doc_processer/app/api/v1/endpoints/image.py

"""Image OCR endpoint."""

import time
import uuid
import cv2
from io import BytesIO

from fastapi import APIRouter, Depends, HTTPException, Request, Response

from app.core.dependencies import (
    get_image_processor,
    get_layout_detector,
    get_ocr_service,
    get_mineru_ocr_service,
    get_glmocr_service,
)
from app.core.config import get_settings
from app.core.logging_config import get_logger, RequestIDAdapter
from app.schemas.image import ImageOCRRequest, ImageOCRResponse
from app.services.image_processor import ImageProcessor
from app.services.layout_detector import LayoutDetector
from app.services.ocr_service import OCRService, MineruOCRService, GLMOCRService

settings = get_settings()

router = APIRouter()
logger = get_logger()


@router.post("/ocr", response_model=ImageOCRResponse)
async def process_image_ocr(
    request: ImageOCRRequest,
    http_request: Request,
    response: Response,
    image_processor: ImageProcessor = Depends(get_image_processor),
    layout_detector: LayoutDetector = Depends(get_layout_detector),
    mineru_service: MineruOCRService = Depends(get_mineru_ocr_service),
    paddle_service: OCRService = Depends(get_ocr_service),
    glmocr_service: GLMOCRService = Depends(get_glmocr_service),
) -> ImageOCRResponse:
    """Process an image and extract content as LaTeX, Markdown, and MathML.

    The processing pipeline:
    1. Load and preprocess image (add 30% whitespace padding)
    2. Detect layout using DocLayout-YOLO
    3. Based on layout:
       - If plain text exists: use PP-DocLayoutV2 for mixed recognition
       - Otherwise: use PaddleOCR-VL with formula prompt
    4. Convert output to LaTeX, Markdown, and MathML formats

    Note: OMML conversion is not included due to performance overhead.
    Use the /convert/latex-to-omml endpoint to convert LaTeX to OMML separately.
    """
    # Get or generate request ID
    request_id = http_request.headers.get("x-request-id", str(uuid.uuid4()))
    response.headers["x-request-id"] = request_id

    # Create logger adapter with request_id
    log = RequestIDAdapter(logger, {"request_id": request_id})
    log.request_id = request_id

    try:
        log.info("Starting image OCR processing")

        # Preprocess image (load only, no padding yet)
        preprocess_start = time.time()
        image = image_processor.preprocess(
            image_url=request.image_url,
            image_base64=request.image_base64,
        )

        # Apply padding only for layout detection
        processed_image = image
        if image_processor and settings.is_padding:
            processed_image = image_processor.add_padding(image)

        preprocess_time = time.time() - preprocess_start
        log.debug(f"Image loading completed in {preprocess_time:.3f}s")

        # Layout detection (using padded image if padding is enabled)
        layout_start = time.time()
        layout_info = layout_detector.detect(processed_image)
        layout_time = time.time() - layout_start
        log.info(f"Layout detection completed in {layout_time:.3f}s")

        # OCR recognition (use original image without padding)
        ocr_start = time.time()
        if layout_info.MixedRecognition:
            recognition_method = "MixedRecognition (MinerU)"
            log.info(f"Using {recognition_method}")

            # Convert original image (without padding) to bytes
            success, encoded_image = cv2.imencode(".png", image)
            if not success:
                raise RuntimeError("Failed to encode image")

            image_bytes = BytesIO(encoded_image.tobytes())
            image_bytes.seek(0)  # Ensure position is at the beginning
            ocr_result = mineru_service.recognize(image_bytes)
        else:
            recognition_method = "FormulaOnly (GLMOCR)"
            log.info(f"Using {recognition_method}")

            # Try GLM-OCR first, fallback to MinerU if token limit exceeded
            try:
                ocr_result = glmocr_service.recognize(image)
            except Exception as e:
                error_msg = str(e)
                # Check if error is due to token limit (max_model_len exceeded)
                if "max_model_len" in error_msg or "decoder prompt" in error_msg or "BadRequestError" in error_msg:
                    log.warning(f"GLM-OCR failed due to token limit: {error_msg}")
                    log.info("Falling back to MinerU for recognition")
                    recognition_method = "FormulaOnly (MinerU fallback)"

                    # Convert original image to bytes for MinerU
                    success, encoded_image = cv2.imencode(".png", image)
                    if not success:
                        raise RuntimeError("Failed to encode image")

                    image_bytes = BytesIO(encoded_image.tobytes())
                    image_bytes.seek(0)
                    ocr_result = mineru_service.recognize(image_bytes)
                else:
                    # Re-raise other errors
                    raise
        ocr_time = time.time() - ocr_start

        total_time = time.time() - preprocess_start
        log.info(f"OCR processing completed - Method: {recognition_method}, " f"Layout time: {layout_time:.3f}s, OCR time: {ocr_time:.3f}s, " f"Total time: {total_time:.3f}s")

    except RuntimeError as e:
        log.error(f"OCR processing failed: {str(e)}", exc_info=True)
        raise HTTPException(status_code=503, detail=str(e))
    except Exception as e:
        log.error(f"Unexpected error during OCR processing: {str(e)}", exc_info=True)
        raise HTTPException(status_code=500, detail="Internal server error")

    return ImageOCRResponse(
        latex=ocr_result.get("latex", ""),
        markdown=ocr_result.get("markdown", ""),
        mathml=ocr_result.get("mathml", ""),
        mml=ocr_result.get("mml", ""),
    )