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| from transformers import AutoModelForCausalLM, AutoTokenizer | |
| import torch | |
| tokenizer = AutoTokenizer.from_pretrained("microsoft/DialoGPT-medium") | |
| model = AutoModelForCausalLM.from_pretrained("microsoft/DialoGPT-medium") | |
| def predict(input, history=[]): | |
| # tokenize the new input sentence | |
| new_user_input_ids = tokenizer.encode(input + tokenizer.eos_token, return_tensors='pt') | |
| # append the new user input tokens to the chat history | |
| bot_input_ids = torch.cat([torch.LongTensor(history), new_user_input_ids], dim=-1) | |
| # generate a response | |
| history = model.generate(bot_input_ids, max_length=1000, pad_token_id=tokenizer.eos_token_id).tolist() | |
| # convert the tokens to text, and then split the responses into lines | |
| response = tokenizer.decode(history[0]).split("<|endoftext|>") | |
| response.remove("") | |
| # write some HTML | |
| html = "<div class='chatbot'>" | |
| for m, msg in enumerate(response): | |
| cls = "user" if m%2 == 0 else "bot" | |
| html += "<div class='msg {}'> {}</div>".format(cls, msg) | |
| html += "</div>" | |
| return html, history | |
| import gradio as gr | |
| css = """ | |
| .chatbox {display:flex;flex-direction:column} | |
| .msg {padding:4px;margin-bottom:4px;border-radius:4px;width:80%} | |
| .msg.user {background-color:cornflowerblue;color:white} | |
| .msg.bot {background-color:lightgray;align-self:self-end} | |
| .footer {display:none !important} | |
| """ | |
| gr.Interface(fn=predict, | |
| theme="default", | |
| inputs=[gr.inputs.Textbox(placeholder="How are you?"), "state"], | |
| outputs=["html", "state"], | |
| css=css).launch() | |