Update app.py

app.py

@@ -7,6 +7,7 @@ import io
 from datetime import datetime
 import xlsxwriter
 from flask_httpauth import HTTPBasicAuth
+import os
 
 app = Flask(__name__, template_folder='templates')
 app.config['WTF_CSRF_ENABLED'] = False  # Disable CSRF for testing
@@ -29,28 +30,35 @@ def verify_password(username, password):
 
 # Initialize SQLite database
 def init_db():
-    conn = sqlite3.connect('/tmp/submissions.db')
-    c = conn.cursor()
-    c.execute('''
-        CREATE TABLE IF NOT EXISTS submissions (
-            id INTEGER PRIMARY KEY AUTOINCREMENT,
-            timestamp TEXT,
-            age REAL,
-            gender TEXT,
-            ever_married TEXT,
-            residence_type TEXT,
-            work_type TEXT,
-            hypertension INTEGER,
-            heart_disease INTEGER,
-            avg_glucose_level REAL,
-            bmi REAL,
-            smoking_status TEXT,
-            probability INTEGER,
-            risk_level TEXT
-        )
-    ''')
-    conn.commit()
-    conn.close()
+    db_path = '/tmp/submissions.db'
+    print(f"Initializing database at {db_path}")
+    try:
+        conn = sqlite3.connect(db_path)
+        c = conn.cursor()
+        c.execute('''
+            CREATE TABLE IF NOT EXISTS submissions (
+                id INTEGER PRIMARY KEY AUTOINCREMENT,
+                timestamp TEXT,
+                age REAL,
+                gender TEXT,
+                ever_married TEXT,
+                residence_type TEXT,
+                work_type TEXT,
+                hypertension INTEGER,
+                heart_disease INTEGER,
+                avg_glucose_level REAL,
+                bmi REAL,
+                smoking_status TEXT,
+                probability INTEGER,
+                risk_level TEXT
+            )
+        ''')
+        conn.commit()
+        print("Database initialized successfully")
+    except Exception as e:
+        print(f"Error initializing database: {str(e)}")
+    finally:
+        conn.close()
 
 init_db()
 
@@ -63,15 +71,24 @@ def home():
 def predict():
     global model
     print("Received predict request at /predict endpoint")
+    
+    # Check model file existence
+    model_path = 'stroke_prediction_model.pkl'
+    if not os.path.exists(model_path):
+        print(f"Model file not found at {model_path}")
+        return jsonify({'success': False, 'error': f'Model file not found at {model_path}'}), 500
+
+    # Load model if not already loaded
     if model is None:
         try:
-            model = joblib.load('stroke_prediction_model.pkl')
-            print("Model loaded successfully during first predict request")
+            model = joblib.load(model_path)
+            print("Model loaded successfully")
         except Exception as e:
             print(f"Error loading model: {str(e)}")
             return jsonify({'success': False, 'error': f'Model failed to load: {str(e)}'}), 500
 
     try:
+        # Parse incoming data
         data = request.json
         print(f"Received JSON data: {data}")
         if not data or 'input' not in data:
@@ -99,6 +116,7 @@ def predict():
         df = pd.DataFrame([features])
         categorical_cols = ['gender', 'ever_married', 'residence_type', 'work_type', 'smoking_status']
         df = pd.get_dummies(df, columns=categorical_cols, drop_first=True)
+        print(f"DataFrame after get_dummies: {df.columns.tolist()}")
 
         # Define expected columns based on model training
         expected_columns = model.feature_names_in_ if hasattr(model, 'feature_names_in_') else [
@@ -116,10 +134,12 @@ def predict():
             if col not in df.columns:
                 df[col] = 0
         df = df[expected_columns]
+        print(f"Aligned DataFrame columns: {df.columns.tolist()}")
 
         # Make prediction
         probability = model.predict_proba(df)[0][1] * 100
         risk_prediction = "Stroke Risk" if probability > 50 else "No Stroke Risk"
+        print(f"Prediction result: probability={probability}%, prediction={risk_prediction}")
 
         # Determine contributing factors
         contributing_factors = {
@@ -128,35 +148,43 @@ def predict():
             'heartDisease': features['heart_disease'] == 1,
             'smoking': features['smoking_status'] in ['smokes', 'formerly smoked']
         }
+        print(f"Contributing factors: {contributing_factors}")
 
-        # Store in database
-        conn = sqlite3.connect('/tmp/submissions.db')
-        c = conn.cursor()
-        c.execute('''
-            INSERT INTO submissions (
-                timestamp, age, gender, ever_married, residence_type, work_type,
-                hypertension, heart_disease, avg_glucose_level, bmi, smoking_status,
-                probability, risk_level
-            ) VALUES (?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?)
-        ''', (
-            datetime.now().isoformat(),
-            features['age'],
-            features['gender'],
-            features['ever_married'],
-            features['residence_type'],
-            features['work_type'],
-            features['hypertension'],
-            features['heart_disease'],
-            features['avg_glucose_level'],
-            features['bmi'],
-            features['smoking_status'],
-            round(probability),
-            risk_prediction
-        ))
-        conn.commit()
-        conn.close()
-
-        print(f"Prediction success: probability={probability}%, prediction={risk_prediction}")
+        # Temporarily comment out database write to isolate the issue
+        """
+        try:
+            conn = sqlite3.connect('/tmp/submissions.db')
+            c = conn.cursor()
+            c.execute('''
+                INSERT INTO submissions (
+                    timestamp, age, gender, ever_married, residence_type, work_type,
+                    hypertension, heart_disease, avg_glucose_level, bmi, smuggling_status,
+                    probability, risk_level
+                ) VALUES (?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?)
+            ''', (
+                datetime.now().isoformat(),
+                features['age'],
+                features['gender'],
+                features['ever_married'],
+                features['residence_type'],
+                features['work_type'],
+                features['hypertension'],
+                features['heart_disease'],
+                features['avg_glucose_level'],
+                features['bmi'],
+                features['smoking_status'],
+                round(probability),
+                risk_prediction
+            ))
+            conn.commit()
+            print("Data successfully written to database")
+        except Exception as db_error:
+            print(f"Database write error: {str(db_error)}")
+        finally:
+            conn.close()
+        """
+
+        # Return prediction result
         return jsonify({
             'success': True,
             'prediction': risk_prediction,