import pandas as pd
import pickle
from sklearn.preprocessing import LabelEncoder
from config import TEXT_COLUMN, LABEL_COLUMNS, METADATA_COLUMNS, LABEL_ENCODERS_PATH

def load_and_preprocess_data(data_path):
    """
    Loads the dataset, fills missing values, converts metadata to numeric if specified,
    and encodes each target label using LabelEncoder.

    Args:
        data_path (str): Path to the CSV file

    Returns:
        data (pd.DataFrame): Preprocessed data
        label_encoders (dict): Fitted LabelEncoders for each label
    """
    data = pd.read_csv(data_path)
    data.fillna("Unknown", inplace=True)

    # Optional: convert numeric metadata
    for col in METADATA_COLUMNS:
        if col in data.columns:
            data[col] = pd.to_numeric(data[col], errors='coerce').fillna(0)

    # Encode target labels
    label_encoders = {}
    for col in LABEL_COLUMNS:
        le = LabelEncoder()
        data[col] = le.fit_transform(data[col])
        label_encoders[col] = le

    return data, label_encoders

def save_label_encoders(label_encoders):
    """
    Save fitted label encoders as a pickle file.
    """
    with open(LABEL_ENCODERS_PATH, "wb") as f:
        pickle.dump(label_encoders, f)

def load_label_encoders():
    """
    Load previously saved label encoders.
    """
    with open(LABEL_ENCODERS_PATH, "rb") as f:
        return pickle.load(f)