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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() | |