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app.py

@@ -54,13 +54,14 @@ class FeatureEngineer(BaseEstimator, TransformerMixin):
         
         return X_copy
 
+# This allows joblib's pickle to find the class reference it saved during training.
+sys.modules['__main__'].FeatureEngineer = FeatureEngineer
+
 # Initialize Flask app with a name
 app = Flask("SuperKart Sales Predictor")
 
 # Load the trained churn prediction model
-#model = joblib.load("XGBoostRegressor_BEST_Pipeline.joblib")
-# passing the custom classes FeatureEngineer in a dictionary to the 'mmap_mode' argument.
-model = joblib.load("XGBoostRegressor_BEST_Pipeline.joblib", mmap_mode={'FeatureEngineer': FeatureEngineer})
+model = joblib.load("XGBoostRegressor_BEST_Pipeline.joblib")
 
 # Define a route for the home page
 @app.get('/')