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 # keep history in the tensor bot_input_ids = torch.cat([torch.LongTensor(chat_history), user_input_ids], dim=-1) # get response chat_history = mdl.generate(bot_input_ids, max_length=1000, pad_token_id=chat_tkn.eos_token_id).tolist() print (chat_history) response = chat_tkn.decode(chat_history[0]).split("<|endoftext|>") print("starting to print response") print(response) # html for display html = "
" for x, mesg in enumerate(response): if x%2!=0 : mesg="Alicia:"+mesg clazz="alicia" else : clazz="user" print("value of x") print(x) print("message") print (mesg) html += "
{}
".format(clazz, mesg) html += "
" print(html) return html, chat_history import gradio as grad css = """ .mychat {display:flex;flex-direction:column} .mesg {padding:5px;margin-bottom:5px;border-radius:5px;width:75%} .mesg.user {background-color:lightblue;color:white} .mesg.alicia {background-color:orange;color:white,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()