renamed text classifier

text-classifier/{text_classifier.py → TextClassifier.py}

@@ -3,7 +3,7 @@ import openai
 import regex as re
 openai.api_key = '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' 
 
-class text_classifier:  
+class TextClassifier:
   
     def classify_topics(tweet_dict): 
         tweet_list = list(tweet_dict.keys())  
@@ -26,7 +26,7 @@ class text_classifier:
             classifications_unclean = response.choices[0]['text'] 
             prediction_dict[tweet] = classifications_unclean 
 
-        return text_classifier.cleanup_topic_results(prediction_dict, tweet_dict) 
+        return TextClassifier.cleanup_topic_results(prediction_dict, tweet_dict)
  
  
     def classify_sentiments(tweet_dict):

text-classifier/main.py

@@ -1,4 +1,4 @@
-from text_classifier import text_classifier
+from TextClassifier import TextClassifier
 
 # Some examples of tweets:
 data_dict = {'25 years ago we made a promise to the people of Hong Kong.   We intend to keep it.  https://t.co/nIN96ZydgV': {'hour': '17',
@@ -40,9 +40,9 @@ data_dict = {'25 years ago we made a promise to the people of Hong Kong.   We in
   }
 
 # Classify the TOPICS and insert the results into the data dictionary found above
-topic_results = text_classifier.classify_topics(data_dict)
+topic_results = TextClassifier.classify_topics(data_dict)
 # Classify the SENTIMENTS and insert the results into the data dictionary found above
-sentiment_results = text_classifier.classify_sentiments(data_dict)   
+sentiment_results = TextClassifier.classify_sentiments(data_dict)
 # Print simple statistics related to TOPICS and SENTIMENTS
-text_classifier.print_stats(sentiment_results) 
+TextClassifier.print_stats(sentiment_results)