input: def classify_emails(df: pd.DataFrame) -> float:
      """
      Creates an AI model in Python to classify emails as either spam or non-spam and 
      returns the precision score of the model on the test set.
      
      >>> df = pd.DataFrame({'EmailText': ["FreeMsg Hi there darling","Dear Friend, I need your urgent help."], 'Label': ['spam', 'non-spam']})
      >>> classify_emails(df)
      1.0
      """
      X = df['EmailText']
      y = df['Label']
  
      X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.20, random_state=42)
  
      count_vectorizer = CountVectorizer()
      X_train_counts = count_vectorizer.fit_transform(X_train)
  
      clf = svm.SVC(kernel='linear')
      clf.fit(X_train_counts, y_train)
  
      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.feature_extraction.text import CountVectorizer
      from sklearn.model_selection import train_test_split
      from sklearn import svm
      from sklearn.metrics import precision_score