input: Write a python function to create a Naive Bayes classifier for text classification.
  assert create_naive_bayes_classifier(["spam message", "help needed", "win big prize"], [1, 0, 1]).predict(["win big prize"])[0] == 1
  assert create_naive_bayes_classifier(["urgent call", "meeting schedule", "lottery winner"], [1, 0, 1]).predict(["meeting schedule"])[0] == 0
  assert create_naive_bayes_classifier(["buy now", "urgent", "important"], [1, 0, 1]).predict(["urgent"])[0] == 0
output: def create_naive_bayes_classifier(texts, labels):
      from sklearn.feature_extraction.text import CountVectorizer
      from sklearn.naive_bayes import MultinomialNB
  
      vectorizer = CountVectorizer()
      X = vectorizer.fit_transform(texts)
      
      clf = MultinomialNB()
      clf.fit(X, labels)
      
      return clf