input: Write a python function to classify text using scikit-learn's CountVectorizer and MultinomialNB.
  assert classify_text(["I love programming", "Python is awesome", "I hate bugs"], ["positive", "positive", "negative"], "I love coding") == "positive"
  assert classify_text(["The movie was great", "I did not like the film", "It was an amazing experience"], ["positive", "negative", "positive"], "The experience was terrible") == "negative"
  assert classify_text(["Happy birthday", "Congratulations on your promotion", "Sorry to hear that"], ["positive", "positive", "negative"], "Happy to see you") == "positive"
output: def classify_text(texts, labels, new_text):
      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)
  
      new_text_vectorized = vectorizer.transform([new_text])
      prediction = clf.predict(new_text_vectorized)
  
      return prediction[0]