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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>"
import torch
from transformers import BlenderbotTokenizer, BlenderbotForConditionalGeneration
tokenizer = BlenderbotTokenizer.from_pretrained("facebook/blenderbot-400M-distill")
model = BlenderbotForConditionalGeneration.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 the right format
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 = ["textbox","state"],
outputs = ["chatbot","state"],
theme ="seafoam",
title = title,
description = description,
article = article
).launch(enable_queue=True)
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