import json
import random
import sys
import time

def main():
    """
    This is a placeholder Python script for ML model inference.
    It simulates a model prediction by returning a random result.
    
    In a real-world application, this script would:
    1. Load a pre-trained machine learning model (e.g., from an .h5 or .pkl file).
    2. Pre-process the input image received from the command-line argument.
    3. Run the image through the model to get a prediction.
    4. Return the prediction results as a JSON string.
    """
    # Simulate processing time
    time.sleep(1.5)

    # Get image path from command line arguments (for context, not used in this placeholder)
    image_path = sys.argv[1] if len(sys.argv) > 1 else "no_image_provided.jpg"

    # Possible outcomes
    predictions = [
        {"label": "Alzheimer Detected", "confidence": round(random.uniform(0.75, 0.98), 2)},
        {"label": "Healthy", "confidence": round(random.uniform(0.80, 0.99), 2)}
    ]
    
    # Choose a random result to simulate a real prediction
    result = random.choice(predictions)

    # Output the result as a JSON string to be captured by the PHP script
    print(json.dumps(result))

if __name__ == "__main__":
    # This check ensures the main function runs only when the script is executed directly
    main()