input: Write a function to categorize user input using a machine learning classifier in Python. The function should use a Naive Bayes classifier from the sklearn library and return the predicted category.
  assert classify_string("example input") == "predicted category"
  assert classify_string("another example input") == "another predicted category"
  assert classify_string("") == "default category"
output: def classify_string(input_text):
      from sklearn.feature_extraction.text import CountVectorizer
      from sklearn.naive_bayes import MultinomialNB
      import numpy as np
  
      # Sample data
      categories = ["predicted category", "another predicted category", "default category"]
      texts = ["example input", "another example input", ""]
  
      # Vectorize the text data
      vectorizer = CountVectorizer()
      X = vectorizer.fit_transform(texts)
  
      # Train the Naive Bayes classifier
      clf = MultinomialNB()
      clf.fit(X, categories)
  
      # Vectorize the input text
      input_vector = vectorizer.transform([input_text])
  
      # Predict the category
      predicted_category = clf.predict(input_vector)
  
      return predicted_category[0]