pip install deepid

# Import the DeepID SDK and required package
import deepid
import os
import json
from datetime import datetime
# Set your API key for authentication
deepid.api_key = 'YOUR_API_KEY_HERE'
# Consider using environment variables 
# or a secure configuration file in production

# Increase this if you're processing large files 
# or experiencing slow network connections
MAX_RETRIES = 10

# Adjust this based on your expected processing time and API response time
RETRY_DELAY = 5

# Ensure this directory exists or has appropriate write permissions
RESULTS_DIR = 'results'

# Dictionary of sample files to be processed, organized by media type
sample_files = {
    # List of image files to analyze
    "image": ["path/to/image1.jpg", "path/to/image2.png"],

    # List of video files to analyze
    "video": ["path/to/video1.mp4"],

    # List of audio files to analyze
    "audio": ["path/to/audio1.wav"]
}

# Tip: For optimal performance,
# use high-quality uncompressed files when possible
# Larger files may take longer to process 
# but often yield more accurate results

def process_file(file_path, modality):
    """
    Submit a file for asynchronous processing by the DeepID API.
    """
    try:
        # Call the DeepID API to process the file asynchronously
        response = deepid.process_file_async(
            file_path,
            run_description=False,  
            # Set to True if you want a detailed description of the analysis
            modalities=[modality]  
            # Specify the type of media being analyzed
        )
        
        # Return the job ID, which will be used to retrieve results later
        return response['id']
    
    except Exception as e:
        # If an error occurs during submission, print the error and return None
        print(f"Error submitting file {file_path}: {e}"

def get_results(file_id):
    """
    Retrieve the results of a processed file from the DeepID API.    """
    # Attempt to retrieve results up to MAX_RETRIES times
    for _ in range(MAX_RETRIES):
        # Check the status of the submitted job
        status_response = deepid.get_file_status(file_id)
        
        # If the file has been processed or results are ready, return them
        if status_response['status'] in ['PROCESSED', 'RESULTS']:
            return status_response['results']
        
        # If results are not ready, 
        # wait for RETRY_DELAY seconds before checking again
        time.sleep(RETRY_DELAY)
    
    # If results are not available after MAX_RETRIES, return None

def save_results(file_path, results):
    """
    Save the analysis results to a JSON file.
    """
    # Create the results directory if it doesn't exist
    if not os.path.exists(RESULTS_DIR):
        os.makedirs(RESULTS_DIR)

    # Extract the filename from the original file path
    filename = os.path.basename(file_path)
    
    # Generate a timestamp for unique file naming
    timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
    
    # Create a new filename for the results, 
    # including the original filename and timestamp
    result_filename = f"{filename}_{timestamp}_result.json"
    
    # Combine the results directory path with the new filename
    result_path = os.path.join(RESULTS_DIR, result_filename)

    # Write the results to a JSON file
    with open(result_path, 'w') as f:
        # Use indent=2 for pretty-printing the JSON for better readability
        json.dump(results, f, indent=2)

    # Print a confirmation message with the path to the saved results
    print(f"Results saved to: {result_path}"

def main():
    """
    Main function to process all files in the sample_files dictionary.
    """
    # Iterate through each modality (image, video, audio) and its file list
    for modality, file_list in sample_files.items():
        print(f"\nProcessing {len(file_list)} {modality} files:")
        
        # Process each file in the current modality
        for file_path in file_list:
            print(f"Processing: {file_path}")
            
            # Submit the file for processing
            file_id = process_file(file_path, modality)
            
            if file_id:
                # If file submission was successful, retrieve the results
                results = get_results(file_id)
                
                if results:
                    # If results were retrieved successfully, 
                    # print and save them
                    print(f"Results for {file_path}: {results}")
                    save_results(file_path, results)
                else:
                    # If no results were retrieved, print an error message
                    print(f"No results received for {file_path}")
            else:
                # If file submission failed, print an error message
                print(f"Failed to process {file_path}")

# Check if this script is being run directly (not imported as a module)
if __name__ == "__main__":
    # Execute the main function

python your_script_name.py