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| import os | |
| import numpy as np | |
| from fastapi import Depends, FastAPI, File, Response, UploadFile, status | |
| from auth import validate_token | |
| from model.model import Model | |
| from routers.fecnet_router import router as fecnet_router | |
| from schema import EmbeddingResponse, SimilarityResponse | |
| app = FastAPI( | |
| title="Facial Expression Embedding Service", | |
| ) | |
| app.include_router(fecnet_router) | |
| model = Model( | |
| os.getenv("MODEL_REPO_ID", ""), | |
| os.getenv("MODEL_FILENAME", ""), | |
| os.getenv("HF_TOKEN", ""), | |
| ) | |
| async def calculate_embedding( | |
| image: UploadFile = File(...), | |
| ): | |
| try: | |
| image_content = await image.read() | |
| if isinstance(image_content, str): | |
| image_content = image_content.encode() | |
| pred = model.predict(model.preprocess(image_content)) | |
| return EmbeddingResponse(embedding=pred[0].tolist()) | |
| except Exception as e: | |
| return Response( | |
| content=str(e), status_code=status.HTTP_500_INTERNAL_SERVER_ERROR | |
| ) | |
| async def calculate_similarity_score( | |
| image1: UploadFile = File(...), | |
| image2: UploadFile = File(...), | |
| ): | |
| try: | |
| image1_content = await image1.read() | |
| if isinstance(image1_content, str): | |
| image1_content = image1_content.encode() | |
| image2_content = await image2.read() | |
| if isinstance(image2_content, str): | |
| image2_content = image2_content.encode() | |
| pred = model.predict( | |
| np.vstack( | |
| [model.preprocess(image1_content), model.preprocess(image2_content)] | |
| ) | |
| ) | |
| return SimilarityResponse( | |
| score=float(model.cosine_similarity(pred[0], pred[1])) | |
| ) | |
| except Exception as e: | |
| return Response( | |
| content=str(e), status_code=status.HTTP_500_INTERNAL_SERVER_ERROR | |
| ) | |