import os

import joblib
import pandas as pd
from sklearn.preprocessing import StandardScaler

import config
from core.generate_dataset import ensure_dataset


class DataPreprocessor:
    def __init__(self):
        self.scaler = StandardScaler()
        self.is_fitted = False
        self.feature_names = None

    def load_raw_data(self):
        ensure_dataset()
        df = pd.read_csv(config.RAW_DATA_PATH, sep=config.CSV_SEPARATOR)
        df.columns = df.columns.str.strip()
        return df

    def clean_data(self, df):
        df = df.copy()
        df = df.drop_duplicates()

        for col in df.columns:
            if df[col].isnull().sum() == 0:
                continue
            if pd.api.types.is_numeric_dtype(df[col]):
                df[col] = df[col].fillna(df[col].median())
            else:
                df[col] = df[col].fillna(df[col].mode()[0])

        return df

    def fit_transform(self, df):
        df = self.clean_data(df)
        if config.TARGET_COLUMN in df.columns:
            y = df[config.TARGET_COLUMN].values
            feature_df = df.drop(columns=[config.TARGET_COLUMN])
        else:
            y = None
            feature_df = df

        self.feature_names = list(feature_df.columns)
        X = self.scaler.fit_transform(feature_df.values)
        self.is_fitted = True
        return X, y

    def transform(self, df):
        if not self.is_fitted:
            raise ValueError("Preprocessor has not been fitted yet.")

        df = self.clean_data(df)
        if config.TARGET_COLUMN in df.columns:
            feature_df = df.drop(columns=[config.TARGET_COLUMN])
        else:
            feature_df = df
        return self.scaler.transform(feature_df.values)

    def save_preprocessor(self):
        os.makedirs(config.MODELS_DIR, exist_ok=True)
        joblib.dump(self.scaler, config.SCALER_PATH)
        joblib.dump(self.feature_names, os.path.join(config.MODELS_DIR, 'feature_names.pkl'))

    def load_preprocessor(self):
        self.scaler = joblib.load(config.SCALER_PATH)
        feature_names_path = os.path.join(config.MODELS_DIR, 'feature_names.pkl')
        if os.path.exists(feature_names_path):
            self.feature_names = joblib.load(feature_names_path)
        self.is_fitted = True


def get_clean_data():
    preprocessor = DataPreprocessor()
    df = preprocessor.load_raw_data()
    return preprocessor.clean_data(df)


def save_clean_data():
    preprocessor = DataPreprocessor()
    df = preprocessor.load_raw_data()
    df = preprocessor.clean_data(df)
    os.makedirs(config.PROCESSED_DATA_DIR, exist_ok=True)
    df.to_csv(config.CLEAN_DATA_PATH, index=False, sep=',')
    return df


if __name__ == '__main__':
    data = save_clean_data()
    print(f"Clean data saved. Shape: {data.shape}")