from fastapi import APIRouter, File, UploadFile, Depends, HTTPException
from fastapi.responses import FileResponse, JSONResponse
from fastapi.concurrency import run_in_threadpool
from enum import Enum
from typing import Optional, Dict, Any
from ..core.config import settings
from app.services.score_service import ScoreService
from app.services.stitch_fusion_service import StitchFusionService
import numpy as np
import cv2
import json
from app.core.logger import get_logger

logger = get_logger(__name__)

router = APIRouter()

score_names = settings.SCORE_TYPE
ScoreType = Enum("InferenceType", {name: name for name in score_names})

stitch_score_names = settings.STITCH_SCORE_TYPE
StitchScoreType = Enum("StitchScoreType", {name: name for name in stitch_score_names})


@router.post("/score_inference", summary="输入卡片类型(正反面, 缺陷类型), 是否为反射卡")
async def card_model_inference(
        score_type: ScoreType,
        is_reflect_card: bool = False,
        file: UploadFile = File(...)
):
    """
    接收一张卡片图片，使用指定类型的模型进行推理，并返回JSON结果。

    - **inference_type**: 要使用的模型类型（从下拉列表中选择）。
    - **file**: 要上传的图片文件。
    """
    service = ScoreService()
    image_bytes = await file.read()

    # 将字节数据转换为numpy数组
    np_arr = np.frombuffer(image_bytes, np.uint8)
    # 从numpy数组中解码图像
    img_bgr = cv2.imdecode(np_arr, cv2.IMREAD_COLOR)

    if img_bgr is None:
        raise ValueError("无法解码图像，请确保上传的是有效的图片格式 (JPG, PNG, etc.)")

    try:
        json_result = await run_in_threadpool(
            service.score_inference,
            score_type=score_type.value,
            is_reflect_card=is_reflect_card,
            img_bgr=img_bgr
        )
        return json_result
    except ValueError as e:
        raise HTTPException(status_code=400, detail=str(e))
    except Exception as e:
        raise HTTPException(status_code=500, detail=f"服务器内部错误: {e}")


@router.post("/score_recalculate", summary="输入卡片类型(正反面, 缺陷类型)",
             description="输入的json数据结构为 "
                         "{'result': {'center_result':..., 'defect_result':...}}")
async def score_recalculate(score_type: ScoreType, json_data: Dict[str, Any]):
    """
    接收分数推理后的结果, 然后重新根据json数据计算居中和缺陷等分数
    """
    service = ScoreService()
    try:
        json_result = await run_in_threadpool(
            service.recalculate_defect_score,
            score_type=score_type.value,
            json_data=json_data
        )
        return json_result
    except ValueError as e:
        raise HTTPException(status_code=400, detail=str(e))
    except Exception as e:
        raise HTTPException(status_code=500, detail=f"服务器内部错误: {e}")


@router.post("/stitch_score_inference",
             summary="StitchFusion 多模态拼接缺陷推理 (一次提交6张同侧图)",
             description="""
一次提交一组 6 张同侧图片, 经 StitchFusion 单 PT 模型大图切片推理后, 返回 labelme 风格 mask 列表。

字段对应 (按正反面分别上传, 一次只提交一侧):
- ring     -> 环光图   (front_ring / back_ring)
- gray     -> 灰度图   (front_gray / back_gray)
- stripe1  -> 调光1    (front_stripe1 / back_stripe1)
- stripe2  -> 调光2    (front_stripe2 / back_stripe2)
- stripe3  -> 调光3    (front_stripe3 / back_stripe3)
- stripe4  -> 调光4    (front_stripe4 / back_stripe4)
""")
async def stitch_score_inference(
        score_type: StitchScoreType,
        card_name: str = "",
        ring: UploadFile = File(..., description="环光图"),
        gray: UploadFile = File(..., description="灰度图"),
        stripe1: UploadFile = File(..., description="调光1"),
        stripe2: UploadFile = File(..., description="调光2"),
        stripe3: UploadFile = File(..., description="调光3"),
        stripe4: UploadFile = File(..., description="调光4"),
):
    """接收同一类型(正面或反面) 6 张图, 输出该组的缺陷 JSON。"""
    variant_files = {
        "ring": ring,
        "gray": gray,
        "stripe1": stripe1,
        "stripe2": stripe2,
        "stripe3": stripe3,
        "stripe4": stripe4,
    }
    variant_bytes: Dict[str, bytes] = {}
    for variant, upload in variant_files.items():
        try:
            variant_bytes[variant] = await upload.read()
        except Exception as e:
            raise HTTPException(status_code=400, detail=f"读取上传文件 {variant} 失败: {e}")
        if not variant_bytes[variant]:
            raise HTTPException(status_code=400, detail=f"上传文件 {variant} 内容为空")

    service = StitchFusionService()
    try:
        json_result = await run_in_threadpool(
            service.stitch_score_inference,
            score_type=score_type.value,
            card_name=card_name,
            variant_bytes=variant_bytes,
        )
        return json_result
    except FileNotFoundError as e:
        raise HTTPException(status_code=500, detail=str(e))
    except ValueError as e:
        raise HTTPException(status_code=400, detail=str(e))
    except Exception as e:
        logger.exception("stitch_score_inference 失败")
        raise HTTPException(status_code=500, detail=f"服务器内部错误: {e}")