Update test_model.py

test_model.py

@@ -1,48 +1,45 @@
-import pandas as pd
-import joblib
-
-def test_heart_disease_model(test_data):
-    """
-    Function to test the trained heart disease prediction model.
-    Loads the model and makes predictions on a sample dataset.
-    """
-    try:
-        # model loading
-        production_model = joblib.load('models/uci_heart_disease_model.pkl')
-        model = production_model['model']
-        optimal_threshold = production_model['metadata']['threshold']
-
-
-
-        # engineered features to match training data
-        test_data['hr_age_ratio'] = test_data['thalach'] / (test_data['age'] + 1e-5)
-        test_data['bp_oldpeak'] = test_data['trestbps'] * (test_data['oldpeak'] + 1)
-        test_data['risk_score'] = (test_data['age']/50 + test_data['chol']/200 + test_data['trestbps']/140)
-
-        # Make predictions
-        probabilities = model.predict_proba(test_data)[:, 1]
-        predictions = (probabilities >= optimal_threshold).astype(int)
-
-        # results DataFrame
-        results = pd.DataFrame({
-            'Prediction': predictions,
-            'Diagnosis': ['Heart Disease' if p == 1 else 'Healthy' for p in predictions],
-            'Probability': probabilities,
-        })
-
-        # data for display
-        display_data = pd.concat([test_data[['age', 'sex', 'cp', 'trestbps', 'chol']], results], axis=1)
-
-        print("=== Heart Disease Prediction Results ===")
-        print(f"Using threshold: {optimal_threshold:.3f}\n")
-        print(display_data.to_string(index=False))
-
-        return results
-
-    except Exception as e:
-        print(f"Error testing model: {str(e)}")
-        return None
-
-
-# pf =pd.read_csv('dataset/test_data.csv')
+import pandas as pd
+import joblib
+
+def test_heart_disease_model(test_data):
+  
+    try:
+        # model loading
+        production_model = joblib.load('models/uci_heart_disease_model.pkl')
+        model = production_model['model']
+        optimal_threshold = production_model['metadata']['threshold']
+
+
+
+        # engineered features to match training data
+        test_data['hr_age_ratio'] = test_data['thalach'] / (test_data['age'] + 1e-5)
+        test_data['bp_oldpeak'] = test_data['trestbps'] * (test_data['oldpeak'] + 1)
+        test_data['risk_score'] = (test_data['age']/50 + test_data['chol']/200 + test_data['trestbps']/140)
+
+        # Make predictions
+        probabilities = model.predict_proba(test_data)[:, 1]
+        predictions = (probabilities >= optimal_threshold).astype(int)
+
+        # results DataFrame
+        results = pd.DataFrame({
+            'Prediction': predictions,
+            'Diagnosis': ['Heart Disease' if p == 1 else 'Healthy' for p in predictions],
+            'Probability': probabilities,
+        })
+
+        # data for display
+        display_data = pd.concat([test_data[['age', 'sex', 'cp', 'trestbps', 'chol']], results], axis=1)
+
+        print("=== Heart Disease Prediction Results ===")
+        print(f"Using threshold: {optimal_threshold:.3f}\n")
+        print(display_data.to_string(index=False))
+
+        return results
+
+    except Exception as e:
+        print(f"Error testing model: {str(e)}")
+        return None
+
+
+# pf =pd.read_csv('dataset/test_data.csv')
 # test_results = test_heart_disease_model(pf)