from dataclasses import dataclass
from typing import Any, Protocol


@dataclass(frozen=True)
class EngineLine:
    text: str
    confidence: float
    bbox: tuple[int, int, int, int]


class OcrEngine(Protocol):
    def recognize(self, image: object) -> list[EngineLine]: ...


class PaddleOcrEngine:
    def __init__(self, pipeline: Any | None = None) -> None:
        self._injected = pipeline is not None
        if pipeline is None:
            from paddleocr import PaddleOCR

            pipeline = PaddleOCR(
                text_detection_model_name="PP-OCRv5_mobile_det",
                text_recognition_model_name="latin_PP-OCRv5_mobile_rec",
                use_doc_orientation_classify=False,
                use_doc_unwarping=False,
                use_textline_orientation=False,
                device="cpu",
            )
        self.pipeline = pipeline

    def recognize(self, image: object) -> list[EngineLine]:
        if not self._injected:
            import numpy

            image = numpy.asarray(image)
        lines: list[EngineLine] = []
        for prediction in self.pipeline.predict(image):
            payload = prediction.json() if callable(prediction.json) else prediction.json
            result = payload.get("res", payload)
            texts = result.get("rec_texts", [])
            scores = result.get("rec_scores", [])
            boxes = result.get("rec_boxes", [])
            if hasattr(boxes, "tolist"):
                boxes = boxes.tolist()
            for text, score, box in zip(texts, scores, boxes, strict=True):
                lines.append(EngineLine(str(text), float(score), tuple(int(value) for value in box)))
        return lines