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