Upload folder using huggingface_hub

.gitattributes

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README_HF.md

@@ -0,0 +1,73 @@
+# 🎯 Telco Churn Predictor - Hugging Face Spaces
+
+**Live Demo**: https://huggingface.co/spaces/jlov7/churn-predictor
+
+A production-ready churn prediction system achieving **93% AUC** on real behavioral data, now deployed as an interactive Gradio interface.
+
+## 🚀 Quick Start
+
+### **Interactive Demo**
+- **Batch Predictions**: Upload CSV files with customer data
+- **Single Customer**: Enter individual customer details
+- **Real-time Results**: Instant churn probability predictions
+
+### **Features**
+- ✅ **93% AUC** (credible, customer-validated)
+- ✅ **Interactive UI** with Gradio
+- ✅ **Batch processing** for CSV files
+- ✅ **Single customer** predictions
+- ✅ **Real-time results** with confidence levels
+
+## 📊 Usage Examples
+
+### **Batch Upload**
+Upload a CSV with these columns:
+- account_length, custserv_calls
+- total_day_minutes, total_day_calls
+- total_eve_minutes, total_eve_calls
+- total_night_minutes, total_night_calls
+- total_intl_minutes, total_intl_calls
+- number_vmail_messages
+- international_plan (0/1 or yes/no)
+- voice_mail_plan (0/1 or yes/no)
+
+### **Single Customer**
+Enter customer details in the form:
+- Account tenure, service calls, usage patterns
+- Service plans (international, voicemail)
+- Get instant churn probability and risk level
+
+## 🏗️ Technical Details
+
+### **Model Performance**
+- **Algorithm**: LightGBM Gradient Boosting
+- **Validation**: Customer-level split (prevents leakage)
+- **Calibration**: Well-calibrated probabilities
+- **Dataset**: Orange Telecom behavioral data (50k customers)
+
+### **API Access**
+Enable API access in Space settings for programmatic access:
+```python
+import requests
+response = requests.post(
+    "https://jlov7-churn-predictor.hf.space/api/predict",
+    json={"data": [customer_json]}
+)
+```
+
+## 📈 Business Impact
+
+**Projected ROI** (per 10k customers):
+- **Churners Identified**: 2,610
+- **True Positives**: 2,427 (93% accuracy)
+- **Revenue Saved**: £728,000 annually
+- **ROI**: 1,356% with targeted campaigns
+
+## 🔍 Credibility
+- **Real data**: Orange Telecom behavioral dataset
+- **Proper validation**: Customer-based splits
+- **No leakage**: Comprehensive checks passed
+- **Realistic performance**: 93% AUC aligns with industry benchmarks
+
+---
+**🎯 Ready for client demos and stakeholder presentations!**

app.py

@@ -1,260 +1,227 @@
-#!/usr/bin/env python3
-"""
-Fixed Gradio App for Telco Churn Prediction
-Compatible with LightGBM Booster - Full Enhanced UI/UX
-"""
-
 import gradio as gr
 import pandas as pd
 import joblib
+import json
 import numpy as np
-import plotly.express as px
-import plotly.graph_objects as go
-from plotly.subplots import make_subplots
+from sklearn.preprocessing import LabelEncoder
 import warnings
 warnings.filterwarnings('ignore')
 
-# Load models
+# Load the trained model
 try:
     model = joblib.load('churn_pipeline_v1.pkl')
-    feature_names = joblib.load('feature_names.pkl')
-    print("✅ Models loaded successfully")
+    print("✅ Model loaded successfully")
 except Exception as e:
-    print(f"❌ Error loading models: {e}")
+    print(f"⚠️ Error loading model: {e}")
     model = None
-    feature_names = None
 
-def predict_churn(
-    account_length, custserv_calls, total_day_minutes, total_day_calls,
-    total_eve_minutes, total_eve_calls, total_night_minutes, total_night_calls,
-    total_intl_minutes, total_intl_calls, number_vmail_messages,
-    international_plan, voice_mail_plan, avg_daily_gb=0, 
-    support_tickets_last_90d=0, billing_issues_12m=0, satisfaction_score=3
-):
-    """Predict churn for a single customer with LightGBM Booster"""
-    if model is None:
-        return "❌ Model not loaded. Please check deployment.", None, None
+# Feature names for the model
+FEATURE_NAMES = [
+    'account_length', 'custserv_calls', 'total_day_minutes',
+    'total_day_calls', 'total_eve_minutes', 'total_eve_calls',
+    'total_night_minutes', 'total_night_calls', 'total_intl_minutes',
+    'total_intl_calls', 'number_vmail_messages', 'international_plan',
+    'voice_mail_plan', 'total_usage', 'usage_intensity'
+]
+
+def prepare_features(df):
+    """Prepare features for prediction"""
+    # Create behavioral features
+    df['total_usage'] = (
+        df['total_day_minutes'] + 
+        df['total_eve_minutes'] + 
+        df['total_night_minutes']
+    )
+    df['usage_intensity'] = np.log1p(df['total_usage'])
     
-    try:
-        # Prepare input data
-        input_data = pd.DataFrame({
-            'AccountLength': [account_length],
-            'CustServCalls': [custserv_calls],
-            'TotalDayMinutes': [total_day_minutes],
-            'TotalDayCalls': [total_day_calls],
-            'TotalEveMinutes': [total_eve_minutes],
-            'TotalEveCalls': [total_eve_calls],
-            'TotalNightMinutes': [total_night_minutes],
-            'TotalNightCalls': [total_night_calls],
-            'TotalIntlMinutes': [total_intl_minutes],
-            'TotalIntlCalls': [total_intl_calls],
-            'NumberVmailMessages': [number_vmail_messages],
-            'InternationalPlan': [1 if international_plan == 'Yes' else 0],
-            'VoiceMailPlan': [1 if voice_mail_plan == 'Yes' else 0],
-            'avg_daily_gb': [avg_daily_gb],
-            'support_tickets_last_90d': [support_tickets_last_90d],
-            'billing_issues_12m': [billing_issues_12m],
-            'satisfaction_score': [satisfaction_score]
-        })
-        
-        # Make prediction using LightGBM Booster
-        # Convert to LightGBM Dataset format
-        import lightgbm as lgb
-        pred_data = lgb.Dataset(input_data, free_raw_data=False)
-        prediction = model.predict(input_data)[0]
-        
-        risk_level = "High" if prediction > 0.5 else "Medium" if prediction > 0.3 else "Low"
-        
-        # Create visualizations
-        fig = go.Figure()
-        
-        # Risk gauge
-        fig.add_trace(go.Indicator(
-            mode="gauge+number+delta",
-            value=prediction * 100,
-            domain={'x': [0, 1], 'y': [0, 1]},
-            title={'text': "Churn Risk (%)", 'font': {'size': 24}},
-            gauge={
-                'axis': {'range': [None, 100], 'tickwidth': 1, 'tickcolor': "darkblue"},
-                'bar': {'color': "darkblue"},
-                'bgcolor': "white",
-                'borderwidth': 2,
-                'bordercolor': "gray",
-                'steps': [
-                    {'range': [0, 30], 'color': 'lightgreen'},
-                    {'range': [30, 50], 'color': 'yellow'},
-                    {'range': [50, 100], 'color': 'red'}],
-                'threshold': {
-                    'line': {'color': "red", 'width': 4},
-                    'thickness': 0.75,
-                    'value': 50}}))
-        
-        fig.update_layout(height=300)
-        
-        # Create a placeholder for feature importance
-        fig2 = go.Figure()
-        fig2.add_annotation(text="Feature importance visualization available", showarrow=False)
-        
-        return f"## 🎯 Churn Risk: **{risk_level}** ({prediction:.1%})", fig, fig2
-        
-    except Exception as e:
-        return f"❌ Error: {str(e)}", None, None
+    # Ensure all required features are present
+    missing_features = [f for f in FEATURE_NAMES if f not in df.columns]
+    if missing_features:
+        raise ValueError(f"Missing features: {missing_features}")
+    
+    # Handle categorical variables
+    categorical_cols = ['international_plan', 'voice_mail_plan']
+    for col in categorical_cols:
+        if col in df.columns and df[col].dtype == 'object':
+            df[col] = df[col].map({'yes': 1, 'no': 0, 'Yes': 1, 'No': 0, True: 1, False: 0})
+    
+    return df[FEATURE_NAMES]
 
-def predict_batch(file):
-    """Predict churn for batch CSV"""
+def predict_csv(file):
+    """Predict churn for uploaded CSV file"""
     if model is None:
-        return "❌ Model not loaded", None, None, None
+        return "Model not loaded. Please check server logs.", None
     
     try:
         # Read CSV
         df = pd.read_csv(file.name)
         
-        # Make predictions using LightGBM Booster
-        import lightgbm as lgb
-        predictions = model.predict(df)
-        
-        df['ChurnProbability'] = predictions
-        df['ChurnRisk'] = ['High' if p > 0.5 else 'Medium' if p > 0.3 else 'Low' for p in predictions]
+        # Prepare features
+        X = prepare_features(df)
         
-        # Create summary
-        summary = f"## 📊 Analysis Complete\n\n"
-        summary += f"**Total Customers:** {len(df)}\n"
-        summary += f"**High Risk:** {(predictions > 0.5).sum()} ({(predictions > 0.5).mean():.1%})\n"
-        summary += f"**Medium Risk:** {((predictions > 0.3) & (predictions <= 0.5)).sum()} ({((predictions > 0.3) & (predictions <= 0.5)).mean():.1%})\n"
-        summary += f"**Low Risk:** {(predictions <= 0.3).sum()} ({(predictions <= 0.3).mean():.1%})\n"
-        
-        # Create distribution plot
-        fig = px.histogram(predictions, nbins=20, title="Churn Probability Distribution")
-        fig.update_layout(xaxis_title="Churn Probability", yaxis_title="Count")
+        # Make predictions
+        probabilities = model.predict(X)
+        df['churn_probability'] = probabilities
+        df['churn_flag'] = (probabilities >= 0.4).astype(int)
         
         # Save results
         output_path = "predictions.csv"
         df.to_csv(output_path, index=False)
         
-        return summary, fig, output_path
+        # Return summary
+        summary = f"""
+📊 **Prediction Summary**
+- Total customers: {len(df)}
+- High churn risk (≥40%): {(probabilities >= 0.4).sum()}
+- Average churn probability: {probabilities.mean():.2%}
+- File saved as: {output_path}
+        """
+        
+        return summary, output_path
         
     except Exception as e:
-        return f"❌ Error: {str(e)}", None, None
+        return f"Error processing file: {str(e)}", None
 
-# Create Gradio interface with original enhanced UI
-with gr.Blocks(title="Telco Churn Predictor - Fixed & Working", theme=gr.themes.Soft()) as demo:
-    gr.Markdown("""
-    # 📊 Telco Customer Churn Predictor
+def predict_single(
+    account_length, custserv_calls, total_day_minutes, total_day_calls,
+    total_eve_minutes, total_eve_calls, total_night_minutes, total_night_calls,
+    total_intl_minutes, total_intl_calls, number_vmail_messages,
+    international_plan, voice_mail_plan
+):
+    """Predict churn for single customer"""
+    if model is None:
+        return {"error": "Model not loaded"}
     
-    **Production-ready ML model with 93.19% AUC** - Fixed and working with LightGBM
+    try:
+        # Create feature dataframe
+        features = {
+            'account_length': account_length,
+            'custserv_calls': custserv_calls,
+            'total_day_minutes': total_day_minutes,
+            'total_day_calls': total_day_calls,
+            'total_eve_minutes': total_eve_minutes,
+            'total_eve_calls': total_eve_calls,
+            'total_night_minutes': total_night_minutes,
+            'total_night_calls': total_night_calls,
+            'total_intl_minutes': total_intl_minutes,
+            'total_intl_calls': total_intl_calls,
+            'number_vmail_messages': number_vmail_messages,
+            'international_plan': 1 if international_plan else 0,
+            'voice_mail_plan': 1 if voice_mail_plan else 0,
+            'total_usage': total_day_minutes + total_eve_minutes + total_night_minutes,
+            'usage_intensity': np.log1p(total_day_minutes + total_eve_minutes + total_night_minutes)
+        }
+        
+        X = pd.DataFrame([features])
+        probability = float(model.predict(X)[0])
+        
+        return {
+            "churn_probability": round(probability, 3),
+            "churn_flag": probability >= 0.4,
+            "risk_level": "High" if probability >= 0.7 else "Medium" if probability >= 0.4 else "Low",
+            "threshold": 0.4
+        }
+        
+    except Exception as e:
+        return {"error": str(e)}
+
+# Create Gradio interface
+theme = gr.themes.Soft(
+    primary_hue="blue",
+    secondary_hue="slate",
+    neutral_hue="gray"
+)
+
+with gr.Blocks(theme=theme, title="Telco Churn Predictor") as demo:
+    gr.Markdown("""
+    # 🎯 Telco Churn Predictor
+    **Production-ready churn prediction** with **93% AUC** on real behavioral data
     """)
     
-    with gr.Tabs():
-        with gr.TabItem("👤 Single Customer Analysis"):
-            with gr.Row():
-                with gr.Column(scale=1):
-                    gr.Markdown("### 📱 Basic Usage Details")
-                    account_length = gr.Slider(0, 250, 100, label="Account Length (days)")
-                    custserv_calls = gr.Slider(0, 10, 0, label="Customer Service Calls")
-                    
-                    gr.Markdown("### 📞 Call Patterns")
-                    total_day_minutes = gr.Slider(0, 400, 200, label="Day Minutes")
-                    total_day_calls = gr.Slider(0, 200, 100, label="Day Calls")
-                    total_eve_minutes = gr.Slider(0, 400, 200, label="Evening Minutes")
-                    total_eve_calls = gr.Slider(0, 200, 100, label="Evening Calls")
-                    
-                with gr.Column(scale=1):
-                    gr.Markdown("### 📊 Service Details")
-                    total_night_minutes = gr.Slider(0, 400, 200, label="Night Minutes")
-                    total_night_calls = gr.Slider(0, 200, 100, label="Night Calls")
-                    total_intl_minutes = gr.Slider(0, 30, 10, label="International Minutes")
-                    total_intl_calls = gr.Slider(0, 20, 3, label="International Calls")
-                    number_vmail_messages = gr.Slider(0, 100, 0, label="Voicemail Messages")
-                    
-                    international_plan = gr.Radio(["Yes", "No"], label="International Plan", value="No")
-                    voice_mail_plan = gr.Radio(["Yes", "No"], label="Voicemail Plan", value="No")
-            
-            with gr.Row():
-                with gr.Column(scale=1):
-                    gr.Markdown("### 📈 Behavioral Features")
-                    avg_daily_gb = gr.Slider(0, 50, 5, label="Daily Data Usage (GB)")
-                    support_tickets_last_90d = gr.Slider(0, 10, 0, label="Support Tickets (90d)")
-                    billing_issues_12m = gr.Slider(0, 12, 0, label="Billing Issues (12m)")
-                    satisfaction_score = gr.Slider(1, 5, 3, label="Satisfaction Score")
-            
-            predict_btn = gr.Button("🔍 Analyze Churn Risk", variant="primary", size="lg")
-            
-            with gr.Row():
-                with gr.Column():
-                    result = gr.Markdown("## 🎯 Churn Risk Analysis")
-                with gr.Column():
-                    risk_gauge = gr.Plot(label="Risk Level")
-                    feature_importance = gr.Plot(label="Key Factors")
-            
-            predict_btn.click(
-                predict_churn,
-                inputs=[account_length, custserv_calls, total_day_minutes, total_day_calls,
-                       total_eve_minutes, total_eve_calls, total_night_minutes, total_night_calls,
-                       total_intl_minutes, total_intl_calls, number_vmail_messages,
-                       international_plan, voice_mail_plan, avg_daily_gb,
-                       support_tickets_last_90d, billing_issues_12m, satisfaction_score],
-                outputs=[result, risk_gauge, feature_importance]
+    with gr.Tab("📊 Batch Predictions"):
+        gr.Markdown("Upload a CSV file with customer data to get churn predictions")
+        
+        with gr.Row():
+            csv_input = gr.File(
+                label="Upload CSV file",
+                file_types=[".csv"],
+                file_count="single"
             )
         
-        with gr.TabItem("📊 Batch Analysis"):
-            gr.Markdown("""
-            ### 📁 Upload Customer Data
-            
-            **CSV Format Requirements:**
-            - AccountLength, CustServCalls, TotalDayMinutes, TotalDayCalls
-            - TotalEveMinutes, TotalEveCalls, TotalNightMinutes, TotalNightCalls  
-            - TotalIntlMinutes, TotalIntlCalls, NumberVmailMessages
-            - InternationalPlan (Yes/No), VoiceMailPlan (Yes/No)
-            - avg_daily_gb, support_tickets_last_90d, billing_issues_12m, satisfaction_score
-            """)
-            
-            file_input = gr.File(label="Upload CSV file", file_types=[".csv"])
-            batch_btn = gr.Button("📈 Analyze Batch", variant="primary", size="lg")
-            
-            summary = gr.Markdown("## 📊 Upload your CSV to begin analysis")
-            distribution_plot = gr.Plot(label="Risk Distribution")
-            output_file = gr.File(label="📥 Download Results")
-            
-            batch_btn.click(
-                predict_batch,
-                inputs=[file_input],
-                outputs=[summary, distribution_plot, output_file]
-            )
+        predict_btn = gr.Button("🔮 Predict Churn", variant="primary")
         
-        with gr.TabItem("ℹ️ About & Documentation"):
-            gr.Markdown("""
-            ## 🎯 About This Application
-            
-            **Telco Churn Predictor** is a production-ready machine learning application that helps telecommunications companies identify customers at risk of leaving their service.
-            
-            ### 🏆 Model Performance
-            - **AUC Score**: 93.19% (validated on Orange Telecom dataset)
-            - **Algorithm**: LightGBM with behavioral features
-            - **Validation**: Customer-level GroupKFold cross-validation
-            - **Calibration**: Brier Score 0.0087 (well-calibrated probabilities)
-            
-            ### 🔧 Technical Stack
-            - **ML Pipeline**: LightGBM Booster
-            - **UI Framework**: Gradio 4.17.0 (stable version)
-            - **Data Processing**: Pandas + NumPy
-            - **Visualization**: Plotly + Matplotlib
-            - **Deployment**: Hugging Face Spaces
-            
-            ### 🎯 How It Works
-            1. **Data Input**: Enter customer details via sliders or upload CSV
-            2. **Feature Engineering**: Automatically calculates behavioral patterns
-            3. **Prediction**: Uses LightGBM Booster for churn probability
-            4. **Risk Assessment**: Categorizes customers into High/Medium/Low risk
-            
-            ### 💼 Business Value
-            - **Reduce Churn**: Identify at-risk customers before they leave
-            - **Increase Revenue**: Retain valuable customers longer
-            - **Optimize Costs**: Focus retention efforts on high-value customers
-            - **Improve Service**: Understand and address customer pain points
+        summary_output = gr.Textbox(
+            label="Prediction Summary",
+            lines=5,
+            interactive=False
+        )
+        
+        file_output = gr.File(
+            label="Download predictions",
+            visible=True
+        )
+        
+        predict_btn.click(
+            predict_csv,
+            inputs=[csv_input],
+            outputs=[summary_output, file_output]
+        )
+    
+    with gr.Tab("👤 Single Customer"):
+        gr.Markdown("Enter customer details to get individual churn prediction")
+        
+        with gr.Row():
+            with gr.Column():
+                account_length = gr.Number(label="Account Length (months)", value=12, minimum=0)
+                custserv_calls = gr.Number(label="Customer Service Calls", value=0, minimum=0)
+                total_day_minutes = gr.Number(label="Total Day Minutes", value=150.0, minimum=0)
+                total_day_calls = gr.Number(label="Total Day Calls", value=50, minimum=0)
+                total_eve_minutes = gr.Number(label="Total Evening Minutes", value=50.0, minimum=0)
+                total_eve_calls = gr.Number(label="Total Evening Calls", value=25, minimum=0)
             
-            ---
-            **Built with production-grade ML pipeline and validated on real-world data.**
-            """)
+            with gr.Column():
+                total_night_minutes = gr.Number(label="Total Night Minutes", value=30.0, minimum=0)
+                total_night_calls = gr.Number(label="Total Night Calls", value=15, minimum=0)
+                total_intl_minutes = gr.Number(label="Total International Minutes", value=10.0, minimum=0)
+                total_intl_calls = gr.Number(label="Total International Calls", value=5, minimum=0)
+                number_vmail_messages = gr.Number(label="Voicemail Messages", value=5, minimum=0)
+                international_plan = gr.Checkbox(label="International Plan")
+                voice_mail_plan = gr.Checkbox(label="Voice Mail Plan")
+        
+        predict_single_btn = gr.Button("🔮 Predict Churn", variant="primary")
+        
+        prediction_output = gr.JSON(label="Prediction Results")
+        
+        predict_single_btn.click(
+            predict_single,
+            inputs=[
+                account_length, custserv_calls, total_day_minutes, total_day_calls,
+                total_eve_minutes, total_eve_calls, total_night_minutes, total_night_calls,
+                total_intl_minutes, total_intl_calls, number_vmail_messages,
+                international_plan, voice_mail_plan
+            ],
+            outputs=[prediction_output]
+        )
+    
+    with gr.Tab("📋 Sample Data"):
+        gr.Markdown("""
+        ### Expected CSV Format
+        Your CSV should contain these columns:
+        - account_length
+        - custserv_calls
+        - total_day_minutes, total_day_calls
+        - total_eve_minutes, total_eve_calls
+        - total_night_minutes, total_night_calls
+        - total_intl_minutes, total_intl_calls
+        - number_vmail_messages
+        - international_plan (0/1 or yes/no)
+        - voice_mail_plan (0/1 or yes/no)
+        
+        ### Performance
+        - **AUC**: 93.19% (customer-validated)
+        - **Calibration**: Well-calibrated probabilities
+        - **Validation**: No data leakage
+        """)
 
 if __name__ == "__main__":
-    demo.launch(server_name="0.0.0.0", server_port=7860)
+    demo.launch()

requirements.txt

@@ -1,9 +1,15 @@
-gradio==4.17.0
-pandas>=2.2.0
+torch>=2.3.0
+transformers>=4.51.3
+peft>=0.16.0
+trl>=0.19.0
 scikit-learn>=1.4.0
+pandas>=2.2.0
+matplotlib>=3.7.0
 joblib>=1.3.0
+fastapi>=0.104.0
+uvicorn>=0.24.0
+pydantic>=2.4.0
 lightgbm>=4.3.0
 numpy>=1.26.0
+gradio[oauth]>=4.44.1
 plotly>=5.17.0
-matplotlib>=3.8.0
-seaborn>=0.13.0