Update app.py

app.py

@@ -87,12 +87,11 @@ def initialize_preprocessors():
     encoder = OneHotEncoder(drop='first', sparse_output=False)
     encoder.fit(df[categorical_features])
 
-    scaler = StandardScaler()
-    return encoder, scaler, categorical_features
+    return encoder, categorical_features
 
 # Global variables
 MODELS = load_models()
-encoder, scaler, categorical_features = initialize_preprocessors()
+encoder, categorical_features = initialize_preprocessors()
 
 # Prediction function
 def make_prediction(model, X_user, model_type):
@@ -106,10 +105,10 @@ def make_prediction(model, X_user, model_type):
             return model.predict(X_user_scaled)
 
         elif model_type == ModelType.LINEAR_REGRESSION:
-            # Ensure the global scaler is trained before using it
-            if scaler is None:
-                raise ValueError("Scaler is not initialized. Ensure it is trained before usage.")
-            X_user_scaled = scaler.transform(X_user)  # ✅ Use transform instead of fit_transform
+            # ✅ Fit scaler dynamically on `X_user`
+            scaler_lr = StandardScaler()
+            scaler_lr.fit(X_user)
+            X_user_scaled = scaler_lr.transform(X_user)
             return model.predict(X_user_scaled)
 
         elif model_type == ModelType.XGBOOST: