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68 lines
2.1 KiB
Python
68 lines
2.1 KiB
Python
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"""OCR LitServe endpoint — detect + rec via operator form field."""
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import logging
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import litserve as ls
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from deepdoc.server.adapters.ocr_adapter import OCRAdapter
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logger = logging.getLogger(__name__)
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class OCREndpoint(ls.LitAPI):
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"""OCR endpoint at /predict/ocr.
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Form field 'operator' (det or rec) selects the mode.
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Form field 'request' carries the JPEG image bytes.
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"""
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def __init__(self, model_dir: str):
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super().__init__()
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self.api_path = "/predict/ocr"
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self.model_dir = model_dir
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self.adapter: OCRAdapter | None = None
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def setup(self, device):
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self.adapter = OCRAdapter(model_dir=self.model_dir)
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self.adapter.load()
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logger.info("OCR model loaded")
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def decode_request(self, request):
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# Handle both old Starlette UploadFile and new Starlette FormData
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if hasattr(request, "file"):
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data = request.file.read()
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# Try to read operator from the underlying request context
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operator = getattr(self, "_request", None)
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if operator is not None:
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operator = operator.query_params.get("operator", "")
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else:
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operator = ""
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else:
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# FormData: get file and operator form fields
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data = request.get("request").file.read()
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op_val = request.get("operator")
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operator = str(op_val) if op_val else ""
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if not data:
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raise ValueError("Empty request body")
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if len(data) > 50 * 1024 * 1024:
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raise ValueError("Image too large")
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operator = operator.strip().lower()
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if operator not in ("det", "rec"):
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raise ValueError(
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f"Invalid or missing operator '{operator}' (must be 'det' or 'rec')"
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)
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return operator, data
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def predict(self, inputs: tuple):
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operator, image_data = inputs
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if operator == "det":
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return self.adapter.detect(image_data)
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else:
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return self.adapter.recognize(image_data)
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def encode_response(self, output):
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return output
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