#!/usr/bin/env python3
"""
社交媒体公开动态采集器
采集公开社交媒体内容（公共账号、公开话题、趋势数据）
"""

import json
import sys
from datetime import datetime
from typing import Dict, List, Any

class SocialCollector:
    """社交媒体公开动态采集"""

    # 可访问的公开来源
    PUBLIC_SOURCES = {
        "Twitter_X": [
            # Twitter/X 的公开页面（需注意访问限制）
            # 实际使用时通过聚合平台或搜索引擎间接获取
            "nitter.net"            # Twitter 的开源前端代理（部分可用）
        ],
        "Reddit": [
            "reddit.com"             # 公开内容
        ],
        "YouTube": [
            "youtube.com"            # 公开视频元数据
        ],
        "LinkedIn": [
            "linkedin.com"           # 公开公司动态、招聘信息
        ],
        " aggregators": [
            "news.google.com",       # Google News（聚合多源）
            "trends.google.com"      # Google Trends（趋势数据）
        ]
    }

    def __init__(self):
        self.collection_log = {
            "start_time": datetime.now().isoformat(),
            "collector": "SocialCollector",
            "posts_collected": 0,
            "sources_accessed": [],
            "warnings": []
        }

    async def collect_twitter_posts(self,
                                     accounts: List[str] = None,
                                     hashtags: List[str] = None,
                                     keywords: List[str] = None,
                                     max_results: int = 50) -> Dict[str, Any]:
        """
        采集 Twitter/X 公开内容

        注意：Twitter 访问限制较多，优先使用搜索引擎和聚合平台

        Args:
            accounts: 公开账号
            hashtags:   话题标签
            keywords: 搜索关键词
            max_results: 最大结果数

        Returns:
            结构化社交媒体数据
        """
        self.collection_log["platform"] = "Twitter/X"
        self.collection_log["accounts"] = accounts or []
        self.collection_log["hashtags"] = hashtags or []
        self.collection_log["keywords"] = keywords or []

        results = []

        # 策略1：使用搜索引擎查找公开推文
        for keyword in (keywords or []):
            query = f"{keyword} site:twitter.com OR site:x.com"
            self.collection_log["sources_accessed"].append({
                "method": "search_engine",
                "query": query,
                "timestamp": datetime.now().isoformat()
            })

        # 策略2：使用聚合平台（如有）
        for hashtag in (hashtags or []):
            query = f"#{hashtag}"
            self.collection_log["sources_accessed"].append({
                "method": "search_engine",
                "query": query,
                "timestamp": datetime.now().isoformat()
            })

        self.collection_log["end_time"] = datetime.now().isoformat()
        self.collection_log["posts_collected"] = len(results)

        return {
            "post_items": results,
            "metadata": {
                "platform": "Twitter/X",
                "count": len(results),
                "access_method": "search_engine_indirect"  # 间接访问
            },
            "collection_log": self.collection_log
        }

    async def collect_reddit_posts(self,
                                    subreddits: List[str] = None,
                                    keywords: List[str] = None,
                                    time_range: str = "1w",
                                    max_results: int = 50) -> Dict[str, Any]:
        """
        采集 Reddit 公开帖子

        Args:
            subreddits: 子版块（如["worldnews", "technology"]）
            keywords: 搜索关键词
            time_range: 时间范围（1d=1天，1w=1周）
            max_results: 最大结果数

        Returns:
            结构化 Reddit 数据
        """
        self.collection_log["platform"] = "Reddit"
        self.collection_log["subreddits"] = subreddits or []
        self.collection_log["keywords"] = keywords or []

        results = []

        for subreddit in (subreddits or []):
            for keyword in (keywords or []):
                query = f"{keyword} site:reddit.com/r/{subreddit}"
                self.collection_log["sources_accessed"].append({
                    "method": "search_engine",
                    "query": query,
                    "timestamp": datetime.now().isoformat()
                })

        self.collection_log["end_time"] = datetime.now().isoformat()
        self.collection_log["posts_collected"] = len(results)

        return {
            "post_items": results,
            "metadata": {
                "platform": "Reddit",
                "count": len(results)
            },
            "collection_log": self.collection_log
        }

    async def collect_youtube_videos(self,
                                       channels: List[str] = None,
                                       keywords: List[str] = None,
                                       max_results: int = 30) -> Dict[str, Any]:
        """
        采集 YouTube 公开视频元数据

        Args:
            channels: 公开频道
            keywords: 搜索关键词
            max_results: 最大结果数

        Returns:
            结构化视频元数据
        """
        self.collection_log["platform"] = "YouTube"
        self.collection_log["channels"] = channels or []
        self.collection_log["keywords"] = keywords or []

        results = []

        for keyword in (keywords or []):
            query = f"{keyword} site:youtube.com/watch"
            self.collection_log["sources_accessed"].append({
                "method": "search_engine",
                "query": query,
                "timestamp": datetime.now().isoformat()
            })

        self.collection_log["end_time"] = datetime.now().isoformat()
        self.collection_log["posts_collected"] = len(results)

        return {
            "post_items": results,
            "metadata": {
                "platform": "YouTube",
                "count": len(results),
                "data_type": "video_metadata"
            },
            "collection_log": self.collection_log
        }

    def extract_post_metadata(self, content: str, url: str, platform: str) -> Dict[str, Any]:
        """提取社交媒体帖子元数据"""
        return {
            "platform": platform,
            "author": self._extract_author(content, platform),
            "text": self._extract_text(content),
            "timestamp": "",
            "likes": 0,
            "shares": 0,
            "comments": 0,
            "url": url
        }

    def _extract_author(self, content: str, platform: str) -> str:
        """提取作者"""
        return "Unknown"

    def _extract_text(self, content: str) -> str:
        """提取帖文内容"""
        return content[:500] + "..." if len(content) > 500 else content

def main():
    print("SocialCollector v1.0")
    print("支持平台：Twitter/X, Reddit, YouTube, LinkedIn")
    print("访问方式：搜索引擎间接访问（合规优先）")

if __name__ == "__main__":
    main()