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PaddlePaddle 3.x hat oneDNN/PIR Executor Bug. Zurueck auf 2.6.2 mit bewaeherter ocr() API statt predict(). Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
96 lines
2.4 KiB
Python
96 lines
2.4 KiB
Python
"""PaddleOCR Remote Service — PP-OCRv4 Latin auf x86_64."""
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import io
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import logging
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import os
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import numpy as np
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from fastapi import FastAPI, File, Header, HTTPException, UploadFile
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from PIL import Image
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logging.basicConfig(level=logging.INFO)
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logger = logging.getLogger(__name__)
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app = FastAPI(title="PaddleOCR Service")
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_engine = None
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_ready = False
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API_KEY = os.environ.get("PADDLEOCR_API_KEY", "")
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def get_engine():
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global _engine
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if _engine is None:
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from paddleocr import PaddleOCR
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logger.info("Loading PaddleOCR model...")
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_engine = PaddleOCR(
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lang="latin",
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use_angle_cls=True,
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show_log=False,
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)
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logger.info("PaddleOCR model loaded successfully")
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return _engine
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@app.on_event("startup")
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def startup_load_model():
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"""Pre-load model at startup so health check passes."""
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global _ready
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try:
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get_engine()
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_ready = True
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logger.info("PaddleOCR ready to serve requests")
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except Exception as e:
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logger.error(f"Failed to load PaddleOCR model: {e}")
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@app.get("/health")
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def health():
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if _ready:
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return {"status": "ok", "model": "PP-OCRv4-latin"}
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return {"status": "loading"}
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@app.post("/ocr")
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async def ocr(
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file: UploadFile = File(...),
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x_api_key: str = Header(default=""),
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):
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if API_KEY and x_api_key != API_KEY:
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raise HTTPException(status_code=401, detail="Invalid API key")
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if not _ready:
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raise HTTPException(status_code=503, detail="Model still loading")
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img_bytes = await file.read()
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img = Image.open(io.BytesIO(img_bytes)).convert("RGB")
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img_np = np.array(img)
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engine = get_engine()
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result = engine.ocr(img_np)
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words = []
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for line in result[0] or []:
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box, (text, conf) = line[0], line[1]
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x_min = min(p[0] for p in box)
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y_min = min(p[1] for p in box)
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x_max = max(p[0] for p in box)
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y_max = max(p[1] for p in box)
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words.append(
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{
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"text": text.strip(),
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"left": int(x_min),
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"top": int(y_min),
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"width": int(x_max - x_min),
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"height": int(y_max - y_min),
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"conf": round(conf * 100, 1),
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}
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)
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return {
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"words": words,
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"image_width": img_np.shape[1],
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"image_height": img_np.shape[0],
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}
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