app.py

import gradio as gr
import pandas as pd
import numpy as np
import json
import subprocess
import sys
import traceback
from pathlib import Path
from datetime import datetime

# ── output folders (same structure Notebook 2 writes to)
ART_DIR = Path("artifacts")
FIG_DIR = ART_DIR / "figures"
TAB_DIR = ART_DIR / "tables"
for p in [FIG_DIR, TAB_DIR]:
    p.mkdir(parents=True, exist_ok=True)

# ────────────────────────────────────────────
# PIPELINE RUNNER
# ────────────────────────────────────────────

def run_notebook(path: str) -> str:
    try:
        result = subprocess.run(
            [sys.executable, "-m", "jupyter", "nbconvert",
             "--to", "notebook", "--execute",
             "--ExecutePreprocessor.timeout=600",
             "--inplace", path],
            capture_output=True, text=True
        )
        if result.returncode != 0:
            return f"❌ Error running {path}:\n{result.stderr[-2000:]}"
        return f"✅ {path} completed successfully."
    except Exception as e:
        return f"❌ Exception: {traceback.format_exc()}"

def run_data_creation():
    log = "▶ Running Notebook 1 — Data Collection & Creation...\n"
    log += run_notebook("datacreation.ipynb")
    return log

def run_analysis():
    log = "▶ Running Notebook 2 — Data Analysis & Modelling...\n"
    log += run_notebook("pythonanalysis.ipynb")
    return log

def run_full_pipeline():
    log = "▶ Running full pipeline...\n\n"
    log += "Step 1 — Data Collection & Creation\n"
    log += run_notebook("datacreation.ipynb") + "\n\n"
    log += "Step 2 — Data Analysis & Modelling\n"
    log += run_notebook("pythonanalysis.ipynb")
    return log

# ────────────────────────────────────────────
# DASHBOARD HELPERS
# ────────────────────────────────────────────

def load_kpis():
    kpi_path = TAB_DIR / "kpis.json"
    if not kpi_path.exists():
        return None
    with open(kpi_path) as f:
        return json.load(f)

def load_shows():
    path = TAB_DIR / "shows_final.csv"
    if not path.exists():
        path = ART_DIR / "shows_master.csv"
    if not path.exists():
        return None
    return pd.read_csv(path)

def load_recommendations():
    path = TAB_DIR / "renewal_recommendations.csv"
    if not path.exists():
        return None
    return pd.read_csv(path)

def load_monthly():
    path = ART_DIR / "monthly_platform_totals.csv"
    if not path.exists():
        return None
    df = pd.read_csv(path)
    df["month"] = pd.to_datetime(df["month"])
    return df

def kpi_html(kpis):
    if not kpis:
        return "<p style='color:#888;text-align:center;padding:40px'>Run the pipeline first to populate the dashboard.</p>"
    return f"""
    <div style="display:flex;gap:16px;flex-wrap:wrap;justify-content:center;padding:16px 0">
      <div class="kpi-card kpi-total">
        <div class="kpi-value">{kpis.get('total_shows','—')}</div>
        <div class="kpi-label">Total Shows</div>
      </div>
      <div class="kpi-card kpi-renew">
        <div class="kpi-value">{kpis.get('shows_to_renew','—')}</div>
        <div class="kpi-label">Renew</div>
      </div>
      <div class="kpi-card kpi-invest">
        <div class="kpi-value">{kpis.get('shows_invest_more','—')}</div>
        <div class="kpi-label">Invest More</div>
      </div>
      <div class="kpi-card kpi-cancel">
        <div class="kpi-value">{kpis.get('shows_to_cancel','—')}</div>
        <div class="kpi-label">Cancel</div>
      </div>
      <div class="kpi-card kpi-roi">
        <div class="kpi-value">{kpis.get('avg_platform_roi','—')}%</div>
        <div class="kpi-label">Avg Platform ROI</div>
      </div>
      <div class="kpi-card kpi-completion">
        <div class="kpi-value">{round(kpis.get('avg_completion_rate',0)*100,1)}%</div>
        <div class="kpi-label">Avg Completion Rate</div>
      </div>
      <div class="kpi-card kpi-rating">
        <div class="kpi-value">{kpis.get('avg_imdb_rating','—')}</div>
        <div class="kpi-label">Avg IMDb Rating</div>
      </div>
      <div class="kpi-card kpi-sentiment">
        <div class="kpi-value">{round(kpis.get('sentiment_alignment',0)*100,1)}%</div>
        <div class="kpi-label">Sentiment Alignment</div>
      </div>
    </div>
    """

def refresh_dashboard():
    kpis  = load_kpis()
    shows = load_recommendations()

    kpi_block = kpi_html(kpis)

    figs = {}
    for name in ["vader_sentiment_analysis", "viewership_trends_sampled",
                 "arima_forecasts", "random_forest_results",
                 "decision_analysis", "platform_overview"]:
        p = FIG_DIR / f"{name}.png"
        figs[name] = str(p) if p.exists() else None

    table_renew = shows[shows["renewal_decision"] == "Renew"][
        ["title","primary_genre","imdb_rating","num_seasons",
         "avg_monthly_streams_k","platform_roi_pct","avg_vader_score"]
    ].round(2).head(20) if shows is not None else pd.DataFrame()

    table_cancel = shows[shows["renewal_decision"] == "Cancel"][
        ["title","primary_genre","imdb_rating","num_seasons",
         "avg_monthly_streams_k","platform_roi_pct","avg_vader_score"]
    ].round(2).head(20) if shows is not None else pd.DataFrame()

    table_invest = shows[shows["renewal_decision"] == "Invest More"][
        ["title","primary_genre","imdb_rating","num_seasons",
         "avg_monthly_streams_k","platform_roi_pct","avg_vader_score"]
    ].round(2).head(20) if shows is not None else pd.DataFrame()

    return (
        kpi_block,
        figs.get("platform_overview"),
        figs.get("viewership_trends_sampled"),
        figs.get("vader_sentiment_analysis"),
        figs.get("arima_forecasts"),
        figs.get("random_forest_results"),
        figs.get("decision_analysis"),
        table_renew,
        table_cancel,
        table_invest
    )

# ────────────────────────────────────────────
# SEARCH
# ────────────────────────────────────────────

def search_shows(query, decision_filter):
    shows = load_recommendations()
    if shows is None:
        return pd.DataFrame({"message": ["Run the pipeline first."]})
    df = shows.copy()
    if query.strip():
        df = df[df["title"].str.contains(query.strip(), case=False, na=False)]
    if decision_filter != "All":
        df = df[df["renewal_decision"] == decision_filter]
    cols = ["title","primary_genre","imdb_rating","num_seasons",
            "avg_monthly_streams_k","platform_roi_pct",
            "avg_vader_score","renewal_decision"]
    return df[cols].round(2).head(50)

# ────────────────────────────────────────────
# AI DASHBOARD — n8n webhook
# ────────────────────────────────────────────

import requests as req

N8N_WEBHOOK = "https://jimkaufmann.app.n8n.cloud/webhook/ai-analyst"

def ask_ai(question, history):
    if not question.strip():
        return history, ""

    shows = load_shows()
    kpis  = load_kpis()

    context = ""
    if kpis:
        context += f"Platform KPIs: {json.dumps(kpis)}\n"
    if shows is not None:
        try:
            summary = shows[["title","renewal_decision","imdb_rating",
                              "platform_roi_pct","avg_monthly_streams_k"]]\
                      .head(30).to_dict(orient="records")
            context += f"Sample shows data: {json.dumps(summary)}\n"
        except:
            context += "Show data available but could not be serialised.\n"

    try:
        response = req.post(
            N8N_WEBHOOK,
            json={"question": question, "context": context},
            timeout=30
        )
        if response.status_code == 200:
            data = response.json()
            answer = data.get("answer") or data.get("text") or str(data)
        else:
            answer = f"Webhook returned status {response.status_code}. Make sure your n8n workflow is active and published."
    except Exception as e:
        answer = f"Could not reach the n8n workflow: {e}"

    history = history or []
    history.append({"role": "user", "content": question})
    history.append({"role": "assistant", "content": answer})
    return history, ""

# ────────────────────────────────────────────
# BUILD UI
# ────────────────────────────────────────────

css_string = open("style.css").read() if Path("style.css").exists() else ""
with gr.Blocks(title="Streaming Cancellation Risk Predictor", css=css_string) as demo:

    # ── HEADER
    gr.HTML("""
    <div class="header-wrap">
      <img src="/file=background_top.png" class="bg-top"/>
      <div class="header-content">
        <h1 class="app-title">🎬 Streaming Cancellation Risk Predictor</h1>
        <p class="app-subtitle">Which shows should we Renew, Cancel, or Invest More in?</p>
      </div>
    </div>
    """)

    with gr.Tabs():

        # ── TAB 1: PIPELINE RUNNER
        with gr.Tab("▶  Pipeline Runner"):
            gr.Markdown("""
Run the two notebooks to collect IMDb data, generate synthetic viewership and reviews,
run VADER sentiment analysis, ARIMA forecasting, and train the Random Forest classifier.
Results are saved automatically and populate the Dashboard tab.
            """)
            with gr.Row():
                btn_nb1  = gr.Button("Step 1 — Data Collection & Creation",  variant="secondary", size="lg")
                btn_nb2  = gr.Button("Step 2 — Data Analysis & Modelling",   variant="secondary", size="lg")
            btn_full = gr.Button("🚀  Run Full Pipeline (Both Steps)", variant="primary", size="lg")
            log_box  = gr.Textbox(label="Execution Log", lines=12, interactive=False)

            btn_nb1.click(run_data_creation, outputs=log_box)
            btn_nb2.click(run_analysis,      outputs=log_box)
            btn_full.click(run_full_pipeline, outputs=log_box)

        # ── TAB 2: DASHBOARD
        with gr.Tab("📊  Dashboard"):

            btn_refresh = gr.Button("🔄  Refresh Dashboard", variant="primary")
            kpi_display = gr.HTML(label="KPIs")

            gr.Markdown("### Platform Overview")
            img_platform = gr.Image(label="Total Monthly Streams", show_label=False)

            gr.Markdown("### Viewership Trends")
            img_trends = gr.Image(label="Viewership Trends", show_label=False)

            gr.Markdown("### Sentiment Analysis")
            img_vader = gr.Image(label="VADER Sentiment", show_label=False)

            gr.Markdown("### ARIMA Forecasts")
            img_arima = gr.Image(label="ARIMA Forecasts", show_label=False)

            gr.Markdown("### Random Forest Results")
            img_rf = gr.Image(label="Random Forest", show_label=False)

            gr.Markdown("### Decision Analysis")
            img_decisions = gr.Image(label="Decision Analysis", show_label=False)

            gr.Markdown("### 🟢 Shows to Renew")
            tbl_renew  = gr.DataFrame(label="Renew")
            gr.Markdown("### 🔴 Shows to Cancel")
            tbl_cancel = gr.DataFrame(label="Cancel")
            gr.Markdown("### 🟡 Shows to Invest More In")
            tbl_invest = gr.DataFrame(label="Invest More")

            all_outputs = [
                kpi_display,
                img_platform, img_trends, img_vader,
                img_arima, img_rf, img_decisions,
                tbl_renew, tbl_cancel, tbl_invest
            ]
            btn_refresh.click(refresh_dashboard, outputs=all_outputs)
            demo.load(refresh_dashboard, outputs=all_outputs)

        # ── TAB 3: SEARCH
        with gr.Tab("🔍  Show Search"):
            gr.Markdown("""
Search across all shows in the dataset. Filter by renewal decision to quickly find
the platform's top renewal candidates or shows flagged for cancellation.
            """)
            with gr.Row():
                search_box    = gr.Textbox(placeholder="Search by show title...", label="", scale=3)
                decision_drop = gr.Dropdown(
                    choices=["All", "Renew", "Invest More", "Cancel"],
                    value="All", label="Filter by decision", scale=1
                )
            search_btn   = gr.Button("Search", variant="primary")
            search_table = gr.DataFrame(label="Results")

            search_btn.click(search_shows,
                             inputs=[search_box, decision_drop],
                             outputs=search_table)
            search_box.submit(search_shows,
                              inputs=[search_box, decision_drop],
                              outputs=search_table)

        # ── TAB 4: AI DASHBOARD
        with gr.Tab("🤖  AI Dashboard"):
            gr.Markdown("""
Ask questions about the platform's content portfolio and get AI-powered answers.
Connected to our n8n workflow which has access to the full show dataset and KPIs.

*Examples: "Which drama shows should we prioritise for renewal?", "What genres have the best ROI?",
"Which shows have high viewership but negative sentiment?"*
            """)
            chatbot  = gr.Chatbot(value=[], height=420, label="")
            with gr.Row():
                msg_box  = gr.Textbox(value="", placeholder="Ask a question about the data...",
                                      label="", scale=4)
                send_btn = gr.Button("Send", variant="primary", scale=1)

            send_btn.click(ask_ai, inputs=[msg_box, chatbot], outputs=[chatbot, msg_box])
            msg_box.submit(ask_ai,  inputs=[msg_box, chatbot], outputs=[chatbot, msg_box])

    # ── FOOTER
    gr.HTML("""
    <div class="footer">
      <img src="/file=background_bottom.png" class="bg-bottom"/>
      <p>ESCP Business School — AI for Big Data Management — Group Project 2026</p>
    </div>
    """)

demo.launch()