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from transformers import AutoModelForCausalLM, AutoTokenizer,BlenderbotForConditionalGeneration
import torch
import gradio as gr
#model_name = "facebook/blenderbot-400M-distill"
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
# keep history in the tensor
bot_input_ids = torch.cat([torch.LongTensor(chat_history), user_input_ids], dim=-1)
# get response
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 for display
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(placeholder="Let's start a chat...")
gr.Interface(fn=converse,
theme="default",
inputs=[text, "state"],
outputs=["html", "state"],
css=css).launch() |