Spaces:
Running
Running
from transformers import AutoTokenizer, AutoModelForCausalLM | |
import torch | |
import gradio as gr | |
tokenizer = AutoTokenizer.from_pretrained("microsoft/DialoGPT-large") | |
model = AutoModelForCausalLM.from_pretrained("microsoft/DialoGPT-large") | |
def predict(input, history=[]): | |
new_user_input_ids = tokenizer.encode(input + tokenizer.eos_token, return_tensors='pt') | |
bot_input_ids = torch.cat([torch.LongTensor(history), new_user_input_ids], dim=-1) | |
history = model.generate(bot_input_ids, max_length=500, pad_token_id=tokenizer.eos_token_id).tolist() | |
response = tokenizer.decode(history[0]).replace("<|endoftext|>", "\n") | |
return response, history | |
css = """ | |
.chatbox {display:flex;flex-direction:column} | |
.msg {padding:4px;margin-bottom:4px;border-radius:4px;width:80%} | |
.msg.user {background-color:cornflowerblue;color:white} | |
.msg.bot {background-color:lightgreen;color:white;align-self:self-end} | |
.footer {display:none !important} | |
""" | |
gr.Interface(fn=predict, theme="grass", css=css, title="Chatbot Two", | |
inputs=[gr.inputs.Textbox(placeholder="Write a text message as if writing a text message to a human."), "state"], outputs=["html", "state"]).launch() |