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from transformers import AutoModelForCausalLM, AutoTokenizer | |
import torch | |
import gradio as gr | |
tokenizer = AutoTokenizer.from_pretrained("microsoft/DialoGPT-large") | |
model = AutoModelForCausalLM.from_pretrained("microsoft/DialoGPT-large") | |
def chat_with_history(message, chat_history=None): | |
# Initialize chat history if not provided | |
if chat_history is None: | |
chat_history = [] | |
# Encode the new user input, add the eos_token, and return a tensor in PyTorch | |
new_user_input_ids = tokenizer.encode(message + tokenizer.eos_token, return_tensors='pt') | |
# Append the new user input tokens to the chat history | |
bot_input_ids = torch.cat([tokenizer.encode(pair[0] + tokenizer.eos_token, return_tensors='pt') for pair in chat_history] + [new_user_input_ids], dim=-1) | |
# Generate a response while limiting the total chat history to 1000 tokens | |
chat_history_ids = model.generate(bot_input_ids, max_length=1000, pad_token_id=tokenizer.eos_token_id) | |
# Decode the last output tokens from bot | |
response = tokenizer.decode(chat_history_ids[:, bot_input_ids.shape[-1]:][0], skip_special_tokens=True) | |
# Update the chat history with the new user message and bot response | |
chat_history.append([message, response]) | |
return response | |
demo = gr.ChatInterface( | |
fn=chat_with_history, | |
examples=["hey how are you ?", "hola", "Yo!"], | |
title="Multi Chat Bot" | |
) | |
demo.launch() | |