# data/data_loader.py

import pandas as pd

import numpy as np

from sklearn.model_selection import train_test_split

from sklearn.preprocessing import StandardScaler

import logging

from app.config import DATA_PATH, FEATURES, TARGET, TEST_SIZE, RANDOM_STATE

def load_data(file_path='data/patient_data.csv'):

    """

    Load data from CSV file

    """

    try:

        df = pd.read_csv(file_path)

        print(f"Data loaded successfully with {len(df)} rows")  # Debug print

        return df

    except Exception as e:

        print(f"Error loading data: {e}")

        return None

def preprocess_data(df):

    """

    Preprocess the data and split into features and target

    Returns:

        X_train, X_test, y_train, y_test, feature_names, scaler

    """

    print("Starting preprocessing...")  # Debug print

    if df is None:

        print("DataFrame is None")  # Debug print

        return None, None, None, None, None, None

    try:

        # Separate features and target

        X = df.drop('readmitted', axis=1)

        y = df['readmitted']

        # Get feature names

        feature_names = X.columns.tolist()

        print(f"Features: {feature_names}")  # Debug print

        # Scale the features

        scaler = StandardScaler()

        X_scaled = scaler.fit_transform(X)

        X_scaled = pd.DataFrame(X_scaled, columns=feature_names)

        # Split the data

        X_train, X_test, y_train, y_test = train_test_split(

            X_scaled, y, test_size=0.2, random_state=42, stratify=y

        )

        print("Preprocessing completed successfully")  # Debug print

        # Explicitly create the return tuple

        result = (X_train, X_test, y_train, y_test, feature_names, scaler)

        print(f"Number of return values: {len(result)}")  # Debug print

        return result

    except Exception as e:

        print(f"Error in preprocessing: {e}")

        return None, None, None, None, None, None