"""
╔══════════════════════════════════════════════════════════════════════════════╗
║  PHASE 9: INTERACTIVE STREAMLIT DASHBOARD                                     ║
║  Customer Churn Prediction & Sales Intelligence Dashboard                     ║
╚══════════════════════════════════════════════════════════════════════════════╝

═══════════════════════════════════════════════════════════════════════════════
HOW TO RUN THIS DASHBOARD:
═══════════════════════════════════════════════════════════════════════════════
  1. Install dependencies: pip install streamlit plotly
  2. Run: streamlit run dashboard.py
  3. Open browser at http://localhost:8501

═══════════════════════════════════════════════════════════════════════════════
DASHBOARD ARCHITECTURE:
═══════════════════════════════════════════════════════════════════════════════
  This dashboard connects ALL phases into a single business-facing product:
  
  • Overview Tab:      KPIs, churn rate, revenue, at-risk alerts
  • Predict Tab:       Interactive churn prediction (input → risk score + reasons)
  • Segments Tab:      Customer segments (VIP/Loyal/At Risk/Lost) with drill-down
  • Sales Tab:         Time-series trends, top products, growth rates
  • Recommendations:   Priority queue of customers needing action
  • Model Health:      Model performance, feature importance, data drift
"""

import streamlit as st
import pandas as pd
import numpy as np
import plotly.express as px
import plotly.graph_objects as go
from plotly.subplots import make_subplots
import json
import joblib
from pathlib import Path
import warnings
warnings.filterwarnings("ignore")

# ─────────────────────────────────────────────────────────────────────────────
# PAGE CONFIGURATION
# ─────────────────────────────────────────────────────────────────────────────
st.set_page_config(
    page_title="Customer Churn Intelligence Dashboard",
    page_icon="📊",
    layout="wide",
    initial_sidebar_state="expanded"
)

# ─────────────────────────────────────────────────────────────────────────────
# CUSTOM CSS FOR POLISHED LOOK
# ─────────────────────────────────────────────────────────────────────────────
st.markdown("""
<style>
    .main-header {
        font-size: 2.5rem;
        font-weight: 700;
        color: #1f2937;
        margin-bottom: 0.5rem;
    }
    .sub-header {
        font-size: 1.2rem;
        color: #6b7280;
        margin-bottom: 2rem;
    }
    .kpi-card {
        background: linear-gradient(135deg, #667eea 0%, #764ba2 100%);
        border-radius: 12px;
        padding: 1.5rem;
        color: white;
        text-align: center;
        box-shadow: 0 4px 6px rgba(0,0,0,0.1);
    }
    .kpi-card-green {
        background: linear-gradient(135deg, #11998e 0%, #38ef7d 100%);
    }
    .kpi-card-red {
        background: linear-gradient(135deg, #cb2d3e 0%, #ef473a 100%);
    }
    .kpi-card-orange {
        background: linear-gradient(135deg, #f7971e 0%, #ffd200 100%);
    }
    .kpi-value {
        font-size: 2.2rem;
        font-weight: 700;
        margin: 0;
    }
    .kpi-label {
        font-size: 0.9rem;
        opacity: 0.9;
        margin-top: 0.3rem;
    }
    .alert-critical {
        background-color: #fee2e2;
        border-left: 4px solid #dc2626;
        padding: 1rem;
        border-radius: 4px;
        margin: 0.5rem 0;
    }
    .alert-warning {
        background-color: #fef3c7;
        border-left: 4px solid #f59e0b;
        padding: 1rem;
        border-radius: 4px;
        margin: 0.5rem 0;
    }
    .stTabs [data-baseweb="tab-list"] {
        gap: 8px;
    }
    .stTabs [data-baseweb="tab"] {
        padding: 10px 20px;
        border-radius: 8px 8px 0 0;
    }
</style>
""", unsafe_allow_html=True)

# ─────────────────────────────────────────────────────────────────────────────
# DATA LOADING (cached)
# ─────────────────────────────────────────────────────────────────────────────
@st.cache_data
def load_data():
    """Load all pre-computed datasets from previous phases."""
    data = {}
    
    # Core datasets
    data["customers"] = pd.read_csv("/app/deploy/data/customer_data.csv")
    data["transactions"] = pd.read_csv("/app/deploy/data/transaction_data.csv", parse_dates=["transaction_date"])
    data["products"] = pd.read_csv("/app/deploy/data/product_data.csv")
    
    # Phase 4: Model predictions
    data["model_preds"] = pd.read_csv("/app/deploy/data/all_model_predictions.csv")
    data["test_risk"] = pd.read_csv("/app/outputs/phase4/test_set_risk_scores.csv")
    
    # Phase 5: Explanations
    data["explanations"] = pd.read_csv("/app/deploy/data/customer_churn_explanations.csv")
    
    # Phase 6: Segmentation
    data["segmented"] = pd.read_csv("/app/deploy/data/customers_segmented.csv")
    data["segment_summary"] = pd.read_csv("/app/deploy/data/segment_summary.csv")
    
    # Phase 7: Sales
    data["monthly_sales"] = pd.read_csv("/app/deploy/data/monthly_sales.csv", parse_dates=["period"])
    data["product_revenue"] = pd.read_csv("/app/deploy/data/product_revenue.csv")
    data["category_revenue"] = pd.read_csv("/app/deploy/data/category_revenue.csv")
    
    with open("/app/deploy/data/sales_kpis.json", "r") as f:
        data["sales_kpis"] = json.load(f)
    
    # Phase 8: Recommendations
    data["recommendations"] = pd.read_csv("/app/deploy/data/customer_recommendations.csv")
    data["priority_queue"] = pd.read_csv("/app/deploy/data/priority_queue_top100.csv")
    
    with open("/app/deploy/data/campaign_roi_summary.json", "r") as f:
        data["campaign_roi"] = json.load(f)
    
    # Model
    try:
        data["model"] = joblib.load("/app/deploy/models/best_churn_model.joblib")
        data["scaler"] = joblib.load("/app/deploy/models/scaler.joblib")
        data["feature_names"] = joblib.load("/app/deploy/models/feature_names.joblib")
    except Exception as e:
        st.warning(f"Model loading issue: {e}")
        data["model"] = None
    
    return data

try:
    data = load_data()
    DATA_LOADED = True
except Exception as e:
    st.error(f"Failed to load data: {e}")
    DATA_LOADED = False
    data = {}

# ─────────────────────────────────────────────────────────────────────────────
# HELPER FUNCTIONS
# ─────────────────────────────────────────────────────────────────────────────
def get_churn_risk_color(score):
    if score < 25: return "#2ecc71", "Low"
    elif score < 50: return "#f1c40f", "Medium"
    elif score < 75: return "#e67e22", "High"
    else: return "#e74c3c", "Critical"

def format_currency(val):
    return f"${val:,.0f}" if val >= 1000 else f"${val:,.2f}"

# ─────────────────────────────────────────────────────────────────────────────
# SIDEBAR
# ─────────────────────────────────────────────────────────────────────────────
with st.sidebar:
    st.image("https://img.icons8.com/color/96/analytics.png", width=60)
    st.title("Churn Intelligence")
    st.markdown("---")
    st.markdown("**Navigation**")
    
    page = st.radio("Go to:", [
        "📊 Overview & KPIs",
        "🔮 Churn Predictor",
        "👥 Customer Segments",
        "📈 Sales Analytics",
        "🎯 Action Recommendations",
        "🧠 Model Performance"
    ])
    
    st.markdown("---")
    st.markdown("**About**")
    st.info("""
    This dashboard combines churn prediction, customer segmentation,
    sales analytics, and automated recommendations into a single
    business intelligence platform.
    
    **Data**: 5,000 customers | 33,037 transactions | 150 products
    """)
    
    if st.button("🔄 Refresh Data"):
        st.cache_data.clear()
        st.rerun()

# ─────────────────────────────────────────────────────────────────────────────
# MAIN CONTENT AREA
# ─────────────────────────────────────────────────────────────────────────────

# ═══════════════════════════════════════════════════════════════════════════════
# PAGE 1: OVERVIEW & KPIs
# ═══════════════════════════════════════════════════════════════════════════════
if page == "📊 Overview & KPIs":
    st.markdown('<div class="main-header">Customer Churn Intelligence Dashboard</div>', unsafe_allow_html=True)
    st.markdown('<div class="sub-header">Real-time insights into customer health, revenue, and retention</div>', unsafe_allow_html=True)
    
    if not DATA_LOADED:
        st.error("Data not loaded. Please run all phases first.")
        st.stop()
    
    # KPI ROW
    col1, col2, col3, col4, col5 = st.columns(5)
    
    with col1:
        churn_rate = data["customers"]["churned"].mean() * 100
        st.markdown(f"""
        <div class="kpi-card kpi-card-red">
            <div class="kpi-value">{churn_rate:.1f}%</div>
            <div class="kpi-label">Churn Rate</div>
        </div>
        """, unsafe_allow_html=True)
    
    with col2:
        total_revenue = data["transactions"]["total_amount"].sum()
        st.markdown(f"""
        <div class="kpi-card">
            <div class="kpi-value">{format_currency(total_revenue)}</div>
            <div class="kpi-label">Total Revenue</div>
        </div>
        """, unsafe_allow_html=True)
    
    with col3:
        total_customers = len(data["customers"])
        st.markdown(f"""
        <div class="kpi-card kpi-card-green">
            <div class="kpi-value">{total_customers:,}</div>
            <div class="kpi-label">Total Customers</div>
        </div>
        """, unsafe_allow_html=True)
    
    with col4:
        at_risk = len(data["recommendations"][data["recommendations"]["priority"] >= 7])
        st.markdown(f"""
        <div class="kpi-card kpi-card-orange">
            <div class="kpi-value">{at_risk:,}</div>
            <div class="kpi-label">High Priority Actions</div>
        </div>
        """, unsafe_allow_html=True)
    
    with col5:
        revenue_at_stake = data["recommendations"]["revenue_at_stake"].sum()
        st.markdown(f"""
        <div class="kpi-card kpi-card-red">
            <div class="kpi-value">{format_currency(revenue_at_stake)}</div>
            <div class="kpi-label">Revenue at Stake</div>
        </div>
        """, unsafe_allow_html=True)
    
    st.markdown("---")
    
    # ALERTS SECTION
    st.subheader("🚨 Live Alerts")
    
    # Critical alerts
    critical_count = len(data["recommendations"][data["recommendations"]["risk_level"] == "Critical"])
    vip_critical = len(data["recommendations"][
        (data["recommendations"]["segment_label"] == "VIP") & 
        (data["recommendations"]["risk_level"] == "Critical")
    ])
    
    col_alert1, col_alert2 = st.columns(2)
    
    with col_alert1:
        st.markdown(f"""
        <div class="alert-critical">
            <strong>⚠️ {critical_count:,} customers at CRITICAL churn risk</strong><br>
            Immediate action required. These customers have >75% churn probability.
        </div>
        """, unsafe_allow_html=True)
    
    with col_alert2:
        st.markdown(f"""
        <div class="alert-warning">
            <strong>💎 {vip_critical:,} VIP customers need urgent attention</strong><br>
            High-value customers at risk. Executive intervention recommended.
        </div>
        """, unsafe_allow_html=True)
    
    # Quick insight cards
    st.markdown("---")
    st.subheader("📋 Quick Insights")
    
    col_q1, col_q2, col_q3 = st.columns(3)
    
    with col_q1:
        seg_dist = data["segmented"]["segment_label"].value_counts()
        fig = px.pie(
            values=seg_dist.values, names=seg_dist.index,
            color=seg_dist.index,
            color_discrete_map={"VIP": "#FFD700", "Loyal": "#2ecc71", 
                               "At Risk": "#e67e22", "Lost": "#e74c3c"},
            hole=0.4
        )
        fig.update_layout(showlegend=True, margin=dict(t=0, b=0, l=0, r=0),
                         height=300, title="Customer Segments")
        st.plotly_chart(fig, use_container_width=True)
    
    with col_q2:
        monthly = data["monthly_sales"].tail(12)
        fig = px.area(
            monthly, x="period", y="revenue",
            color_discrete_sequence=["#3498db"],
            title="Revenue Trend (Last 12 Months)"
        )
        fig.update_layout(height=300, margin=dict(t=30, b=0, l=0, r=0))
        st.plotly_chart(fig, use_container_width=True)
    
    with col_q3:
        risk_dist = data["recommendations"]["risk_level"].value_counts()
        risk_order = ["Low", "Medium", "High", "Critical"]
        risk_dist = risk_dist.reindex(risk_order).fillna(0)
        fig = px.bar(
            x=risk_dist.index, y=risk_dist.values,
            color=risk_dist.index,
            color_discrete_map={"Low": "#2ecc71", "Medium": "#f1c40f",
                               "High": "#e67e22", "Critical": "#e74c3c"},
            title="Risk Distribution"
        )
        fig.update_layout(height=300, margin=dict(t=30, b=0, l=0, r=0), showlegend=False)
        st.plotly_chart(fig, use_container_width=True)

# ═══════════════════════════════════════════════════════════════════════════════
# PAGE 2: CHURN PREDICTOR
# ═══════════════════════════════════════════════════════════════════════════════
elif page == "🔮 Churn Predictor":
    st.markdown('<div class="main-header">Churn Risk Predictor</div>', unsafe_allow_html=True)
    st.markdown('<div class="sub-header">Input customer data to predict churn probability and get actionable insights</div>', unsafe_allow_html=True)
    
    if not DATA_LOADED or data.get("model") is None:
        st.warning("⚠️ Model not available. Please run Phase 4 first.")
        st.stop()
    
    # TWO MODES: Single Customer or Batch Upload
    pred_mode = st.radio("Prediction Mode:", ["Single Customer", "Batch Upload (CSV)"], horizontal=True)
    
    if pred_mode == "Single Customer":
        # Use an existing customer as template
        sample_customers = data["customers"]["customer_id"].sample(5, random_state=42).tolist()
        selected_customer = st.selectbox("Select a customer (or enter ID manually):", 
                                          ["Manual Entry"] + sample_customers)
        
        if selected_customer != "Manual Entry":
            # Pre-fill with existing customer data
            cust_row = data["customers"][data["customers"]["customer_id"] == selected_customer].iloc[0]
            default_age = int(cust_row["age"])
            default_tickets = int(cust_row["support_tickets_6m"])
            default_email = float(cust_row["email_open_rate"])
            default_web = int(cust_row["web_sessions_30d"])
        else:
            default_age, default_tickets, default_email, default_web = 35, 1, 0.3, 5
        
        st.markdown("---")
        
        # Input form
        col1, col2 = st.columns(2)
        
        with col1:
            st.subheader("📋 Customer Profile")
            age = st.slider("Age", 18, 80, default_age)
            gender = st.selectbox("Gender", ["M", "F", "Other"])
            region = st.selectbox("Region", ["North", "South", "East", "West", "Central"])
            segment = st.selectbox("Business Segment", ["Retail", "SMB", "Enterprise"])
            channel = st.selectbox("Acquisition Channel", ["Online", "In-Store", "Mobile App", "Phone"])
            ltv_baseline = st.number_input("Baseline LTV ($)", 0.0, 15000.0, 500.0)
        
        with col2:
            st.subheader("📊 Behavioral Data")
            support_tickets = st.slider("Support Tickets (6m)", 0, 10, default_tickets)
            email_rate = st.slider("Email Open Rate", 0.0, 1.0, default_email)
            web_sessions = st.slider("Web Sessions (30d)", 0, 30, default_web)
            recency = st.slider("Days Since Last Purchase", 0, 500, 120)
            total_spent = st.number_input("Total Spent ($)", 0.0, 20000.0, 800.0)
            txn_count = st.slider("Transaction Count", 0, 50, 8)
        
        st.markdown("---")
        
        if st.button("🔮 Predict Churn Risk", type="primary", use_container_width=True):
            # Build feature vector
            # Create a DataFrame with same structure as training
            feature_names = data["feature_names"]
            
            # Compute derived features
            tenure_days = 365  # placeholder
            purchase_frequency = txn_count / (tenure_days / 30 + 1)
            avg_txn_value = total_spent / (txn_count + 1)
            rfm_score = 8  # placeholder
            engagement = (email_rate * 0.35 + min(web_sessions/20, 1) * 0.4 + 
                         (1 - min(support_tickets/5, 1)) * 0.25)
            
            input_dict = {
                "age": age,
                "lifetime_value_baseline": ltv_baseline,
                "support_tickets_6m": support_tickets,
                "email_open_rate": email_rate,
                "web_sessions_30d": web_sessions,
                "total_spent": total_spent,
                "txn_count": txn_count,
                "avg_order_value": avg_txn_value,
                "max_order_value": avg_txn_value * 1.5,
                "min_order_value": avg_txn_value * 0.5,
                "std_order_value": avg_txn_value * 0.3,
                "total_quantity": txn_count * 2,
                "total_discount": txn_count * 0.02,
                "unique_products": min(txn_count, 20),
                "recency_days": recency,
                "R_score": max(1, 6 - recency // 60),
                "F_score": min(5, max(1, txn_count // 3)),
                "M_score": min(5, max(1, int(total_spent // 500))),
                "RFM_score": 10,  # placeholder
                "tenure_days": tenure_days,
                "days_between_first_last": tenure_days,
                "purchase_frequency": purchase_frequency,
                "avg_transaction_value": avg_txn_value,
                "engagement_score": engagement,
                "spending_trend_ratio": 1.0,
                "purchase_regularity": 1.0,
                "category_diversity": min(txn_count, 10),
                "discount_sensitivity": 0.02,
            }
            
            # Gender dummies
            for g in ["F", "M", "Other"]:
                input_dict[f"gender_{g}"] = 1 if gender == g else 0
            
            # Region dummies
            for r in ["Central", "East", "North", "South", "West"]:
                input_dict[f"region_{r}"] = 1 if region == r else 0
            
            # Segment dummies
            for s in ["Enterprise", "Retail", "SMB"]:
                input_dict[f"segment_{s}"] = 1 if segment == s else 0
            
            # Channel dummies
            for c in ["In-Store", "Mobile App", "Online", "Phone"]:
                input_dict[f"acquisition_channel_{c}"] = 1 if channel == c else 0
            
            # Create feature vector
            X_input = pd.DataFrame([{k: input_dict.get(k, 0) for k in feature_names}])
            X_scaled = data["scaler"].transform(X_input)
            
            # Predict
            proba = data["model"].predict_proba(X_scaled)[0][1]
            risk_score = proba * 100
            color, risk_label = get_churn_risk_color(risk_score)
            prediction = 1 if risk_score >= 50 else 0
            
            # DISPLAY RESULTS
            st.markdown("---")
            
            res_col1, res_col2, res_col3 = st.columns([1, 2, 1])
            
            with res_col2:
                st.markdown(f"""
                <div style="text-align: center; padding: 2rem; background: {color}20; border-radius: 16px; border: 3px solid {color};">
                    <div style="font-size: 1.2rem; color: {color}; font-weight: 600;">Churn Risk: {risk_label}</div>
                    <div style="font-size: 4rem; font-weight: 800; color: {color}; margin: 0.5rem 0;">{risk_score:.1f}%</div>
                    <div style="font-size: 1rem; color: #666;">Predicted Outcome: {'🔴 CHURN' if prediction == 1 else '🟢 RETAIN'}</div>
                </div>
                """, unsafe_allow_html=True)
            
            # Risk meter
            fig = go.Figure(go.Indicator(
                mode="gauge+number",
                value=risk_score,
                domain={'x': [0, 1], 'y': [0, 1]},
                gauge={
                    'axis': {'range': [0, 100]},
                    'bar': {'color': color},
                    'steps': [
                        {'range': [0, 25], 'color': '#d5f5e3'},
                        {'range': [25, 50], 'color': '#fcf3cf'},
                        {'range': [50, 75], 'color': '#f5cba7'},
                        {'range': [75, 100], 'color': '#f5b7b1'}
                    ],
                    'threshold': {
                        'line': {'color': 'black', 'width': 4},
                        'thickness': 0.75,
                        'value': risk_score
                    }
                }
            ))
            fig.update_layout(height=300, margin=dict(t=0, b=0, l=0, r=0))
            st.plotly_chart(fig, use_container_width=True)
            
            # Reasons
            st.subheader("📊 Key Risk Factors")
            
            reasons = []
            if recency > 90:
                reasons.append(("No purchase in 90+ days", "HIGH", f"{recency} days since last purchase"))
            if txn_count < 3:
                reasons.append(("Very few transactions", "HIGH", f"Only {txn_count} total purchases"))
            if total_spent < 200:
                reasons.append(("Low lifetime spend", "MEDIUM", f"${total_spent:.0f} total spent"))
            if email_rate < 0.2:
                reasons.append(("Low email engagement", "MEDIUM", f"{email_rate:.1%} open rate"))
            if support_tickets > 3:
                reasons.append(("High support activity", "MEDIUM", f"{support_tickets} recent tickets"))
            
            if not reasons:
                reasons.append(("Overall profile stable", "LOW", "No major risk flags detected"))
            
            for reason, severity, detail in reasons:
                sev_color = {"HIGH": "🔴", "MEDIUM": "🟡", "LOW": "🟢"}[severity]
                st.markdown(f"""
                <div style="padding: 0.75rem; background: #f8f9fa; border-radius: 8px; margin: 0.3rem 0;">
                    <strong>{sev_color} {reason}</strong> — {detail}
                </div>
                """, unsafe_allow_html=True)
            
            # Recommendation
            st.subheader("🎯 Recommended Action")
            if risk_score >= 75:
                st.error("🚨 **URGENT**: Executive-level intervention recommended. Personal call + exclusive offer.")
            elif risk_score >= 50:
                st.warning("⚠️ **HIGH PRIORITY**: Re-engagement campaign + targeted discount.")
            elif risk_score >= 25:
                st.info("ℹ️ **MONITOR**: Increase touchpoints. Watch for further decline.")
            else:
                st.success("✅ **HEALTHY**: Consider upsell/cross-sell opportunities.")
    
    else:  # Batch mode
        st.info("Upload a CSV with customer features to predict churn risk for multiple customers.")
        uploaded = st.file_uploader("Upload CSV", type=["csv"])
        
        if uploaded:
            batch_df = pd.read_csv(uploaded)
            st.write(f"📄 Loaded {len(batch_df)} customers")
            st.dataframe(batch_df.head())
            st.info("Batch prediction coming soon — single customer mode is fully functional above.")

# ═══════════════════════════════════════════════════════════════════════════════
# PAGE 3: CUSTOMER SEGMENTS
# ═══════════════════════════════════════════════════════════════════════════════
elif page == "👥 Customer Segments":
    st.markdown('<div class="main-header">Customer Segmentation</div>', unsafe_allow_html=True)
    st.markdown('<div class="sub-header">Explore VIP, Loyal, At Risk, and Lost customer segments</div>', unsafe_allow_html=True)
    
    if not DATA_LOADED:
        st.error("Data not loaded.")
        st.stop()
    
    seg = data["segmented"]
    
    # Segment selector
    selected_segments = st.multiselect(
        "Filter Segments:",
        ["VIP", "Loyal", "At Risk", "Lost"],
        default=["VIP", "Loyal", "At Risk", "Lost"]
    )
    
    filtered = seg[seg["segment_label"].isin(selected_segments)]
    
    col1, col2 = st.columns(2)
    
    with col1:
        # 3D-ish scatter: Recency vs Spend, colored by segment
        fig = px.scatter(
            filtered, x="recency_days", y="total_spent",
            color="segment_label", size="txn_count",
            hover_data=["customer_id", "RFM_score", "engagement_score"],
            color_discrete_map={"VIP": "#FFD700", "Loyal": "#2ecc71",
                                "At Risk": "#e67e22", "Lost": "#e74c3c"},
            title="Customers: Recency vs Lifetime Spend",
            labels={"recency_days": "Days Since Last Purchase", "total_spent": "Total Spent ($)"}
        )
        fig.update_layout(height=500)
        st.plotly_chart(fig, use_container_width=True)
    
    with col2:
        # RFM distribution by segment
        fig = px.box(
            filtered, x="segment_label", y="RFM_score",
            color="segment_label",
            color_discrete_map={"VIP": "#FFD700", "Loyal": "#2ecc71",
                                "At Risk": "#e67e22", "Lost": "#e74c3c"},
            title="RFM Score Distribution by Segment",
            category_orders={"segment_label": ["VIP", "Loyal", "At Risk", "Lost"]}
        )
        fig.update_layout(height=500, showlegend=False)
        st.plotly_chart(fig, use_container_width=True)
    
    # Segment metrics table
    st.subheader("📊 Segment Metrics")
    
    metrics = filtered.groupby("segment_label").agg(
        Count=("customer_id", "count"),
        Avg_Spend=("total_spent", "mean"),
        Avg_Recency=("recency_days", "mean"),
        Avg_Frequency=("txn_count", "mean"),
        Avg_RFM=("RFM_score", "mean"),
        Churn_Rate=("churned", "mean"),
    ).round(2)
    
    metrics["Churn_Rate"] = (metrics["Churn_Rate"] * 100).round(1).astype(str) + "%"
    metrics["Avg_Spend"] = "$" + metrics["Avg_Spend"].round(0).astype(int).astype(str)
    
    st.dataframe(metrics, use_container_width=True)
    
    # Customer table with search
    st.subheader("🔍 Customer Explorer")
    
    search_id = st.text_input("Search Customer ID (e.g., C00001):")
    if search_id:
        cust = seg[seg["customer_id"].str.contains(search_id, case=False, na=False)]
    else:
        cust = filtered.sample(min(20, len(filtered)), random_state=42)
    
    display_cols = ["customer_id", "segment_label", "recency_days", "txn_count", 
                    "total_spent", "RFM_score", "engagement_score", "churned"]
    st.dataframe(cust[display_cols].reset_index(drop=True), use_container_width=True)

# ═══════════════════════════════════════════════════════════════════════════════
# PAGE 4: SALES ANALYTICS
# ═══════════════════════════════════════════════════════════════════════════════
elif page == "📈 Sales Analytics":
    st.markdown('<div class="main-header">Sales Analytics</div>', unsafe_allow_html=True)
    st.markdown('<div class="sub-header">Revenue trends, product performance, and growth metrics</div>', unsafe_allow_html=True)
    
    if not DATA_LOADED:
        st.error("Data not loaded.")
        st.stop()
    
    tab1, tab2, tab3, tab4 = st.tabs(["📊 Revenue Trends", "🏆 Top Products", "📅 Seasonality", "📈 Growth"])
    
    with tab1:
        monthly = data["monthly_sales"]
        
        fig = make_subplots(rows=2, cols=1, shared_xaxes=True,
                           subplot_titles=("Monthly Revenue", "Monthly Orders"),
                           vertical_spacing=0.1)
        
        fig.add_trace(
            go.Scatter(x=monthly["period"], y=monthly["revenue"],
                      mode="lines+markers", name="Revenue", line=dict(color="#3498db")),
            row=1, col=1
        )
        fig.add_trace(
            go.Scatter(x=monthly["period"], y=monthly["orders"],
                      mode="lines+markers", name="Orders", line=dict(color="#e67e22")),
            row=2, col=1
        )
        
        fig.update_layout(height=600, showlegend=False,
                         title_text="Revenue & Order Trends Over Time")
        st.plotly_chart(fig, use_container_width=True)
        
        # KPI row
        kpi_col1, kpi_col2, kpi_col3 = st.columns(3)
        with kpi_col1:
            st.metric("Total Revenue", data["sales_kpis"]["Total Revenue (All Time)"])
        with kpi_col2:
            st.metric("Avg Order Value", data["sales_kpis"]["Average Order Value"])
        with kpi_col3:
            st.metric("Latest MoM Growth", data["sales_kpis"]["Revenue Growth (MoM, latest)"])
    
    with tab2:
        top_n = st.slider("Show Top N Products", 5, 50, 10)
        top_products = data["product_revenue"].head(top_n)
        
        fig = px.bar(
            top_products, x="revenue", y="product_name",
            orientation="h", color="category",
            title=f"Top {top_n} Products by Revenue",
            labels={"revenue": "Revenue ($)", "product_name": ""}
        )
        fig.update_layout(height=500, yaxis=dict(autorange="reversed"))
        st.plotly_chart(fig, use_container_width=True)
        
        # Category breakdown
        fig2 = px.pie(
            data["category_revenue"], values="revenue", names="category",
            title="Revenue by Category", hole=0.4
        )
        fig2.update_layout(height=400)
        st.plotly_chart(fig2, use_container_width=True)
    
    with tab3:
        # Seasonal patterns
        seasonal = data["transactions"].copy()
        seasonal["month"] = seasonal["transaction_date"].dt.month
        seasonal["dayofweek"] = seasonal["transaction_date"].dt.dayofweek
        seasonal["quarter"] = seasonal["transaction_date"].dt.quarter
        
        month_pattern = seasonal.groupby("month")["total_amount"].sum().reset_index()
        month_pattern["month_name"] = pd.to_datetime(month_pattern["month"], format="%m").dt.strftime("%b")
        
        dow_pattern = seasonal.groupby("dayofweek")["total_amount"].sum().reset_index()
        dow_pattern["day_name"] = ["Mon", "Tue", "Wed", "Thu", "Fri", "Sat", "Sun"]
        
        col_s1, col_s2 = st.columns(2)
        
        with col_s1:
            fig = px.bar(
                month_pattern, x="month_name", y="total_amount",
                color_discrete_sequence=["#3498db"],
                title="Revenue by Month (Seasonality)",
                labels={"total_amount": "Revenue ($)", "month_name": "Month"}
            )
            fig.update_layout(height=350)
            st.plotly_chart(fig, use_container_width=True)
        
        with col_s2:
            fig = px.bar(
                dow_pattern, x="day_name", y="total_amount",
                color_discrete_sequence=["#e67e22"],
                title="Revenue by Day of Week",
                labels={"total_amount": "Revenue ($)", "day_name": "Day"}
            )
            fig.update_layout(height=350)
            st.plotly_chart(fig, use_container_width=True)
    
    with tab4:
        monthly = data["monthly_sales"].copy()
        monthly["revenue_mom"] = monthly["revenue"].pct_change() * 100
        monthly["revenue_3ma"] = monthly["revenue"].rolling(3, min_periods=1).mean()
        
        fig = go.Figure()
        fig.add_trace(go.Bar(
            x=monthly["period"], y=monthly["revenue_mom"],
            name="MoM Growth %", marker_color="#3498db"
        ))
        fig.add_trace(go.Scatter(
            x=monthly["period"], y=monthly["revenue_3ma"],
            name="3-Month MA ($)", yaxis="y2", line=dict(color="#e74c3c")
        ))
        
        fig.update_layout(
            title="Month-over-Month Growth Rate",
            yaxis=dict(title="MoM Growth (%)"),
            yaxis2=dict(title="Revenue ($)", overlaying="y", side="right"),
            height=400, legend=dict(orientation="h", yanchor="bottom", y=1.02)
        )
        st.plotly_chart(fig, use_container_width=True)

# ═══════════════════════════════════════════════════════════════════════════════
# PAGE 5: ACTION RECOMMENDATIONS
# ═══════════════════════════════════════════════════════════════════════════════
elif page == "🎯 Action Recommendations":
    st.markdown('<div class="main-header">Action Recommendations</div>', unsafe_allow_html=True)
    st.markdown('<div class="sub-header">Priority queue of customers needing immediate business action</div>', unsafe_allow_html=True)
    
    if not DATA_LOADED:
        st.error("Data not loaded.")
        st.stop()
    
    recs = data["recommendations"]
    
    # Filters
    col_f1, col_f2, col_f3 = st.columns(3)
    with col_f1:
        seg_filter = st.multiselect("Segment:", recs["segment_label"].unique(), default=recs["segment_label"].unique())
    with col_f2:
        risk_filter = st.multiselect("Risk Level:", recs["risk_level"].unique(), default=recs["risk_level"].unique())
    with col_f3:
        priority_min = st.slider("Min Priority Score:", 0, 130, 50)
    
    filtered_recs = recs[
        (recs["segment_label"].isin(seg_filter)) &
        (recs["risk_level"].isin(risk_filter)) &
        (recs["final_priority_score"] >= priority_min)
    ].sort_values("final_priority_score", ascending=False)
    
    st.markdown(f"**{len(filtered_recs):,} customers match your filters**")
    
    # Priority stats
    stat_col1, stat_col2, stat_col3, stat_col4 = st.columns(4)
    with stat_col1:
        st.metric("Total at Risk", len(filtered_recs))
    with stat_col2:
        st.metric("Revenue at Stake", f"${filtered_recs['revenue_at_stake'].sum():,.0f}")
    with stat_col3:
        st.metric("Est. Campaign Cost", f"${filtered_recs['cost_per_customer'].sum():,.0f}")
    with stat_col4:
        st.metric("Est. ROI", f"{((filtered_recs['expected_revenue_saved'].sum() - filtered_recs['cost_per_customer'].sum()) / max(filtered_recs['cost_per_customer'].sum(), 1) * 100):.0f}%")
    
    st.markdown("---")
    
    # Display priority table
    display_cols = [
        "customer_id", "segment_label", "risk_level", "churn_risk_score",
        "total_spent", "revenue_at_stake", "final_priority_score",
        "recommended_action", "recommended_channel", "recommended_offer",
        "urgency", "behavioral_enhancements"
    ]
    
    st.dataframe(
        filtered_recs[display_cols].head(100),
        use_container_width=True,
        column_config={
            "churn_risk_score": st.column_config.ProgressColumn("Risk %", min_value=0, max_value=100, format="%.0f%%"),
            "final_priority_score": st.column_config.NumberColumn("Priority", format="%.1f"),
            "revenue_at_stake": st.column_config.NumberColumn("Revenue at Stake", format="$%.0f"),
        }
    )
    
    # Campaign ROI summary
    st.markdown("---")
    st.subheader("💰 Campaign ROI Summary")
    
    roi = data["campaign_roi"]
    col_roi1, col_roi2 = st.columns(2)
    
    with col_roi1:
        st.markdown("**Full Campaign (All Customers)**")
        fc = roi["full_campaign"]
        st.write(f"- Customers: {fc['customers']:,}")
        st.write(f"- Revenue at Stake: ${fc['revenue_at_stake']:,.0f}")
        st.write(f"- Campaign Cost: ${fc['campaign_cost']:,.0f}")
        st.write(f"- Expected Saved: ${fc['expected_revenue_saved']:,.0f}")
        st.write(f"- ROI: **{fc['roi_percent']:.0f}%**")
    
    with col_roi2:
        st.markdown("**High Priority Only (Priority ≥ 7)**")
        hp = roi["high_priority_campaign"]
        st.write(f"- Customers: {hp['customers']:,}")
        st.write(f"- Revenue at Stake: ${hp['revenue_at_stake']:,.0f}")
        st.write(f"- Campaign Cost: ${hp['campaign_cost']:,.0f}")
        st.write(f"- Expected Saved: ${hp['expected_revenue_saved']:,.0f}")
        st.write(f"- ROI: **{hp['roi_percent']:.0f}%**")

# ═══════════════════════════════════════════════════════════════════════════════
# PAGE 6: MODEL PERFORMANCE
# ═══════════════════════════════════════════════════════════════════════════════
elif page == "🧠 Model Performance":
    st.markdown('<div class="main-header">Model Performance & Explainability</div>', unsafe_allow_html=True)
    st.markdown('<div class="sub-header">Churn prediction accuracy, feature importance, and model health</div>', unsafe_allow_html=True)
    
    if not DATA_LOADED:
        st.error("Data not loaded.")
        st.stop()
    
    tab1, tab2, tab3 = st.tabs(["📊 Model Metrics", "🔍 Feature Importance", "📋 Explanations"])
    
    with tab1:
        # Model comparison
        try:
            model_comp = pd.read_csv("/app/outputs/phase4/model_comparison.csv")
            
            fig = px.bar(
                model_comp, x="Model", y=["Accuracy", "Precision", "Recall", "F1-Score", "AUC-ROC"],
                barmode="group", title="Model Comparison",
                color_discrete_sequence=["#3498db", "#2ecc71", "#e74c3c", "#9b59b6", "#f1c40f"]
            )
            fig.update_layout(height=500)
            st.plotly_chart(fig, use_container_width=True)
            
            st.dataframe(model_comp.round(3), use_container_width=True)
        except:
            st.info("Model comparison data not available.")
        
        # Confusion matrix
        st.subheader("Confusion Matrix (Best Model)")
        
        preds = data["model_preds"]
        from sklearn.metrics import confusion_matrix
        cm = confusion_matrix(preds["actual_churn"], preds["rf_pred"])
        
        fig = px.imshow(
            cm, text_auto=True,
            labels=dict(x="Predicted", y="Actual", color="Count"),
            x=["Active", "Churned"], y=["Active", "Churned"],
            color_continuous_scale="Blues"
        )
        fig.update_layout(height=400)
        st.plotly_chart(fig, use_container_width=True)
    
    with tab2:
        st.subheader("Top Churn Drivers")
        
        try:
            # Feature importance from Phase 5 summary or recompute
            if data.get("model") and hasattr(data["model"], "feature_importances_"):
                importances = data["model"].feature_importances_
                feat_names = data["feature_names"]
                
                imp_df = pd.DataFrame({
                    "Feature": [f.replace("_scaled", "") for f in feat_names],
                    "Importance": importances
                }).sort_values("Importance", ascending=False).head(15)
                
                fig = px.bar(
                    imp_df, x="Importance", y="Feature", orientation="h",
                    color="Importance", color_continuous_scale="Viridis",
                    title="Top 15 Feature Importances (Random Forest)"
                )
                fig.update_layout(height=500, yaxis=dict(autorange="reversed"))
                st.plotly_chart(fig, use_container_width=True)
            else:
                st.info("Feature importance data not available.")
        except Exception as e:
            st.error(f"Could not load feature importance: {e}")
    
    with tab3:
        st.subheader("Customer Explanations")
        
        try:
            explanations = data["explanations"]
            
            cust_search = st.text_input("Search Customer ID:")
            if cust_search:
                exp = explanations[explanations["customer_id"].str.contains(cust_search, case=False, na=False)]
            else:
                exp = explanations.head(10)
            
            for _, row in exp.iterrows():
                with st.expander(f"Customer {row['customer_id']} | P(churn)={row['predicted_churn_prob']:.1%} | {'🔴 CHURNED' if row['actual_churn']==1 else '🟢 ACTIVE'}"):
                    st.markdown(f"**Top Risk Factors:** {row['top_reasons_for_churn']}")
                    st.markdown(f"**Loyalty Signs:** {row['top_reasons_against_churn']}")
        except:
            st.info("Explanation data not available.")

# ─────────────────────────────────────────────────────────────────────────────
# FOOTER
# ─────────────────────────────────────────────────────────────────────────────
st.markdown("---")
st.markdown("""
<div style="text-align: center; color: #9ca3af; padding: 1rem;">
    <small>Customer Churn Intelligence Dashboard | Built with Streamlit | Data: Synthetic (5,000 customers, 33,037 transactions)</small>
</div>
""", unsafe_allow_html=True)