"""
图片处理结果质检模块

处理完成后，用视觉 AI 对比原图和结果图，判断是否符合客户需求。
评分 0-100，低于阈值则判定不合格，触发重试或人工跟进。
"""
import base64
import os
import time
import asyncio
from typing import Optional
from dotenv import load_dotenv

load_dotenv()

_QA_PASS_SCORE = int(os.getenv("QA_PASS_SCORE", "70"))  # 合格分数线，默认70

QA_PROMPT_TEMPLATE = """\
你是一名专业的图片处理质检员，需要评估处理结果是否满足要求。

【处理类型】{proc_type}
【客户需求/Gemini提示词】{gemini_prompt}
【原图描述】主体：{subject}，类型：{proc_type}，质量：{quality}

请对比左图（原图）和右图（处理结果），从以下维度打分（每项0-25分）：

1. 内容完整性：主体图案/内容是否完整保留，有无缺失、截断
2. 畸变去除：褶皱/透视变形/背景是否已被清除
3. 细节还原：颜色、线条、纹理等细节与原图的匹配程度
4. 输出干净度：背景是否干净，有无多余内容、AI幻觉、模糊块

输出格式（严格按照此格式，每行一个字段）：
完整性: <0-25>
畸变: <0-25>
细节: <0-25>
干净: <0-25>
总分: <0-100>
结论: <pass|fail>
问题: <简述主要问题，不超过30字，无问题填"无">
建议: <如果fail，给出重试改进建议，不超过40字，pass则填"无">
"""


class ImageQA:
    """处理结果质检器"""

    def __init__(self):
        self.api_key  = os.getenv("OPENAI_API_KEY")
        self.base_url = os.getenv("OPENAI_BASE_URL", "https://open.bigmodel.cn/api/paas/v4")
        self.model    = os.getenv("VISION_MODEL", "glm-4v-flash")
        self.pass_score = _QA_PASS_SCORE

    def _to_base64(self, path: str) -> Optional[str]:
        try:
            with open(path, "rb") as f:
                return base64.b64encode(f.read()).decode("utf-8")
        except Exception as e:
            print(f"[ImageQA] 读取图片失败 {path}: {e}")
            return None

    def _parse(self, text: str) -> dict:
        def p(key):
            for line in text.splitlines():
                line = line.strip()
                for k in [f"{key}:", f"{key}："]:
                    if line.startswith(k):
                        return line[len(k):].strip()
            return ""

        try:
            score = int(p("总分"))
        except ValueError:
            score = 0

        conclusion = p("结论").lower()
        if conclusion not in ("pass", "fail"):
            conclusion = "pass" if score >= self.pass_score else "fail"

        return {
            "score":       score,
            "pass":        conclusion == "pass",
            "issue":       p("问题"),
            "suggestion":  p("建议"),
            "detail": {
                "completeness": p("完整性"),
                "distortion":   p("畸变"),
                "detail":       p("细节"),
                "clean":        p("干净"),
            },
            "raw": text,
        }

    async def check(
        self,
        original_path: str,
        result_path: str,
        proc_type: str = "",
        subject: str = "",
        quality: str = "",
        gemini_prompt: str = "",
    ) -> dict:
        """
        质检处理结果。

        Args:
            original_path: 原图本地路径
            result_path:   处理结果本地路径
            proc_type:     处理类型（印花提取 / 高清修复等）
            subject:       主体描述
            quality:       原图质量
            gemini_prompt: 传给 Gemini 的提示词（体现客户需求）

        Returns:
            {
                "score": int,       # 0-100
                "pass": bool,       # 是否合格
                "issue": str,       # 主要问题
                "suggestion": str,  # 重试改进建议
                "detail": dict,     # 各维度分数
            }
        """
        if not self.api_key:
            print("[ImageQA] 未配置 API Key，跳过质检，默认通过")
            return {"score": 80, "pass": True, "issue": "无", "suggestion": "无", "detail": {}}

        orig_b64   = self._to_base64(original_path)
        result_b64 = self._to_base64(result_path)
        if not orig_b64 or not result_b64:
            print("[ImageQA] 图片读取失败，跳过质检")
            return {"score": 75, "pass": True, "issue": "质检图片读取失败", "suggestion": "无", "detail": {}}

        prompt = QA_PROMPT_TEMPLATE.format(
            proc_type=proc_type or "图片处理",
            subject=subject or "未知",
            quality=quality or "未知",
            gemini_prompt=gemini_prompt or "按标准处理",
        )

        start = time.monotonic()
        try:
            from openai import AsyncOpenAI
            client = AsyncOpenAI(base_url=self.base_url, api_key=self.api_key)

            response = await client.responses.create(
                model=self.model,
                input=[
                    {
                        "role": "user",
                        "content": [
                            {
                                "type": "input_image",
                                "image_url": f"data:image/jpeg;base64,{orig_b64}",
                            },
                            {
                                "type": "input_image",
                                "image_url": f"data:image/jpeg;base64,{result_b64}",
                            },
                            {
                                "type": "input_text",
                                "text": prompt,
                            },
                        ],
                    }
                ],
            )
            content = response.output_text
            elapsed = time.monotonic() - start
            result = self._parse(content)
            result["elapsed"] = round(elapsed, 1)

            status = "✓ 合格" if result["pass"] else "✗ 不合格"
            print(f"[ImageQA] {status} | 得分: {result['score']}/100 | 问题: {result['issue']} | 耗时: {elapsed:.1f}s")
            if not result["pass"]:
                print(f"[ImageQA] 改进建议: {result['suggestion']}")
            try:
                from utils.api_cost_tracker import record
                record("gemini_vision", count=1)
            except Exception:
                pass
            return result

        except Exception as e:
            elapsed = time.monotonic() - start
            print(f"[ImageQA] 质检失败 ({elapsed:.1f}s): {e}")
            return {"score": 75, "pass": True, "issue": f"质检异常: {e}", "suggestion": "无", "detail": {}}


# 全局实例
image_qa = ImageQA()