import argparse
import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import MinMaxScaler
from sklearn.preprocessing import LabelEncoder
import os



def parse(csv_path):
    print(f"Location of the file: {csv_path}")
    
    # Step 1: Load the dataset
    # file_path = "dataset.csv"  # Path to the original dataset
    data = pd.read_csv(csv_path)

    # Drop NA and duplicates
    data = data.dropna()
    data.to_csv('data/01 dropna.csv', index=False)
    data = data.drop_duplicates()
    data.to_csv('data/02 drop_duplicates.csv', index=False)
    
    # Step 2: Define the feature columns (X) and target column (y)
    # X = data[["name", "attendance percentage", "average sleep time", "average screen time"]]  # Feature columns
    X = data[["DateTime","product","campaign_id","webpage_id","product_category_1","product_category_2","user_group_id","gender","age_level","user_depth","city_development_index","var_1"]]  # Feature columns
    y = data["is_click"]  # Target column

    # Extract datetime features
    X.loc[:,'DateTime'] = pd.to_datetime(X['DateTime'], errors='coerce')
    X = X.dropna(subset=['DateTime'])
    # print(X.columns)
    # print(X.iloc[:5,0])
    # print(X.iloc[:5,0].dt.weekday)
    X.loc[:,'weekday'] = pd.to_numeric(pd.to_datetime(X['DateTime'], errors='coerce').dt.weekday, errors='coerce', downcast='integer')
    X.loc[:,'month'] = pd.to_numeric(pd.to_datetime(X['DateTime'], errors='coerce').dt.month, errors='coerce', downcast='integer')
    X.loc[:,"hour"] = pd.to_datetime(X['DateTime'], errors='coerce').dt.hour.values
    X = X.drop('DateTime', axis=1)

    # Product label to number
    le = LabelEncoder()
    X.loc[:,"product"] = le.fit_transform(X["product"])
    # Gender label to number
    X['gender'] = X['gender'].map({'Female': 1,
                                   'Male': 0,
                                   'M': 0})
    
    
    # Normalize numerical features
    scaler = MinMaxScaler()
    numerical_features = ['campaign_id','webpage_id','user_depth',"product_category_1","product_category_2","user_group_id", 'age_level',"user_depth", 'city_development_index', 'var_1']
    X[numerical_features] = scaler.fit_transform(X[numerical_features])
    data = pd.concat([X, y.to_frame(name="is_click")], axis=1)
    data.to_csv('data/03 normalize.csv', index=False)

    
    
    # Step 3: Split the dataset into training and testing sets
    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
    
    # Step 4: Combine X and y back into dataframes for train and test
    train_data = pd.concat([X_train, y_train], axis=1)  # Combine features and target for training data
    test_data = pd.concat([X_test, y_test], axis=1)    # Combine features and target for testing data
    
    # Step 5: Create the 'data' folder if it doesn't exist
    output_folder = "data"
    os.makedirs(output_folder, exist_ok=True)
    
    # Step 6: Save the train and test sets as CSV files
    # train_file_path = os.path.join(output_folder, "train.csv")
    train_file_path = "data/train.csv"
    test_file_path = "data/test.csv"
    
    train_data.to_csv(train_file_path, index=False)
    test_data.to_csv(test_file_path, index=False)
    
    print(f"Train and test datasets saved in '{output_folder}' folder.")
    




if __name__ == '__main__':
    parser = argparse.ArgumentParser()
    parser.add_argument("--csv-path", type=str)
    
    
    args = parser.parse_args()
    parse(args.csv_path)