import re
import requests
from multiprocessing.dummy import Pool
from tqdm import tqdm
import pandas as pd
from collections import Counter
from wordcloud import WordCloud
import matplotlib.pyplot as plt

# 配置常量
KEYWORD = "2024 巴黎奥运会"
DANMU_KEYWORD = "AI"  # 过滤弹幕中的关键字
PAGENUM = 10  # 设置要爬取的页面数量
WORKERS = 6  # 线程池工作线程数

# HTTP请求头部
HEADERS = {
    "cookie": "your_cookie_here",  # 替换为实际cookie
    'origin': 'https://www.bilibili.com',
    "User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/91.0.4472.124 Safari/537.36",
    "referer": "https://t.bilibili.com/?spm_id_from=333.337.0.0",
}

def get_search_results_html(page: int) -> str:
    """获取搜索结果页面的HTML内容"""
    url = f"https://search.bilibili.com/all?keyword={KEYWORD}&order=click&page={page}"
    try:
        response = requests.get(url, headers=HEADERS)
        response.raise_for_status()
        return response.text
    except requests.RequestException as e:
        print(f"Error fetching page {page}: {e}")
        return ""

def get_bvs(html: str) -> list:
    """从HTML内容中提取BVs"""
    return re.findall(r'bvid:"([^"]+)"', html)

def get_info(vid: str) -> dict:
    """获取视频信息"""
    url = f"https://api.bilibili.com/x/web-interface/view/detail?bvid={vid}"
    try:
        response = requests.get(url)
        response.raise_for_status()
        data = response.json()
        
        if 'data' in data:
            info = {
                "标题": data["data"]["View"]["title"],
                "cid": [dic["cid"] for dic in data["data"]["View"]["pages"]]
            }
            return info
    except requests.RequestException as e:
        print(f"Error fetching info for vid {vid}: {e}")
    return {}

def get_danmu(info: dict) -> list:
    """获取视频的弹幕"""
    all_dms = []
    for cid in info.get("cid", []):
        url = f"https://api.bilibili.com/x/v1/dm/list.so?oid={cid}"
        try:
            response = requests.get(url)
            response.encoding = "utf-8"
            data = re.findall('<d p="(.*?)">(.*?)</d>', response.text)
            dms = [d[1] for d in data if DANMU_KEYWORD in d[1]]  # 过滤包含AI的弹幕
            all_dms += dms
        except requests.RequestException as e:
            print(f"Error fetching danmu for cid {cid}: {e}")
    
    print(f"获取弹幕{len(all_dms)}条！")
    return all_dms

def save_danmu(bv: str, danmu_data: list):
    """将弹幕保存到文本文件和Excel中"""
    df = pd.DataFrame(danmu_data, columns=['弹幕'])
    df.to_excel(f"./{KEYWORD}弹幕.xlsx", index=False, mode='a', header=not pd.io.common.file_exists(f"./{KEYWORD}弹幕.xlsx"))

def main():
    """主函数：爬取视频信息和弹幕"""
    pool = Pool(WORKERS)
    htmls = pool.map(get_search_results_html, range(1, PAGENUM + 1))
    bvs = []
    
    for html in htmls:
        bvs.extend(get_bvs(html))

    # 限制为前三百个视频
    bvs = bvs[:300]
    
    all_danmu = []
    
    # 爬取弹幕
    for bv in tqdm(bvs, desc="正在爬取弹幕"):
        info = get_info(bv继续完成上述Python代码，确保我们可以爬取B站弹幕、保存到Excel文件，并生成词云图。

        if info:
            danmu = get_danmu(info)
            all_danmu.extend(danmu)

    # 统计AI相关弹幕数量
    counter = Counter(all_danmu)
    top_danmu = counter.most_common(8)

    # 输出前8的弹幕
    print("AI相关弹幕统计（数量排名前8）：")
    for text, count in top_danmu:
        print(f"{text}: {count}")

    # 将弹幕数据写入Excel
    save_danmu(KEYWORD, all_danmu)

    # 生成词云图
    generate_wordcloud(all_danmu)

def generate_wordcloud(danmu_data):
    """生成弹幕的词云图"""
    text = " ".join(danmu_data)
    wordcloud = WordCloud(width=800, height=400, background_color='white').generate(text)

    plt.figure(figsize=(10, 5))
    plt.imshow(wordcloud, interpolation='bilinear')
    plt.axis('off')
    plt.title("弹幕词云图")
    plt.show()

if __name__ == "__main__":
    main()