input: Write a python function to predict sentiment of a given text using a pre-trained model.
  assert sentiment_model("This movie was horrible and I never want to watch it again.") == 'Negative'
  assert sentiment_model("I loved every moment of this wonderful film!") == 'Positive'
  assert sentiment_model("It was an average experience, not too bad but not great either.") == 'Neutral'
output: def sentiment_model(text):
      from transformers import pipeline
      sentiment_pipeline = pipeline("sentiment-analysis")
      
      result = sentiment_pipeline(text)[0]
      label = result['label']
      
      if label == 'POSITIVE':
          return 'Positive'
      elif label == 'NEGATIVE':
          return 'Negative'
      else:
          return 'Neutral'