input: Write a python function to train a Naive Bayes classifier on a given dataset of emails to identify spam and non-spam emails, and return the accuracy of the model. 
  assert train_and_evaluate_model('spam.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 dataset
      data = pd.read_csv(file_path)
  
      # Features and labels
      X = data['email']
      y = data['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 Naive Bayes classifier
      clf = MultinomialNB()
      clf.fit(X_train_vec, y_train)
  
      # Predict and evaluate
      y_pred = clf.predict(X_test_vec)
      accuracy = accuracy_score(y_test, y_pred)
  
      return accuracy