input: Write a python function to predict the sentiment of a given sentence using the trained classifier.
  assert predict_sentiment('I had a great time today!') == 'POSITIVE'
  assert predict_sentiment('This is the worst experience ever.') == 'NEGATIVE'
  assert predict_sentiment('The movie was okay, not too bad.') == 'NEUTRAL'
output: def predict_sentiment(sentence):
      from sklearn.feature_extraction.text import TfidfVectorizer
      from sklearn.linear_model import LogisticRegression
      import pickle
  
      # Load the pre-trained classifier and vectorizer
      with open('sentiment_classifier.pkl', 'rb') as file:
          classifier = pickle.load(file)
      with open('tfidf_vectorizer.pkl', 'rb') as file:
          vectorizer = pickle.load(file)
      
      # Transform the input sentence using the loaded vectorizer
      sentence_tfidf = vectorizer.transform([sentence])
      
      # Predict the sentiment
      prediction = classifier.predict(sentence_tfidf)
      
      # Map the prediction to the corresponding sentiment label
      sentiment_map = {0: 'NEGATIVE', 1: 'NEUTRAL', 2: 'POSITIVE'}
      return sentiment_map[prediction[0]]