from abc import ABC, abstractmethod
from dataclasses import dataclass
from typing import List

import numpy as np

from .defect_classes import max_severity


@dataclass
class DefectBox:
    class_id: int
    class_name: str
    class_name_zh: str
    confidence: float
    severity: str          # fatal / major / minor / rework / none
    box_xyxy: List[float]


@dataclass
class DetectResult:
    defects: List[DefectBox]
    duration_ms: float
    image_width: int
    image_height: int
    model_version: str

    @property
    def defect_count(self) -> int:
        return len(self.defects)

    @property
    def max_severity(self) -> str:
        return max_severity([d.severity for d in self.defects]) if self.defects else "none"

    @property
    def avg_confidence(self) -> float:
        if not self.defects:
            return 0.0
        return round(sum(d.confidence for d in self.defects) / len(self.defects), 4)

    @property
    def verdict(self) -> str:
        return "fail" if self.defects else "pass"


class BaseInferenceEngine(ABC):
    @abstractmethod
    def detect(self, image: np.ndarray, conf: float = 0.45) -> DetectResult:
        ...

    @abstractmethod
    def warmup(self) -> None:
        ...

    @property
    @abstractmethod
    def model_version(self) -> str:
        ...