update predict_page.py

predict_page.py

@@ -10,23 +10,6 @@ import joblib
 
 
 
-# Load the numerical imputer
-#num_imputer = joblib.load("numerical_imputer.joblib")
-
-# Load the categorical imputer
-#cat_imputer = joblib.load("categorical_imputer.joblib")
-
-# Load the scaler
-#scaler = joblib.load("scaler.joblib")
-
-# Load the label encoder for 'family' feature
-#le_family = joblib.load("le_family.joblib")
-
-# Load the label encoder for 'holiday_type' feature
-#le_holiday_type = joblib.load("le_holiday_type.joblib")
-
-# Load the label encoder for 'city' feature
-#le_city = joblib.load("le_city.joblib")
 
 # Load the final model
 regressor = joblib.load("Best_model.joblib")
@@ -66,41 +49,6 @@ def show_predict_page():
         # Convert the input data to a pandas DataFrame
         input_df = pd.DataFrame([input_data])
 
-
-# Selecting categorical and numerical columns separately
-#        cat_columns = [col for col in input_df.columns if input_df[col].dtype == 'object']
-#        num_columns = [col for col in input_df.columns if input_df[col].dtype != 'object']
-
-
-# Apply the imputers
-#        input_df_imputed_cat = cat_imputer.transform(input_df[cat_columns])
-#        input_df_imputed_num = num_imputer.transform(input_df[num_columns])
-
-# Convert the NumPy arrays to DataFrames
-#        input_df_imputed_cat = pd.DataFrame(input_df_imputed_cat, columns=cat_columns)
-#        input_df_imputed_num = pd.DataFrame(input_df_imputed_num, columns=num_columns)
-        
-
-# Scale the numerical columns
-#        input_df_scaled = scaler.transform(input_df_imputed_num)
-#        input_scaled_df = pd.DataFrame(input_df_scaled , columns = num_columns)
-
-#        input_df_imputed  = pd.concat([input_df_imputed_cat, input_scaled_df], axis=1)
-        
- # Encode the categorical columns
-        # Encode the categorical columns
-#        input_df_imputed['family'] = le_family.transform(input_df_imputed['family'])
-#        input_df_imputed['holiday_type'] = le_holiday_type.transform(input_df_imputed['holiday_type'])
-#        input_df_imputed['city'] = le_city.transform(input_df_imputed['city'])
-
-
-        #input_encoded_df = pd.DataFrame(encoder.transform(input_df_imputed_cat))
-        #input_encoded_df.columns = input_encoded_df.columns.astype(str)
-        
-
-#joining the cat encoded and num scaled
-#        final_df = input_df_imputed 
-
 # Make a prediction
         prediction = round(regressor.predict(input_df)[0], 2)