input: Write a python function to classify text into categories using Naive Bayes classifier.
  assert classify_text("It wasn't the best night.") in ['positive', 'negative', 'neutral']
  assert classify_text("I love this movie!") in ['positive', 'negative', 'neutral']
  assert classify_text("The food was terrible.") in ['positive', 'negative', 'neutral']
output: def classify_text(text):
      from sklearn.feature_extraction.text import CountVectorizer
      from sklearn.naive_bayes import MultinomialNB
      from sklearn.pipeline import make_pipeline
  
      # Example training data
      train_data = [
          "I love this movie!",
          "This film is amazing!",
          "The food was terrible.",
          "It wasn't the best night.",
          "I'm so happy with this product!",
          "I'm very disappointed.",
          "This is the worst experience.",
          "Absolutely fantastic!",
      ]
      train_labels = [
          "positive",
          "positive",
          "negative",
          "neutral",
          "positive",
          "negative",
          "negative",
          "positive",
      ]
  
      # Create a model pipeline
      model = make_pipeline(CountVectorizer(), MultinomialNB())
  
      # Train the model
      model.fit(train_data, train_labels)
  
      # Predict the category
      return model.predict([text])[0]