#!/usr/bin/env python3
"""
P0 问题实时检测与分发

功能：
  1. 从 MySQL 读取最近一段时间的飞书群消息
  2. 复用 sync_feishu_feedback.py 的聚类 + 优先级判定逻辑
  3. 过滤已推送过的 P0 簇（去重）
  4. 仅推送新增 P0 到「小葵小葵」群

设计：
  - 每分钟由 crontab 调用一次
  - 查询最近 2 小时的消息，确保聚类质量
  - 用「簇签名」（sorted message_ids）做去重
  - 每天 10:00 清空去重状态（与全量分发错开）

用法:
  python3 detect_p0_realtime.py [--dry-run] [--lookback-minutes 120]
"""

import sys, os, re, json, urllib.request, argparse, hashlib
from datetime import datetime, timedelta
from pathlib import Path

# 将 sync_feishu_feedback.py 所在目录加入 sys.path，以便 import
SKILL_DIR = os.path.join(os.path.dirname(os.path.abspath(__file__)), "..", "skills", "feishu-feedback-sync", "scripts")
sys.path.insert(0, SKILL_DIR)

from sync_feishu_feedback import (
    get_db_connection, query_messages, sort_threads, get_tenant_token, content_similarity,
    DISPATCH_CHAT_ID, DISPATCH_CRED_DIR, P0_NOTIFY_USERS,
    MYSQL_HOST, MYSQL_PORT, MYSQL_USER, MYSQL_PASS, MYSQL_DB,
)
from priority_classifier import compute_final_priority

# === 配置 ===
STATE_FILE = os.path.join(os.path.dirname(os.path.abspath(__file__)), "..", "tmp", "p0_dispatched_state.json")
LOOKBACK_MINUTES = 120  # 默认回顾 2 小时的消息
CLUSTER_MIN_SIZE = 2     # 至少 2 条消息才算有效簇


def load_dispatched_state():
    """加载已推送的 P0 簇状态。兼容新旧格式。"""
    try:
        with open(STATE_FILE, "r") as f:
            state = json.load(f)
    except (FileNotFoundError, json.JSONDecodeError):
        state = {}
    cutoff = (datetime.now() - timedelta(hours=24)).isoformat()
    cleaned = {}
    for k, v in state.items():
        ts = v if isinstance(v, str) else v.get("time", "")
        if ts > cutoff:
            cleaned[k] = v
    return cleaned


def save_dispatched_state(state):
    """保存已推送状态"""
    os.makedirs(os.path.dirname(STATE_FILE), exist_ok=True)
    tmp = STATE_FILE + ".tmp"
    with open(tmp, "w") as f:
        json.dump(state, f, ensure_ascii=False, indent=2)
    os.rename(tmp, STATE_FILE)


def cluster_signature(cluster_msgs):
    """
    生成簇的唯一签名。
    用簇内所有 message_id 排序后拼接的 MD5，对簇成员变化敏感但适度容忍轻微变化。
    """
    ids = sorted(str(m[0]) for m in cluster_msgs)
    joined = ",".join(ids)
    return hashlib.md5(joined.encode()).hexdigest()


def cluster_content_fingerprint(cluster_msgs):
    """生成基于内容语义的簇指纹，用于跨扫描去重（不依赖消息ID集合）"""
    all_contents = []
    for m in cluster_msgs:
        c = str(m[3]).strip() if m[3] else ""
        if c and len(c) > 8:
            all_contents.append(c[:300])
    aggregated = " | ".join(all_contents[:5])
    senders = sorted(set(m[1] for m in cluster_msgs if m[1]))
    times = [m[6] for m in cluster_msgs if m[6]]
    hour = times[0][:13] if times else "unknown"
    return {
        "content": aggregated,
        "senders": senders,
        "hour": hour,
        "msg_count": len(cluster_msgs),
    }


def is_duplicate_p0(new_fp, dispatched_entries):
    """
    基于内容语义判断新 P0 是否与已推送 P0 重复。
    dispatched_entries: {sig: {"time": str, "fp": dict}}
    """
    for entry in dispatched_entries.values():
        old_fp = entry.get("fp")
        if not old_fp:
            continue
        same_hour = new_fp["hour"] == old_fp["hour"]
        sender_overlap = len(set(new_fp["senders"]) & set(old_fp["senders"]))
        if same_hour and sender_overlap >= 1:
            sim = content_similarity(new_fp["content"], old_fp["content"])
            if sim > 0.20:
                return True
        if sender_overlap >= 2:
            sim = content_similarity(new_fp["content"], old_fp["content"])
            if sim > 0.35:
                return True
    return False


def is_probably_p0(cluster_msgs):
    """
    快速判断一个簇是否是 P0 级别问题。
    返回 (is_p0: bool, priority_info: dict)
    """
    if len(cluster_msgs) < CLUSTER_MIN_SIZE:
        return False, None
    info = compute_final_priority(cluster_msgs)
    return info["priority"] == "P0", info


def _clean_summary(text):
    """清洗摘要文本，提取核心问题描述（处理转发消息、内部讨论等噪音）。"""
    # 去掉 [聊天记录] 等转发标记
    text = re.sub(r'^\[聊天记录\]\s*', '', text)
    # 去掉完整XML块
    text = re.sub(r'<msg>.*?</msg>', '', text, flags=re.DOTALL)
    # 去掉所有XML标签（含不完整的）
    text = re.sub(r'</?[a-zA-Z][a-zA-Z0-9]*(?:\s[^>]*)?>?', '', text)
    # 去掉残留的XML属性片段
    text = re.sub(r'\b[a-zA-Z]+="[^"]*"', '', text)
    # 截断 "↳ 回复 xxx:" 及之后的所有内容（内部讨论引用，非用户原始反馈）
    text = re.split(r'↳\s*回复', text)[0].strip()
    # 去掉媒体标记
    text = re.sub(r'\[视频\]|\[图片\]|\[语音\]|\[文件\]|\[表情\]', '', text)
    # 拆分发送人标记（emoji/符号 + 名字 + emoji/符号 + :），取最后一条用户消息
    sender_pat = r'(?:[^\u4e00-\u9fff\w\d\s]+\s*)+[\u4e00-\u9fff\s]{1,20}\s*(?:[^\u4e00-\u9fff\w\d\s]+\s*)*:\s*'
    parts = re.split(sender_pat, text)
    meaningful = [p.strip() for p in parts if p.strip() and len(p.strip()) > 3]
    if meaningful:
        text = meaningful[-1]
    # 去掉手机号/用户ID
    text = re.sub(r'(?:^|(?<=[^\d]))1[3-9]\d{9}(?=[^\d]|$)', '', text)
    # 去掉话术后缀
    text = re.sub(r'[,，]?\s*(老师|辛苦|麻烦|帮忙)\s*(看下|看一下|看看|看)[。！!]*$', '', text)
    text = re.sub(r'[,，]?\s*@\S+\s*', '', text)
    # 去掉残留的数字+XML碎片
    text = re.sub(r'\d+[a-zA-Z<>/]+$', '', text)
    # 清理多余空格和标点
    text = re.sub(r'\s+', ' ', text).strip()
    text = re.sub(r'^[,，\s]+|[,，\s]+$', '', text)

    # === 内部讨论话术改写：疑问句 → 陈述句 ===
    # 去掉"这个反馈可以跟用户确认下..."等讨论话术前缀
    text = re.sub(r'^(?:这个|该)?反馈可以(?:跟|和)用户确认下?', '', text)
    text = re.sub(r'^(?:这个|该)?问题可以(?:跟|和)用户确认下?', '', text)
    text = re.sub(r'^(?:这个|该)?(?:反馈|问题)?(?:可以)?(?:跟|和)用户确认下?', '', text)
    text = re.sub(r'是在进行什么操作时', '', text)
    text = re.sub(r'具体是什么操作时', '', text)
    text = re.sub(r'是在什么情况下', '', text)
    # 去掉句末疑问词
    text = re.sub(r'[？?！!。．]+$', '', text)
    text = re.sub(r'[吗呢吧啊呀]$', '', text)
    # 碎片化症状词 → 补全为陈述句
    symptom_map = {
        r'^闪退的?$': '用户反馈闪退，需确认操作场景',
        r'^崩溃的?$': '用户反馈崩溃，需确认操作场景',
        r'^卡退的?$': '用户反馈卡退，需确认操作场景',
        r'^卡死的?$': '用户反馈卡死，需确认操作场景',
        r'^打不开的?$': '用户反馈打不开，需确认操作场景',
    }
    for pat, repl in symptom_map.items():
        if re.match(pat, text):
            text = repl
            break
    # 再次清理
    text = re.sub(r'\s+', ' ', text).strip()
    text = re.sub(r'^[,，\s]+|[,，\s]+$', '', text)
    return text


def _pick_best_summary(cluster_msgs):
    """从簇中选出最能代表 P0 问题的摘要消息。
    优先选择匹配 P0 关键词且非转发/非内部讨论的消息。"""
    from priority_classifier import P0_KEYWORDS

    # 收集所有 P0 关键词正则
    p0_patterns = []
    for cat_pats in P0_KEYWORDS.values():
        p0_patterns.append(cat_pats)
    combined_p0 = re.compile('|'.join(p0_patterns), re.IGNORECASE)

    # 判断是否为转发消息/内部讨论（包含 [聊天记录]、↳ 回复 等标记）
    def _is_forward_or_discussion(t):
        return bool(re.search(r'^\[聊天记录\]|↳\s*回复|<msg>|<appmsg', t))

    best = None
    best_noise = None  # 兜底：第一条非空消息（即使有噪音）
    for m in cluster_msgs:
        t = str(m[3]).strip() if m[3] else ""
        if not t or len(t) <= 3:
            continue
        if best_noise is None:
            best_noise = t
        # 跳过转发消息和内部讨论，优先找用户原始反馈
        if _is_forward_or_discussion(t):
            continue
        if best is None:
            best = t
        if combined_p0.search(t):
            return _clean_summary(t)[:150]

    # 如果没有非转发的消息，从有噪音的消息中找 P0 关键词
    if best is None:
        for m in cluster_msgs:
            t = str(m[3]).strip() if m[3] else ""
            if not t or len(t) <= 3:
                continue
            if combined_p0.search(t):
                return _clean_summary(t)[:150]
        best = best_noise

    return _clean_summary(best or "")[:150]


def generate_p0_alert_text(cluster_msgs, priority_info):
    """
    生成 P0 问题的简短告警文本（精简版，不含完整文档链接）。
    """
    root_sender = cluster_msgs[0][1]
    root_time = cluster_msgs[0][6]
    root_text = _pick_best_summary(cluster_msgs)

    lines = [
        f"🚨 P0 实时告警",
        f"",
        f"问题描述： {root_text}",
        f"报告人： {root_sender}",
        f"报告时间： {root_time}",
        f"判定依据： {priority_info.get('reasoning', 'P0')}",
        f"修复时限： {priority_info.get('deadline', '2小时内')}",
    ]

    return "\n".join(lines)


def dispatch_p0_alert(alert_text):
    """
    将 P0 告警发送到「小葵小葵」群，@ 指定人员。
    使用飞书 post 富文本格式。
    """
    import urllib.request
    token = get_tenant_token(cred_dir=DISPATCH_CRED_DIR)

    # 构建富文本内容
    content_parts = []
    for line in alert_text.split("\n"):
        if line.strip():
            content_parts.append([{"tag": "text", "text": line + "\n"}])

    # 追加 @ 通知
    if P0_NOTIFY_USERS:
        at_line = [{"tag": "text", "text": "\n⚠️ 请关注: "}]
        for uid in P0_NOTIFY_USERS:
            at_line.append({"tag": "at", "user_id": uid})
            at_line.append({"tag": "text", "text": " "})
        content_parts.append(at_line)

    post_content = json.dumps({
        "zh_cn": {
            "title": "🚨 P0 问题实时告警",
            "content": content_parts
        }
    }, ensure_ascii=False)

    body = json.dumps({
        "receive_id": DISPATCH_CHAT_ID,
        "msg_type": "post",
        "content": post_content
    }, ensure_ascii=False).encode()

    req = urllib.request.Request(
        "https://open.feishu.cn/open-apis/im/v1/messages?receive_id_type=chat_id",
        data=body,
        headers={
            "Authorization": f"Bearer {token}",
            "Content-Type": "application/json"
        },
        method="POST"
    )
    resp = urllib.request.urlopen(req, timeout=10)
    d = json.loads(resp.read())
    if d.get("code") == 0:
        return True
    else:
        print(f"   ⚠️ 实时分发失败: {d.get('msg', '')[:100]}")
        return False


def should_clear_state():
    """检查是否到了每天清空状态的时间（10:00-10:01 之间清空，配合全量分发）"""
    now = datetime.now()
    return now.hour == 10 and now.minute <= 1


def main():
    parser = argparse.ArgumentParser(description="P0 问题实时检测与分发")
    parser.add_argument("--dry-run", action="store_true", help="仅打印不推送")
    parser.add_argument("--lookback-minutes", type=int, default=LOOKBACK_MINUTES,
                        help=f"查询最近 N 分钟的消息（默认 {LOOKBACK_MINUTES}）")
    args = parser.parse_args()

    # 清空逻辑：每天 10:00-10:01 之间清空去重状态，让全量分发正常进行
    if should_clear_state():
        print("[P0-detect] 10:00 清空去重状态，配合全量分发")
        save_dispatched_state({})
        # 10:00 的全量流程会处理，这里直接退出避免重复
        print("[P0-detect] 全量分发时段，跳过实时检测")
        return

    print(f"[P0-detect] 扫描最近 {args.lookback_minutes} 分钟消息...")
    lookback_start = (datetime.now() - timedelta(minutes=args.lookback_minutes)).strftime("%Y-%m-%d %H:%M:%S")
    now_str = datetime.now().strftime("%Y-%m-%d %H:%M:%S")

    # 查询最近消息（直接用 SQL，避免 query_messages 的按天查询限制）
    import pymysql
    conn = pymysql.connect(
        host=MYSQL_HOST, port=MYSQL_PORT,
        user=MYSQL_USER, password=MYSQL_PASS,
        database=MYSQL_DB, charset="utf8mb4"
    )
    cursor = conn.cursor()
    cursor.execute("""
        SELECT message_id, sender_name, msg_type, content, media_url, quote_message_id,
               DATE_FORMAT(msg_time, '%%Y-%%m-%%d %%H:%%i:%%s') as msg_time, msg_timestamp
        FROM lark_group_message
        WHERE msg_time >= %s AND msg_time <= %s
        ORDER BY msg_time ASC
    """, (lookback_start, now_str))
    rows = cursor.fetchall()
    conn.close()

    print(f"[P0-detect] 查询到 {len(rows)} 条消息")

    if len(rows) < 2:
        print("[P0-detect] 消息不足，退出")
        return

    # 聚类
    sorted_msgs, clusters, cluster_order = sort_threads(rows)
    print(f"[P0-detect] 聚类完成：{len(clusters)} 个簇")

    # 加载已推送状态
    state = load_dispatched_state()
    print(f"[P0-detect] 已记录 {len(state)} 个已推送簇")

    # 遍历簇，找出 P0 且未推送的
    new_p0_count = 0
    for cid in cluster_order:
        cmsgs = clusters[cid]
        is_p0, info = is_probably_p0(cmsgs)
        if not is_p0:
            continue

        sig = cluster_signature(cmsgs)
        if sig in state:
            print(f"[P0-detect] 已推送过(精确匹配)，跳过: sig={sig[:8]}...")
            continue

        # 内容语义去重
        fp = cluster_content_fingerprint(cmsgs)
        if is_duplicate_p0(fp, state):
            print(f"[P0-detect] 已推送过(内容匹配)，跳过: senders={fp['senders'][:2]}... hour={fp['hour']}")
            continue

        print(f"[P0-detect] 🚨 发现新 P0! sig={sig[:8]}... {len(cmsgs)}条消息")

        if args.dry_run:
            alert = generate_p0_alert_text(cmsgs, info)
            print(f"[DRY-RUN] 将发送：\n{alert}")
            state[sig] = {"time": datetime.now().isoformat(), "fp": fp}
            new_p0_count += 1
        else:
            alert = generate_p0_alert_text(cmsgs, info)
            success = dispatch_p0_alert(alert)
            if success:
                print(f"[P0-detect] ✅ P0 已实时推送")
                state[sig] = {"time": datetime.now().isoformat(), "fp": fp}
                new_p0_count += 1
            else:
                print(f"[P0-detect] ❌ 推送失败")

    if new_p0_count > 0:
        save_dispatched_state(state)
        print(f"[P0-detect] 共推送 {new_p0_count} 个新 P0")

    print("[P0-detect] 完成")
    return


if __name__ == "__main__":
    main()