"""
services/topic_engine.py
智能选题引擎 — 聚合热点数据 + 历史权重，推荐高潜力选题
"""
import logging
import os
import json
import re
from datetime import datetime, timedelta

logger = logging.getLogger("autobot")


class TopicEngine:
    """
    智能选题推荐引擎

    职责: 聚合热点探测结果与历史互动权重，为用户推荐高潜力选题。
    不直接访问 MCP / LLM，通过注入的 AnalyticsService 获取数据。
    """

    def __init__(self, analytics_service):
        """
        Args:
            analytics_service: AnalyticsService 实例，提供权重和笔记数据
        """
        self.analytics = analytics_service

    # ========== 核心: 多维度评分 ==========

    def score_topic(self, topic: str, hotspot_data: dict = None) -> dict:
        """
        为单个候选主题计算综合评分

        维度:
          - hotspot_score (0-40): 热点热度
          - weight_score  (0-30): 历史互动权重
          - scarcity_score(0-20): 内容稀缺度
          - timeliness_score(0-10): 时效性

        Args:
            topic: 候选主题文本
            hotspot_data: 可选的热点分析数据（包含 hot_topics, suggestions 等）

        Returns:
            dict with total_score, hotspot_score, weight_score, scarcity_score, timeliness_score
        """
        hotspot_score = self._calc_hotspot_score(topic, hotspot_data)
        weight_score = self._calc_weight_score(topic)
        scarcity_score = self._calc_scarcity_score(topic)
        timeliness_score = self._calc_timeliness_score(topic)

        total = hotspot_score + weight_score + scarcity_score + timeliness_score

        return {
            "total_score": total,
            "hotspot_score": hotspot_score,
            "weight_score": weight_score,
            "scarcity_score": scarcity_score,
            "timeliness_score": timeliness_score,
        }

    # ========== 推荐主题列表 ==========

    def recommend_topics(self, count: int = 5, hotspot_data: dict = None) -> list[dict]:
        """
        推荐排序后的选题列表

        逻辑:
          1. 收集候选主题 (热点 + 权重主题)
          2. 对每个主题评分
          3. 去重 (语义相近合并)
          4. 按总分降序取 top-N
          5. 为每个主题生成创作角度建议

        Args:
            count: 返回推荐数量 (默认 5)
            hotspot_data: 可选的热点分析数据

        Returns:
            list of dict, 每项包含:
              topic, score, reason, source, angles,
              score_detail (各维度分数)
        """
        candidates = self._collect_candidates(hotspot_data)

        if not candidates:
            logger.warning("选题引擎: 无候选主题可推荐")
            return []

        # 评分
        scored = []
        for topic, source in candidates:
            detail = self.score_topic(topic, hotspot_data)
            scored.append({
                "topic": topic,
                "score": detail["total_score"],
                "source": source,
                "score_detail": detail,
            })

        # 去重
        scored = self._deduplicate(scored)

        # 排序
        scored.sort(key=lambda x: x["score"], reverse=True)
        scored = scored[:count]

        # 生成 reason 和 angles
        for item in scored:
            item["reason"] = self._generate_reason(item)
            item["angles"] = self._generate_angles(item["topic"], item["source"])

        return scored

    # ========== 候选收集 ==========

    def _collect_candidates(self, hotspot_data: dict = None) -> list[tuple[str, str]]:
        """
        收集所有候选主题，返回 [(topic, source), ...]

        source: "hotspot" | "weight" | "trend"
        """
        candidates = []
        seen = set()

        # 1. 从热点数据收集
        if hotspot_data:
            for topic in hotspot_data.get("hot_topics", []):
                topic_clean = self._clean_topic(topic)
                if topic_clean and topic_clean not in seen:
                    candidates.append((topic_clean, "hotspot"))
                    seen.add(topic_clean)

            for suggestion in hotspot_data.get("suggestions", []):
                topic_clean = self._clean_topic(suggestion.get("topic", ""))
                if topic_clean and topic_clean not in seen:
                    candidates.append((topic_clean, "hotspot"))
                    seen.add(topic_clean)

        # 2. 从权重数据收集
        topic_weights = self.analytics._weights.get("topic_weights", {})
        for topic, info in topic_weights.items():
            topic_clean = self._clean_topic(topic)
            if topic_clean and topic_clean not in seen:
                candidates.append((topic_clean, "weight"))
                seen.add(topic_clean)

        # 3. 从分析历史提取趋势主题
        history = self.analytics._weights.get("analysis_history", [])
        for entry in history[-5:]:
            top_topic = entry.get("top_topic", "")
            if top_topic and top_topic not in seen:
                candidates.append((top_topic, "trend"))
                seen.add(top_topic)

        return candidates

    # ========== 评分子模块 ==========

    def _calc_hotspot_score(self, topic: str, hotspot_data: dict = None) -> int:
        """热点热度评分 (0-40)"""
        if not hotspot_data:
            return 0

        score = 0

        # 检查是否在热门主题中
        hot_topics = hotspot_data.get("hot_topics", [])
        for i, ht in enumerate(hot_topics):
            if self._topic_similar(topic, ht):
                # 排名越靠前分越高
                score = max(score, 40 - i * 5)
                break

        # 检查是否在推荐建议中
        suggestions = hotspot_data.get("suggestions", [])
        for suggestion in suggestions:
            if self._topic_similar(topic, suggestion.get("topic", "")):
                score = max(score, 30)
                break

        return min(40, score)

    def _calc_weight_score(self, topic: str) -> int:
        """历史互动权重评分 (0-30)"""
        topic_weights = self.analytics._weights.get("topic_weights", {})
        if not topic_weights:
            return 0

        # 精确匹配
        if topic in topic_weights:
            weight = topic_weights[topic].get("weight", 0)
            # weight 原始范围 0-100，映射到 0-30
            return min(30, int(weight * 0.3))

        # 模糊匹配
        best_score = 0
        for existing_topic, info in topic_weights.items():
            if self._topic_similar(topic, existing_topic):
                weight = info.get("weight", 0)
                best_score = max(best_score, min(30, int(weight * 0.3)))

        return best_score

    def _calc_scarcity_score(self, topic: str) -> int:
        """
        内容稀缺度评分 (0-20)

        近 7 天已发布 >= 2 篇的主题: scarcity_score <= 5
        """
        notes = self.analytics._analytics_data.get("notes", {})
        seven_days_ago = (datetime.now() - timedelta(days=7)).isoformat()

        recent_count = 0
        for nid, note in notes.items():
            collected = note.get("collected_at", "")
            if collected >= seven_days_ago:
                note_topic = note.get("topic", "")
                if self._topic_similar(topic, note_topic):
                    recent_count += 1

        if recent_count >= 2:
            return min(5, max(0, 5 - recent_count))  # 发的越多越低
        elif recent_count == 1:
            return 12  # 有一篇，中等稀缺
        else:
            return 20  # 完全空白，高稀缺

    def _calc_timeliness_score(self, topic: str) -> int:
        """
        时效性评分 (0-10)

        基于主题是否包含时效性关键词（季节、节日等）
        """
        now = datetime.now()
        month = now.month

        # 季节关键词
        season_keywords = {
            "春": [2, 3, 4, 5],
            "夏": [5, 6, 7, 8],
            "秋": [8, 9, 10, 11],
            "冬": [11, 12, 1, 2],
            "早春": [2, 3],
            "初夏": [5, 6],
            "初秋": [8, 9],
        }

        # 节日关键词
        festival_windows = {
            "情人节": (2, 10, 2, 18),
            "三八": (3, 1, 3, 12),
            "妇女节": (3, 1, 3, 12),
            "母亲节": (5, 5, 5, 15),
            "618": (6, 1, 6, 20),
            "七夕": (7, 20, 8, 15),
            "中秋": (9, 1, 9, 30),
            "国庆": (9, 25, 10, 10),
            "双十一": (10, 20, 11, 15),
            "双11": (10, 20, 11, 15),
            "双十二": (12, 1, 12, 15),
            "圣诞": (12, 15, 12, 28),
            "元旦": (12, 25, 1, 5),
            "年货": (1, 5, 2, 10),
            "春节": (1, 10, 2, 10),
            "开学": (8, 20, 9, 15),
        }

        score = 5  # 基础分

        # 季节匹配
        for keyword, months in season_keywords.items():
            if keyword in topic and month in months:
                score = max(score, 8)
                break

        # 节日窗口匹配
        for keyword, (m1, d1, m2, d2) in festival_windows.items():
            if keyword in topic:
                start = datetime(now.year, m1, d1)
                end = datetime(now.year, m2, d2)
                # 处理跨年
                if start > end:
                    if now >= start or now <= end:
                        score = 10
                        break
                elif start <= now <= end:
                    score = 10
                    break
                else:
                    score = max(score, 3)  # 不在窗口期但有时效关键词

        return score

    # ========== 去重 ==========

    def _deduplicate(self, scored: list[dict]) -> list[dict]:
        """
        去重: 语义相近的主题合并，保留分数较高者

        例: "春季穿搭" 和 "早春穿搭" 合并为高分项
        """
        if len(scored) <= 1:
            return scored

        result = []
        merged_indices = set()

        for i in range(len(scored)):
            if i in merged_indices:
                continue
            best = scored[i]
            for j in range(i + 1, len(scored)):
                if j in merged_indices:
                    continue
                if self._topic_similar(scored[i]["topic"], scored[j]["topic"]):
                    merged_indices.add(j)
                    if scored[j]["score"] > best["score"]:
                        best = scored[j]
            result.append(best)

        return result

    # ========== 辅助方法 ==========

    @staticmethod
    def _clean_topic(topic: str) -> str:
        """清理主题文本"""
        if not topic:
            return ""
        # 去除序号、emoji、多余空格
        t = re.sub(r'^[\d.、)\]】]+\s*', '', topic.strip())
        t = re.sub(r'[•·●]', '', t)
        return t.strip()

    @staticmethod
    def _topic_similar(a: str, b: str) -> bool:
        """
        判断两个主题是否语义相近 (简单规则匹配)

        策略:
          1. 完全相同 → True
          2. 一方包含另一方 → True
          3. 去除修饰词后相同 → True
          4. 共享核心词比例 > 60% → True
        """
        if not a or not b:
            return False

        a_clean = a.strip().lower()
        b_clean = b.strip().lower()

        # 完全相同
        if a_clean == b_clean:
            return True

        # 包含关系
        if a_clean in b_clean or b_clean in a_clean:
            return True

        # 去修饰词
        modifiers = ["早", "初", "晚", "新", "最", "超", "巨", "真的", "必看"]
        a_core = a_clean
        b_core = b_clean
        for mod in modifiers:
            a_core = a_core.replace(mod, "")
            b_core = b_core.replace(mod, "")
        if a_core and b_core and a_core == b_core:
            return True

        # 核心词重叠
        # 按字分词 (中文简单分词)
        a_chars = set(a_clean)
        b_chars = set(b_clean)
        if len(a_chars) >= 2 and len(b_chars) >= 2:
            intersection = a_chars & b_chars
            union = a_chars | b_chars
            if len(intersection) / len(union) > 0.6:
                return True

        return False

    @staticmethod
    def _generate_reason(item: dict) -> str:
        """根据评分生成推荐理由"""
        detail = item.get("score_detail", {})
        parts = []

        if detail.get("hotspot_score", 0) >= 25:
            parts.append("当前热点话题")
        if detail.get("weight_score", 0) >= 15:
            parts.append("历史互动表现好")
        if detail.get("scarcity_score", 0) >= 15:
            parts.append("内容空白可抢占")
        if detail.get("timeliness_score", 0) >= 8:
            parts.append("时效性强")

        source = item.get("source", "")
        if source == "hotspot" and not parts:
            parts.append("热点趋势推荐")
        elif source == "weight" and not parts:
            parts.append("基于历史表现推荐")
        elif source == "trend" and not parts:
            parts.append("持续趋势主题")

        if not parts:
            parts.append("综合推荐")

        return "，".join(parts)

    @staticmethod
    def _generate_angles(topic: str, source: str) -> list[str]:
        """
        为主题生成 1-3 个创作角度建议

        注意: 这里用规则生成，不调用 LLM
        """
        angles = []

        # 通用角度模板
        templates_by_type = {
            "穿搭": [
                f"从预算角度分享{topic}的平替选择",
                f"身材不同如何驾驭{topic}",
                f"一周{topic}不重样的实穿记录",
            ],
            "美食": [
                f"零失败的{topic}详细做法",
                f"外卖 vs 自己做{topic}的对比",
                f"{topic}的隐藏吃法",
            ],
            "护肤": [
                f"不同肤质的{topic}选择指南",
                f"踩雷vs回购：{topic}真实体验",
                f"平价替代大牌{topic}推荐",
            ],
            "好物": [
                f"用了半年的{topic}真实测评",
                f"后悔没早买的{topic}清单",
                f"从使用场景出发推荐{topic}",
            ],
        }

        # 根据主题关键词匹配模板
        matched = False
        for keyword, templates in templates_by_type.items():
            if keyword in topic:
                angles = templates[:3]
                matched = True
                break

        if not matched:
            # 通用角度
            angles = [
                f"个人真实体验分享{topic}",
                f"新手入门{topic}的详细攻略",
                f"关于{topic}的冷知识和避坑指南",
            ]

        # 限制每个角度不超过 30 字
        return [a[:30] for a in angles]