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()