input: Write a python function to classify emails as spam or non-spam using Naive Bayes classifier.
  assert classify_email("Hey, I'm offering a 50% discount on our services!") == [1]
  assert classify_email("Did you see the latest news about the software update?") == [0]
  assert classify_email("Do you want to make money fast and easy?") == [1]
  assert classify_email("Please save the date for our upcoming event on April 15th.") == [0]
  assert classify_email("You have one last chance to get this limited offer!") == [1]
output: def classify_email(email):
      from sklearn.feature_extraction.text import CountVectorizer
      from sklearn.naive_bayes import MultinomialNB
      
      # Training data (for simplicity, this is hardcoded here)
      emails = [
          "Hey, I'm offering a 50% discount on our services!",
          "Did you see the latest news about the software update?",
          "Do you want to make money fast and easy?",
          "Please save the date for our upcoming event on April 15th.",
          "You have one last chance to get this limited offer!"
      ]
      labels = [1, 0, 1, 0, 1]  # 1 for spam, 0 for non-spam
      
      # Create a CountVectorizer
      vectorizer = CountVectorizer()
      X_train = vectorizer.fit_transform(emails)
      
      # Create and train the Naive Bayes classifier
      clf = MultinomialNB()
      clf.fit(X_train, labels)
      
      # Transform the input email
      X_test = vectorizer.transform([email])
      
      # Predict and return the result
      return clf.predict(X_test).tolist()