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from transformers import BlenderbotTokenizer, BlenderbotForConditionalGeneration
import torch
import gradio as gr
mname = "facebook/blenderbot-400M-distill"
model = BlenderbotForConditionalGeneration.from_pretrained(mname)
tokenizer = BlenderbotTokenizer.from_pretrained(mname)
def take_last_tokens(inputs, note_history, history):
"""Filter the last 128 tokens"""
if inputs['input_ids'].shape[1] > 128:
inputs['input_ids'] = torch.tensor([inputs['input_ids'][0][-128:].tolist()])
inputs['attention_mask'] = torch.tensor([inputs['attention_mask'][0][-128:].tolist()])
note_history = ['</s> <s>'.join(note_history[0].split('</s> <s>')[2:])]
history = history[1:]
return inputs, note_history, history
def add_note_to_history(note, note_history):
"""Add a note to the historical information"""
note_history.append(note)
note_history = '</s> <s>'.join(note_history)
return [note_history]
title = "Blenderbot Tokenizer with Conditional Generation State of the Art"
description = """Blenderbot"""
def chat(message, history):
history = history or []
if history:
history_useful = ['</s> <s>'.join([str(a[0])+'</s> <s>'+str(a[1]) for a in history])]
else:
history_useful = []
history_useful = add_note_to_history(message, history_useful)
inputs = tokenizer(history_useful, return_tensors="pt")
inputs, history_useful, history = take_last_tokens(inputs, history_useful, history)
reply_ids = model.generate(**inputs)
response = tokenizer.batch_decode(reply_ids, skip_special_tokens=True)[0]
history_useful = add_note_to_history(response, history_useful)
list_history = history_useful[0].split('</s> <s>')
history.append((list_history[-2], list_history[-1]))
return history, history
gr.Interface(
fn=chat,
theme="huggingface",
css=".footer {display:none !important}",
inputs=["text", "state"],
outputs=["chatbot", "state"],
title=title,
description=description,
allow_flagging="never",
).launch() |