import re
import requests
import json
from wordcloud import WordCloud
from collections import Counter
import pandas as pd
import jieba

# 用于获得请求的头，应对B站的反爬虫机制
headers = {
    "user-agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/128.0.0.0 Safari/537.36 Edg/128.0.0.0",
    "Referer": "https://www.bilibili.com/video/BV1ji421r7PB/?spm_id_from=333.337.search-card.all.click&vd_source=6d043574d81a3f216c41cc281767c59e",
    "Cookie": "buvid4=F793982E-B1BE-6615-D504-4A3BF6DC079274572-022081216-zrNi2ZH69Dhc8fOw09IIaA%3D%3D; buvid_fp_plain=undefined; DedeUserID=254629681; DedeUserID__ckMd5=23182279794f6f43; header_theme_version=CLOSE; enable_web_push=DISABLE; rpdid=0zbfVHb84m|bujW4qg1|H3r|3w1RkXNz; FEED_LIVE_VERSION=V_WATCHLATER_PIP_WINDOW3; hit-dyn-v2=1; CURRENT_FNVAL=4048; LIVE_BUVID=AUTO3317219069887705; buvid3=575DAC67-0FE1-B3C3-8A9F-32190F231A1F24035infoc; b_nut=1723377925; _uuid=24521A4B-895E-10E6A-2555-A9EF812A1AC136123infoc; fingerprint=45b7e451fad392b858fc6e95bbb4231d; PVID=1; SESSDATA=7bb24682%2C1741874111%2Cf9490%2A92CjB3NDHhJCIr34LBlg2TTS7vPGQ72Kh3s3DVcaEn0JkQ_hXc7q6MgsK9Wjb-Z6NTf68SVnlnTkJQZmtmRXMxZnc1RDZpYUFnS1BlTmptUklUSkcyQUNxaWg4Smpla1VCZHdkLVc4dW9OWU4zZm1PaVhuMzFtcnNvQ2JMQzFiTFVleC1HbXc0Sm53IIEC; bili_jct=895f9e091815ee492e57345dc62b8757; bili_ticket=eyJhbGciOiJIUzI1NiIsImtpZCI6InMwMyIsInR5cCI6IkpXVCJ9.eyJleHAiOjE3MjY1ODEzMjMsImlhdCI6MTcyNjMyMjA2MywicGx0IjotMX0.47Js9n97GQecalPWdc-MNrDVUOz2-FfY_DlB72DUlZM; bili_ticket_expires=1726581263; CURRENT_QUALITY=32; bp_t_offset_254629681=977402825627664384; buvid_fp=45b7e451fad392b858fc6e95bbb4231d; home_feed_column=5; browser_resolution=1528-704; b_lsid=18C6BCCB_191F83A85C1"
}

def get_bvlist():
    # 获取BV号
    bvlist = []
    for page in range(1, 11):
        # 搜索“2024巴黎奥运会”的URL
        url = f'https://api.bilibili.com/x/web-interface/wbi/search/type?category_id=&search_type=video&ad_resource=5654&__refresh__=true&_extra=&context=&page={page}&page_size=30&pubtime_begin_s=0&pubtime_end_s=0&from_source=&from_spmid=333.337&platform=pc&highlight=1&single_column=0&keyword=2024%E5%B7%B4%E9%BB%8E%E5%A5%A5%E8%BF%90%E4%BC%9A&#39'
        # 发送请求
        response = requests.get(url, headers=headers)
        response.encoding='urf-8'
        # 使用正则表达式得到BV号
        tem_bvlist = re.findall('"bvid":"(.*?)"', response.text)
        bvlist.extend(list(set(tem_bvlist)))
    print("BV号获取完成")
    return bvlist

def get_cidlist(bvlist):
    # 传入获取到的BV号，获取对应的cid
    cidlist=[]
    for bvid in bvlist:
        # 由于获取cid的URL
        url = f'https://api.bilibili.com/x/player/pagelist?bvid={bvid}&jsonp=jsonp'
        # 发送请求
        res = requests.get(url,headers=headers)
        res.encoding = 'urf-8'
        # 利用JSON提取cid
        json_dict = json.loads(res.text)
        cidlist.append(json_dict["data"][0]["cid"])
    print("cid获取完成")
    return cidlist

def get_danmu(cidlist):
    # 利用传入的cid，获取对应的弹幕
    count = 1
    for cid in cidlist:
        # 弹幕所在的URL
        url = f'http://comment.bilibili.com/{cid}.xml'
        # 发送请求
        response = requests.get(url, headers=headers)
        response.encoding = 'utf-8'
        # 利用正则表达式获取弹幕
        content_list = re.findall('<d p=".*?">(.*?)</d>', response.text)
        for danmu in content_list:
            with open('弹幕.txt', 'a', encoding='utf-8') as f:
                f.write(danmu + '\n')
        print(f'----------第{count}个视频弹幕获取完毕！----------')
        count +=1

def get_ai_related_danmu():
    # 统计与AI技术应用相关的弹幕
    ai_related_danmu = []
    # AI相关的关键词列表
    ai_keywords = {'ai', 'ai生成', 'ai合成', 'ai配音', 'gpt', '人工智能', '机器学习', '深度学习', '神经网络',
                   '自然语言处理', '计算机视觉', '语音识别', '人脸识别', '无人驾驶'}
    # 读取弹幕文件
    with open('弹幕.txt', 'r', encoding='utf-8') as file:
        danmu_lines = file.readlines()
    # 去除原本的换行符和空格，方便统计
    danmu_cleaned = [line.strip() for line in danmu_lines]
    # 统计与AI技术应用相关的弹幕
    for danmu in danmu_cleaned:
        if any(keyword in danmu.lower() for keyword in ai_keywords):
            # 对含有“ai”关键字但内容并不是AI技术应用相关的弹幕需另外判断
            if 'ai' in danmu.lower() and re.search(r'(?<![\u4e00-\u9fa5])[aiAI](?![\u4e00-\u9fa5])', danmu):
                continue
            ai_related_danmu.append(danmu)

    # 统计每种弹幕的数量
    danmu_count = Counter(ai_related_danmu)

    # 获取数量排名前8的弹幕
    top_8_danmu = danmu_count.most_common(8)

    # 创建一个DataFrame来存储数据
    df = pd.DataFrame(top_8_danmu, columns=['弹幕内容', '数量'])

    # 将DataFrame写入Excel文件
    df.to_excel('AI弹幕统计.xlsx', index=False)
    return(ai_related_danmu)

def get_wordcloud(ai_danmu):
    # 首先，将弹幕列表中的每个元素连接成一个长字符串，元素之间用空格分隔
    ai = ' '.join(ai_danmu)
    # 使用jieba分词对连接后的字符串进行分词，并将分词结果再次连接成一个长字符串，分词之间用空格分隔
    cut_t = ' '.join(jieba.cut(ai))
    # 设置停用词，减少无效信息
    stop_words = set()
    content = [line.strip() for line in open('停用词.txt', 'r', encoding='utf-8').readlines()]
    stop_words.update(content)
    # 创建一个WordCloud对象，设置词云图的参数
    wordcloud = WordCloud(background_color="white",     # 设置背景颜色为白色
                          width=800,                    # 设置词云图的宽度
                          height=600,                   # 设置词云图的高度
                          max_words=150,                # 设置词云图中显示的最大单词数
                          max_font_size=80,             # 设置词云图中最大字号
                          font_path='msyh.ttc',         # 设置字体路径，确保能够显示中文
                          contour_width=2,              # 设置词云图的轮廓宽度
                          contour_color='steelblue',    # 设置词云图轮廓的颜色
                          repeat='true',                # 设置单词课重复出现
                          stopwords = stop_words,       # 设置停用词
                          ).generate(cut_t)
    # 将生成的词云图保存为PNG文件
    wordcloud.to_file('词云图.png')

if __name__ == '__main__':
    bvlist = get_bvlist()
    cidlist = get_cidlist(bvlist)
    get_danmu(cidlist)
    ai_danmu = get_ai_related_danmu()
    get_wordcloud(ai_danmu)