from transformers import AutoModelForCausalLM, AutoTokenizer import torch tokenizer = AutoTokenizer.from_pretrained("ncoop57/DiGPTame-medium") model = AutoModelForCausalLM.from_pretrained("ncoop57/DiGPTame-medium") 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 import gradio as gr gr.Interface(fn=predict, theme="default", css=".footer {display:none !important}", inputs=["text", "state"], outputs=["chatbot", "state"]).launch()