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| 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 = "<div class='mybot'>" | |
| 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 += "<div class='mesg {}'> {}</div>".format(clazz, mesg) | |
| html += "</div>" | |
| 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() | |