input: Write a python function to classify an article as either 'Sports' or 'Politics' using a trained Naive Bayes classifier.
  assert predictClass("The hockey game was intense and thrilling.") == 'Sports'
  assert predictClass("The peace talks in the Middle East are ongoing.") == 'Politics'
  assert predictClass("The team scored a last-minute goal to win the championship.") == 'Sports'
output: def predictClass(article):
      from sklearn.feature_extraction.text import CountVectorizer
      from sklearn.naive_bayes import MultinomialNB
      import pickle
  
      # Load the trained Naive Bayes classifier and CountVectorizer
      with open('naive_bayes_classifier.pkl', 'rb') as model_file:
          classifier = pickle.load(model_file)
      
      with open('count_vectorizer.pkl', 'rb') as vectorizer_file:
          vectorizer = pickle.load(vectorizer_file)
  
      # Transform the article using the same vectorizer
      article_vector = vectorizer.transform([article])
      
      # Predict the class
      predicted_class = classifier.predict(article_vector)
      
      return predicted_class[0]