Update app.py

app.py

@@ -20,197 +20,197 @@ except Exception as e:
     print(f"Error loading model: {str(e)}")
     model = None
 
-   # Admin credentials for report access
+# Admin credentials for report access
 users = {
-       "admin": "your_secure_password"  # Replace with a strong password
-   }
+    "admin": "your_secure_password"  # Replace with a strong password
+}
 
 @auth.verify_password
 def verify_password(username, password):
-       if username in users and users[username] == password:
-           return username
-       return None
+    if username in users and users[username] == password:
+        return username
+    return None
 
-   # Initialize SQLite database
+# Initialize SQLite database
 def init_db():
-       conn = sqlite3.connect('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()
+    conn = sqlite3.connect('/tmp/submissions.db')  # Use /tmp for writable storage
+    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()
 
 init_db()
 
 @app.route('/')
 def home():
-       return render_template('index.html')
+    return render_template('index.html')
 
 @app.route('/predict', methods=['POST'])
 def predict():
-       if model is None:
-           return jsonify({'success': False, 'error': 'Model failed to load'}), 500
-
-       try:
-           # Get form data
-           data = request.json
-           if not data or 'input' not in data:
-               return jsonify({'success': False, 'error': 'Invalid input data'}), 400
-           input_data = data['input']
-           print(f"Received input data: {input_data}")
-
-           # Prepare data for model
-           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'),
-               'work_type': input_data.get('work_type', 'Unknown'),
-               'hypertension': int(input_data.get('hypertension', 0)),
-               'heart_disease': int(input_data.get('heart_disease', 0)),
-               'avg_glucose_level': float(input_data.get('avg_glucose_level', 0)),
-               'bmi': float(input_data.get('bmi', 0)),
-               'smoking_status': input_data.get('smoking_status', 'Unknown')
-           }
-           print(f"Processed features: {features}")
-
-           # Convert to DataFrame
-           df = pd.DataFrame([features])
-
-           # One-hot encode categorical variables
-           categorical_cols = ['gender', 'ever_married', 'Residence_type', 'work_type', 'smoking_status']
-           df = pd.get_dummies(df, columns=categorical_cols, drop_first=True)
-
-           # Get expected columns from the model
-           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',
-               'work_type_Govt_job', 'work_type_Never_worked', 'work_type_Private',
-               'work_type_Self-employed',
-               'smoking_status_formerly smoked', 'smoking_status_never smoked',
-               'smoking_status_smokes'
-           ]
-           print(f"Expected columns: {expected_columns}")
-
-           # Align DataFrame with expected 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  # Probability of stroke
-           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,
-               'smoking': features['smoking_status'] in ['smokes', 'formerly smoked']
-           }
-
-           # Log submission to database
-           conn = sqlite3.connect('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_level
-           ))
-           conn.commit()
-           conn.close()
-
-           # Return prediction result
-           return jsonify({
-               'success': True,
-               'probability': round(probability),
-               'riskLevel': risk_level,
-               'contributingFactors': contributing_factors
-           })
-       except Exception as e:
-           print(f"Error during prediction: {str(e)}")
-           return jsonify({'success': False, 'error': str(e)}), 500
+    if model is None:
+        return jsonify({'success': False, 'error': 'Model failed to load'}), 500
+
+    try:
+        # Get form data
+        data = request.json
+        if not data or 'input' not in data:
+            return jsonify({'success': False, 'error': 'Invalid input data'}), 400
+        input_data = data['input']
+        print(f"Received input data: {input_data}")
+
+        # Prepare data for model
+        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'),
+            'work_type': input_data.get('work_type', 'Unknown'),
+            'hypertension': int(input_data.get('hypertension', 0)),
+            'heart_disease': int(input_data.get('heart_disease', 0)),
+            'avg_glucose_level': float(input_data.get('avg_glucose_level', 0)),
+            'bmi': float(input_data.get('bmi', 0)),
+            'smoking_status': input_data.get('smoking_status', 'Unknown')
+        }
+        print(f"Processed features: {features}")
+
+        # Convert to DataFrame
+        df = pd.DataFrame([features])
+
+        # One-hot encode categorical variables
+        categorical_cols = ['gender', 'ever_married', 'Residence_type', 'work_type', 'smoking_status']
+        df = pd.get_dummies(df, columns=categorical_cols, drop_first=True)
+
+        # Get expected columns from the model
+        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',
+            'work_type_Govt_job', 'work_type_Never_worked', 'work_type_Private',
+            'work_type_Self-employed',
+            'smoking_status_formerly smoked', 'smoking_status_never smoked',
+            'smoking_status_smokes'
+        ]
+        print(f"Expected columns: {expected_columns}")
+
+        # Align DataFrame with expected 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  # Probability of stroke
+        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,
+            'smoking': features['smoking_status'] in ['smokes', 'formerly smoked']
+        }
+
+        # Log submission to 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_level
+        ))
+        conn.commit()
+        conn.close()
+
+        # Return prediction result
+        return jsonify({
+            'success': True,
+            'probability': round(probability),
+            'riskLevel': risk_level,
+            'contributingFactors': contributing_factors
+        })
+    except Exception as e:
+        print(f"Error during prediction: {str(e)}")
+        return jsonify({'success': False, 'error': str(e)}), 500
 
 @app.route('/admin/report', methods=['GET'])
 @auth.login_required
 def admin_report():
-       try:
-           today = datetime.now().strftime('%Y-%m-%d')
-           conn = sqlite3.connect('submissions.db')
-           df = pd.read_sql_query('SELECT * FROM submissions WHERE DATE(timestamp) = ?', conn, params=[today])
-           conn.close()
-           
-           if df.empty:
-               return "No submissions for today.", 200
-           
-           # Prepare data for Excel
-           df['hypertension'] = df['hypertension'].apply(lambda x: 'Yes' if x == 1 else 'No')
-           df['heart_disease'] = df['heart_disease'].apply(lambda x: 'Yes' if x == 1 else 'No')
-           df = df[[
-               'timestamp', 'age', 'gender', 'ever_married', 'residence_type', 'work_type',
-               'hypertension', 'heart_disease', 'avg_glucose_level', 'bmi', 'smoking_status',
-               'probability', 'risk_level'
-           ]]
-           df.columns = [
-               'Timestamp', 'Age', 'Gender', 'Ever Married', 'Residence Type', 'Work Type',
-               'Hypertension', 'Heart Disease', 'Avg Glucose Level', 'BMI', 'Smoking Status',
-               'Probability (%)', 'Risk Level'
-           ]
-           
-           # Create Excel file in memory
-           output = io.BytesIO()
-           with pd.ExcelWriter(output, engine='xlsxwriter') as writer:
-               df.to_excel(writer, sheet_name='Daily Report', index=False)
-               worksheet = writer.sheets['Daily Report']
-               for idx, col in enumerate(df.columns):
-                   max_len = max(df[col].astype(str).map(len).max(), len(col)) + 2
-                   worksheet.set_column(idx, idx, max_len)
-           output.seek(0)
-           
-           # Send file
-           return send_file(
-               output,
-               download_name=f'report_{today}.xlsx',
-               as_attachment=True,
-               mimetype='application/vnd.openxmlformats-officedocument.spreadsheetml.sheet'
-           )
-       except Exception as e:
-           print(f"Error generating report: {str(e)}")
-           return f"Error generating report: {str(e)}", 500
+    try:
+        today = datetime.now().strftime('%Y-%m-%d')
+        conn = sqlite3.connect('/tmp/submissions.db')
+        df = pd.read_sql_query('SELECT * FROM submissions WHERE DATE(timestamp) = ?', conn, params=[today])
+        conn.close()
+        
+        if df.empty:
+            return "No submissions for today.", 200
+        
+        # Prepare data for Excel
+        df['hypertension'] = df['hypertension'].apply(lambda x: 'Yes' if x == 1 else 'No')
+        df['heart_disease'] = df['heart_disease'].apply(lambda x: 'Yes' if x == 1 else 'No')
+        df = df[[
+            'timestamp', 'age', 'gender', 'ever_married', 'residence_type', 'work_type',
+            'hypertension', 'heart_disease', 'avg_glucose_level', 'bmi', 'smoking_status',
+            'probability', 'risk_level'
+        ]]
+        df.columns = [
+            'Timestamp', 'Age', 'Gender', 'Ever Married', 'Residence Type', 'Work Type',
+            'Hypertension', 'Heart Disease', 'Avg Glucose Level', 'BMI', 'Smoking Status',
+            'Probability (%)', 'Risk Level'
+        ]
+        
+        # Create Excel file in memory
+        output = io.BytesIO()
+        with pd.ExcelWriter(output, engine='xlsxwriter') as writer:
+            df.to_excel(writer, sheet_name='Daily Report', index=False)
+            worksheet = writer.sheets['Daily Report']
+            for idx, col in enumerate(df.columns):
+                max_len = max(df[col].astype(str).map(len).max(), len(col)) + 2
+                worksheet.set_column(idx, idx, max_len)
+        output.seek(0)
+        
+        # Send file
+        return send_file(
+            output,
+            download_name=f'report_{today}.xlsx',
+            as_attachment=True,
+            mimetype='application/vnd.openxmlformats-officedocument.spreadsheetml.sheet'
+        )
+    except Exception as e:
+        print(f"Error generating report: {str(e)}")
+        return f"Error generating report: {str(e)}", 500
 
 if __name__ == '__main__':
-       app.run(debug=True, host='0.0.0.0', port=5000)
+    app.run(debug=False, host='0.0.0.0', port=5000)