input: Write a python function to train a Naive Bayes model for email spam detection.
  assert train_naive_bayes('email_dataset.csv').shape[0] > 0
  assert 'spam' in train_naive_bayes('email_dataset.csv')
  assert 'ham' in train_naive_bayes('email_dataset.csv')
output: def train_naive_bayes(file_path):
      import pandas as pd
      from sklearn.feature_extraction.text import CountVectorizer
      from sklearn.naive_bayes import MultinomialNB
      from sklearn.pipeline import Pipeline
      from sklearn.model_selection import train_test_split
      from sklearn import metrics
      
      # Load the dataset
      data = pd.read_csv(file_path)
      
      # Split the dataset into training and testing sets
      X_train, X_test, y_train, y_test = train_test_split(data['email'], data['label'], test_size=0.2, random_state=42)
      
      # Create a pipeline to vectorize the data and train the model
      pipeline = Pipeline([
          ('vectorizer', CountVectorizer()),
          ('classifier', MultinomialNB())
      ])
      
      # Train the model
      pipeline.fit(X_train, y_train)
      
      # Predict on the test set
      y_pred = pipeline.predict(X_test)
      
      # Calculate accuracy
      accuracy = metrics.accuracy_score(y_test, y_pred)
      print(f'Accuracy: {accuracy}')
      
      return pipeline