input: Write a python function to classify the sentiment of a given sentence using the trained Naive Bayes classifier.
  assert sentiment_classifier("This is a fantastic product!") == 'positive'
  assert sentiment_classifier("I am very disappointed with this item.") == 'negative'
  assert sentiment_classifier("The service was excellent and the staff was friendly.") == 'positive'
output: def sentiment_classifier(sentence):
      import joblib
      
      # Load the pre-trained Naive Bayes model and the vectorizer
      model = joblib.load('naive_bayes_model.pkl')
      vectorizer = joblib.load('vectorizer.pkl')
      
      # Transform the input sentence using the loaded vectorizer
      transformed_sentence = vectorizer.transform([sentence])
      
      # Predict the sentiment using the model
      prediction = model.predict(transformed_sentence)
      
      return 'positive' if prediction[0] == 1 else 'negative'