import pandas as pd

from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler, OneHotEncoder
from sklearn.compose import ColumnTransformer


def prepare_data(df: pd.DataFrame):

    # Drop leakage / unnecessary columns
    df = df.drop(columns=["AQI_Bucket"], errors="ignore")
    df = df.drop(columns=["Date"], errors="ignore")

    # Split features & target
    X = df.drop(columns=["AQI"])
    y = df["AQI"]

    # Train-test split
    X_train, X_test, y_train, y_test = train_test_split(
        X, y, test_size=0.2, random_state=42
    )

    # Column selection
    num_cols = X.select_dtypes(include=["number"]).columns
    cat_cols = X.select_dtypes(include=["object"]).columns

    # Preprocessing pipeline
    preprocessor = ColumnTransformer(
        transformers=[
            ("num", StandardScaler(), num_cols),
            ("cat", OneHotEncoder(handle_unknown="ignore"), cat_cols)
        ]
    )

    return X_train, X_test, y_train, y_test, preprocessor