import requests
import re
import time
import pandas as pd
import matplotlib.pyplot as plt
from wordcloud import WordCloud
from collections import Counter

headers = {
    'cookie':'CURRENT_FNVAL=4048; buvid4=CDB22228-76EA-BC93-F037-78FC6CEC077D36275-023090719-X83v1qigvaVs%2BeTu3%2F5T2g%3D%3D; rpdid=|(u)luk)m|)R0J\'uYmR|)Y)J); enable_web_push=DISABLE; header_theme_version=CLOSE; DedeUserID=567151924; DedeUserID__ckMd5=3d51b3cb3879b2e0; PVID=1; buvid3=909EA327-0037-5349-5CF4-5B2C4EF5300103254infoc; b_nut=1726374703; bsource=search_bing; _uuid=A28E1582-10535-12B8-49B2-D1F1026722D1607876infoc; buvid_fp=1ed033837a881c0f6fee6ce1ae293ed0; bili_ticket=eyJhbGciOiJIUzI1NiIsImtpZCI6InMwMyIsInR5cCI6IkpXVCJ9.eyJleHAiOjE3MjY2MzM5MDQsImlhdCI6MTcyNjM3NDY0NCwicGx0IjotMX0.52ZE1RDG5tSqHh-ZOGgEHDzj6W1UyOtiMkcxw_a2WNY; bili_ticket_expires=1726633844; SESSDATA=701189c6%2C1741926705%2C58e19%2A91CjCANFin8L-nRK6CjxH9_BSgRe6HHUSWybFilZklu8yORRObfCV2cnJswJPECKKy1UcSVkMtcE4ydHItZF9lOW43ZFpyelRVVEUzZUVCdlh6S2ltWWJIaTg0MU1DclRIeG8wbE84cE1pSFBkOXA1alNxTkp3bDJuLWNCN2IzV2JXX2p4SGIxaW9BIIEC; bili_jct=98cbd1d0535939dc4a5c474a44d27ad7; sid=7wfo5xb3; home_feed_column=5; browser_resolution=1432-776; CURRENT_QUALITY=80; bp_t_offset_567151924=977713175669506048; b_lsid=811052DA4_191FB79926A',
    'Referer': 'https://search.bilibili.com/all?vt=82484714&keyword=2024%E5%B7%B4%E9%BB%8E%E5%A5%A5%E8%BF%90%E4%BC%9A&from_source=webtop_search&spm_id_from=333.1007&search_source=5',
    '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'
}

def get_response(url):
    response = requests.get(url = url, headers = headers)
    return response

def get_bv(page_num):
    bv_list = []
    link = 'https://api.bilibili.com/x/web-interface/wbi/search/type'
    data = {
        'category_id':'',
        'search_type': 'video',
        'ad_resource': '5654',
        '__refresh__': 'true',
        '_extra':'',
        'context':'',
        'page': page_num,
        'page_size': '42',
        'pubtime_begin_s': '0',
        'pubtime_end_s': '0',
        'from_source':'',
        'from_spmid': '333.337',
        'platform': 'pc',
        'highlight': '1',
        'single_column': '0',
        'keyword': '2024巴黎奥运会',
        'qv_id': 'Hz50pRYmKFQYlX2AorY3bJUTNJbLRwnX',
        'source_tag': '3',
        'gaia_vtoken':'',
        'dynamic_offset': '30',
        'web_location': '1430654',
        'w_rid': '1b994979977a17ee8010f012d43fa7b6',
        'wts': '1726501056'
    }
    link_data = requests.get(url = link, headers = headers, params = data).json()
    for index in link_data['data']['result']:
        bv_list.append(index['bvid'])
#    pprint(bv_list)
    return bv_list

def get_cid(bvid):
    url = f"https://api.bilibili.com/x/web-interface/view?bvid={bvid}"
    data = get_response(url).json()
#    pprint(data)
    return data['data']['cid']

def get_danmaku(cid):
    url = f"https://comment.bilibili.com/{cid}.xml"
    response = get_response(url)
    response.encoding = 'utf-8'
#    pprint(response.text)
    return response.text

def parse_danmaku(danmaku_xml):
    danmaku_list = re.findall('">(.*?)</d>',danmaku_xml)
    return danmaku_list

# 弹幕相关关键词
ai_keywords = ["AI", "人工智能", "机器学习", "深度学习", "AI技术", "自动驾驶", "智能", "图像识别", "AI应用","智造"]

# 筛选与AI相关的弹幕
def get_ai_danmaku(danmaku_list_all, ai_keywords):
    ai_related_danmaku = [danmaku for danmaku in danmaku_list_all if any(keyword in danmaku for keyword in ai_keywords)]
    return ai_related_danmaku

# 统计弹幕出现频次
def count_danmaku(danmaku_list):
    danmaku_counter = Counter(danmaku_list)
    return danmaku_counter

# 获取前n个弹幕及其出现次数
def get_top_n_danmaku(danmaku_counter, n=8):
    return danmaku_counter.most_common(n)

# 写入 Excel
def write_to_excel(data, filename='danmaku_AI_top8.xlsx'):
    df = pd.DataFrame(data, columns=['弹幕内容', '出现次数'])
    df.to_excel(filename, index=False)

# 生成并显示词云图
def get_wordcloud(danmaku_counter):
    wordcloud = WordCloud(
        width=800,  # 宽度
        height=400,  # 高度
        background_color='white',  # 背景色
        max_words=100,  # 显示的最大词语数量
        colormap='viridis',  # 颜色映射
        font_path='msyh.ttc'  # 指定字体路径，适应中文显示
    ).generate_from_frequencies(danmaku_counter)
    plt.figure(figsize=(10, 5))  # 图像大小
    plt.imshow(wordcloud, interpolation="bilinear")
    plt.axis("off")  # 关闭坐标轴显示
    plt.show()

if __name__ == '__main__':

    # 收集前300个视频的bvid
    videos = []
    page_num = 1
    while len(videos) < 300:
        bv_list = get_bv(str(page_num))
        videos.extend(bv_list)
        page_num += 1
        time.sleep(1)  # 防止请求过于频繁

    # 仅保留前300个视频号
    videos = videos[:300]

    all_danmaku = []
    all_ai_related_danmaku = []

    # 获取每个视频的弹幕并打印
    for bvid in videos:
        try:
            cid = get_cid(bvid)
            danmaku_xml = get_danmaku(cid)
            danmaku_list = parse_danmaku(danmaku_xml)

            # 筛选AI相关弹幕
            ai_related_danmaku = get_ai_danmaku(danmaku_list, ai_keywords)
            all_ai_related_danmaku.extend(ai_related_danmaku)

            for danmaku in danmaku_list:
                all_danmaku.append(danmaku)
        except Exception as e:
            print(f"Error fetching danmaku for video {bvid}: {e}")
        time.sleep(1) # 防止请求过于频繁

    # 统计ai弹幕出现频次
    ai_danmaku_counter = count_danmaku(all_ai_related_danmaku)

    # 获取前8个频次最高的弹幕
    top_8_danmaku = get_top_n_danmaku(ai_danmaku_counter, n=8)
    for dm,cnt in top_8_danmaku:
        print(dm)

    # 输出到Excel
    write_to_excel(top_8_danmaku)

    # 生成词云
    get_wordcloud(ai_danmaku_counter)

#    print("弹幕统计结果已保存到Excel: danmaku_AI_top8.xlsx")