removed unnecessary file in TextClassifier

textclassifier/TextClassifier.py

@@ -4,19 +4,16 @@ import regex as re
 from twitterscraper import TwitterScraper
 from datetime import date
 import os
-
+from dotenv import find_dotenv, load_dotenv
 
 # Set one directory up into ROOT_PATH
 ROOT_PATH = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
 
-from dotenv import find_dotenv, load_dotenv
-
 dotenv_path = find_dotenv()
 load_dotenv(dotenv_path)
 OPENAI_AUTHTOKEN = os.environ.get("OPENAI_AUTHTOKEN")
 
 
-
 class TextClassifier:
     def __init__(self, model_name="text-davinci-002", from_date='2022-01-01', to_date=str(date.today()),
 
@@ -48,12 +45,6 @@ class TextClassifier:
         # self.api_key = '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'
         openai.api_key = OPENAI_AUTHTOKEN
 
-    def scrape_tweets(self):
-        """
-        Scrapes tweets from the given date range.
-        """
-        self.ts.scrape_tweets()
-
     @staticmethod
     def cleanup_sentiment_results(classification_unclean):
         """
@@ -220,8 +211,6 @@ class TextClassifier:
         df_topic['topic'] = df_topic['tweet'].apply(self.classify_topic)
         return df_topic
 
-
-
     @staticmethod
     def cleanup_topic_results(prediction_dict, text):
         new_item = text.replace("\n", " ")
@@ -252,7 +241,6 @@ class TextClassifier:
                     return row
             return None
 
-
     def __repr__(self):
         """
         Gives a string that describes which user is classified
@@ -260,7 +248,6 @@ class TextClassifier:
         """
         return "Classifier for user: " + self.user_name + " with model: " + self.model_name + "."
 
-
 # if __name__ == "__main__":
 #     import pandas as pd
 #     from datetime import datetime
@@ -276,4 +263,3 @@ class TextClassifier:
 #     print(df)
 #     # save to csv in a folder under politweet with timestamp in name
 #     df.to_csv(f"{datetime.now().strftime('%Y-%m-%d %H-%M-%S')}_tweets.csv")
-