| from transformers import AutoModelForCausalLM, AutoTokenizer,BlenderbotForConditionalGeneration |
| import torch |
| import gradio as gr |
|
|
| |
| model_name = "microsoft/DialoGPT-medium" |
| chat_token = AutoTokenizer.from_pretrained(model_name) |
| model = AutoModelForCausalLM.from_pretrained(model_name) |
|
|
| def converse(user_input, chat_history=[]): |
| user_input_ids = chat_token(user_input + chat_token.eos_token, return_tensors='pt').input_ids |
|
|
| |
| bot_input_ids = torch.cat([torch.LongTensor(chat_history), user_input_ids], dim=-1) |
|
|
| |
| chat_history = model.generate(bot_input_ids, max_length=1000, pad_token_id=chat_token.eos_token_id).tolist() |
| print (chat_history) |
|
|
| response = chat_token.decode(chat_history[0]).split("<|endoftext|>") |
| |
| print("Starting to print response...") |
| print(response) |
| |
| |
| html = "<div class='mybot'>" |
| for x, mesg in enumerate(response): |
| if x%2!=0 : |
| mesg="BOT: " + mesg |
| clazz="bot" |
| 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 |
|
|
| 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.bot {background-color:orange;color:white,align-self:self-end} |
| .footer {display:none !important} |
| """ |
| text=gr.inputs.Textbox(label="User Input", placeholder="Let's start a chat...") |
| gr.Interface(fn=converse, |
| theme="default", |
| inputs=[text, "state"], |
| outputs=["html", "state"], |
| css=css).launch() |
