Upload main.py

main.py

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+from datasets import Dataset
+import json
+from datasets import concatenate_datasets, Dataset
+
+arrow_files = ['data/data-00000-of-00003.arrow','data/data-00001-of-00003.arrow','data/data-00002-of-00003.arrow']
+ds = concatenate_datasets([Dataset.from_file(arrow_file) for arrow_file in arrow_files])
+
+threshold = 6000
+df = ds.to_pandas()
+df['question_id'] = df['METADATA'].apply(lambda x: int(json.loads(x)['question_id']))
+df = df.drop('SOURCE', axis=1)
+df = df.drop('METADATA', axis=1)
+df = df[df['upvotes'] > threshold]
+
+import pandas as pd
+pd.set_option('display.max_columns', None)  # Show all columns
+pd.set_option('display.max_rows', None)
+pd.set_option('display.max_colwidth', 200)
+
+# Set display options
+
+filtered_df = df[df['INSTRUCTION'].str.contains('吃饭')]
+
+class1 = ['船','高中历史', '陈情令','流浪地球', '厦门','鞋','购买','高中化学','考研政治','高中政治','英语','数学','语文','解题','高二','高三','演员','周星驰','王宝强','口吻','编程','免费下载', 
+      'iphone','壁纸','购买的游戏','有什么好玩的手机游戏','诗歌','图片','视频是什么','买什么书','饮品','推荐','做饭','手机','APP', 
+     '考研', '5G', '成都', '旅游', '深度学习','如何入门','足球', '篮球','周杰伦', '演唱会','高考','歌','法律','中医','LeetCode',
+     '面试','iPad','工具']
+
+mask = df['INSTRUCTION'].str.contains('|'.join(class1))
+class1_df = df[mask]
+
+result = df[~df.isin(class1_df).all(axis=1)]
+
+threshold = 10000
+df = ds.to_pandas()
+df['question_id'] = df['METADATA'].apply(lambda x: int(json.loads(x)['question_id']))
+df = df.drop('SOURCE', axis=1)
+df = df.drop('METADATA', axis=1)
+df = df[df['upvotes'] > threshold]
+filtered_df = df[df['INSTRUCTION'].str.contains('历史')]
+
+class1 = ['魔戒','奥运','梅西','船','高中历史', '陈情令','流浪地球', '厦门','鞋','购买','高中化学','考研政治','高中政治','英语','数学','语文','解题','高二','高三','演员','周星驰','王宝强','口吻','编程','免费下载', 
+      'iphone','壁纸','购买的游戏','有什么好玩的手机游戏','诗歌','图片','视频是什么','买什么书','饮品','做饭','手机','APP', 
+     '考研', '5G', '成都', '旅游', '深度学习','如何入门','足球', '篮球','周杰伦', '演唱会','高考','歌','法律','中医','LeetCode',
+     '面试','iPad','大罗小罗','破门绝平创历','大气二氧化碳浓度','大雁塔」','帕克太阳探测器','CPU','NBA','霍比特人','冯绍峰','历史中考',
+    '曼联','奥会单板','冬奥会','射手王', '浏览器的历史记录','2020赛季的F2','TES正在开创自己', 'x86','极地涡旋','百度百科',
+    '马刺','chrome']
+
+mask = filtered_df['INSTRUCTION'].str.contains('|'.join(class1))
+removedf = filtered_df[mask]
+resultdf = filtered_df[~filtered_df.isin(removedf).all(axis=1)]
+grouped_sorted_df = resultdf.sort_values(['question_id','upvotes'], ascending=[False, False])
+# grouped_sorted_df['INSTRUCTION'].unique()
+# grouped_sorted_df
+
+idx = -1
+with open('history.md', 'w', encoding='utf-8') as f:
+    for index, row in grouped_sorted_df.iterrows():
+        # Write the question with markdown formatting
+        if idx != row['question_id']:
+            f.write("#### " + row['INSTRUCTION'] +"\n")
+            idx = row['question_id']
+            
+        f.write("- " +'['+str(row['upvotes'])+'] ' +  row['RESPONSE']+"\n")
+        
+        f.write("\n\n")