Asep Haryana SaputraandClaude Opus 4.7
ed8cecc3f5
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
57 lines
1.8 KiB
Python
57 lines
1.8 KiB
Python
from fastapi import FastAPI, File, HTTPException, UploadFile
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from model import ImageDecodeError, LABELS, SERVICE_NAME, SERVICE_VERSION, model_service
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from schemas import HealthResponse, MetadataResponse, PredictionResponse
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app = FastAPI(title="ZeaVis ML Service", version=SERVICE_VERSION)
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@app.on_event("startup")
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def load_model() -> None:
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model_service.load()
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@app.get("/health", response_model=HealthResponse)
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def health() -> HealthResponse:
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return HealthResponse(status="ok", model_loaded=model_service.model_loaded)
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@app.get("/metadata", response_model=MetadataResponse)
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def metadata() -> MetadataResponse:
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return MetadataResponse(
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service_name=SERVICE_NAME,
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service_version=SERVICE_VERSION,
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model_path=str(model_service.model_path),
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model_loaded=model_service.model_loaded,
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input_size=model_service.input_size,
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labels=LABELS,
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)
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@app.post("/predict", response_model=PredictionResponse)
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async def predict(file: UploadFile = File(...)) -> PredictionResponse:
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if file.content_type is None or not file.content_type.startswith("image/"):
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raise HTTPException(status_code=400, detail="Uploaded file must be an image")
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if not model_service.model_loaded:
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raise HTTPException(status_code=503, detail="Model is not loaded")
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image_bytes = await file.read()
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try:
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label, confidence, probabilities = model_service.predict(image_bytes)
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except ImageDecodeError as exc:
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raise HTTPException(status_code=400, detail=str(exc)) from exc
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except Exception as exc:
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import logging
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logging.exception("Prediction failed")
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raise HTTPException(status_code=500, detail="Prediction failed") from exc
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return PredictionResponse(
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label=label,
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confidence=confidence,
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probabilities=probabilities,
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)
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