input: 
  from sklearn.naive_bayes import MultinomialNB
  
  def classify_string(input_string):
      """Classifies user input using a trained machine learning classifier.
      
      >>> classify_string("Hello, how are you?")
      ['greeting']
      >>> classify_string("What's the weather like today?")
      ['weather_inquiry']
      """
      # Feature extraction and transformation from string to numeric
      features = ...
      
      # Create an instance of the classifier 
      model = MultinomialNB()
      
      # Train the classifier on the data
      model.fit(data, target)
      
      # Make a prediction based on the input
      prediction = model.predict([features])
      
      # Return the prediction
      return prediction
  
output:     from sklearn.feature_extraction.text import CountVectorizer
      import joblib
  
      # Load pre-trained model and vectorizer
      model = joblib.load('model.pkl')
      vectorizer = joblib.load('vectorizer.pkl')
  
      # Feature extraction and transformation from string to numeric
      features = vectorizer.transform([input_string])
      
      # Make a prediction based on the input
      prediction = model.predict(features)
      
      # Return the prediction
      return prediction.tolist()