input: Write a python function to train a Multinomial Naive Bayes model on a given dataset and calculate its accuracy.
  assert train_and_evaluate_model('emails.csv') > 0.9
  assert train_and_evaluate_model('spam.csv') > 0.85
  assert train_and_evaluate_model('ham.csv') > 0.8
output: def train_and_evaluate_model(file_path):
      import pandas as pd
      from sklearn.feature_extraction.text import CountVectorizer
      from sklearn.model_selection import train_test_split
      from sklearn.naive_bayes import MultinomialNB
      from sklearn.metrics import accuracy_score
  
      # Load the dataset
      data = pd.read_csv(file_path)
      
      # Assuming the dataset has two columns: 'text' and 'label'
      X = data['text']
      y = data['label']
      
      # Split the dataset into training and testing sets
      X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
      
      # Convert text data into feature vectors
      vectorizer = CountVectorizer()
      X_train_vectors = vectorizer.fit_transform(X_train)
      X_test_vectors = vectorizer.transform(X_test)
      
      # Train the Multinomial Naive Bayes model
      model = MultinomialNB()
      model.fit(X_train_vectors, y_train)
      
      # Make predictions on the test set
      y_pred = model.predict(X_test_vectors)
      
      # Calculate the accuracy of the model
      accuracy = accuracy_score(y_test, y_pred)
      
      return accuracy