"""推荐结果 LLM 增强

扫描完成后异步调用 LLM，为每只推荐股票生成深度分析。
"""

import asyncio
import json
import logging
from app.llm.client import chat_completion
from app.llm.prompts import ENHANCE_SYSTEM_PROMPT, ENHANCE_USER_TEMPLATE
from app.config import settings

logger = logging.getLogger(__name__)


async def enhance_recommendations(result: dict) -> None:
    """对推荐结果进行 LLM 增强分析（fire-and-forget）"""
    if not settings.deepseek_api_key:
        return

    recommendations = result.get("recommendations", [])
    if not recommendations:
        return

    market_temp = result.get("market_temp")
    hot_sectors = result.get("hot_sectors", [])

    # 构建板块文本
    sectors_text = "\n".join(
        f"- {s.sector_name}: 涨幅{s.pct_change}%, 资金流入{s.capital_inflow}万, "
        f"涨停{s.limit_up_count}家, 热度{s.heat_score}分, 阶段={s.stage}"
        for s in hot_sectors[:5]
    ) if hot_sectors else "暂无板块数据"

    # 温度等级
    temp_val = market_temp.temperature if market_temp else 0
    if temp_val >= 60:
        temp_level = "积极"
    elif temp_val >= 30:
        temp_level = "谨慎"
    else:
        temp_level = "低迷"

    enhanced_count = 0
    for rec in recommendations:
        try:
            user_msg = ENHANCE_USER_TEMPLATE.format(
                temperature=market_temp.temperature if market_temp else "N/A",
                temp_level=temp_level,
                up_count=market_temp.up_count if market_temp else 0,
                down_count=market_temp.down_count if market_temp else 0,
                limit_up_count=market_temp.limit_up_count if market_temp else 0,
                max_streak=market_temp.max_streak if market_temp else 0,
                broken_rate=market_temp.broken_rate if market_temp else 0,
                sectors_text=sectors_text,
                name=rec.name,
                ts_code=rec.ts_code,
                sector=rec.sector,
                score=rec.score,
                level=rec.level,
                market_temp_score=rec.market_temp_score,
                sector_score=rec.sector_score,
                capital_score=rec.capital_score,
                technical_score=rec.technical_score,
                position_score=rec.position_score,
                valuation_score=rec.valuation_score,
                signal=rec.signal,
                entry_price=rec.entry_price or "N/A",
                target_price=rec.target_price or "N/A",
                stop_loss=rec.stop_loss or "N/A",
                reasons="；".join(rec.reasons) if rec.reasons else "无",
            )

            messages = [
                {"role": "system", "content": ENHANCE_SYSTEM_PROMPT},
                {"role": "user", "content": user_msg},
            ]

            resp = await chat_completion(messages)
            if resp and resp.content:
                rec.llm_analysis = resp.content.strip()
                enhanced_count += 1

        except asyncio.CancelledError:
            logger.warning(f"LLM 增强 {rec.ts_code} 被取消")
            break
        except Exception as e:
            logger.error(f"LLM 增强 {rec.ts_code} 失败: {e}")

    if enhanced_count > 0:
        # 更新数据库
        await _save_llm_analysis_to_db(recommendations)
        # 通过 WebSocket 通知前端
        await _broadcast_llm_ready(recommendations)

    logger.info(f"LLM 增强完成: {enhanced_count}/{len(recommendations)} 条")


async def _save_llm_analysis_to_db(recommendations: list) -> None:
    """将 LLM 分析结果更新到数据库"""
    try:
        from app.db.database import get_db
        from sqlalchemy import text

        async with get_db() as db:
            for rec in recommendations:
                if not rec.llm_analysis:
                    continue
                await db.execute(
                    text(
                        "UPDATE recommendations SET llm_analysis = :analysis "
                        "WHERE ts_code = :code AND date(created_at) = date('now', 'localtime') "
                        "AND scan_session = :session"
                    ),
                    {
                        "analysis": rec.llm_analysis,
                        "code": rec.ts_code,
                        "session": rec.scan_session,
                    },
                )
            await db.commit()
    except Exception as e:
        logger.error(f"保存 LLM 分析到数据库失败: {e}")


async def _broadcast_llm_ready(recommendations: list) -> None:
    """通过 WebSocket 广播 LLM 分析完成事件"""
    try:
        from app.api.websocket import broadcast_update
        await broadcast_update({
            "type": "llm_analysis_ready",
            "count": len([r for r in recommendations if r.llm_analysis]),
        })
    except Exception as e:
        logger.error(f"广播 LLM 分析完成失败: {e}")