input: Write a python function to train and evaluate a Naive Bayes model on a text dataset and return the model's accuracy.
  assert train_and_evaluate_model('data.csv') > 0.5
output: def train_and_evaluate_model(file_path):
      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 the data
      data = pd.read_csv(file_path)
  
      # Assuming the dataset has 'text' and 'label' columns
      X = data['text']
      y = data['label']
  
      # Split the data into training and test sets
      X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
  
      # Convert text data to numerical data
      vectorizer = CountVectorizer()
      X_train_vec = vectorizer.fit_transform(X_train)
      X_test_vec = vectorizer.transform(X_test)
  
      # Train the Naive Bayes model
      model = MultinomialNB()
      model.fit(X_train_vec, y_train)
  
      # Make predictions
      y_pred = model.predict(X_test_vec)
  
      # Evaluate the model
      accuracy = accuracy_score(y_test, y_pred)
      
      return accuracy