input: Write a python function to train a Naive Bayes classifier on text data and return the model accuracy.
  assert 0 <= train_naive_bayes_classifier('data.csv') <= 1
output: def train_naive_bayes_classifier(file_path):
      import pandas as pd
      from sklearn.feature_extraction.text import CountVectorizer
      from sklearn.naive_bayes import MultinomialNB
      from sklearn.model_selection import train_test_split
      from sklearn.metrics import accuracy_score
  
      # Load the data
      data = pd.read_csv(file_path)
  
      # Assuming the data has two columns: 'text' and 'label'
      X = data['text']
      y = data['label']
  
      # Convert text data into feature vectors
      vectorizer = CountVectorizer()
      X_vectorized = vectorizer.fit_transform(X)
  
      # Split the data into training and testing sets
      X_train, X_test, y_train, y_test = train_test_split(X_vectorized, y, test_size=0.2, random_state=42)
  
      # Train a Naive Bayes classifier
      model = MultinomialNB()
      model.fit(X_train, y_train)
  
      # Make predictions on the test set
      y_pred = model.predict(X_test)
  
      # Calculate and return the accuracy
      accuracy = accuracy_score(y_test, y_pred)
      return accuracy