input: Write a python function to train a Naive Bayes model to classify text data and another function to make predictions using the trained model.
  assert accuracy_score(predict(['this is a test'], *train_model(['text data', 'some more text'], [0, 1])), [1]) >= 0
  assert isinstance(train_model(['example text'], [0])[0], GaussianNB)
  assert isinstance(train_model(['example text'], [0])[1], CountVectorizer)
output: from sklearn.naive_bayes import GaussianNB
  from sklearn.feature_extraction.text import CountVectorizer
  from sklearn.metrics import accuracy_score
  
  def train_model(text_data, labels):
      vectorizer = CountVectorizer()
      X = vectorizer.fit_transform(text_data).toarray()
      model = GaussianNB()
      model.fit(X, labels)
      return model, vectorizer
  
  def predict(texts, model, vectorizer):
      X = vectorizer.transform(texts).toarray()
      return model.predict(X)