import pandas as pd
import re
from collections import defaultdict, Counter


def comments_analysis(search_word, keywords_list):
    print("正在处理弹幕数据...")
    
    # 读取弹幕文件
    def read_comments(file_path):
        with open(file_path, 'r', encoding='utf-8') as f:
            comments = f.readlines()
        return [comment.strip() for comment in comments]

    # 统计与 AI 技术应用相关的弹幕数量，并记录匹配的弹幕
    def count_comments(comments, keywords):
        count = Counter()
        matched_comments = defaultdict(list)  # 存储每个关键词对应的匹配弹幕
        # 提前将关键词转化为小写
        keywords_lower = {keyword.lower() for keyword in keywords}
        for comment in comments:
            comment_lower = comment.lower()  # 将弹幕转换为小写
            for keyword in keywords_lower:
                if keyword == 'ai':
                    # 使用正则表达式判断 "AI" 前后是否有英文字母
                    if re.search(r'(?<![a-zA-Z])ai(?![a-zA-Z])', comment_lower):
                        count[keyword] += 1
                        matched_comments[keyword].append(comment)  # 记录匹配的弹幕
                else:
                    if keyword in comment_lower:
                        count[keyword] += 1
                        matched_comments[keyword].append(comment)

        return count, matched_comments

    # 将弹幕统计结果按数量排序并选择前 8 项 
    def process_data(count):
        data = [{'弹幕': keyword, '数量': num} for keyword, num in count.items()]  
        df_top8 = pd.DataFrame(data).sort_values(by='数量', ascending=False).head(8)
        return df_top8

    # 处理与统计结果匹配的弹幕数据
    def process_matched_comments(matched_comments):
        matched_data = []
        for keyword, comments in matched_comments.items():
            for comment in comments:
                matched_data.append({'关键词': keyword, '匹配弹幕': comment})

        df_matched = pd.DataFrame(matched_data)
        return df_matched

    # 将统计结果写入 Excel
    def write_to_excel(count, matched_comments, output_file):
        # 调用数据处理函数
        df_top8 = process_data(count)

        # 调用匹配弹幕处理函数
        df_matched = process_matched_comments(matched_comments)

        # 写入不同的工作表
        with pd.ExcelWriter(output_file, engine='openpyxl') as writer:
            df_top8.to_excel(writer, sheet_name='Sheet1', index=False)  # 写入前 8 个关键词及其数量
            df_matched.to_excel(writer, sheet_name='Sheet2', index=False)  # 写入匹配的弹幕

        # 将匹配弹幕写入文本文件
        txt_output_file = f'{search_word}统计弹幕.txt'
        with open(txt_output_file, mode='w', encoding='utf-8') as f:
            f.writelines(comment + '\n' for comment in df_matched['匹配弹幕'])

    comments_file = f'{search_word}弹幕.txt'  # 需要分析的弹幕的文件路径
    output_file = f'{search_word}统计弹幕结果.xlsx'  # 结果输出的文件路径
    # keywords = ['AI', '人工智能', '智能', 'GPT', '计算', '数据分析', '识别','gtp','合成','监测']  # 相关关键词
    keywords = keywords_list

    comments = read_comments(comments_file) # 读取需要处理的弹幕数据
    comment_count, matched_comments = count_comments(comments, keywords) # 统计与 AI 技术应用相关的弹幕数量，并记录匹配的弹幕
    write_to_excel(comment_count, matched_comments, output_file) # 数据处理与保存
    print(f"统计结果已写入 {output_file}")

# # 以上函数性能测试
# if __name__ == "__main__":
#     import cProfile

#     # 设置测试关键词列表
#     keywords_list = ['AI', '人工智能', '智能', 'GPT', '计算', '数据分析', '识别', 'gtp', '合成', '监测']
    
#     # 创建 cProfile 对象
#     pr = cProfile.Profile()
#     pr.enable()  # 开始性能分析

#     # 调用功能
#     comments_analysis("2024巴黎奥运会", keywords_list)

#     pr.disable()  # 停止性能分析
#     pr.dump_stats('output.prof')  # 保存性能数据到文件
#     print("性能分析结果已保存到 output.prof")