input: 
  def classify_string(input_string):
      """
      Create a machine learning classifier in Python for categorizing user input using 
      Naive Bayes.
      
      Example:
      >>> classify_string("Hello, how are you?")
      array(['greeting'], dtype=object)
      >>> classify_string("What is the weather today?")
      array(['weather'], dtype=object)
      """
  
output:     from sklearn.feature_extraction.text import CountVectorizer
      from sklearn.naive_bayes import MultinomialNB
      from sklearn.pipeline import make_pipeline
      import numpy as np
  
      # Sample training data
      data = [
          ("Hello, how are you?", "greeting"),
          ("What is the weather today?", "weather"),
          ("Good morning!", "greeting"),
          ("How's the weather?", "weather"),
          ("Hi there!", "greeting"),
          ("What's the forecast?", "weather")
      ]
  
      texts, labels = zip(*data)
  
      # Create a pipeline with CountVectorizer and MultinomialNB
      model = make_pipeline(CountVectorizer(), MultinomialNB())
      model.fit(texts, labels)
  
      # Predict the category of the input string
      return np.array([model.predict([input_string])[0]], dtype=object)