input: Write a python function to predict sentiment of a given text using trained LinearSVC model.
  assert predict_sentiment('The food at this restaurant is terrible')=='Negative'
  assert predict_sentiment('I love this place!')=='Positive'
  assert predict_sentiment('Not worth the money')=='Negative'
output: def predict_sentiment(text):
      import joblib
      
      # Load the trained model and the vectorizer
      model = joblib.load('linear_svc_model.pkl')
      vectorizer = joblib.load('vectorizer.pkl')
      
      # Vectorize the input text
      text_vector = vectorizer.transform([text])
      
      # Predict the sentiment
      prediction = model.predict(text_vector)
      
      # Map the prediction to sentiment
      sentiment = 'Positive' if prediction[0] == 1 else 'Negative'
      
      return sentiment