| ```python | |
| from transformers import GPT2Tokenizer, GPT2LMHeadModel | |
| def generate_response(input_text): | |
| inputs = tokenizer(input_text, return_tensors="pt") | |
| output_sequences = model.generate( | |
| input_ids=inputs['input_ids'], | |
| attention_mask=inputs['attention_mask'], | |
| max_length=100, # Adjusted max_length | |
| temperature=0.3, | |
| top_k=40, | |
| top_p=0.85, | |
| num_return_sequences=1, | |
| no_repeat_ngram_size=2, | |
| pad_token_id=tokenizer.eos_token_id, | |
| eos_token_id=tokenizer.eos_token_id, | |
| early_stopping=True, | |
| do_sample=True, | |
| use_cache=True, | |
| ) | |
| full_generated_text = tokenizer.decode(output_sequences[0], skip_special_tokens=True) | |
| bot_response_start = full_generated_text.find('[Bot]') + len('[Bot]') | |
| bot_response = full_generated_text[bot_response_start:] | |
| return bot_response | |
| model_name = 'KhantKyaw/Chat_GPT-2' | |
| tokenizer = GPT2Tokenizer.from_pretrained(model_name) | |
| model = GPT2LMHeadModel.from_pretrained(model_name) | |
| response = generate_response(user_input) | |
| print("Chatbot:", response) | |
| ``` |