Update yuqingpredict.py

 from utils.getPublicData import *
 from utils.getPublicData import *
-from utils.predict import *
-articleList = getAllArticleData()
-commentList = getAllCommentsData()
+from utils.predict import predict_future_values  # Use the new function
 import csv
 import csv
 import os
 import os
 import datetime
 import datetime
-def getTopicByArticle():# 返回文章内容的话题字典
-    articleTopicDic = {}
-    for i in articleList:
-        if i[14] != None:
-            if i[14] in articleTopicDic.keys():
-                articleTopicDic[i[14]] += 1
-            else:
-                articleTopicDic[i[14]] = 1
-    resultData = []
-    for key,value in articleTopicDic.items():
-        resultData.append({
-            'name':key,
-            'value':value
-        })
-    return resultData
-
-def getTopicByComments():# 返回评论内容的话题字典
-    commentsTopicDic = {}
-    for i in commentList:
-        if i[9] != None:
-            if i[9] in commentsTopicDic:
-                commentsTopicDic[i[9]] += 1
-            else:
-                commentsTopicDic[i[9]] = 1
-    resultData = []
-    for key,value in commentsTopicDic.items():
-        resultData.append({
-            'name':key,
-            'value':value
-        })
-    return resultData
-
-def mergeTopics(article_topics, comment_topics):# 合并话题
-    merged_dict = {}
-    for topic in article_topics + comment_topics:
-        if topic['name'] in merged_dict:
-            merged_dict[topic['name']] += topic['value']
-        else:
-            merged_dict[topic['name']] = topic['value']
-    merged_dict = sorted(merged_dict.items(), key=lambda item: item[1], reverse=True)
-    merged_list = [[key, str(value)] for key, value in merged_dict]
-    return merged_list
-def getAllTopicData():
-    # 读取合并文件 merge.csv
-    # data = []
-    # df = pd.read_csv('./merged_topics.csv',encoding='utf8')
-    # for i in df.values:
-    #     try:
-    #         data.append([
-    #             re.search('[\u4e00-\u9fa5]+',str(i)).group(),
-    #             re.search('\d+',str(i)).group()
-    #         ])
-    #     except:
-    #         continue
-    return mergeTopics(getTopicByArticle(), getTopicByComments())
+import pandas as pd
 
 
-def getTopicCreatedAtandpredictData(topic):# 统计特定话题的评论在每个日期的数量，并返回日期和对应的评论数量
+def getTopicCreatedAtandpredictData(topic):
     createdAt = {}
     createdAt = {}
     for i in articleList:
     for i in articleList:
         if i[14]==topic:
         if i[14]==topic:
@@ -75,30 +19,13 @@ def getTopicCreatedAtandpredictData(topic):# 统计特定话题的评论在每
                 createdAt[i[1]] += 1
                 createdAt[i[1]] += 1
             else:
             else:
                 createdAt[i[1]] = 1
                 createdAt[i[1]] = 1
-    createdAt = {k: createdAt[k] for k in sorted(createdAt, key=lambda date: datetime.datetime.strptime(date, "%Y-%m-%d"))}
-    createdAt.update(predict_future_values(createdAt))
-    sorted_data = {k: createdAt[k] for k in sorted(createdAt, key=lambda date: datetime.datetime.strptime(date, "%Y-%m-%d"))}
-    # result_list = [0] * (len(sorted_data) - 5) + [1] * 5
-    print(list(createdAt.keys()),list(createdAt.values()))
-    return list(createdAt.keys()),list(createdAt.values())
 
 
-def writeTopicsToCSV(topics, file_name):
-    # 检查文件是否存在，如果存在则附加写入，否则新建一个
-    file_exists = os.path.isfile(file_name)
-    # 按值的降序排序
-    sorted_topics = sorted(topics, key=lambda x: x['value'], reverse=True)
-    with open(file_name, 'w', newline='', encoding='utf-8') as csvfile:
-        fieldnames = ['name', 'value']
-        writer = csv.DictWriter(csvfile, fieldnames=fieldnames)
-        # 如果文件不存在，则写入表头
-        if not file_exists:
-            writer.writeheader()
-        # 写入数据
-        for topic in sorted_topics:
-            writer.writerow(topic)
-if __name__ == '__main__':
-    # 将话题数据写入 CSV 文件
-    # print(mergeTopics(getTopicByArticle(), getTopicByComments()))
-    # writeTopicsToCSV(merged_topics, 'merged_topics.csv')
-    print(getAllTopicData())
+    # Use the improved time series prediction approach
+    predictions = predict_future_values(createdAt, forecast_days=5)
+
+    # Merge historical data and predictions
+    combined_data = {**createdAt, **predictions}
+    combined_data = {k: combined_data[k] for k in sorted(combined_data, key=lambda date: datetime.datetime.strptime(date, "%Y-%m-%d"))}
 
 
+    print(list(combined_data.keys()), list(combined_data.values()))
+    return list(combined_data.keys()), list(combined_data.values())