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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()
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