58 lines
2.1 KiB
Python
58 lines
2.1 KiB
Python
from fastapi import FastAPI, File, UploadFile, HTTPException
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import io
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from PIL import Image
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import logging
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from inference import DotsOcr
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from datetime import datetime
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import asyncio
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# 配置日志
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logging.basicConfig(level=logging.INFO)
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logger = logging.getLogger(__name__)
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app = FastAPI(title="DotsOCR API")
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inference_semaphore = asyncio.Semaphore(1)
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model_path = "DotsOCR"
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try:
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ocr_engine = DotsOcr(model_path)
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logger.info(f"{datetime.now()} - DotsOCR model loaded successfully")
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except Exception as e:
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logger.error(f"{datetime.now()} - DotsOCR model loading failed: {e}")
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ocr_engine = None
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@app.post("/predict")
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async def predict(file: UploadFile = File(...)):
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"""
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上传一张图像，返回布局识别结果（字典列表）。
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使用信号量确保同一时间只有一个请求进行推理，防止 OOM。
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"""
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if ocr_engine is None:
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raise HTTPException(status_code=500, detail="模型未正确加载")
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if file.content_type is not None and not file.content_type.startswith("image/"):
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raise HTTPException(status_code=400, detail="文件必须是图像格式")
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async with inference_semaphore:
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try:
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image_bytes = await file.read()
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try:
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image = Image.open(io.BytesIO(image_bytes)).convert("RGB")
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except Exception:
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raise HTTPException(status_code=400, detail="无法识别的图像文件，请上传有效的图像")
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logger.info(f"处理图像: {file.filename}, 尺寸: {image.size}")
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result = ocr_engine.sample_inference(image)
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return result
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except HTTPException:
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# 直接抛出 HTTP 异常，避免被通用异常捕获导致状态码错误
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raise
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except Exception as e:
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logger.exception("推理过程中发生错误")
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raise HTTPException(status_code=500, detail=str(e))
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@app.get("/health")
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async def health_check():
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return {"status": "ok", "model_loaded": ocr_engine is not None} |