input: Write a python function to predict if a tweet is positive or negative using a trained Naive Bayes classifier.
  assert predict_tweet_sentiment("I am feeling great today!") == "The tweet is positive!"
  assert predict_tweet_sentiment("I am feeling sad today.") == "The tweet is negative!"
  assert predict_tweet_sentiment("Today was an incredible day!") == "The tweet is positive!"
  assert predict_tweet_sentiment("Today was an awful day!") == "The tweet is negative!"
output: def predict_tweet_sentiment(tweet):
      import pickle
      from sklearn.feature_extraction.text import CountVectorizer
  
      # Load the trained Naive Bayes classifier and the vectorizer
      with open('naive_bayes_model.pkl', 'rb') as model_file:
          classifier = pickle.load(model_file)
      with open('vectorizer.pkl', 'rb') as vectorizer_file:
          vectorizer = pickle.load(vectorizer_file)
  
      # Transform the tweet into the format expected by the classifier
      tweet_vector = vectorizer.transform([tweet])
  
      # Predict the sentiment
      prediction = classifier.predict(tweet_vector)[0]
  
      if prediction == 1:
          return "The tweet is positive!"
      else:
          return "The tweet is negative!"