| from transformers import GPT3LMHeadModel, GPT3Tokenizer | |
| # Load pre-trained model and tokenizer | |
| model_name = "gpt3" # You could choose another model here | |
| model = GPT2LMHeadModel.from_pretrained(model_name) | |
| tokenizer = GPT2Tokenizer.from_pretrained(model_name) | |
| def generate_feedback(user_answer): | |
| # Encode user input to model's input format | |
| inputs = tokenizer.encode(user_answer, return_tensors='pt') | |
| # Generate response from the model | |
| outputs = model.generate(inputs, max_length=150, num_return_sequences=1, no_repeat_ngram_size=2) | |
| # Decode the model's output to text | |
| feedback = tokenizer.decode(outputs[0], skip_special_tokens=True) | |
| return feedback | |