135 lines
4.6 KiB
Python
135 lines
4.6 KiB
Python
# File: specs/04-Infrastructure-OPS/04-00-docker-compose/Desk-5439/ocr-sidecar/app.py
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# Tesseract OCR HTTP Sidecar API — รับ POST /ocr แล้วคืนข้อความที่สกัดจาก PDF/Image
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# ตาม ADR-023A: OCR auto-detect (PyMuPDF chars > 100 → Fast path, else Tesseract)
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# Change Log:
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# - 2026-05-25: Initial FastAPI server สำหรับ PaddleOCR sidecar
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# - 2026-05-30: เปลี่ยน lang='en' เป็น lang='ch' (CTJK) เพื่อรองรับภาษาไทย
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# - 2026-05-30: เปลี่ยนจาก PaddleOCR เป็น Tesseract OCR เพื่อความเข้ากันได้กับ CPU เก่า
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import os
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import logging
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import re
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import fitz # PyMuPDF
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from pathlib import Path
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from typing import Optional
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from PIL import Image
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import pytesseract
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import io
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from fastapi import FastAPI, HTTPException
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from pydantic import BaseModel
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from pythainlp.tokenize import word_tokenize
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from pythainlp.util import normalize as thai_normalize
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logging.basicConfig(level=logging.INFO)
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logger = logging.getLogger("ocr-sidecar")
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app = FastAPI(title="Tesseract OCR Sidecar", version="1.0.0")
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# อ่านค่า config จาก environment
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OCR_CHAR_THRESHOLD = int(os.getenv("OCR_CHAR_THRESHOLD", "100"))
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MAX_PAGES = int(os.getenv("OCR_MAX_PAGES", "0")) # 0 = ทุกหน้า
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OCR_LANG = os.getenv("OCR_LANG", "tha+eng") # Tesseract language code (tha+eng = Thai + English)
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logger.info(f"Tesseract OCR Sidecar initialized (lang={OCR_LANG})")
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class OcrRequest(BaseModel):
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pdfPath: str
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maxPages: Optional[int] = None
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class OcrResponse(BaseModel):
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text: str
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ocrUsed: bool
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pageCount: int
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charCount: int
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@app.get("/health")
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def health():
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return {"status": "ok", "engine": "tesseract"}
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@app.post("/ocr", response_model=OcrResponse)
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def ocr_extract(req: OcrRequest):
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pdf_path = Path(req.pdfPath)
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if not pdf_path.exists():
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raise HTTPException(status_code=404, detail=f"ไม่พบไฟล์: {req.pdfPath}")
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max_pages = req.maxPages or MAX_PAGES
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try:
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doc = fitz.open(str(pdf_path))
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except Exception as e:
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raise HTTPException(status_code=422, detail=f"เปิดไฟล์ PDF ล้มเหลว: {e}")
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pages_to_process = list(range(min(len(doc), max_pages) if max_pages > 0 else len(doc)))
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page_count = len(pages_to_process)
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# Fast path: ลอง extract text layer ก่อน
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fast_text_parts = []
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for i in pages_to_process:
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page = doc[i]
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fast_text_parts.append(page.get_text())
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fast_text = "\n".join(fast_text_parts).strip()
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total_chars = len(fast_text)
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if total_chars > OCR_CHAR_THRESHOLD:
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logger.info(f"Fast path: {total_chars} chars extracted from {pdf_path.name}")
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return OcrResponse(
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text=fast_text,
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ocrUsed=False,
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pageCount=page_count,
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charCount=total_chars,
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)
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# Slow path: ใช้ Tesseract OCR กับทุกหน้า
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logger.info(f"Slow path (Tesseract): {total_chars} chars too few for {pdf_path.name}")
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ocr_text_parts = []
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for i in pages_to_process:
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page = doc[i]
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pix = page.get_pixmap(dpi=200)
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img_bytes = pix.tobytes("png")
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img = Image.open(io.BytesIO(img_bytes))
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text = pytesseract.image_to_string(img, lang=OCR_LANG)
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ocr_text_parts.append(text.strip())
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ocr_text = "\n".join(ocr_text_parts).strip()
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logger.info(f"Tesseract extracted {len(ocr_text)} chars from {pdf_path.name}")
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return OcrResponse(
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text=ocr_text,
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ocrUsed=True,
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pageCount=page_count,
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charCount=len(ocr_text),
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)
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class NormalizeRequest(BaseModel):
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text: str
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class NormalizeResponse(BaseModel):
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normalized: str
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@app.post("/normalize", response_model=NormalizeResponse)
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def normalize_text(req: NormalizeRequest):
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"""Normalize Thai text ด้วย PyThaiNLP สำหรับ rag-thai-preprocess queue"""
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try:
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# normalize unicode + ตัดคำแล้วต่อกลับด้วย space เพื่อ embedding
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normalized = thai_normalize(req.text)
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tokens = word_tokenize(normalized, engine="newmm", keep_whitespace=False)
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result = " ".join(tokens)
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return NormalizeResponse(normalized=result)
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except Exception as e:
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logger.warning(f"Thai normalize failed, returning raw text: {e}")
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return NormalizeResponse(normalized=req.text)
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if __name__ == "__main__":
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import uvicorn
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port = int(os.getenv("OCR_PORT", "8765"))
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uvicorn.run(app, host="0.0.0.0", port=port)
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