import requests
import os
import logging
from check_structure import check_existing_file, check_existing_folder


def import_raw_data(raw_data_relative_path, filenames, bucket_folder_url):
    """import filenames from bucket_folder_url in raw_data_relative_path"""
    if check_existing_folder(raw_data_relative_path):
        os.makedirs(raw_data_relative_path)
    # download all the files
    for filename in filenames:
        input_file = os.path.join(bucket_folder_url, filename)
        output_file = os.path.join(raw_data_relative_path, filename)
        if check_existing_file(output_file):
            object_url = input_file
            print(f"downloading {input_file} as {os.path.basename(output_file)}")
            response = requests.get(object_url)
            if response.status_code == 200:
                # Process the response content as needed
                content = (
                    response.content
                )  # Utilisez response.content pour les fichiers binaires
                with open(output_file, "wb") as file:
                    file.write(content)
            else:
                print(f"Error accessing the object {input_file}:", response.status_code)

    # Téléchargez le dossier 'img_train'
    img_train_folder_url = os.path.join(bucket_folder_url, "image_train/")
    img_train_local_path = os.path.join(raw_data_relative_path, "image_train/")
    if check_existing_folder(img_train_local_path):
        os.makedirs(img_train_local_path)

    try:
        response = requests.get(img_train_folder_url)
        if response.status_code == 200:
            file_list = response.text.splitlines()
            for img_url in file_list:
                img_filename = os.path.basename(img_url)
                output_file = os.path.join(img_train_local_path, img_filename)
                if check_existing_file(output_file):
                    print(f"downloading {img_url} as {img_filename}")
                    img_response = requests.get(img_url)
                    if img_response.status_code == 200:
                        with open(output_file, "wb") as img_file:
                            img_file.write(img_response.content)
                    else:
                        print(f"Error downloading {img_url}:", img_response.status_code)
        else:
            print(
                f"Error accessing the object list {img_train_folder_url}:",
                response.status_code,
            )
    except Exception as e:
        # nonlocalprint(f"An error occurred: {str(e)}")
        print(f"An error occurred: {str(e)}")


def main(
    raw_data_relative_path="./data/raw",
    filenames=["X_test_update.csv", "X_train_update.csv", "Y_train_CVw08PX.csv"],
    bucket_folder_url="https://mlops-project-db.s3.eu-west-1.amazonaws.com/classification_e-commerce/",
):
    """Upload data from AWS s3 in ./data/raw"""
    import_raw_data(raw_data_relative_path, filenames, bucket_folder_url)
    logger = logging.getLogger(__name__)
    logger.info("making raw data set")


if __name__ == "__main__":
    log_fmt = "%(asctime)s - %(name)s - %(levelname)s - %(message)s"
    logging.basicConfig(level=logging.INFO, format=log_fmt)

    main()