input: Write a python function to train and evaluate a Multinomial Naive Bayes model using a given CSV file with pandas and scikit-learn.
  ```python
  import pandas as pd
  from sklearn.naive_bayes import MultinomialNB
  
  def train_and_evaluate_naive_bayes(csv_file):
      df = pd.read_csv(csv_file, header=None)
      X = df[df.columns[:-1]]
      y = df[df.columns[-1]]
      model = MultinomialNB()
      model.fit(X, y)
      accuracy = model.score(X, y)
      return accuracy
  
  assert train_and_evaluate_naive_bayes('spam.csv') > 0.90
output: from sklearn.model_selection import train_test_split
  from sklearn.metrics import accuracy_score
  
  def train_and_evaluate_naive_bayes(csv_file):
      df = pd.read_csv(csv_file, header=None)
      X = df[df.columns[:-1]]
      y = df[df.columns[-1]]
      X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
      model = MultinomialNB()
      model.fit(X_train, y_train)
      y_pred = model.predict(X_test)
      accuracy = accuracy_score(y_test, y_pred)
      return accuracy