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# coding=utf-8
# author: xusong <xusong28@jd.com>
# 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() |