input: Write a python function to read a CSV file, split it into training and testing sets, and train a Multinomial Naive Bayes classifier. The function should return the accuracy on both the training and testing sets.
  assert train_and_evaluate('spam.csv')[0] > 0.9
  assert train_and_evaluate('spam.csv')[1] > 0.8
output: def train_and_evaluate(csv_file):
      import pandas as pd
      from sklearn.model_selection import train_test_split
      from sklearn.feature_extraction.text import CountVectorizer
      from sklearn.naive_bayes import MultinomialNB
      from sklearn.metrics import accuracy_score
  
      # Load data
      df = pd.read_csv(csv_file)
      
      # Assuming the CSV has two columns: 'text' and 'label'
      X = df['text']
      y = df['label']
      
      # Split 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)
      
      # Vectorize text data
      vectorizer = CountVectorizer()
      X_train_vec = vectorizer.fit_transform(X_train)
      X_test_vec = vectorizer.transform(X_test)
      
      # Train Multinomial Naive Bayes classifier
      clf = MultinomialNB()
      clf.fit(X_train_vec, y_train)
      
      # Predict and calculate accuracy
      y_train_pred = clf.predict(X_train_vec)
      y_test_pred = clf.predict(X_test_vec)
      
      train_accuracy = accuracy_score(y_train, y_train_pred)
      test_accuracy = accuracy_score(y_test, y_test_pred)
      
      return train_accuracy, test_accuracy