Spaces:
Running
Running
import os | |
import gradio as gr | |
title = "Have Fun With ChubbyBot" | |
description = """ | |
<p> | |
<center> | |
The bot is trained on blended_skill_talk dataset using facebook/blenderbot-400M-distill. | |
<img src="https://huggingface.co/spaces/EXFINITE/BlenderBot-UI/resolve/main/img/cover.png" alt="rick" width="250"/> | |
</center> | |
</p> | |
""" | |
article = "<p style='text-align: center'><a href='https://arxiv.org/abs/1907.06616' target='_blank'>Recipes for building an open-domain chatbot</a></p><p style='text-align: center'><a href='https://parl.ai/projects/recipes/' target='_blank'>Original PARLAI Code</a></p></center></p>" | |
from transformers import AutoModelForCausalLM, AutoTokenizer | |
import torch | |
tokenizer = AutoTokenizer.from_pretrained("facebook/blenderbot-400M-distill") | |
model = AutoModelForCausalLM.from_pretrained("facebook/blenderbot-400M-distill") | |
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() | |
# convert the tokens to text, and then split the responses into lines | |
response = tokenizer.decode(history[0]).replace("<|endoftext|>", "\n") | |
return response, history | |
gr.Interface( | |
fn = predict, | |
inputs = ["textbox","state"], | |
outputs = ["chatbot","state"], | |
theme ="grass", | |
title = title, | |
description = description, | |
article = article | |
).launch(enable_queue=True) | |