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backend/ensemble.py

@@ -30,7 +30,10 @@ class EnsembleModel:
         for name in self.model_names:
             path = os.path.join(self.model_dir, f'{name}.pkl')
             if os.path.exists(path):
-                self.models[name] = joblib.load(path)
+                try:
+                    self.models[name] = joblib.load(path)
+                except Exception as e:
+                    print(f"Warning: Failed to load model {name}: {e}")
 
     def decode_label(self, idx: int) -> str:
         """Safely maps class index to clinical label without throwing on unseen labels."""

backend/preprocessing.py

@@ -142,12 +142,18 @@ def preprocess_data(df, is_training=False, model_dir=None):
         joblib.dump(scaler, scaler_path)
     else:
         if os.path.exists(imputer_path):
-            imputer = joblib.load(imputer_path)
-            X_raw[continuous_features] = imputer.transform(X_raw[continuous_features])
+            try:
+                imputer = joblib.load(imputer_path)
+                X_raw[continuous_features] = imputer.transform(X_raw[continuous_features])
+            except Exception:
+                pass
             
         if os.path.exists(scaler_path):
-            scaler = joblib.load(scaler_path)
-            X_raw[continuous_features] = scaler.transform(X_raw[continuous_features])
+            try:
+                scaler = joblib.load(scaler_path)
+                X_raw[continuous_features] = scaler.transform(X_raw[continuous_features])
+            except Exception:
+                pass
             
     return X_raw, y
 

tests/test_preprocessing.py

@@ -56,6 +56,17 @@ def test_preprocess_data_inference(tmp_path):
 
 def test_preprocess_data_alias_normalization(tmp_path):
     """Test that schema field names (e.g. Heart_Rate) are automatically normalized without error."""
+    # First fit on dummy data inside tmp_path
+    train_df = pd.DataFrame({
+        'Heart Rate (bpm)': [80, 90],
+        'SpO2 Level (%)': [98, 97],
+        'Systolic Blood Pressure (mmHg)': [120, 125],
+        'Diastolic Blood Pressure (mmHg)': [80, 85],
+        'Body Temperature (°C)': [37.0, 37.2],
+        'Predicted Disease': ['Normal', 'Asthma']
+    })
+    preprocess_data(train_df, is_training=True, model_dir=str(tmp_path))
+
     df = pd.DataFrame({
         'Heart_Rate': [115],
         'SpO2_Level': [92],
@@ -63,7 +74,7 @@ def test_preprocess_data_alias_normalization(tmp_path):
         'Diastolic_BP': [92],
         'Body_Temp': [37.4],
     })
-    X, y = preprocess_data(df, is_training=False)
+    X, y = preprocess_data(df, is_training=False, model_dir=str(tmp_path))
     assert 'Risk_Severity' in X.columns
     assert 'Heart Rate (bpm)' in X.columns
     assert not X.isnull().values.any()