| | 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() |
