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| from transformers import AutoModelWithLMHead, AutoTokenizer | |
| import torch | |
| import gradio as gr | |
| tokenizer = AutoTokenizer.from_pretrained('microsoft/DialoGPT-medium') | |
| model = AutoModelWithLMHead.from_pretrained('output-medium') | |
| chat_history_ids = None | |
| step = 0 | |
| def predict(input, chat_history_ids=chat_history_ids, step=step): | |
| # encode the new user input, add the eos_token and return a tensor in Pytorch | |
| new_user_input_ids = tokenizer.encode( | |
| input + tokenizer.eos_token, return_tensors='pt') | |
| # append the new user input tokens to the chat history | |
| bot_input_ids = torch.cat( | |
| [chat_history_ids, new_user_input_ids], dim=-1) if step > 0 else new_user_input_ids | |
| # generated a response while limiting the total chat history to 1000 tokens, | |
| chat_history_ids = model.generate( | |
| bot_input_ids, max_length=1000, | |
| pad_token_id=tokenizer.eos_token_id, | |
| no_repeat_ngram_size=3, | |
| do_sample=True, | |
| top_k=100, | |
| top_p=0.7, | |
| temperature=0.8 | |
| ) | |
| step = step + 1 | |
| output = tokenizer.decode( | |
| chat_history_ids[:, bot_input_ids.shape[-1]:][0], skip_special_tokens=True) | |
| return output | |
| gr.Interface(fn=predict, inputs="text", outputs="text").launch(debug=True) | |