surya-ocr/surya/endpoint/legacy.py

import io
import time

from fastapi import APIRouter, File, Request, UploadFile
from PIL import Image

from tools import (
    ocr,
    text_detection,
    layout_detection,
    table_recognition,
    extract_text_from_image,
)
from vllm_batcher import vllm_ocr_batcher
from vllm_tools import vllm_backend_info
from surya.endpoint.schemas import ApiResponse, Info
from surya.endpoint.service import (
    _dump_model_or_list,
    _error_response,
    _load_base64_image,
    _log_exception,
    _ocr_response_data,
    _success_response,
    logger,
)

router = APIRouter()


@router.post("/home")
def home():
    return "<h1>Welcome to SURYA OCR API!</h1>"


@router.post("/v1/api/ai/suya_ocr", include_in_schema=False, response_model=ApiResponse)
@router.post("/v1/api/ai/suya_ocr/", response_model=ApiResponse)
def run_ocr(p: Info, request: Request):
    try:
        pil_image, pil_image_highres = _load_base64_image(p)
        rec_img, pred, box_img = ocr(
            pil_image,
            pil_image_highres,
            p.skip_text_detection,
            p.recognize_math,
            with_bboxes=p.ocr_with_boxes,
        )
        return _success_response(_ocr_response_data(pred))
    except Exception as e:
        results = _error_response(e)
        _log_exception("suya_ocr", request, e, results)
        return results


@router.get("/v1/api/ai/suya_ocr_vllm/health", response_model=ApiResponse)
async def suya_ocr_vllm_health():
    return _success_response(vllm_backend_info())


@router.post("/v1/api/ai/suya_ocr_vllm", include_in_schema=False, response_model=ApiResponse)
@router.post("/v1/api/ai/suya_ocr_vllm/", response_model=ApiResponse)
def run_ocr_vllm(p: Info, request: Request):
    try:
        pil_image, pil_image_highres = _load_base64_image(p)
        start = time.perf_counter()
        _, pred, _ = vllm_ocr_batcher.submit(
            pil_image,
            pil_image_highres,
            skip_text_detection=p.skip_text_detection,
            recognize_math=p.recognize_math,
            with_bboxes=False,
            request_id=getattr(request.state, "request_id", "-"),
        )
        logger.info(
            "api_vllm_submit_wait_complete request_id=%s duration_ms=%.2f",
            getattr(request.state, "request_id", "-"),
            (time.perf_counter() - start) * 1000,
        )
        return _success_response(_ocr_response_data(pred))
    except Exception as e:
        results = _error_response(e)
        _log_exception("suya_ocr_vllm", request, e, results)
        return results


@router.post("/v1/api/ai/suya_text_det", include_in_schema=False, response_model=ApiResponse)
@router.post("/v1/api/ai/suya_text_det/", response_model=ApiResponse)
def run_text_det(p: Info, request: Request):
    try:
        pil_image, _ = _load_base64_image(p)
        det_img, text_pred = text_detection(pil_image)
        text_lines = text_pred.model_dump(exclude=["heatmap", "affinity_map"])
        return _success_response({"text_lines": text_lines})
    except Exception as e:
        results = _error_response(e)
        _log_exception("suya_text_det", request, e, results)
        return results


@router.post("/v1/api/ai/suya_layout_det", include_in_schema=False, response_model=ApiResponse)
@router.post("/v1/api/ai/suya_layout_det/", response_model=ApiResponse)
def run_layout_det(p: Info, request: Request):
    try:
        pil_image, _ = _load_base64_image(p)
        layout_img, pred = layout_detection(pil_image)
        text_lines = pred.model_dump(exclude=["segmentation_map"])
        return _success_response({"text_lines": text_lines})
    except Exception as e:
        results = _error_response(e)
        _log_exception("suya_layout_det", request, e, results)
        return results


@router.post("/v1/api/ai/suya_table_rec", include_in_schema=False, response_model=ApiResponse)
@router.post("/v1/api/ai/suya_table_rec/", response_model=ApiResponse)
def run_table_rec(p: Info, request: Request):
    try:
        pil_image, pil_image_highres = _load_base64_image(p)

        table_img, pred = table_recognition(
            pil_image, pil_image_highres, p.skip_table_detection
        )

        text_json = _dump_model_or_list(pred)
        text_lines = ""
        if isinstance(pred, list):
            text_lines = "\n".join(
                [item.html for item in pred if getattr(item, "html", None)]
            )
        elif hasattr(pred, "text_lines"):
            text_lines = "\n".join([p.text for p in pred.text_lines])

        return _success_response({"ocr_text_json": text_json, "text_lines": text_lines})
    except Exception as e:
        results = _error_response(e)
        _log_exception("suya_table_rec", request, e, results)
        return results


@router.post("/image2text", response_model=ApiResponse)
async def image_to_text(request: Request, file: UploadFile = File(...)):
    try:
        contents = await file.read()
        image = Image.open(io.BytesIO(contents))
        logger.info(
            "image2text_upload request_id=%s filename=%s content_type=%s size_bytes=%s image_size=%s",
            getattr(request.state, "request_id", "-"),
            file.filename,
            file.content_type,
            len(contents),
            image.size,
        )
        full_text = extract_text_from_image(image)
        return _success_response(full_text)
    except Exception as e:
        results = _error_response(e)
        _log_exception("image2text", request, e, results)
        return results