#from transformers import BlenderbotTokenizer, BlenderbotForConditionalGeneration from transformers import AutoModelForCausalLM, AutoTokenizer,BlenderbotForConditionalGeneration import torch chat_tkn = AutoTokenizer.from_pretrained("microsoft/DialoGPT-medium") mdl = AutoModelForCausalLM.from_pretrained("microsoft/DialoGPT-medium") #chat_tkn = AutoTokenizer.from_pretrained("facebook/blenderbot-400M-distill") #mdl = BlenderbotForConditionalGeneration.from_pretrained("facebook/blenderbot-400M-distill") def converse(user_input, chat_history=[]): user_input_ids = chat_tkn(user_input + chat_tkn.eos_token, return_tensors='pt').input_ids # create a combined tensor with chat history bot_input_ids = torch.cat([torch.LongTensor(chat_history), user_input_ids], dim=-1) # generate a response chat_history = mdl.generate(bot_input_ids, max_length=1000, pad_token_id=chat_tkn.eos_token_id).tolist() print (chat_history) # convert the tokens to text, and then split the responses into lines response = chat_tkn.decode(chat_history[0]).split("<|endoftext|>") #response.remove("") print("starting to print response") print(response) # write some HTML html = "
" for m, msg in enumerate(response): cls = "user" if m%2 == 0 else "bot" print("value of m") print(m) print("message") print (msg) html += "
{}
".format(cls, msg) html += "
" print(html) return html, chat_history import gradio as grad css = """ .chatbox {display:flex;flex-direction:column} .msg {padding:4px;margin-bottom:4px;border-radius:4px;width:80%} .msg.user {background-color:blue;color:white} .msg.bot {background-color:orange;align-self:self-end} .footer {display:none !important} """ text=grad.inputs.Textbox(placeholder="Lets chat") grad.Interface(fn=converse, theme="default", inputs=[text, "state"], outputs=["html", "state"], css=css).launch()