6737bd6db5
Python microservice (ocr-service/): - FastAPI app with /ocr and /health endpoints - Surya engine: transformer-based OCR for typewritten/modern handwriting - Kraken engine: historical HTR for Kurrent/Suetterlin with pure-Python polygon-to-quad approximation (gift wrapping + rotating calipers) - Eager model loading at startup via lifespan context manager - PDF download via httpx, page rendering via pypdfium2 at 300 DPI Java RestClientOcrClient: - Implements OcrClient + OcrHealthClient interfaces - Calls Python service via Spring RestClient - Health check with graceful fallback Docker Compose: - New ocr-service container (mem_limit 6g, no host ports) - Health check with start_period 60s for model loading - ocr_models volume for Kraken model files - Backend depends on ocr-service health Refs #226, #227 Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
94 lines
2.8 KiB
Python
94 lines
2.8 KiB
Python
"""OCR microservice — FastAPI app with Surya and Kraken engine support."""
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import io
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import logging
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from contextlib import asynccontextmanager
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import httpx
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import pypdfium2 as pdfium
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from fastapi import FastAPI, HTTPException
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from PIL import Image
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from engines import kraken as kraken_engine
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from engines import surya as surya_engine
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from models import OcrBlock, OcrRequest
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logging.basicConfig(level=logging.INFO)
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logger = logging.getLogger(__name__)
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_models_ready = False
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@asynccontextmanager
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async def lifespan(app: FastAPI):
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"""Load all OCR models at startup before accepting requests."""
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global _models_ready
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logger.info("Loading OCR models at startup...")
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surya_engine.load_models()
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kraken_engine.load_models()
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_models_ready = True
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logger.info("All OCR models loaded — ready to accept requests")
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yield
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logger.info("Shutting down OCR service")
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app = FastAPI(title="Familienarchiv OCR Service", lifespan=lifespan)
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@app.get("/health")
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def health():
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"""Health endpoint — returns 200 only after models are loaded."""
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if not _models_ready:
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raise HTTPException(status_code=503, detail="Models not loaded yet")
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return {"status": "ok", "surya": True, "kraken": kraken_engine.is_available()}
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@app.post("/ocr", response_model=list[OcrBlock])
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async def run_ocr(request: OcrRequest):
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"""Run OCR on a PDF document.
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Downloads the PDF from the provided URL, converts pages to images,
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and runs the appropriate OCR engine based on scriptType.
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"""
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if not _models_ready:
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raise HTTPException(status_code=503, detail="Models not loaded yet")
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images = await _download_and_convert_pdf(request.pdf_url)
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script_type = request.script_type.upper()
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if script_type == "HANDWRITING_KURRENT":
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if not kraken_engine.is_available():
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raise HTTPException(
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status_code=400,
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detail="Kraken model not available — cannot process Kurrent script",
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)
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blocks = kraken_engine.extract_blocks(images, request.language)
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else:
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# TYPEWRITER, HANDWRITING_LATIN, UNKNOWN — all use Surya
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blocks = surya_engine.extract_blocks(images, request.language)
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return [OcrBlock(**b) for b in blocks]
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async def _download_and_convert_pdf(url: str) -> list[Image.Image]:
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"""Download a PDF from URL and convert each page to a PIL Image."""
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async with httpx.AsyncClient(timeout=httpx.Timeout(300.0)) as client:
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response = await client.get(url)
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response.raise_for_status()
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pdf = pdfium.PdfDocument(io.BytesIO(response.content))
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images = []
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for page_idx in range(len(pdf)):
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page = pdf[page_idx]
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# Render at 300 DPI for good OCR quality
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bitmap = page.render(scale=300 / 72)
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pil_image = bitmap.to_pil()
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images.append(pil_image)
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return images
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