test_cleanup.py

import os
import json
from PIL import Image
from app import predict, process_image

# Mock DETECTOR_AVAILABLE to avoid import errors during test
import app
app.DETECTOR_AVAILABLE = True
app.run_detect = lambda args: None # Mock run_detect

def test_cleanup():
    # Create a large dummy image
    large_img_path = "test_large_cleanup.png"
    Image.new('RGB', (2000, 2000), color='green').save(large_img_path)
    
    # Create a dummy output file because predict expects it
    output_path = "temp_result.json"
    with open(output_path, 'w') as f:
        json.dump({"prediction": "real", "confidence": 0.9, "elapsed_time": 0.1}, f)
        
    # We need to monkeypatch process_image to track the file it creates
    original_process_image = app.process_image
    created_files = []
    
    def tracked_process_image(path):
        new_path = original_process_image(path)
        if new_path != path:
            created_files.append(new_path)
        return new_path
        
    app.process_image = tracked_process_image
    
    # Run predict
    try:
        print("Running predict...")
        app.predict(large_img_path, "R50_TF")
    except Exception as e:
        print(f"Predict failed: {e}")
        
    # Check if cropped file was created
    assert len(created_files) > 0, "Should have created a cropped file"
    cropped_path = created_files[0]
    print(f"Cropped path was: {cropped_path}")
    
    # Check if cropped file was deleted
    assert not os.path.exists(cropped_path), f"Cropped file {cropped_path} should have been deleted"
    print("Cleanup test passed!")
    
    # Cleanup original
    if os.path.exists(large_img_path):
        os.remove(large_img_path)
    if os.path.exists(output_path):
        os.remove(output_path)

if __name__ == "__main__":
    test_cleanup()