"""
资讯子图API调用工具（使用MCP统一接口）
"""
from typing import Dict, Any, Optional, List
import random
from datetime import datetime
from dataclasses import dataclass

from ...mcp.mcp_manager import mcp_manager
from ...mcp.adapters import NewsAdapter


@dataclass
class NewsAPIClient:
    """
    资讯API客户端 - 使用MCP统一接口
    
    保持向后兼容，内部使用MCP适配器
    """
    
    # 保留配置字段用于向后兼容
    newsapi_key: Optional[str] = None
    news_repository: Optional[Any] = None
    
    def __post_init__(self):
        """初始化后设置（延迟初始化标志）"""
        self._mcp_initialized = False  # 延迟初始化标志

    async def _init_mcp(self):
        """初始化MCP系统（延迟初始化）"""
        if self._mcp_initialized:
            return  # 已初始化，跳过
        if not mcp_manager.get_adapter("news"):
            mcp_manager.register_adapter(
                NewsAdapter(news_repo=self.news_repository)
            )
            await mcp_manager.initialize()
        self._mcp_initialized = True
    
    async def query_news(
        self, 
        user_id: str = "default", 
        query: str = "", 
        use_cache: bool = True
    ) -> List[Dict[str, Any]]:
        """查询新闻（统一入口）"""
        await self._init_mcp()
        result = await mcp_manager.execute(
            "news", "query_news",
            user_id=user_id, query=query, use_cache=use_cache
        )
        if result.success:
            return result.data
        return self.query_news_mock(query)
    
    def query_news_mock(self, query: str) -> List[Dict[str, Any]]:
        """模拟查询（保留用于向后兼容）"""
        mock_news = [
            {
                "title": "OpenAI发布GPT-5：智能再升级",
                "source": "Tech News",
                "summary": "最新消息，OpenAI刚刚发布了GPT-5模型，智能水平再次取得重大突破...",
                "keywords": ["AI", "GPT-5", "OpenAI"],
                "author": "AI Team",
                "published_at": datetime.now().isoformat()
            },
            {
                "title": "大模型在医疗领域的应用",
                "source": "Health Tech",
                "summary": "大模型AI技术正在医疗领域展现巨大潜力，从辅助诊断到药物研发...",
                "keywords": ["医疗", "大模型", "应用"],
                "author": "Medical Team",
                "published_at": datetime.now().isoformat()
            }
        ]
        
        results = []
        query_lower = query.lower()
        for news in mock_news:
            if (query_lower in news["title"].lower() or 
                query_lower in news["summary"].lower() or
                any(keyword.lower() in query_lower for keyword in news["keywords"])):
                results.append(news)
        
        return results if results else mock_news[:2]
    
    def analyze_url_mock(self, url: str) -> Dict[str, Any]:
        """模拟URL分析（保留用于向后兼容）"""
        return {
            "title": f"分析结果：{url}",
            "source": "URL Analyzer",
            "summary": "已完成对该URL的内容分析，包含文章摘要和情感倾向判断...",
            "keywords": ["News", "Analysis"]
        }
    
    def extract_keywords_mock(self, text: str) -> List[str]:
        """模拟关键词提取（保留用于向后兼容）"""
        keywords = ["AI", "大模型", "应用场景", "行业趋势", "创新", "技术"]
        result = [k for k in keywords if k.lower() in text.lower()]
        return result if result else keywords[:4]
    
    def generate_report_mock(self, query: str) -> str:
        """模拟报告生成（保留用于向后兼容）"""
        return f"""═══════════════════════════════════════════
📊 资讯分析报告
═══════════════════════════════════════════

主题：{query}

📋 摘要：
这是关于 {query} 的资讯分析综合报告。

🔍 主要发现：
1. AI技术持续快速发展
2. 大模型应用场景不断拓展
3. 行业数字化转型加速

🏷️ 关键词：
- AI
- 大模型
- 数字化转型

═══════════════════════════════════════════
"""


# 全局单例（保持向后兼容）
news_api = NewsAPIClient()