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app.py

@@ -1,3 +1,5 @@
+
+import .FeatureEngineering
 import joblib
 import pandas as pd
 from flask import Flask, request, jsonify
@@ -47,64 +49,3 @@ def predict_churn():
 # Run the Flask app in debug mode
 if __name__ == '__main__':
     app.run(debug=True)
-
-
-class FeatureEngineer(BaseEstimator, TransformerMixin):
-    def __init__(self):
-        # We need to store the LabelEncoders 
-        # so they can be applied consistently to new data.
-        self.le_prod = LabelEncoder()
-        self.le_store = LabelEncoder()
-
-    def fit(self, X, y=None):
-        # Create a new feature 'Product_Id_Cd' from the first two characters of Product_Id.
-        X['Product_Id_Cd'] = X['Product_Id'].apply(lambda x: x[:2])
-
-        # Correct 'Product_Sugar_Content' to 'Product_Sugar_Content_Corr'
-        X['Product_Sugar_Content_Corr'] = X['Product_Sugar_Content'].str.replace('reg', 'Regular', regex=True)
-        
-        
-        # Calculate 'Operation_Years'
-        X['Operation_Years'] = 2025 - X['Store_Establishment_Year']
-        
-        self.le_prod.fit(X['Product_Id_Cd'])        
-        le_feat=['Product_Sugar_Content_Corr','Store_Size','Store_Location_City_Type','Store_Type','Product_Id_Cd']
-        for i in le_feat:
-            self.le_prod.fit(X[i])
-        
-        # Fit LabelEncoder for 'Store'
-        self.le_store.fit(X['Store_Id'])
-        return self
-
-    def transform(self, X):
-        X_copy = X.copy()
-        
-        # Apply the transformations
-        X_copy['Product_Id_Cd'] = X_copy['Product_Id'].apply(lambda x: x[:2])
-        
-        X_copy['Product_Sugar_Content_Corr'] = X_copy['Product_Sugar_Content'].str.replace('reg', 'Regular', regex=True)
-        
-        X_copy['Operation_Years'] = 2013 - X_copy['Store_Establishment_Year']
-        
-        # Using a try-except block to handle unseen categories gracefully
-        try:
-          
-            le_feat=['Product_Sugar_Content_Corr','Store_Size','Store_Location_City_Type','Store_Type','Product_Id_Cd']
-            for i in le_feat:
-                X_copy[i] = self.le_prod.transform(X_copy[i])
-        except ValueError:
-            # Handling unknown categories in production data
-            X_copy['Product_Id_Cd'] = -1
-        
-        # Apply LabelEncoder to 'Store_Id'
-        try:
-            X_copy['Store'] = self.le_store.transform(X_copy['Store_Id'])
-        except ValueError:
-            X_copy['Store'] = -1
-        
-        
-        # Droping the features which have been processed into new features already
-        rem_feat=['Product_Id','Store_Id','Product_Sugar_Content','Product_Type', 'Store_Establishment_Year']
-        X_copy.drop(rem_feat, axis=1, inplace=True)
-        
-        return X_copy