# -*- coding: utf-8 -*-
"""
AI 分析引擎：DeepSeek 深度研报 + RAG 增强
- 检索：股票相关新闻（向量语义）+ 公司概况 + 机构动向/基金持仓（结构化）
- 生成：结构化工研报（公司概况/基本面/技术面/消息面/机构动向/风险提示/操作建议）
"""
import logging
import threading
import time
import requests

from config import (LLM_API_KEY, LLM_BASE_URL, LLM_MAX_TOKENS, LLM_MODEL,
                    LLM_TEMPERATURE, LLM_TIMEOUT, CHROMA_NEWS_COLLECTION,
                    CHROMA_PROFILE_COLLECTION)
from database import query, query_one, execute
from rag.vector_store import query_vectors

log = logging.getLogger("analyst")

_jobs = {}          # code -> {status, report, error, ts}
_jobs_lock = threading.Lock()


# ------------------------------------------------------------------ LLM
def llm_chat(messages, max_tokens=None, temperature=None, timeout=None):
    """调用 DeepSeek（OpenAI 兼容）。返回最终 content（忽略推理过程）
    模型/Key/地址从设置区读取（settings 覆盖 config 默认）"""
    from settings import llm_config
    cfg = llm_config()
    resp = requests.post(
        f"{cfg['base_url']}/chat/completions",
        headers={"Authorization": f"Bearer {cfg['api_key']}"},
        json={
            "model": cfg["model"],
            "messages": messages,
            "max_tokens": max_tokens or cfg["max_tokens"],
            "temperature": cfg["temperature"] if temperature is None else temperature,
            "stream": False,
        },
        timeout=timeout or cfg["timeout"],
    )
    resp.raise_for_status()
    data = resp.json()
    try:
        return data["choices"][0]["message"].get("content") or ""
    except (KeyError, IndexError):
        return ""


# ------------------------------------------------------------------ RAG 检索
def _rag_news(code, stock_name, query_text, top_k=6):
    """检索个股相关新闻（向量语义，按 code 过滤）"""
    try:
        where = {"code": code}
        hits = query_vectors(query_text, n_results=top_k, where=where,
                             name=CHROMA_NEWS_COLLECTION)
        out = []
        for h in hits:
            m = h.get("metadata", {})
            out.append({
                "title": m.get("title", ""),
                "date": m.get("date", ""),
                "sentiment": m.get("sentiment", 0),
                "text": h.get("document", "")[:400],
                "distance": round(h.get("distance", 0), 3),
            })
        return out
    except Exception as e:
        log.warning("RAG news fail: %s", e)
        return []


def _rag_profile(code):
    try:
        hits = query_vectors("公司主营业务与基本面", n_results=1,
                             where={"code": code}, name=CHROMA_PROFILE_COLLECTION)
        if hits:
            return hits[0].get("document", "")
    except Exception:
        pass
    return ""


def _inst_summary(code):
    """机构评级 + 基金持仓摘要（结构化）"""
    ratings = query(
        "SELECT inst_name, rating, target_price, rating_date, prev_rating "
        "FROM inst_ratings WHERE stock_code=? ORDER BY rating_date DESC LIMIT 6", (code,))
    holdings = query(
        "SELECT inst_name, quarter, hold_value, change_pct FROM fund_holdings "
        "WHERE stock_code=? ORDER BY quarter DESC, hold_value DESC LIMIT 6", (code,))
    return ratings, holdings


# ------------------------------------------------------------------ 报告生成
def _fmt_indicators(ind):
    if not ind:
        return "（暂无技术数据）"
    lines = [
        f"- 最新价 {ind.get('close')}，当日 {ind.get('change_pct', 0):+.2f}%",
        f"- MA5={ind.get('ma5')} / MA10={ind.get('ma10')} / MA20={ind.get('ma20')} / MA60={ind.get('ma60')}",
        f"- RSI(14)={ind.get('rsi')}，KDJ K/D/J={ind.get('kdj_k')}/{ind.get('kdj_d')}/{ind.get('kdj_j')}",
        f"- MACD DIF={ind.get('dif')} / DEA={ind.get('dea')} / 柱={ind.get('macd')}",
        f"- 量比 {ind.get('vol_ratio')}，5日涨幅 {ind.get('chg_5d', 0):+.2f}%，20日涨幅 {ind.get('chg_20d', 0):+.2f}%",
        f"- 近120日区间 {ind.get('low_52w')} ~ {ind.get('high_52w')}，20日波动率 {ind.get('volatility')}%",
    ]
    return "\n".join(lines)


def _build_prompt(stock, ind, news_hits, profile, ratings, holdings, score, focus):
    rated = "、".join(f"{r['inst_name']}({r['rating']},目标{r['target_price']})" for r in ratings) or "暂无"
    held = "；".join(f"{h['inst_name']} {h['quarter']}持仓{h['hold_value']:.0f}万 环比{h['change_pct']:+.1f}%" for h in holdings) or "暂无"
    news_text = "\n\n".join(
        f"【{n['date']}｜{n['title']}】(情感{n['sentiment']:+.2f})\n{n['text']}" for n in news_hits
    ) or "（检索到相关资讯较少）"

    return f"""你是资深A股投顾，请基于下方【资料】对股票 {stock['name']}({stock['code']}) 输出一份结构化工研报。

【资料】
公司概况：
{profile or stock.get('description', '暂无')}

技术面：
{_fmt_indicators(ind)}

综合评分：{score.get('total')} 分（评级：{score.get('rating')}），分项：趋势{score.get('trend')}/动量{score.get('momentum')}/技术{score.get('technical')}/量能{score.get('volume')}/消息{score.get('news')}/机构{score.get('institutional')}

机构评级：{rated}
基金持仓：{held}

相关资讯（RAG 语义检索）：
{news_text}

用户关注点：{focus or '整体投资价值'}

【输出要求】用 Markdown 输出，结构如下：
## 一、公司概况与基本面
## 二、技术面解读
## 三、消息面与市场情绪
## 四、机构动向
## 五、风险提示
## 六、操作建议（给出 目标区间 / 支撑位 / 压力位，说明短线与中线思路）
注意：内容需严格基于上述资料，数据为模拟数据，结尾加一句「以上内容基于模拟数据生成，仅供系统演示，不构成投资建议」。"""


def generate_report_sync(code, focus=""):
    """同步生成报告（后台线程调用），并记录历史 + 数据源"""
    import json
    stock = query_one("SELECT * FROM stocks WHERE code=?", (code,))
    if not stock:
        return {"error": "股票不存在"}
    ind = _indicators_for(code)
    score = _score_for(code, ind)
    hits = _rag_news(code, stock["name"], f"{stock['name']} {focus or '投资价值 业绩 利好利空'} {ind.get('close','')}")
    profile = _rag_profile(code)
    ratings, holdings = _inst_summary(code)
    prompt = _build_prompt(stock, ind, hits, profile, ratings, holdings, score, focus)
    # 记录大模型参考的数据源（供详情页展示）
    sources = {
        "focus": focus,
        "score": score,
        "indicators": _fmt_indicators(ind),
        "profile": profile or stock.get("description", ""),
        "news": hits,
        "ratings": ratings,
        "holdings": holdings,
        "prompt": prompt,
    }
    try:
        report = llm_chat([
            {"role": "system", "content": "你是一名严谨专业的A股投资顾问，输出结构化、简洁、可执行的研报。"},
            {"role": "user", "content": prompt},
        ])
        report = report.strip()
        if not report:
            raise RuntimeError("LLM 返回为空")
        execute("INSERT OR REPLACE INTO analysis_cache(code, report, created_at) VALUES(?,?,datetime('now','localtime'))",
                (code, report))
        execute(
            "INSERT INTO analysis_history(code, stock_name, focus, report, sources, created_at) "
            "VALUES(?,?,?,?,?,datetime('now','localtime'))",
            (code, stock["name"], focus, report, json.dumps(sources, ensure_ascii=False)))
        return {"report": report, "ts": time.time()}
    except Exception as e:
        log.exception("gen report fail")
        return {"error": str(e)}


def _indicators_for(code):
    """从 DB 读取日线并算指标（避免循环依赖 app）"""
    from engine.indicators import compute_indicators
    rows = query("SELECT date,open,high,low,close,volume FROM stock_daily WHERE code=? ORDER BY date ASC", (code,))
    return compute_indicators(rows)


def _score_for(code, ind):
    from engine.scoring import score_stock
    # 新闻情感（与 app 端口径一致：精确/前缀/后缀三种匹配）
    n = query_one(
        "SELECT AVG(sentiment) AS s FROM news WHERE (related_stocks=? OR related_stocks LIKE ? OR related_stocks LIKE ?) "
        "AND publish_date >= date('now','-7 day')",
        (code, f"%,{code}", f"{code},%"))
    news_score = n["s"] if n and n["s"] is not None else 0.0
    # 机构热度
    st = query_one(
        "SELECT COUNT(*) AS c FROM inst_ratings WHERE stock_code=? AND rating IN ('买入','增持') "
        "AND rating_date >= date('now','-30 day')", (code,))
    inst_count = st["c"] if st else 0
    inst_score = min(1.0, inst_count / 4.0)
    return score_stock(ind, news_score, inst_score)


# ------------------------------------------------------------------ 异步任务
def submit_report(code, focus=""):
    """提交后台生成任务，立即返回"""
    with _jobs_lock:
        if _jobs.get(code, {}).get("status") == "running":
            return {"status": "running"}
        _jobs[code] = {"status": "running", "report": None, "error": None, "ts": time.time()}
    threading.Thread(target=_run_job, args=(code, focus), daemon=True).start()
    return {"status": "running"}


def _run_job(code, focus):
    try:
        res = generate_report_sync(code, focus)
        with _jobs_lock:
            if res.get("error"):
                _jobs[code] = {"status": "error", "error": res["error"], "ts": time.time()}
            else:
                _jobs[code] = {"status": "done", "report": res["report"], "ts": time.time()}
    except Exception as e:
        with _jobs_lock:
            _jobs[code] = {"status": "error", "error": str(e), "ts": time.time()}


def report_status(code):
    with _jobs_lock:
        return dict(_jobs.get(code, {}))


def get_cached_report(code):
    return query_one("SELECT report, created_at FROM analysis_cache WHERE code=?", (code,))


# ------------------------------------------------------------------ 历史记录
def list_history(code, limit=20):
    """某股票的历史 AI 分析记录（不含正文，只返回摘要）"""
    rows = query(
        "SELECT id, code, stock_name, focus, created_at, sources, LENGTH(report) AS len, "
        "SUBSTR(report, 1, 60) AS excerpt FROM analysis_history "
        "WHERE code=? ORDER BY id DESC LIMIT ?", (code, limit))
    out = []
    for r in rows:
        try:
            import json
            src = json.loads(r.get("sources") or "{}")
        except Exception:
            src = {}
        out.append({
            "id": r["id"], "code": r["code"], "stock_name": r["stock_name"],
            "focus": r["focus"], "created_at": r["created_at"],
            "chars": r["len"], "excerpt": (r["excerpt"] or "").strip(),
            "news_count": len(src.get("news") or []),
        })
    return out


def get_history(aid):
    """单条分析详情：正文 + 数据源 JSON"""
    import json
    row = query_one("SELECT * FROM analysis_history WHERE id=?", (aid,))
    if not row:
        return None
    try:
        src = json.loads(row.get("sources") or "{}")
    except Exception:
        src = {}
    return {
        "id": row["id"], "code": row["code"], "stock_name": row["stock_name"],
        "focus": row["focus"], "report": row["report"], "created_at": row["created_at"],
        "sources": src,
    }