input: 
  def classify_emails_as_spam_or_non_spam():
      """
      Classifies emails from the Enron Spam Dataset as either spam or non-spam using an AI model.
      The function loads the dataset, processes the text data into a bag of words model, trains an SVM classifier,
      tests the model, and prints the precision score.
  
      >>> classify_emails_as_spam_or_non_spam()
      Precision score of the model: 0.95
      """
  
output:     import pandas as pd
      from sklearn.feature_extraction.text import CountVectorizer
      from sklearn.model_selection import train_test_split
      from sklearn.svm import SVC
      from sklearn.metrics import precision_score
  
      # Load the dataset
      df = pd.read_csv('enron_spam_data.csv')
  
      # Preprocess the text data
      vectorizer = CountVectorizer()
      X = vectorizer.fit_transform(df['text'])
      y = df['label']
  
      # Split the dataset 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)
  
      # Train the SVM classifier
      svm = SVC(kernel='linear')
      svm.fit(X_train, y_train)
  
      # Test the model
      y_pred = svm.predict(X_test)
  
      # Calculate and print the precision score
      precision = precision_score(y_test, y_pred, pos_label='spam')
      print(f"Precision score of the model: {precision:.2f}")