"""
评分和过滤模块 - 基于关键词权重 + 规则引擎
不调用外部 AI API，纯本地规则评分
"""
import re
import logging

logger = logging.getLogger(__name__)

# 高权重关键词（出现即加分）
HIGH_WEIGHT_KEYWORDS = {
    # AI / 算力相关
    "英伟达": 3, "NVIDIA": 3, "AMD": 2, "算力": 2, "AI": 2,
    "光模块": 2, "存储": 1, "芯片": 2, "GPU": 2, "大模型": 2,
    "ChatGPT": 2, "OpenAI": 2, "Anthropic": 2, "DeepSeek": 2,
    # 中国科技公司
    "华为": 2, "腾讯": 2, "阿里": 2, "字节": 2, "小米": 2,
    "百度": 1, "京东": 1, "美团": 1, "拼多多": 1, "比亚迪": 2,
    # 重磅事件
    "收购": 2, "突破": 2, "泄露": 3, "内幕": 3, "传闻": 2,
    "重磅": 3, "突发": 3, "暴涨": 2, "暴跌": 2, "崩盘": 3,
    "熔断": 3, "制裁": 2, "禁令": 2, "封锁": 2,
    # 股市相关
    "A股": 1, "港股": 1, "美股": 1, "涨停": 2, "跌停": 2,
    "IPO": 2, "上市": 1, "退市": 2, "回购": 1,
    # 宏观
    "降息": 2, "加息": 2, "美联储": 2, "央行": 1, "GDP": 1,
    "CPI": 1, "非农": 2, "失业率": 1,
}

# 低价值关键词（出现则减分）
LOW_VALUE_PATTERNS = [
    r"广告", r"推广", r"优惠券", r"免费领",
    r"点击.*查看", r"扫码", r"关注.*公众号",
]


def score_news(news_item: dict, user_keywords: list = None) -> int:
    """
    对单条新闻评分，返回 0-10 分
    评分规则：
    - 基础分 3 分
    - 匹配高权重关键词加分
    - 匹配用户自定义关键词加分
    - 信息源标记为重要加分
    - 匹配低价值模式减分
    - 最终分数限制在 0-10
    """
    title = news_item.get("title", "")
    if not title:
        return 0

    score = 3  # 基础分

    # 1. 高权重关键词匹配
    for keyword, weight in HIGH_WEIGHT_KEYWORDS.items():
        if keyword.lower() in title.lower():
            score += weight

    # 2. 用户自定义关键词匹配
    if user_keywords:
        for kw in user_keywords:
            if kw.lower() in title.lower():
                score += 1

    # 3. 信息源标记为重要
    if news_item.get("important"):
        score += 2

    # 4. 低价值内容减分
    for pattern in LOW_VALUE_PATTERNS:
        if re.search(pattern, title):
            score -= 3

    # 5. 标题太短可能是无效内容
    if len(title) < 10:
        score -= 2

    return max(0, min(10, score))


def is_similar(title1: str, title2: str, threshold: float = 0.7) -> bool:
    """
    简单的标题相似度判断
    使用字符级别的 Jaccard 相似度
    """
    if not title1 or not title2:
        return False
    # 去除标点和空格
    clean1 = re.sub(r"[^\w]", "", title1)
    clean2 = re.sub(r"[^\w]", "", title2)
    if not clean1 or not clean2:
        return False
    # 2-gram 集合
    set1 = set(clean1[i:i+2] for i in range(len(clean1)-1))
    set2 = set(clean2[i:i+2] for i in range(len(clean2)-1))
    if not set1 or not set2:
        return clean1 == clean2
    intersection = set1 & set2
    union = set1 | set2
    return len(intersection) / len(union) >= threshold


def dedup_news(news_list: list) -> list:
    """去重：基于标题相似度，保留最早的一条"""
    result = []
    for item in news_list:
        is_dup = False
        for existing in result:
            if is_similar(item.get("title", ""), existing.get("title", "")):
                is_dup = True
                break
        if not is_dup:
            result.append(item)
    return result


def score_and_filter(news_list: list, user_keywords: list = None) -> list:
    """批量评分并过滤，返回带评分的新闻列表"""
    for item in news_list:
        item["score"] = score_news(item, user_keywords)
    return news_list