input: Write a python function to train a Naive Bayes classifier to detect spam emails from a given CSV file.
  assert train_naive_bayes('test_emails.csv') is not None
  assert 'spam' in train_naive_bayes('test_emails.csv')
  assert 'ham' in train_naive_bayes('test_emails.csv')
output: def train_naive_bayes(csv_file):
      import pandas as pd
      from sklearn.feature_extraction.text import CountVectorizer
      from sklearn.naive_bayes import MultinomialNB
      from sklearn.pipeline import make_pipeline
  
      # Load the data
      df = pd.read_csv(csv_file)
  
      # Assume the CSV has columns 'label' and 'message'
      X = df['message']
      y = df['label']
  
      # Create a Naive Bayes model
      model = make_pipeline(CountVectorizer(), MultinomialNB())
  
      # Train the model
      model.fit(X, y)
  
      # Return the trained model
      return model