input: Write a function to classify text documents into two classes: positive and negative using a Naive Bayes classifier. The function should return the predicted class label ('positive' or 'negative'). If the input is empty, return an error message. 
  def classify_sentiment(text):
      if not text:
          return "Error: Input text is empty."
      
      positiveText = "This is a great movie!"
      negativeText = "This movie was terrible"
      
      import nltk
      from nltk.classify import NaiveBayesClassifier 
    
      def label_words(words): 
          return dict([(word, True) for word in words]) 
  
      positive_tokens = nltk.word_tokenize(positiveText) 
      negative_tokens = nltk.word_tokenize(negativeText)
  
      positive_data = [(label_words(positive_tokens), 'positive')] 
      negative_data = [(label_words(negative_tokens), 'negative')]
   
      train_data = positive_data + negative_data 
      classifier = NaiveBayesClassifier.train(train_data) 
    
      return classifier.classify(label_words(nltk.word_tokenize(text)))
  
  assert classify_sentiment("This is a great movie!")=='positive'
  assert classify_sentiment("This movie was terrible")=='negative'
  assert classify_sentiment("This movie was ok")=='negative'
  assert classify_sentiment("")=="Error: Input text is empty."
output: def classify_sentiment(text):
      if not text:
          return "Error: Input text is empty."
      
      positiveText = "This is a great movie!"
      negativeText = "This movie was terrible"
      
      import nltk
      from nltk.classify import NaiveBayesClassifier 
    
      def label_words(words): 
          return dict([(word, True) for word in words]) 
  
      positive_tokens = nltk.word_tokenize(positiveText) 
      negative_tokens = nltk.word_tokenize(negativeText)
  
      positive_data = [(label_words(positive_tokens), 'positive')] 
      negative_data = [(label_words(negative_tokens), 'negative')]
   
      train_data = positive_data + negative_data 
      classifier = NaiveBayesClassifier.train(train_data) 
    
      return classifier.classify(label_words(nltk.word_tokenize(text)))