts",
+                        "What are the most important features?",
+                        "Give me the full recommendations table",
+                    ],
+                    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)])

requirements.txt

@@ -0,0 +1,14 @@
+gradio==6.0.0
+pandas>=2.0.0
+numpy>=1.24.0
+matplotlib>=3.7.0
+seaborn>=0.13.0
+statsmodels>=0.14.0
+scikit-learn>=1.3.0
+papermill>=2.5.0
+nbformat>=5.9.0
+pillow>=10.0.0
+requests>=2.31.0
+vaderSentiment>=3.3.2
+huggingface_hub>=0.20.0
+plotly>=5.18.0

style.css

@@ -0,0 +1,324 @@
+/* --- Target the Gradio app wrapper for backgrounds --- */
+gradio-app,
+.gradio-app,
+.main,
+#app,
+[data-testid="app"] {
+  background-color: rgb(40,9,109) !important;
+  background-image:
+    url('https://huggingface.co/spaces/JimKau/Coding_Project_II/resolve/main/background_top.png'),
+    url('https://huggingface.co/spaces/JimKau/Coding_Project_II/resolve/main/background_mid.png'),
+    url('https://huggingface.co/spaces/JimKau/Coding_Project_II/resolve/main/background_bottom.png') !important;
+  background-position:
+    top center,
+    0 913px,
+    bottom center !important;
+  background-repeat:
+    no-repeat,
+    repeat-y,
+    no-repeat !important;
+  background-size:
+    100% auto,
+    100% auto,
+    100% auto !important;
+  min-height: 100vh !important;
+}
+
+/* --- Fallback on html/body --- */
+html, body {
+  background-color: rgb(40,9,109) !important;
+  margin: 0 !important;
+  padding: 0 !important;
+  min-height: 100vh !important;
+}
+
+/* --- Main container --- */
+.gradio-container {
+  max-width: 1400px !important;
+  width: 94vw !important;
+  margin: 0 auto !important;
+  padding-top: 220px !important;
+  padding-bottom: 150px !important;
+  background: transparent !important;
+}
+
+/* --- Title in ESCP gold --- */
+#escp_title h1 {
+  color: rgb(242,198,55) !important;
+  font-size: 3rem !important;
+  font-weight: 800 !important;
+  text-align: center !important;
+  margin: 0 0 12px 0 !important;
+}
+
+/* --- Subtitle --- */
+#escp_title p, #escp_title em {
+  color: rgba(255,255,255,0.85) !important;
+  text-align: center !important;
+}
+
+/* --- Tab bar background --- */
+.tabs > .tab-nav,
+.tab-nav,
+div[role="tablist"],
+.svelte-tabs > .tab-nav {
+  background: rgba(40,9,109,0.6) !important;
+  border-radius: 10px 10px 0 0 !important;
+  padding: 4px !important;
+}
+
+/* --- ALL tab buttons: force white text --- */
+.tabs > .tab-nav button,
+.tab-nav button,
+div[role="tablist"] button,
+button[role="tab"],
+.svelte-tabs button,
+.tab-nav > button,
+.tabs button {
+  color: #ffffff !important;
+  font-weight: 600 !important;
+  border: none !important;
+  background: transparent !important;
+  padding: 10px 20px !important;
+  border-radius: 8px 8px 0 0 !important;
+  opacity: 1 !important;
+}
+
+/* --- Selected tab: ESCP gold --- */
+.tabs > .tab-nav button.selected,
+.tab-nav button.selected,
+button[role="tab"][aria-selected="true"],
+button[role="tab"].selected,
+div[role="tablist"] button[aria-selected="true"],
+.svelte-tabs button.selected {
+  color: rgb(242,198,55) !important;
+  background: rgba(255,255,255,0.12) !important;
+}
+
+/* --- Unselected tabs: ensure visibility --- */
+.tabs > .tab-nav button:not(.selected),
+.tab-nav button:not(.selected),
+button[role="tab"][aria-selected="false"],
+button[role="tab"]:not(.selected),
+div[role="tablist"] button:not([aria-selected="true"]) {
+  color: #ffffff !important;
+  opacity: 1 !important;
+}
+
+/* --- White card panels --- */
+.gradio-container .gr-block,
+.gradio-container .gr-box,
+.gradio-container .gr-panel,
+.gradio-container .gr-group {
+  background: #ffffff !important;
+  border-radius: 10px !important;
+}
+
+/* --- Tab content area --- */
+.tabitem {
+  background: rgba(255,255,255,0.95) !important;
+  border-radius: 0 0 10px 10px !important;
+  padding: 16px !important;
+}
+
+/* --- Inputs --- */
+.gradio-container input,
+.gradio-container textarea,
+.gradio-container select {
+  background: #ffffff !important;
+  border: 1px solid #d1d5db !important;
+  border-radius: 8px !important;
+}
+
+/* --- Buttons: ESCP purple primary --- */
+.gradio-container button:not([role="tab"]) {
+  font-weight: 600 !important;
+  padding: 10px 16px !important;
+  border-radius: 10px !important;
+}
+
+button.primary {
+  background-color: rgb(40,9,109) !important;
+  color: #ffffff !important;
+  border: none !important;
+}
+
+button.primary:hover {
+  background-color: rgb(60,20,140) !important;
+}
+
+button.secondary {
+  background-color: #ffffff !important;
+  color: rgb(40,9,109) !important;
+  border: 2px solid rgb(40,9,109) !important;
+}
+
+button.secondary:hover {
+  background-color: rgb(240,238,250) !important;
+}
+
+/* --- Dataframes --- */
+[data-testid="dataframe"] {
+  background-color: #ffffff !important;
+  border-radius: 10px !important;
+}
+
+table {
+  font-size: 0.85rem !important;
+}
+
+/* --- Chatbot (AI Dashboard tab) --- */
+.gr-chatbot {
+  min-height: 380px !important;
+  background-color: #ffffff !important;
+  border-radius: 12px !important;
+}
+
+.gr-chatbot .message.user {
+  background-color: rgb(232,225,250) !important;
+  border-radius: 12px !important;
+}
+
+.gr-chatbot .message.bot {
+  background-color: #f3f4f6 !important;
+  border-radius: 12px !important;
+}
+
+/* --- Gallery --- */
+.gallery {
+  background: #ffffff !important;
+  border-radius: 10px !important;
+}
+
+/* --- Log textbox --- */
+textarea {
+  font-family: monospace !important;
+  font-size: 0.8rem !important;
+}
+
+/* --- Markdown headings inside tabs --- */
+.tabitem h3 {
+  color: rgb(40,9,109) !important;
+  font-weight: 700 !important;
+}
+
+.tabitem h4 {
+  color: #374151 !important;
+}
+
+/* --- Examples row (AI Dashboard) --- */
+.examples-row button {
+  background: rgb(240,238,250) !important;
+  color: rgb(40,9,109) !important;
+  border: 1px solid rgb(40,9,109) !important;
+  border-radius: 8px !important;
+  font-size: 0.85rem !important;
+}
+
+.examples-row button:hover {
+  background: rgb(232,225,250) !important;
+}
+
+/* --- Header / footer: transparent over banner --- */
+header, header *,
+footer, footer * {
+  background: transparent !important;
+  box-shadow: none !important;
+}
+
+footer a, footer button,
+header a, header button {
+  background: transparent !important;
+  border: none !important;
+  box-shadow: none !important;
+}
+
+#footer, #footer *,
+[class*="footer"], [class*="footer"] *,
+[class*="chip"], [class*="pill"], [class*="chip"] *, [class*="pill"] * {
+  background: transparent !important;
+  border: none !important;
+  box-shadow: none !important;
+}
+
+[data-testid*="api"], [data-testid*="settings"],
+[id*="api"], [id*="settings"],
+[class*="api"], [class*="settings"],
+[class*="bottom"], [class*="toolbar"], [class*="controls"] {
+  background: transparent !important;
+  box-shadow: none !important;
+}
+
+[data-testid*="api"] *, [data-testid*="settings"] *,
+[id*="api"] *, [id*="settings"] *,
+[class*="api"] *, [class*="settings"] * {
+  background: transparent !important;
+  box-shadow: none !important;
+}
+
+section footer {
+  background: transparent !important;
+}
+
+section footer button,
+section footer a {
+  background: transparent !important;
+  background-color: transparent !important;
+  border: none !important;
+  box-shadow: none !important;
+  color: white !important;
+}
+
+section footer button:hover,
+section footer button:focus,
+section footer a:hover,
+section footer a:focus {
+  background: transparent !important;
+  background-color: transparent !important;
+  box-shadow: none !important;
+}
+
+section footer button,
+section footer button * {
+  background: transparent !important;
+  background-color: transparent !important;
+  background-image: none !important;
+  box-shadow: none !important;
+  filter: none !important;
+}
+
+section footer button::before,
+section footer button::after {
+  background: transparent !important;
+  background-color: transparent !important;
+  background-image: none !important;
+  box-shadow: none !important;
+  filter: none !important;
+}
+
+section footer a,
+section footer a * {
+  background: transparent !important;
+  background-color: transparent !important;
+  box-shadow: none !important;
+}
+
+.gradio-container footer button,
+.gradio-container footer button *,
+.gradio-container .footer button,
+.gradio-container .footer button * {
+  background: transparent !important;
+  background-color: transparent !important;
+  background-image: none !important;
+  box-shadow: none !important;
+}
+
+.gradio-container footer button::before,
+.gradio-container footer button::after,
+.gradio-container .footer button::before,
+.gradio-container .footer button::after {
+  background: transparent !important;
+  background-color: transparent !important;
+  background-image: none !important;
+  box-shadow: none !important;
+}