import warnings

import pandas as pd

import os

warnings.filterwarnings('ignore')

# Load the noisy dataset

try:

    original = pd.read_csv(open(os.path.join(os.path.dirname(__file__), '../../data/raw/original.csv'), 'r'))

    synthetic = pd.read_csv(open(os.path.join(os.path.dirname(__file__), '../../data/raw/synthetic.csv'), 'r'))

except FileNotFoundError as err:

    print(f'Ann error occoured: {err}')

# Process the original set

def process_original_dataset():

    return original.drop_duplicates().dropna().replace({'M': 1, 'F': 2, 'YES': 1, 'NO': 0})

# Process the synthetic set

def process_synthetic_dataset():

    return synthetic.drop_duplicates().dropna().replace({'M': 1, 'F': 2, 'YES': 1, 'NO': 0})

# Processing results

def processing():

    process_original_dataset().to_csv('../../data/processed/original.csv', index=False)

    process_synthetic_dataset().to_csv('../../data/processed/synthetic.csv', index=False)

    return 'Dataset Processed.'

if __name__ == '__main__':

   print(processing())