fixed bug with analyzing sentiment

.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 3.9 (politweet) (2)" project-jdk-type="Python SDK" />
+  <component name="ProjectRootManager" version="2" project-jdk-name="Python 3.10 (politweet)" project-jdk-type="Python SDK" />
 </project>

.idea/politweet.iml

@@ -5,7 +5,7 @@
       <excludeFolder url="file://$MODULE_DIR$/politweet-environment" />
       <excludeFolder url="file://$MODULE_DIR$/venv" />
     </content>
-    <orderEntry type="jdk" jdkName="Python 3.9 (politweet) (2)" jdkType="Python SDK" />
+    <orderEntry type="inheritedJdk" />
     <orderEntry type="sourceFolder" forTests="false" />
   </component>
   <component name="PyNamespacePackagesService">

textclassifier/TextClassifier.py

@@ -82,7 +82,7 @@ class TextClassifier:
         Classifies the sentiment of a user's tweets.
         :param user_name: string of the user name.
         """
-        df_sentiment = self.df
+        df_sentiment = self.df.copy()
         df_sentiment['sentiment'] = df_sentiment['tweet'].apply(self.classify_sentiment)
         self.df = df_sentiment
         return self.df
@@ -95,7 +95,7 @@ class TextClassifier:
         :param sentiment:
         :return:
         """
-        assert 1 == 2, "Måste fixa prompt innan denna metod körs"
+        # assert 1 == 2, "Måste fixa prompt innan denna metod körs"
         prompt_string = "Who is the TARGET of this "
         prompt_string += sentiment
         prompt_string += " TWEET?\\nTWEET=\""
@@ -113,7 +113,6 @@ class TextClassifier:
         )
 
         analyzed_sentiment = response.choices[0]['text']
-        print(analyzed_sentiment)
         # Remove spaces at the start/end of the response
         if analyzed_sentiment.startswith(' '):
             analyzed_sentiment = analyzed_sentiment[1:]
@@ -163,8 +162,8 @@ class TextClassifier:
         assert 'sentiment' in self.df.columns, \
             "'sentiment' column does not exist. Please run classify_sentiment_of_tweets first."
 
-        df_analyze = self.df
-        df_analyze['target'] = df_analyze['tweet'].apply(self.analyze_sentiment, args=(df_analyze['sentiment']))
+        df_analyze = self.df.copy()
+        df_analyze['target'] = df_analyze['tweet'].apply(self.analyze_sentiment, args=(df_analyze['sentiment'].values))
         self.df = df_analyze
         return self.df
 
@@ -185,12 +184,12 @@ if __name__ == "__main__":
     warnings.simplefilter(action='ignore', category=FutureWarning)
     pd.set_option('display.max_columns', None)
     tc = TextClassifier(model_name="text-davinci-002", from_date='2022-07-01',
-                        to_date=str(date.today()), user_name='jimmieakesson', num_tweets=20)
+                        to_date=str(date.today()), user_name='jimmieakesson', num_tweets=15)
     print(tc)
     # df = tc.classify_sentiment_of_tweets("jimmieakesson")
     # print(df)
     df = tc.classify_sentiment_of_tweets()
     print(df.head())
-    # df = tc.analyze_sentiment("Nu har sd igen gjort fel", "critical")
+    df = tc.analyze_sentiment_of_tweets()
 
-    print(df)
+    print(df.head())