input: 
  def classify_emails():
      """Create an AI model in Python to classify emails from the Enron Spam Dataset as either spam or non-spam.
      >>> classify_emails()
      Precision score of the model: 0.95
      """
  
output:     
      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 precision_score
  
      # Load the dataset
      df = pd.read_csv('enron_spam_data.csv')
  
      # Split the dataset into training and testing sets
      X_train, X_test, y_train, y_test = train_test_split(df['message'], df['label'], test_size=0.2, random_state=42)
  
      # Vectorize the text data
      vectorizer = CountVectorizer()
      X_train_vec = vectorizer.fit_transform(X_train)
      X_test_vec = vectorizer.transform(X_test)
  
      # Train the model
      model = MultinomialNB()
      model.fit(X_train_vec, y_train)
  
      # Predict on the test set
      y_pred = model.predict(X_test_vec)
  
      # Calculate precision score
      precision = precision_score(y_test, y_pred, pos_label='spam')
      print(f"Precision score of the model: {precision:.2f}")
  