"""
新闻文章数据库模型
"""
from datetime import datetime
from sqlalchemy import Column, Integer, String, Text, DateTime, JSON, Boolean, Float
from sqlalchemy.orm import relationship

from app.models.database import Base


class NewsArticle(Base):
    """新闻文章表"""
    __tablename__ = "news_articles"

    id = Column(Integer, primary_key=True, index=True)

    # 新闻基本信息
    title = Column(String(500), nullable=False)
    content = Column(Text, nullable=True)  # 完整内容或摘要
    content_hash = Column(String(64), nullable=False, index=True)  # 内容哈希，用于去重
    url = Column(String(1000), nullable=False, unique=True)  # 原文链接
    source = Column(String(100), nullable=False, index=True)  # 来源网站
    author = Column(String(200), nullable=True)  # 作者

    # 新闻分类
    category = Column(String(50), nullable=False, index=True)  # 'crypto', 'stock', 'forex', 'commodity'
    tags = Column(JSON, nullable=True)  # 标签列表

    # 时间信息
    published_at = Column(DateTime, nullable=True, index=True)  # 发布时间
    crawled_at = Column(DateTime, default=datetime.utcnow, index=True)  # 爬取时间

    # LLM 分析结果
    llm_analyzed = Column(Boolean, default=False, index=True)  # 是否已分析
    market_impact = Column(String(20), nullable=True, index=True)  # 'high', 'medium', 'low'
    impact_type = Column(String(50), nullable=True)  # 'bullish', 'bearish', 'neutral'
    relevant_symbols = Column(JSON, nullable=True)  # 相关的币种/股票代码

    # LLM 分析详情
    sentiment = Column(String(20), nullable=True)  # 'positive', 'negative', 'neutral'
    summary = Column(Text, nullable=True)  # LLM 生成的摘要
    key_points = Column(JSON, nullable=True)  # 关键点列表
    trading_advice = Column(Text, nullable=True)  # 交易建议

    # 优先级队列
    priority = Column(Float, default=0.0, index=True)  # 优先级分数
    priority_reason = Column(Text, nullable=True)  # 优先级原因

    # 通知状态
    notified = Column(Boolean, default=False, index=True)  # 是否已发送通知
    notification_sent_at = Column(DateTime, nullable=True)
    notification_channel = Column(String(50), nullable=True)  # 'feishu', 'telegram', etc.

    # 质量控制
    quality_score = Column(Float, nullable=True)  # 质量分数 0-1
    duplicate_of = Column(Integer, nullable=True)  # 如果是重复，指向原始文章ID

    # 状态
    is_active = Column(Boolean, default=True, index=True)  # 是否有效

    # 时间戳
    created_at = Column(DateTime, default=datetime.utcnow, index=True)
    updated_at = Column(DateTime, default=datetime.utcnow, onupdate=datetime.utcnow)

    def __repr__(self):
        return f"<NewsArticle({self.category} {self.source} {self.title[:50]}...)>"

    def to_dict(self):
        """转换为字典"""
        return {
            'id': self.id,
            'title': self.title,
            'content': self.content,
            'url': self.url,
            'source': self.source,
            'author': self.author,
            'category': self.category,
            'tags': self.tags,
            'published_at': self.published_at.isoformat() if self.published_at else None,
            'crawled_at': self.crawled_at.isoformat() if self.crawled_at else None,
            'llm_analyzed': self.llm_analyzed,
            'market_impact': self.market_impact,
            'impact_type': self.impact_type,
            'relevant_symbols': self.relevant_symbols,
            'sentiment': self.sentiment,
            'summary': self.summary,
            'key_points': self.key_points,
            'trading_advice': self.trading_advice,
            'priority': self.priority,
            'priority_reason': self.priority_reason,
            'notified': self.notified,
            'notification_sent_at': self.notification_sent_at.isoformat() if self.notification_sent_at else None,
            'notification_channel': self.notification_channel,
            'quality_score': self.quality_score,
            'duplicate_of': self.duplicate_of,
            'is_active': self.is_active,
            'created_at': self.created_at.isoformat() if self.created_at else None,
            'updated_at': self.updated_at.isoformat() if self.updated_at else None,
        }