input: Write a python function to predict the sentiment score of a given text using a trained LinearSVC model and CountVectorizer.
  assert predict_sentiment('I hated this movie')==0
  assert predict_sentiment('I loved this movie')==1
  assert predict_sentiment('It was an average movie')==0
output: def predict_sentiment(text):
      import pickle
      from sklearn.feature_extraction.text import CountVectorizer
      from sklearn.svm import LinearSVC
      
      # Load the trained model and vectorizer from disk
      with open('linear_svc_model.pkl', 'rb') as model_file:
          model = pickle.load(model_file)
      
      with open('count_vectorizer.pkl', 'rb') as vectorizer_file:
          vectorizer = pickle.load(vectorizer_file)
      
      # Transform the input text using the count vectorizer
      text_vectorized = vectorizer.transform([text])
      
      # Predict the sentiment using the trained model
      sentiment = model.predict(text_vectorized)
      
      return sentiment[0]