input: Write a python function to predict the quality of a given text using a trained SVM model and count vectorizer.
  assert predict_quality("This is a great product!") in ["Good", "Bad"]
  assert predict_quality("I had a terrible experience.") in ["Good", "Bad"]
  assert predict_quality("Average performance and service.") in ["Good", "Bad"]
output: def predict_quality(text):
      import joblib
      # Load the trained SVM model and count vectorizer
      model = joblib.load('svm_model.pkl')
      vectorizer = joblib.load('count_vectorizer.pkl')
      
      # Transform the input text using the count vectorizer
      text_vector = vectorizer.transform([text])
      
      # Predict the quality using the SVM model
      prediction = model.predict(text_vector)
      
      # Map the prediction to "Good" or "Bad"
      return "Good" if prediction == 1 else "Bad"