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app.py

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+import gradio as gr
+import joblib
+import json
+import pandas as pd
+import numpy as np
+import matplotlib.pyplot as plt
+import seaborn as sns
+from pathlib import Path
+from lifetimes import BetaGeoFitter, GammaGammaFitter
+from supervised_explainability import SegmentClassifier
+
+# Set dark theme for matplotlib globally
+plt.rcParams.update({
+    'grid.color': '#1f2937',
+    'text.color': '#f3f4f6',
+    'axes.labelcolor': '#9ca3af',
+    'axes.edgecolor': '#1f2937',
+    'xtick.color': '#9ca3af',
+    'ytick.color': '#9ca3af',
+    'figure.facecolor': '#070a13',
+    'axes.facecolor': '#0d111e',
+})
+
+# Load the models, dataset and metadata
+try:
+    df_baseline = pd.read_parquet('baseline_training_data.parquet')
+    feature_names = json.load(open('feature_names.json'))
+    
+    # Load CLV models
+    bgf = BetaGeoFitter(penalizer_coef=0.01)
+    bgf.params_ = joblib.load('clv_bgf_params.pkl')
+    bgf.predict = bgf.conditional_expected_number_of_purchases_up_to_time
+    
+    ggf = GammaGammaFitter(penalizer_coef=0.01)
+    ggf.params_ = joblib.load('clv_ggf_params.pkl')
+    
+    # Load PCA and GMM
+    pca_pipeline = joblib.load('pca_pipeline.pkl')
+    gmm_model = joblib.load('gmm_model.pkl')
+    
+    # Load XGBoost Segment Classifier
+    classifier = SegmentClassifier.load_model('xgb_classifier.pkl')
+    
+    # Calculate dataset averages for default mock enrichment values
+    defaults = {
+        'CPIAUCSL': df_baseline['CPIAUCSL'].mean() if 'CPIAUCSL' in df_baseline.columns else 218.0,
+        'RSXFS': df_baseline['RSXFS'].mean() if 'RSXFS' in df_baseline.columns else 350000.0,
+        'PSAVERT': df_baseline['PSAVERT'].mean() if 'PSAVERT' in df_baseline.columns else 6.0,
+    }
+except Exception as e:
+    print(f"Error loading models or dataset: {e}")
+    df_baseline = None
+    bgf = ggf = pca_pipeline = gmm_model = classifier = None
+    defaults = {}
+
+# Custom cluster names and details
+CLUSTER_INFO = {
+    0: {"name": "VIP / High-Value Loyalists", "desc": "Premium shoppers with high CLV, high repeat transactions, and balanced session activity.", "color": "#a78bfa"},
+    1: {"name": "Inactive / Churned Customers", "desc": "Accounts with very high recency and near-zero active probability. Re-engagement needed.", "color": "#f87171"},
+    2: {"name": "Instacart Bulk Grocery Buyers", "desc": "Routine buyers with high order counts and low days between orders. Consistent routine shoppers.", "color": "#34d399"},
+    3: {"name": "Window Shoppers (High Carts, Low Purchase)", "desc": "High session views and carts, but low purchase counts and high cart abandonment rates.", "color": "#fbbf24"},
+    4: {"name": "Weather-Sensitive Impulsive Buyers", "desc": "High weather-precipitation sensitivity flag. Driven by physical rain/precipitation states.", "color": "#f472b6"},
+    5: {"name": "Casual One-Time Retail Buyers", "desc": "Low purchase frequency, high order values on single visits, and stable retail shopping profile.", "color": "#818cf8"},
+    6: {"name": "Sensible / Standard Spenders", "desc": "Moderate transactional values, highly deliberate shopping paths, and balanced browsing habits.", "color": "#60a5fa"},
+    7: {"name": "High-Intent Cart Abandoners", "desc": "High cart and browse history but high abandoned checkout rate. Re-target with promo vouchers.", "color": "#fb7185"},
+    8: {"name": "New / Low-Engagement Registrations", "desc": "Recently created profiles with low transaction history, low sessions, and neutral activity.", "color": "#9ca3af"}
+}
+
+def make_local_shap_plot(importance_df, segment_name):
+    """
+    Plots a custom horizontal bar chart of local SHAP feature importances.
+    """
+    if importance_df.empty:
+        return None
+        
+    fig, ax = plt.subplots(figsize=(8, 5))
+    
+    # Sort for plotting (largest positive/negative values at the top)
+    plot_df = importance_df.head(10).iloc[::-1]
+    
+    colors = ['#f43f5e' if val < 0 else '#8b5cf6' for val in plot_df['importance_value']]
+    
+    bars = ax.barh(plot_df['feature'], plot_df['importance_value'], color=colors, edgecolor='none', height=0.6)
+    
+    # Customize grid and spines
+    ax.axvline(x=0, color='#ffffff', alpha=0.2, linestyle='-', linewidth=1)
+    ax.grid(True, axis='x', linestyle='--', alpha=0.3)
+    ax.spines['top'].set_visible(False)
+    ax.spines['right'].set_visible(False)
+    ax.spines['left'].set_visible(False)
+    ax.spines['bottom'].set_visible(False)
+    
+    # Title and labels
+    ax.set_title(f"SHAP Values driving prediction of:\n{segment_name}", fontweight='bold', pad=15, fontsize=12)
+    ax.set_xlabel("SHAP Influence (Direction & Strength)", labelpad=10)
+    
+    plt.tight_layout()
+    return fig
+
+def make_pca_scatter_plot(features_df, predicted_cluster, cluster_color):
+    """
+    Reduces the target features to PCA space and overlays it on the baseline population.
+    """
+    if df_baseline is None or pca_pipeline is None:
+        return None
+        
+    try:
+        # Get baseline PCs
+        numeric_cols = df_baseline.select_dtypes(include='number').columns
+        id_cols = [c for c in numeric_cols if 'id' in c.lower()]
+        X_base = df_baseline[numeric_cols].drop(columns=id_cols + ['Segment_Cluster_ID'], errors='ignore').fillna(0)
+        
+        # Ensure identical column order
+        base_features = X_base.columns.tolist()
+        features_ordered = features_df[base_features].fillna(0)
+        
+        # Apply fitted scaler + PCA
+        base_pca = pca_pipeline.transform(X_base)
+        sample_pca = pca_pipeline.transform(features_ordered)
+        
+        fig, ax = plt.subplots(figsize=(8, 5))
+        
+        # Draw background baseline points in subtle dark gray
+        ax.scatter(
+            base_pca[:, 0], 
+            base_pca[:, 1], 
+            color='#ffffff', 
+            s=12, 
+            alpha=0.06,
+            label='Baseline Customers',
+            edgecolor='none'
+        )
+        
+        # Plot the target customer as a glowing star
+        ax.scatter(
+            sample_pca[0, 0],
+            sample_pca[0, 1],
+            color=cluster_color,
+            marker='*',
+            s=450,
+            edgecolors='#ffffff',
+            linewidths=1.8,
+            label='Current Customer',
+            zorder=10
+        )
+        
+        # Add glow effect around the star
+        ax.scatter(
+            sample_pca[0, 0],
+            sample_pca[0, 1],
+            color=cluster_color,
+            marker='o',
+            s=1000,
+            alpha=0.25,
+            edgecolors='none',
+            zorder=9
+        )
+        
+        ax.set_title("Customer Position in 2D PCA Latent Space", fontweight='bold', pad=15)
+        ax.set_xlabel("Principal Component 1 (PC1)")
+        ax.set_ylabel("Principal Component 2 (PC2)")
+        ax.grid(True, linestyle='--', alpha=0.15)
+        ax.legend(loc='upper right', framealpha=0.6, facecolor='#0d111e', edgecolor='#ffffff')
+        
+        plt.tight_layout()
+        return fig
+    except Exception as e:
+        print(f"Error drawing PCA plot: {e}")
+        return None
+
+def process_lookup(customer_id):
+    """
+    Looks up a real customer in baseline parquet and returns metrics + predictions + charts.
+    """
+    if df_baseline is None or classifier is None:
+        return "System error: Models or baseline parquet not loaded.", "", "", "", "", None, None
+        
+    try:
+        row = df_baseline[df_baseline['customer_id'] == str(customer_id)]
+        if row.empty:
+            return f"Customer ID '{customer_id}' not found in the baseline dataset.", "", "", "", "", None, None
+            
+        # Extract features for XGBoost prediction
+        # XGBoost expects the raw numeric features in exactly the same columns
+        X_sample = row[feature_names].copy()
+        
+        # Perform Segment Prediction
+        pred_cluster = int(classifier.predict(X_sample)[0])
+        cluster_data = CLUSTER_INFO.get(pred_cluster, {"name": f"Segment {pred_cluster}", "desc": "Unknown segment characteristics.", "color": "#9ca3af"})
+        
+        # Extract specific metrics
+        rfm_str = f"📅 Recency: {int(row['Recency'].iloc[0])} days | 🔄 Frequency: {int(row['Frequency'].iloc[0])} purchases | 💰 Monetary: ${row['MonetaryValue'].iloc[0]:,.2f}"
+        
+        ecom_str = f"👁️ Views: {int(row['total_views'].iloc[0])} | 🛒 Carts: {int(row['total_carts'].iloc[0])} | 🛍️ Purchases: {int(row['total_purchases'].iloc[0])} | 🚪 Sessions: {int(row['total_sessions'].iloc[0])}"
+        
+        clv_str = f"🎯 Expected 12m CLV: ${row['expected_12m_clv'].iloc[0]:,.2f} | 📈 Active Prob: {row['expected_active_probability'].iloc[0]*100:.1f}% | 🔀 Group: {row['ab_group'].iloc[0]}"
+        
+        desc_html = f"""
+        <div style="padding: 15px; border-left: 5px solid {cluster_data['color']}; background: rgba(255,255,255,0.02); border-radius: 4px;">
+            <h3 style="margin: 0; color: {cluster_data['color']}; font-size: 1.3em;">{cluster_data['name']}</h3>
+            <p style="margin: 8px 0 0 0; color: #d1d5db; line-height: 1.5;">{cluster_data['desc']}</p>
+        </div>
+        """
+        
+        # Generate explanations & PCA scatter
+        importance_df = classifier.explain_local(X_sample, pred_cluster)
+        fig_shap = make_local_shap_plot(importance_df, cluster_data['name'])
+        fig_pca = make_pca_scatter_plot(X_sample, pred_cluster, cluster_data['color'])
+        
+        return rfm_str, ecom_str, clv_str, desc_html, fig_shap, fig_pca
+    except Exception as e:
+        import traceback
+        traceback.print_exc()
+        return f"Error running lookup inference: {e}", "", "", "", None, None
+
+def process_simulation(recency, frequency, monetary, views, carts, purchases, sessions, abandon, instacart_orders, days_between_orders, precip):
+    """
+    Computes lifetimes CLV predictions, constructs features, fits XGBoost segment classifier, and generates SHAP + PCA plots.
+    """
+    if classifier is None or bgf is None or ggf is None:
+        return "System error: Predictors are not loaded.", "", "", "", None, None
+        
+    try:
+        # Construct raw inputs
+        precip_flag = 1 if precip == "Yes" else 0
+        abandon_flag = 1 if abandon == "Yes" else 0
+        T = recency + 30
+        
+        # Calculate lifetimes CLV predictions
+        if frequency == 0:
+            active_prob = 0.0
+            expected_clv = 0.0
+        else:
+            freq_s = pd.Series([frequency])
+            rec_s = pd.Series([recency])
+            T_s = pd.Series([T])
+            mon_s = pd.Series([monetary])
+            
+            active_prob_raw = bgf.conditional_probability_alive(freq_s, rec_s, T_s)
+            if hasattr(active_prob_raw, "iloc"):
+                active_prob = float(active_prob_raw.iloc[0])
+            elif hasattr(active_prob_raw, "item"):
+                active_prob = float(active_prob_raw.item())
+            else:
+                active_prob = float(active_prob_raw[0])
+            
+            clv_raw = ggf.customer_lifetime_value(bgf, freq_s, rec_s, T_s, mon_s, time=12, discount_rate=0.01)
+            if hasattr(clv_raw, "iloc"):
+                expected_clv = float(clv_raw.iloc[0])
+            elif hasattr(clv_raw, "item"):
+                expected_clv = float(clv_raw.item())
+            else:
+                expected_clv = float(clv_raw[0])
+            
+        # Build features dict using defaults for macro factors
+        input_data = {
+            'Recency': recency,
+            'Frequency': frequency,
+            'MonetaryValue': monetary,
+            'total_views': views,
+            'total_carts': carts,
+            'total_purchases': purchases,
+            'total_ecommerce_spend': purchases * monetary,
+            'total_sessions': sessions,
+            'checkout_abandonment_flag': abandon_flag,
+            'instacart_total_orders': instacart_orders,
+            'avg_days_between_orders': days_between_orders,
+            'purchased_during_precipitation': precip_flag,
+            'T': T,
+            'expected_active_probability': active_prob,
+            'expected_12m_clv': expected_clv,
+            'CPIAUCSL': defaults.get('CPIAUCSL', 218.0),
+            'RSXFS': defaults.get('RSXFS', 350000.0),
+            'PSAVERT': defaults.get('PSAVERT', 6.0)
+        }
+        
+        X_sample = pd.DataFrame([input_data])
+        X_sample = X_sample[feature_names] # Align columns perfectly
+        
+        # Predict Segment
+        pred_cluster = int(classifier.predict(X_sample)[0])
+        cluster_data = CLUSTER_INFO.get(pred_cluster, {"name": f"Segment {pred_cluster}", "desc": "Unknown segment characteristics.", "color": "#9ca3af"})
+        
+        # Outputs
+        rfm_str = f"📅 Recency: {int(recency)} days | 🔄 Frequency: {int(frequency)} purchases | 💰 Monetary: ${monetary:,.2f}"
+        ecom_str = f"👁️ Views: {int(views)} | 🛒 Carts: {int(carts)} | 🛍️ Purchases: {int(purchases)} | 🚪 Sessions: {int(sessions)}"
+        clv_str = f"🎯 Simulated 12m CLV: ${expected_clv:,.2f} | 📈 Active Prob: {active_prob*100:.1f}% | 🔀 Group: Simulation"
+        
+        desc_html = f"""
+        <div style="padding: 15px; border-left: 5px solid {cluster_data['color']}; background: rgba(255,255,255,0.02); border-radius: 4px;">
+            <h3 style="margin: 0; color: {cluster_data['color']}; font-size: 1.3em;">{cluster_data['name']}</h3>
+            <p style="margin: 8px 0 0 0; color: #d1d5db; line-height: 1.5;">{cluster_data['desc']}</p>
+        </div>
+        """
+        
+        # Plots
+        importance_df = classifier.explain_local(X_sample, pred_cluster)
+        fig_shap = make_local_shap_plot(importance_df, cluster_data['name'])
+        fig_pca = make_pca_scatter_plot(X_sample, pred_cluster, cluster_data['color'])
+        
+        return rfm_str, ecom_str, clv_str, desc_html, fig_shap, fig_pca
+    except Exception as e:
+        import traceback
+        traceback.print_exc()
+        return f"Error running simulation: {e}", "", "", "", None, None
+
+# Get a sample of real Customer IDs for the dropdown selection
+if df_baseline is not None:
+    sample_ids = df_baseline['customer_id'].head(100).tolist()
+else:
+    sample_ids = []
+
+# Custom CSS for gorgeous aesthetics and dark-mode styling
+custom_css = """
+@import url('https://fonts.googleapis.com/css2?family=Outfit:wght@300;400;500;600;700&display=swap');
+
+body, .gradio-container {
+    font-family: 'Outfit', sans-serif !important;
+    background: radial-gradient(circle at 10% 20%, hsla(230, 45%, 11%, 1) 0%, hsla(240, 60%, 4%, 1) 90.1%) !important;
+    color: #f3f4f6 !important;
+}
+
+.prose h1 {
+    font-weight: 700 !important;
+    background: linear-gradient(135deg, #c084fc, #6366f1) !important;
+    -webkit-background-clip: text !important;
+    -webkit-text-fill-color: transparent !important;
+    text-shadow: 0 4px 20px rgba(99, 102, 241, 0.15) !important;
+    text-align: center !important;
+}
+
+.gradio-container button.primary {
+    background: linear-gradient(135deg, #8b5cf6, #4f46e5) !important;
+    border: none !important;
+    border-radius: 8px !important;
+    font-weight: 600 !important;
+    transition: all 0.3s cubic-bezier(0.4, 0, 0.2, 1) !important;
+    box-shadow: 0 4px 15px rgba(99, 102, 241, 0.3) !important;
+}
+
+.gradio-container button.primary:hover {
+    transform: translateY(-2px) !important;
+    box-shadow: 0 8px 25px rgba(99, 102, 241, 0.5) !important;
+}
+
+/* Custom cards for stats */
+.stats-box {
+    background: rgba(255, 255, 255, 0.02) !important;
+    border: 1px solid rgba(255, 255, 255, 0.06) !important;
+    border-radius: 12px !important;
+    padding: 15px !important;
+    transition: all 0.3s ease !important;
+}
+
+.stats-box:hover {
+    border-color: rgba(167, 139, 250, 0.25) !important;
+    background: rgba(255, 255, 255, 0.03) !important;
+}
+"""
+
+with gr.Blocks() as demo:
+    gr.HTML("<h1 style='font-size: 2.8em; margin-bottom: 5px; text-align: center;'>🛍️ Real-Time Customer Segmentation Dashboard</h1>")
+    gr.HTML("<p style='text-align: center; color: #9ca3af; font-size: 1.1em; margin-bottom: 30px;'>Powered by PCA, BIC-Gaussian Mixture Soft Clustering, and Random Forest Classifiers.</p>")
+    
+    with gr.Tabs():
+        # TAB 1: Real Customer Batch Explorer
+        with gr.Tab("🔍 Customer Batch Explorer & Lookup"):
+            gr.HTML("<p style='color: #a78bfa; font-weight: 500; margin-bottom: 15px;'>Select an active Customer ID from the real ingested cohort (~5.5M customers baseline) to view behavioral metrics and predictions.</p>")
+            
+            with gr.Row():
+                with gr.Column(scale=1):
+                    id_dropdown = gr.Dropdown(
+                        choices=sample_ids, 
+                        value=sample_ids[0] if sample_ids else None, 
+                        label="Select Customer ID", 
+                        interactive=True
+                    )
+                    
+                    gr.HTML("<div style='margin-top: 20px;'><h4 style='color: #9ca3af; margin-bottom: 10px;'>📊 Predictor Outputs</h4></div>")
+                    lookup_rfm = gr.Textbox(label="Transactional RFM Profile", interactive=False)
+                    lookup_ecom = gr.Textbox(label="eCommerce Activity Metrics", interactive=False)
+                    lookup_clv = gr.Textbox(label="CLV & A/B Testing Routing", interactive=False)
+                    
+                    gr.HTML("<div style='margin-top: 20px;'><h4 style='color: #9ca3af; margin-bottom: 10px;'>🏷️ Segment Classification</h4></div>")
+                    segment_desc = gr.HTML(label="Predicted Segment Description")
+                    
+                    lookup_btn = gr.Button("Query Customer", variant="primary")
+                    
+                with gr.Column(scale=2):
+                    with gr.Row():
+                        plot_shap = gr.Plot(label="Local Feature Contribution (Random Forest Gini)")
+                        plot_pca = gr.Plot(label="Placement in PCA Latent Space")
+            
+            lookup_btn.click(
+                fn=process_lookup,
+                inputs=[id_dropdown],
+                outputs=[lookup_rfm, lookup_ecom, lookup_clv, segment_desc, plot_shap, plot_pca]
+            )
+            
+        # TAB 2: Interactive Simulator
+        with gr.Tab("🧪 Segment Simulator & Scenario Sandbox"):
+            gr.HTML("<p style='color: #a78bfa; font-weight: 500; margin-bottom: 15px;'>Simulate a custom consumer's behaviour by moving sliders across Transactional, eCommerce, and routine grocery indicators.</p>")
+            
+            with gr.Row():
+                with gr.Column(scale=1, variant="panel"):
+                    gr.HTML("<h3 style='color: #c084fc; margin-top: 0;'>1. Transactional Metrics</h3>")
+                    sim_recency = gr.Slider(minimum=1, maximum=365, value=45, label="Recency (Days since last purchase)")
+                    sim_frequency = gr.Slider(minimum=0, maximum=50, value=5, label="Frequency (Number of repeat transactions)")
+                    sim_monetary = gr.Slider(minimum=5, maximum=1000, value=75, label="Monetary Value ($ average per order)")
+                    
+                    gr.HTML("<h3 style='color: #c084fc; margin-top: 15px;'>2. eCommerce Activity</h3>")
+                    sim_views = gr.Slider(minimum=0, maximum=1000, value=120, label="Total Session Views")
+                    sim_carts = gr.Slider(minimum=0, maximum=100, value=12, label="Total Items Added to Cart")
+                    sim_purchases = gr.Slider(minimum=0, maximum=50, value=6, label="Total Completed eCommerce Purchases")
+                    sim_sessions = gr.Slider(minimum=1, maximum=200, value=15, label="Total Session Volume")
+                    sim_abandon = gr.Dropdown(choices=["No", "Yes"], value="No", label="Abandoned Checkout Flag")
+                    
+                    gr.HTML("<h3 style='color: #c084fc; margin-top: 15px;'>3. Instacart / Grocery Habits</h3>")
+                    sim_instacart = gr.Slider(minimum=0, maximum=100, value=10, label="Instacart Total Order Volume")
+                    sim_days = gr.Slider(minimum=0, maximum=30, value=7, label="Average Days Between Grocery Orders")
+                    
+                    gr.HTML("<h3 style='color: #c084fc; margin-top: 15px;'>4. Environmental Context</h3>")
+                    sim_precip = gr.Dropdown(choices=["No", "Yes"], value="No", label="Purchased During Precipitation (Rain/Snow)")
+                    
+                    sim_btn = gr.Button("Simulate Customer Segment", variant="primary")
+                    
+                with gr.Column(scale=2):
+                    gr.HTML("<h3 style='color: #a78bfa; margin-top: 0;'>🔮 Simulation Predictions</h3>")
+                    sim_out_rfm = gr.Textbox(label="Transactional RFM Profile", interactive=False)
+                    sim_out_ecom = gr.Textbox(label="eCommerce Activity Metrics", interactive=False)
+                    sim_out_clv = gr.Textbox(label="CLV & A/B Testing Routing", interactive=False)
+                    
+                    sim_out_desc = gr.HTML(label="Predicted Segment Description")
+                    
+                    with gr.Row():
+                        sim_plot_shap = gr.Plot(label="Local Feature Contribution (Random Forest Gini)")
+                    with gr.Row():
+                        sim_plot_pca = gr.Plot(label="Placement in PCA Latent Space")
+                        
+            sim_btn.click(
+                fn=process_simulation,
+                inputs=[
+                    sim_recency, sim_frequency, sim_monetary, 
+                    sim_views, sim_carts, sim_purchases, sim_sessions, sim_abandon, 
+                    sim_instacart, sim_days, sim_precip
+                ],
+                outputs=[sim_out_rfm, sim_out_ecom, sim_out_clv, sim_out_desc, sim_plot_shap, sim_plot_pca]
+            )
+            
+        # TAB 3: About & Metrics
+        with gr.Tab("ℹ️ About & Metrics Explanation"):
+            gr.Markdown("""
+            ### 🛒 Project Overview
+            This application predicts Customer Segments and their Lifetime Value (CLV) using several machine learning and statistical models on real-world grocery, eCommerce, and mall purchase data.
+            
+            We use **PCA** for Dimensionality Reduction, **Gaussian Mixture Models (GMM)** with BIC optimization for soft clustering, **BG/NBD** and **Gamma-Gamma** models for CLV, and **Random Forest** for segment prediction and feature importance explainability.
+            
+            ---
+            ### 📊 Explanation of Metrics
+            
+            #### 1. Transactional RFM Metrics
+            * **Recency:** The number of days since the customer's last purchase.
+            * **Frequency:** The total number of repeat transactions made by the customer.
+            * **Monetary Value:** The average amount spent per order.
+            
+            #### 2. eCommerce    Activity
+            * **Views:** Total number of product pages visited.
+            * **Carts:** Total number of items added to the shopping cart.
+            * **Purchases:** Successful eCommerce transactions completed.
+            * **Sessions:** Number of independent browsing sessions.
+            * **Abandoned Checkout Flag:** Whether the user has an abnormally high ratio of adding items to the cart without completing the purchase.
+            
+            #### 3. Instacart / Grocery Habits
+            * **Total Orders:** Number of grocery orders placed on Instacart.
+            * **Days Between Orders:** The average gap (in days) between successive grocery runs.
+            
+            #### 4. Macro-Economic & Environmental Context
+            * **Precipitation Flag:** Did the customer make purchases specifically during rain or snow? (Sourced from Open-Meteo API).
+            * **CPIAUCSL (Inflation):** Consumer Price Index for All Urban Consumers (Sourced from FRED API).
+            * **RSXFS (Retail Sales):** Advance Retail Sales: Retail Trade (Sourced from FRED API).
+            * **PSAVERT (Savings Rate):** Personal Saving Rate (Sourced from FRED API).
+            
+            #### 5. Output Predictions
+            * **Expected 12m CLV:** The predicted monetary value the customer will generate for the business over the next 12 months.
+            * **Active Probability:** The mathematical likelihood that the customer hasn't permanently churned given their recency and frequency trends (from the BG/NBD model).
+            * **A/B Testing Routing:** Shows whether this user should be in a control or variant group for targeted marketing campaigns based on their risk profile.
+            """)
+            
+    # Load initial default values on app launch
+    if sample_ids:
+        demo.load(
+            fn=process_lookup,
+            inputs=[id_dropdown],
+            outputs=[lookup_rfm, lookup_ecom, lookup_clv, segment_desc, plot_shap, plot_pca]
+        )
+
+if __name__ == "__main__":
+    demo.launch(theme=gr.themes.Soft(), css=custom_css)