input: 
  def classify_emails_as_spam_or_non_spam():
      """This function creates an AI model in Python to classify emails from the Enron Spam Dataset as either spam or non-spam.
      It uses a bag-of-words model and a Linear SVM classifier to achieve the classification. The function loads the dataset,
      preprocesses the data, trains the model, and then evaluates its precision score.
      Example:
      >>> # Assuming "spam.csv" is correctly located and contains the appropriate columns 'EmailText' and 'Label'
      >>> classify_emails_as_spam_or_non_spam()
      Precision score of the model: 0.95
      """
  
  import pandas as pd
  from sklearn.feature_extraction.text import CountVectorizer
  from sklearn.model_selection import train_test_split
  from sklearn import svm
  from sklearn.metrics import precision_score
  
  # Load the dataset
  df = pd.read_csv("spam.csv", encoding='iso-8859-1')
  # Load label and data
  X = df['EmailText']
  y = df['Label']
  
  # Split data into train-test sets
  X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.20, random_state=42)
  
  # Create bag of words model
  count_vectorizer = CountVectorizer()
  X_train_counts = count_vectorizer.fit_transform(X_train)
  
  # Train the model
  clf = svm.SVC(kernel='linear')
  clf.fit(X_train_counts, y_train)
  
  # Test the model
  X_test_counts = count_vectorizer.transform(X_test)
  predictions = clf.predict(X_test_counts)
  score = precision_score(y_test, predictions, average='binary')
  
  print('Precision score of the model:', score)
  
output:     
      return score
  