lite clean-up i TextClassifier

.idea/misc.xml

@@ -1,4 +1,4 @@
 <?xml version="1.0" encoding="UTF-8"?>
 <project version="4">
-  <component name="ProjectRootManager" version="2" project-jdk-name="Python 3.10 (politweet)" project-jdk-type="Python SDK" />
+  <component name="ProjectRootManager" version="2" project-jdk-name="Python 3.9 (politweet)" project-jdk-type="Python SDK" />
 </project>

textclassifier/TextClassifier.py

@@ -46,7 +46,6 @@ class TextClassifier:
             classification_clean = classification_clean.replace(" ", "")
 
         return classification_clean
-        return response.choices[0]['text']
 
     def classify_sentiment(self, text: str):
         """
@@ -200,16 +199,31 @@ class TextClassifier:
         df_topic['topic'] = df_topic['tweet'].apply(self.classify_topic)
         return df_topic
 
-    def __repr__(self):
-        return "TwitterScraper(from_date={}, to_date={}, num_tweets={})".format(self.from_date, self.to_date,
-                                                                                self.num_tweets)
-
-    def cleanup_topic_results(prediction_dict, text):
+    @staticmethod
+    def cleanup_topic_results(text):
             new_item = text.replace("\n", " ")
             new_item = new_item.replace("  ", " ")
             return new_item
 
+    def __repr__(self):
+        return "TwitterScraper(from_date={}, to_date={}, num_tweets={})".format(self.from_date, self.to_date,
+                                                                                self.num_tweets)
+
 
 
-if __name__ == "__main__":
+# if __name__ == "__main__":
+#     import pandas as pd
+#     from datetime import datetime
+#     import os
+#     # show all columns
+#     pd.set_option('display.max_columns', None)
+#
+#     tc = TextClassifier(from_date="2019-01-01", to_date="2019-05-31", user_name='jimmieakesson', num_tweets=20)
+#     tc.classify_sentiment_of_tweets()
+#     # df = tc.analyze_sentiment_of_tweets()
+#     # print(df)
+#     df = tc.classify_topics_of_tweets()
+#     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")