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| """Test script for explainability features.""" | |
| import asyncio | |
| import traceback | |
| import numpy as np | |
| from PIL import Image | |
| import io | |
| async def main(): | |
| from app.services.model_registry import get_model_registry | |
| from app.core.config import settings | |
| registry = get_model_registry() | |
| # Load models from fusion repo | |
| print("Loading models...") | |
| await registry.load_from_fusion_repo(settings.HF_FUSION_REPO_ID) | |
| print("Models loaded!") | |
| # Create a test image | |
| img = Image.fromarray(np.random.randint(0, 255, (224, 224, 3), dtype=np.uint8)) | |
| buf = io.BytesIO() | |
| img.save(buf, format='PNG') | |
| img_bytes = buf.getvalue() | |
| # Test each model | |
| models = ['cnn-transfer', 'gradfield-cnn', 'vit-base', 'deit-distilled'] | |
| for model_name in models: | |
| print(f"\nTesting {model_name}...") | |
| try: | |
| model = registry.get_submodel(model_name) | |
| result = model.predict(image_bytes=img_bytes, explain=True) | |
| has_heatmap = 'heatmap_base64' in result | |
| print(f" Success! pred={result['pred']}, has_heatmap={has_heatmap}") | |
| if has_heatmap: | |
| # Check heatmap is valid base64 | |
| import base64 | |
| decoded = base64.b64decode(result['heatmap_base64']) | |
| print(f" Heatmap size: {len(decoded)} bytes") | |
| except Exception as e: | |
| print(f" ERROR: {e}") | |
| traceback.print_exc() | |
| if __name__ == "__main__": | |
| asyncio.run(main()) | |