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#from transformers import BlenderbotTokenizer, BlenderbotForConditionalGeneration
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
# create a combined tensor with chat history
bot_input_ids = torch.cat([torch.LongTensor(chat_history), user_input_ids], dim=-1)
# generate a response
chat_history = mdl.generate(bot_input_ids, max_length=1000, pad_token_id=chat_tkn.eos_token_id).tolist()
print (chat_history)
# convert the tokens to text, and then split the responses into lines
response = chat_tkn.decode(chat_history[0]).split("<|endoftext|>")
#response.remove("")
print("starting to print response")
print(response)
# write some HTML
html = "<div class='chatbot'>"
for m, msg in enumerate(response):
cls = "user" if m%2 == 0 else "bot"
print("value of m")
print(m)
print("message")
print (msg)
html += "<div class='msg {}'> {}</div>".format(cls, msg)
html += "</div>"
print(html)
return html, chat_history
import gradio as grad
css = """
.chatbox {display:flex;flex-direction:column}
.msg {padding:4px;margin-bottom:4px;border-radius:4px;width:80%}
.msg.user {background-color:blue;color:white}
.msg.bot {background-color:orange;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() |