"""STEP 02 — Simulation Panel CRUD + multi-source question intake.

Question bank for the AI quality audit. Sources:
  manual    手工添加
  imported  批量导入的真实用户问题
  resolved  从"已答好"的 question_traces 采纳的回归用例
  ai_gen    AI 生成的覆盖性问题
"""
from fastapi import APIRouter, HTTPException

from app.auth import CurrentUser
from app.config import settings
from app.contracts import SimulationQuestion, SimulationQuestionUpdate
from app.db import (
    list_simulation_questions,
    create_simulation_question,
    update_simulation_question,
    delete_simulation_question,
    bulk_create_simulation_questions,
    adopt_hit_question_traces,
)

router = APIRouter()
_T = settings.default_tenant
_P = settings.default_project


@router.get("/simulation-panel")
async def _list(_user: CurrentUser = None):
    return await list_simulation_questions(_T, _P)


@router.post("/simulation-panel")
async def _create(body: SimulationQuestion, _user: CurrentUser):
    data = body.model_dump()
    data["tenant_id"] = _T
    data["project_id"] = _P
    data["source"] = "manual"
    return await create_simulation_question(data)


@router.post("/simulation-panel/import")
async def _import(body: dict, _user: CurrentUser):
    """Bulk import real user questions — one per line, or a list."""
    raw = body.get("text") or body.get("questions") or ""
    texts = raw if isinstance(raw, list) else str(raw).splitlines()
    added = await bulk_create_simulation_questions(_T, _P, texts, "imported")
    return {"added": added}


@router.post("/simulation-panel/adopt-hits")
async def _adopt_hits(_user: CurrentUser):
    """Adopt well-answered questions as regression cases (source=resolved)."""
    added = await adopt_hit_question_traces(_T, _P)
    return {"added": added}


@router.post("/simulation-panel/ai-generate")
async def _ai_generate(body: dict, _user: CurrentUser):
    """Let the LLM propose representative coverage questions."""
    from app.llm_client import LlmClient

    llm = LlmClient.from_settings() if settings.llm_api_key else None
    if not llm or not llm.available():
        raise HTTPException(400, "未配置 LLM（Agent 设置里填好 API Key 后可用）")

    count = max(1, min(int(body.get("count", 10)), 30))
    topic = (body.get("topic") or "城市本地生活知识图谱（地点、区域、路线、夜间体验、标签）").strip()
    try:
        out = llm.chat_json(
            system=(
                "你是知识图谱质量稽查助手。请基于给定主题，生成有代表性、"
                "覆盖不同场景的真实用户中文问题，用于检验知识图谱数据的完善度。"
                '严格返回 JSON：{"questions": ["...", "..."]}'
            ),
            user=f"主题：{topic}。生成 {count} 个不同角度、不重复的问题。",
        )
        qs = out.get("questions") or []
        qs = [str(q).strip() for q in qs if str(q).strip()][:count]
    except Exception as e:
        raise HTTPException(400, f"AI 生成失败：{str(e)[:160]}")

    added = await bulk_create_simulation_questions(_T, _P, qs, "ai_gen")
    return {"added": added, "generated": len(qs)}


@router.patch("/simulation-panel/{q_id}")
async def _update(q_id: int, body: SimulationQuestionUpdate, _user: CurrentUser):
    row = await update_simulation_question(q_id, body.model_dump(exclude_none=True))
    if not row:
        raise HTTPException(404, "Simulation question not found")
    return row


@router.delete("/simulation-panel/{q_id}")
async def _delete(q_id: int, _user: CurrentUser):
    await delete_simulation_question(q_id)
    return {"ok": True}