Update app.py

app.py

@@ -61,6 +61,7 @@ def home():
 @app.route('/predict', methods=['POST'])
 def predict():
     global model
+    print("Received predict request")
     if model is None:
         try:
             model = joblib.load('stroke_prediction_model.pkl')
@@ -71,16 +72,19 @@ def predict():
 
     try:
         data = request.json
+        print(f"Received JSON data: {data}")
         if not data or 'input' not in data:
+            print("Invalid input data format")
             return jsonify({'success': False, 'error': 'Invalid input data'}), 400
         input_data = data['input']
-        print(f"Received input data: {input_data}")
+        print(f"Processed input data: {input_data}")
 
+        # Validate and convert input data
         features = {
             'age': float(input_data.get('age', 0)),
             'gender': input_data.get('gender', 'Unknown'),
             'ever_married': input_data.get('ever_married', 'No'),
-            'Residence_type': input_data.get('residence_type', 'Unknown'),
+            'residence_type': input_data.get('residence_type', 'Unknown'),  # Matches form
             'work_type': input_data.get('work_type', 'Unknown'),
             'hypertension': int(input_data.get('hypertension', 0)),
             'heart_disease': int(input_data.get('heart_disease', 0)),
@@ -88,15 +92,17 @@ def predict():
             'bmi': float(input_data.get('bmi', 0)),
             'smoking_status': input_data.get('smoking_status', 'Unknown')
         }
-        print(f"Processed features: {features}")
+        print(f"Converted features: {features}")
 
+        # Create DataFrame and handle categorical variables
         df = pd.DataFrame([features])
-        categorical_cols = ['gender', 'ever_married', 'Residence_type', 'work_type', 'smoking_status']
+        categorical_cols = ['gender', 'ever_married', 'residence_type', 'work_type', 'smoking_status']
         df = pd.get_dummies(df, columns=categorical_cols, drop_first=True)
 
+        # Define expected columns based on model training
         expected_columns = model.feature_names_in_ if hasattr(model, 'feature_names_in_') else [
             'age', 'hypertension', 'heart_disease', 'avg_glucose_level', 'bmi',
-            'gender_Male', 'gender_Other', 'ever_married_Yes', 'Residence_type_Urban',
+            'gender_Male', 'gender_Other', 'ever_married_Yes', 'residence_type_Urban',
             'work_type_Govt_job', 'work_type_Never_worked', 'work_type_Private',
             'work_type_Self-employed',
             'smoking_status_formerly smoked', 'smoking_status_never smoked',
@@ -104,22 +110,25 @@ def predict():
         ]
         print(f"Expected columns: {expected_columns}")
 
+        # Align DataFrame columns
         for col in expected_columns:
             if col not in df.columns:
                 df[col] = 0
         df = df[expected_columns]
 
+        # Make prediction
         probability = model.predict_proba(df)[0][1] * 100
         risk_level = 'High' if probability > 50 else 'Moderate' if probability > 25 else 'Low'
 
+        # Determine contributing factors
         contributing_factors = {
-            'age': features['age'] > 45,
-            'hypertension': features['hypertension'] == 1,
-            'heart_disease': features['heart_disease'] == 1,
             'glucose': features['avg_glucose_level'] > 140,
+            'hypertension': features['hypertension'] == 1,
+            'heartDisease': features['heart_disease'] == 1,  # Matches index.html
             'smoking': features['smoking_status'] in ['smokes', 'formerly smoked']
         }
 
+        # Store in database
         conn = sqlite3.connect('/tmp/submissions.db')
         c = conn.cursor()
         c.execute('''
@@ -133,7 +142,7 @@ def predict():
             features['age'],
             features['gender'],
             features['ever_married'],
-            features['Residence_type'],
+            features['residence_type'],
             features['work_type'],
             features['hypertension'],
             features['heart_disease'],
@@ -146,6 +155,7 @@ def predict():
         conn.commit()
         conn.close()
 
+        print(f"Prediction success: probability={probability}%, risk_level={risk_level}")
         return jsonify({
             'success': True,
             'probability': round(probability),