Spaces:
Running
Running
| import requests | |
| import os | |
| def test_image_embedding(): | |
| url = "http://localhost:8000/image_embedding/image_to_embedding" | |
| image_path = r"C:\Users\itg\.gemini\antigravity\brain\a2d1bd2b-b329-461a-ab89-c0d64934f5fb\test_image_for_embedding_1772686600102.png" | |
| if not os.path.exists(image_path): | |
| print(f"Error: {image_path} not found.") | |
| return | |
| with open(image_path, "rb") as f: | |
| files = {"file": (image_path, f, "image/png")} | |
| try: | |
| response = requests.post(url, files=files) | |
| if response.status_code == 200: | |
| result = response.json() | |
| if result["success"]: | |
| embedding = result["data"]["embedding"] | |
| print(f"Successfully retrieved embedding. Dimension: {len(embedding)}") | |
| # EfficientNetV2-S embedding dimension should be 1280 | |
| if len(embedding) == 1280: | |
| print("Verification PASSED: Embedding dimension is 1280.") | |
| else: | |
| print(f"Verification FAILED: Expected dimension 1280, got {len(embedding)}.") | |
| else: | |
| print(f"API Error: {result['msg']}") | |
| else: | |
| print(f"HTTP Error: {response.status_code}") | |
| except Exception as e: | |
| print(f"Request failed: {e}") | |
| if __name__ == "__main__": | |
| test_image_embedding() | |