"""简历诊断接口"""

import time

from fastapi import APIRouter, Depends
from pydantic import BaseModel, Field

from app.ai.resume_diagnoser.diagnoser import diagnose_all, generate_summary, polish_content
from app.core.auth import func_permission
from app.core.context import RequestContext
from app.core.database import get_db
from app.core.logger import log
from app.services.resume_diagnose_service import ResumeDiagnoseService, aggregate_results

router = APIRouter(prefix="/resume/diagnose", tags=["简历诊断"])


class DiagnoseParam(BaseModel):
    resume_id: int = Field(..., alias="resumeId")


@router.post("", summary="触发简历诊断")
async def diagnose_resume(param: DiagnoseParam, _: None = Depends(func_permission("resume_diag"))):
    """触发简历AI诊断，返回报告ID"""
    user_id = RequestContext.user_id.get()
    log.info(f"简历诊断开始, resumeId={param.resume_id}, userId={user_id}")

    # 1. 短事务：加载简历数据
    async for session in get_db():
        service = ResumeDiagnoseService(session)
        resume, tasks = await service.load_resume_data(param.resume_id, user_id)

    if not tasks:
        log.warning(f"简历无可诊断内容, resumeId={param.resume_id}")
        raise ValueError("简历没有可诊断的描述内容")

    log.info(f"加载简历数据完成, 待诊断任务数={len(tasks)}")

    # 2. 并行 AI 诊断（不持有数据库连接）
    ai_tasks = [{k: v for k, v in t.items() if not k.startswith("_")} for t in tasks]
    t0 = time.time()
    ai_results = await diagnose_all(ai_tasks)
    log.info(f"AI诊断全部完成, 耗时{time.time() - t0:.2f}s")

    # 3. 统计 + 评级（纯计算）
    stats = aggregate_results(tasks, ai_results)
    log.info(f"统计完成, grade={stats['grade']}, urgent={stats['urgent_total']}, important={stats['important_total']}, expression={stats['expression_total']}")

    # 4. AI 生成整体评价（不持有数据库连接）
    t1 = time.time()
    summary = await generate_summary(
        grade=stats["grade"], urgent_total=stats["urgent_total"],
        important_total=stats["important_total"], expression_total=stats["expression_total"],
        target_position=resume.target_position or "", all_findings=stats["all_findings"],
    )
    log.info(f"AI生成整体评价完成, 耗时{time.time() - t1:.2f}s")

    # 5. 短事务：纯写入
    async for session in get_db():
        service = ResumeDiagnoseService(session)
        report_id = await service.save_report(
            param.resume_id, user_id, stats["grade"], summary,
            stats["urgent_total"], stats["important_total"], stats["expression_total"],
            tasks, ai_results,
        )

    log.info(f"简历诊断完成, reportId={report_id}")
    return {"reportId": report_id}


@router.get("/{resume_id}", summary="查询最近一次诊断报告")
async def get_diagnosis_report(resume_id: int):
    """查询指定简历的最近一次诊断报告 + 所有诊断问题"""
    user_id = RequestContext.user_id.get()
    async for session in get_db():
        service = ResumeDiagnoseService(session)
        result = await service.get_latest_report(resume_id, user_id)
    return result


@router.put("/issue/{issue_id}/resolve", summary="标记问题已处理")
async def resolve_issue(issue_id: int):
    """标记诊断问题已处理"""
    user_id = RequestContext.user_id.get()
    async for session in get_db():
        service = ResumeDiagnoseService(session)
        await service.resolve_issue(issue_id, user_id)


class FeedbackParam(BaseModel):
    user_feedback: int = Field(..., alias="userFeedback")


@router.put("/issue/{issue_id}/feedback", summary="用户评价诊断问题")
async def feedback_issue(issue_id: int, param: FeedbackParam):
    """单独更新用户评价（1=符合 2=不符合）"""
    user_id = RequestContext.user_id.get()
    async for session in get_db():
        service = ResumeDiagnoseService(session)
        await service.update_feedback(issue_id, user_id, param.user_feedback)


class PolishParam(BaseModel):
    content: list[str] = Field(..., description="用户编辑后的文本段落数组")


@router.post("/issue/{issue_id}/polish", summary="AI润色用户编辑的文本")
async def polish_issue_content(issue_id: int, param: PolishParam):
    """基于诊断问题上下文，AI润色用户编辑后的文本"""
    user_id = RequestContext.user_id.get()

    async for session in get_db():
        service = ResumeDiagnoseService(session)
        ctx = await service.get_issue_for_polish(issue_id, user_id)

    result = await polish_content(
        module_type=ctx["module_label"],
        reference_content=ctx["optimized_content"],
        user_content=param.content,
        is_summary=ctx["is_summary"],
    )
    return {"content": result}