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| # coding=utf-8 | |
| # author: xusong <[email protected]> | |
| # time: 2022/8/25 16:57 | |
| """ | |
| https://gradio.app/creating_a_chatbot/ | |
| https://huggingface.co/spaces/abidlabs/chatbot-stylized/blob/main/app.py | |
| """ | |
| from transformers import AutoModelForCausalLM, AutoTokenizer | |
| import torch | |
| import gradio as gr | |
| tokenizer = AutoTokenizer.from_pretrained("microsoft/DialoGPT-medium") | |
| model = AutoModelForCausalLM.from_pretrained("microsoft/DialoGPT-medium") | |
| def predict(input, history=[]): | |
| # tokenize the new input sentence | |
| 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([torch.LongTensor(history), new_user_input_ids], dim=-1) | |
| # generate a response | |
| history = model.generate(bot_input_ids, max_length=1000, pad_token_id=tokenizer.eos_token_id).tolist() # gpt2生成结果会拼接上输入。 | |
| # convert the tokens to text, and then split the responses into lines | |
| response = tokenizer.decode(history[0]).split("<|endoftext|>") | |
| response = [(response[i], response[i+1]) for i in range(0, len(response)-1, 2)] # convert to tuples of list | |
| return response, history | |
| gr.Interface(fn=predict, | |
| inputs=["text", "state"], | |
| outputs=["chatbot", "state"]).launch() |