902d423f3c
- Surya models lazy-load on first OCR request instead of at startup (saves ~3-4GB idle RAM — Kraken stays eager at ~16MB) - Process one page at a time in Surya engine (limits peak memory) - RECOGNITION_BATCH_SIZE=1, DETECTOR_BATCH_SIZE=1 (slower but fits in RAM) - Revert mem_limit back to 6GB (sufficient with these optimizations) - Render DPI stays at 200 Idle memory: ~2GB (Kraken only). Peak during OCR: ~5-6GB (Surya loaded). Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
101 lines
3.2 KiB
Python
101 lines
3.2 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 confidence import apply_confidence_markers, get_threshold
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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 lightweight models at startup. Surya loads lazily on first request."""
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global _models_ready
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logger.info("Loading Kraken model at startup (Surya loads lazily on first OCR request)...")
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kraken_engine.load_models()
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_models_ready = True
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logger.info("Startup complete — 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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threshold = get_threshold(script_type)
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for block in blocks:
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if block.get("words"):
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block["text"] = apply_confidence_markers(block["words"], threshold)
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block.pop("words", None)
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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 a presigned 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 200 DPI — balances OCR quality vs memory usage
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# (Surya 0.17 models use ~5GB idle; 300 DPI causes OOM on multi-page docs)
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bitmap = page.render(scale=200 / 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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