input: 
  def classify_emails(df: pd.DataFrame) -> float:
      """ Classify emails from the Enron Spam Dataset as either spam or non-spam using a SVM model.
      The model uses a bag of words approach with a CountVectorizer and is evaluated using precision score.
      >>> df = pd.DataFrame({
      ... 'EmailText': ['Dear Sir, I am a Nigerian prince.', 'Meeting at 3 PM today.'],
      ... 'Label': ['spam', 'ham']
      ... })
      >>> classify_emails(df) >= 0
      True
      """
      # Split data into features and labels
      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')
      
      return score
  
output:     from sklearn.model_selection import train_test_split
      from sklearn.feature_extraction.text import CountVectorizer
      from sklearn import svm
      from sklearn.metrics import precision_score