Update app.py

app.py

@@ -7,10 +7,6 @@ from PIL import Image
 import google.generativeai as genai
 
 # --- Gemini API Configuration ---
-# IMPORTANT: Set your GOOGLE_API_KEY as an environment variable
-# For local testing, you can uncomment the line below and paste your key
-# os.environ['GOOGLE_API_KEY'] = "YOUR_GOOGLE_API_KEY"
-
 try:
     GOOGLE_API_KEY = os.getenv('GOOGLE_API_KEY')
     if GOOGLE_API_KEY:
@@ -30,7 +26,7 @@ def load_models():
         scaler = joblib.load('models/scaler.pkl')
         return model, encoders, scaler
     except FileNotFoundError:
-        print("Error: Model files not found. Please ensure 'churn_model.pkl', 'label_encoders.pkl', and 'scaler.pkl' are in the 'models/' directory.")
+        print("Error: Model files not found in 'models/' directory.")
         return None, None, None
     except Exception as e:
         print(f"An unexpected error occurred while loading models: {e}")
@@ -64,22 +60,35 @@ def predict_churn(customer_id, region, plan_type, monthly_charges, total_charges
         return "🔴 **Error:** Model components are not loaded. Please check the server logs.", 0.0
 
     try:
-        # 1. Create Input DataFrame
-        input_data = pd.DataFrame({
-            'region': [region],
-            'plan_type': [plan_type],
-            'monthly_charges': [float(monthly_charges)],
-            'total_charges': [float(total_charges)],
-            'tenure_months': [int(tenure_months)],
-            'contract_type': [contract_type],
-            'paperless_billing': [1 if paperless_billing else 0],
-            'payment_method': [payment_method],
-            'data_usage_gb': [float(data_usage_gb)],
-            'call_minutes': [int(call_minutes)],
-            'sms_count': [int(sms_count)],
-            'complaint_status': [complaint_status],
-            'complaint_count': [int(complaint_count)]
-        })
+        # ===== THIS IS THE FIX =====
+        # Define the exact column order that the model was trained on.
+        # This prevents errors caused by mismatched column orders.
+        TRAINING_COLUMNS = [
+            'region', 'plan_type', 'monthly_charges', 'total_charges', 'tenure_months',
+            'contract_type', 'paperless_billing', 'payment_method', 'data_usage_gb',
+            'call_minutes', 'sms_count', 'complaint_status', 'complaint_count'
+        ]
+
+        # 1. Create a dictionary with input data
+        input_dict = {
+            'region': region,
+            'plan_type': plan_type,
+            'monthly_charges': float(monthly_charges),
+            'total_charges': float(total_charges),
+            'tenure_months': int(tenure_months),
+            'contract_type': contract_type,
+            'paperless_billing': 1 if paperless_billing else 0,
+            'payment_method': payment_method,
+            'data_usage_gb': float(data_usage_gb),
+            'call_minutes': int(call_minutes),
+            'sms_count': int(sms_count),
+            'complaint_status': complaint_status,
+            'complaint_count': int(complaint_count)
+        }
+
+        # Create the DataFrame, enforcing the correct column order
+        input_data = pd.DataFrame([input_dict], columns=TRAINING_COLUMNS)
+        # ==========================
 
         # 2. Encode Categorical Features
         for col, encoder in encoders.items():
@@ -113,7 +122,7 @@ def predict_churn(customer_id, region, plan_type, monthly_charges, total_charges
 
     except Exception as e:
         # --- Gemini Error Handling ---
-        print(f"Prediction error: {e}") # Log the real error for debugging
+        print(f"Prediction error: {e}")
         if GOOGLE_API_KEY:
             try:
                 gemini_model = genai.GenerativeModel('gemini-2.5-flash')
@@ -126,16 +135,15 @@ def predict_churn(customer_id, region, plan_type, monthly_charges, total_charges
                 response = gemini_model.generate_content(prompt)
                 return response.text, 0.0
             except Exception as gemini_e:
-                print(f"Gemini API error: {gemini_e}") # Log Gemini error
+                print(f"Gemini API error: {gemini_e}")
                 return "An unexpected error occurred. Please verify your inputs and try again.", 0.0
         else:
             return "An unexpected error occurred. Please check your inputs are valid and try again.", 0.0
 
 
 # --- Gradio UI ---
-
 with gr.Blocks(title="Telecom Churn Prediction - BRBRAITT Group 5", theme=gr.themes.Soft()) as app:
-    # Header
+    # Header and other UI elements... (No changes needed here, keeping it the same as before)
     gr.Markdown("""
     # 🔮 Telecom Churn Prediction System
     **TIRTC Course: Advance AI/ML Training (Nokia) | Institution: BRBRAITT, Jabalpur | Group 5**
@@ -176,11 +184,7 @@ with gr.Blocks(title="Telecom Churn Prediction - BRBRAITT Group 5", theme=gr.the
                 with gr.Column(scale=1):
                     gr.Markdown("### 📊 Prediction Result")
                     prediction_output = gr.Markdown(value="*Results will be displayed here...*")
-                    
-                    # ===== THIS IS THE FIX =====
-                    # Replaced gr.Gauge with gr.Number to fix the crash
                     probability_gauge = gr.Number(label="Churn Probability", value=0.0, interactive=False)
-                    # ==========================
 
             predict_btn.click(
                 fn=predict_churn,
@@ -198,15 +202,11 @@ with gr.Blocks(title="Telecom Churn Prediction - BRBRAITT Group 5", theme=gr.the
             - **Accuracy:** 90%
             - **AUC Score:** 0.95
             - **Best Algorithm:** Random Forest Classifier
-
             #### 💼 Business Impact
-            - **Current Churn Rate:** 50% in the sample dataset.
             - **Monthly Revenue at Risk:** Over ₹12,250.
             - **Potential Annual Loss:** Over ₹147,000.
-            - **Savings Opportunity:** A 25% reduction in churn could save over ₹36,750 annually.
-
             #### 🔴 Top Churn Drivers
-            1.  **Contract Type:** `Month-to-month` customers have a near 100% churn rate in high-risk groups.
+            1.  **Contract Type:** `Month-to-month` customers are at highest risk.
             2.  **Tenure:** New customers (0-12 months) are most likely to churn.
             3.  **Complaints:** A single open complaint doubles the likelihood of churn.
             """)
@@ -234,11 +234,10 @@ with gr.Blocks(title="Telecom Churn Prediction - BRBRAITT Group 5", theme=gr.the
             ### 🎓 Academic Project Details
             - **Course:** TIRTC - Advance AI/ML Training (Nokia)
             - **Institution:** BRBRAITT, Jabalpur
-            - **Project:** Capstone Project 1
             - **Team (Group 5):** Abhay Gupta, Jay Kumar, Kripanshu Gupta, Ruhy Namdeo
             - **Tech Stack:** Scikit-learn, Pandas, Gradio, Gemini, Hugging Face
             ---
-            **🏆 Project Status:** Complete | **📅 Last Updated:** October 2025 | **🔢 Version:** 1.2.0
+            **🏆 Project Status:** Complete | **📅 Last Updated:** October 2025 | **🔢 Version:** 1.3.0
             """)
     
     gr.Markdown("--- \n © 2025 BRBRAITT Group 5 | TIRTC Advance AI/ML Training")