import subprocess
import os
import pandas as pd
import concurrent.futures

def download_image(bucket_url, directory_path, destination_folder, destination_filename):
    """Download images from a specific directory in Google Cloud Storage if they don't exist locally."""
    os.makedirs(destination_folder, exist_ok=True)
    
    # List images in the GCS directory
    gsutil_ls_command = f"gsutil ls '{bucket_url}/{directory_path}/*.jpg'"
    result = subprocess.run(gsutil_ls_command, shell=True, capture_output=True, text=True)

    # Extract image filenames from the gsutil ls command output
    image_filenames = result.stdout.strip().split('\n')

    for image_filename in image_filenames:
        image_filename = os.path.basename(image_filename)
        # local_path = os.path.join(destination_folder, image_filename)
        local_path = os.path.join(destination_folder, destination_filename)

        # Check if the image already exists locally
        if not os.path.exists(local_path):
            # Download the image only if it doesn't exist locally
            gsutil_cp_command = f"gsutil cp '{bucket_url}/{directory_path}/{image_filename}' '{local_path}'"
            subprocess.run(gsutil_cp_command, shell=True, check=True)
            # print(f"Downloaded: {local_path}")
        # else:
        #     print(f"Skipped (Already Exists): {local_path}")

    # print(f"All images downloaded to: {destination_folder}")

def user_image(sample_id):
    bucket_url = "gs://hpos-prod.appspot.com"
    directory_path = f"{sample_id}"
    destination_folder = "data/images"
    destination_filename = f"{sample_id}.jpg"

    local_path = os.path.join(destination_folder, destination_filename)

    if not os.path.exists(local_path):
        download_image(bucket_url, directory_path, destination_folder, destination_filename)


if __name__ == "__main__":
    curdir = os.getcwd()
    path_delim = '/'
    df = pd.read_excel(curdir + path_delim + "data/CardPrint.xlsx", sheet_name="op")
    
     # Using ThreadPoolExecutor for parallel processing
    with concurrent.futures.ThreadPoolExecutor() as executor:
        # Map the user_image function to the list of sample IDs, allowing parallel execution
        executor.map(user_image, df['Sample ID'])