input: Write a python function to classify the category of a book title using a trained Naive Bayes model.
  assert classify_book(["Pride and Prejudice"]) == "Romance"
  assert classify_book(["The Great Gatsby"]) == "Fiction"
  assert classify_book(["A Brief History of Time"]) == "Science"
output: def classify_book(titles):
      from sklearn.feature_extraction.text import CountVectorizer
      from sklearn.naive_bayes import MultinomialNB
      import numpy as np
      
      # Example training data
      book_titles = [
          "Pride and Prejudice", "Emma", "Sense and Sensibility",  # Romance
          "The Great Gatsby", "To Kill a Mockingbird", "1984",  # Fiction
          "A Brief History of Time", "The Selfish Gene", "The Elegant Universe"  # Science
      ]
      categories = [
          "Romance", "Romance", "Romance",
          "Fiction", "Fiction", "Fiction",
          "Science", "Science", "Science"
      ]
      
      # Vectorizing the book titles
      vectorizer = CountVectorizer()
      X = vectorizer.fit_transform(book_titles)
      
      # Training the Naive Bayes model
      model = MultinomialNB()
      model.fit(X, categories)
      
      # Classifying the input titles
      X_input = vectorizer.transform(titles)
      predictions = model.predict(X_input)
      
      return predictions[0]