input: Write a function to train a Naive Bayes text classification model using scikit-learn.
  assert train_naive_bayes_model(pd.DataFrame({'text': ['I love programming', 'Python is great', 'I hate bugs'], 'label': [1, 1, 0]})) is not None
  assert train_naive_bayes_model(pd.DataFrame({'text': ['Cats are cute', 'Dogs are loyal', 'I prefer cats'], 'label': [1, 1, 1]})).predict([vectorizer.transform(['Dogs are loyal'])]) == [1]
  assert train_naive_bayes_model(pd.DataFrame({'text': ['Summer is hot', 'Winter is cold', 'Spring is pleasant'], 'label': [0, 0, 1]})).predict([vectorizer.transform(['Spring is pleasant'])]) == [1]
output: def train_naive_bayes_model(df):
      from sklearn.feature_extraction.text import CountVectorizer
      from sklearn.naive_bayes import MultinomialNB
      from sklearn.pipeline import make_pipeline
      
      vectorizer = CountVectorizer()
      model = MultinomialNB()
      pipeline = make_pipeline(vectorizer, model)
      
      pipeline.fit(df['text'], df['label'])
      return pipeline