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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 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=4000, 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]).split("<|endoftext|>")
#print('decoded_response-->>'+str(response))
response = [(response[i], response[i+1]) for i in range(0, len(response)-1, 2)] # convert to tuples of list
#print('response-->>'+str(response))
return response, history
description = "This is a chatbot application based on the DialoGPT model of Microsoft."
title = "Chat with DialoGPT"
examples = [["What is the meaning of life?"]]
gr.Interface(fn=predict,
title=title,
description=description,
inputs=["text", "state"],
outputs=["chatbot", "state"]).launch()
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