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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() | |