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| import sys | |
| import os | |
| from PIL import Image | |
| def test(): | |
| print("Testing Image Authenticity lazy-load and predict...") | |
| # Create a dummy image (e.g. noise) | |
| import numpy as np | |
| dummy_img = Image.fromarray(np.random.randint(0, 256, (400, 400, 3), dtype=np.uint8)) | |
| from app import get_image_detector | |
| detector = get_image_detector() | |
| result, visuals = detector.predict_with_visuals( | |
| dummy_img, | |
| include_gradcam=True, | |
| include_fft=True, | |
| include_result_card=False | |
| ) | |
| print("\n--- TEST RESULT ---") | |
| print(f"Label: {result['label']}") | |
| print(f"Fake Prob: {result['fake_prob']*100:.1f}%") | |
| print(f"Real Prob: {result['real_prob']*100:.1f}%") | |
| print(f"Scores: {result['scores']}") | |
| print(f"Has GradCAM: {visuals.get('gradcam') is not None}") | |
| print(f"Has FFT: {visuals.get('fft_spectrum') is not None}") | |
| print("-------------------") | |
| print("SUCCESS: Pipeline runs correctly.") | |
| if __name__ == "__main__": | |
| test() | |