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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("staring 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() |