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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")
def converse(user_input, chat_history=[]):
user_input_ids = chat_tkn(user_input + chat_tkn.eos_token, return_tensors='pt').input_ids
bot_input_ids = torch.cat([torch.LongTensor(chat_history),user_input_ids], dim=-1)
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")
text=grad.components.Textbox(placeholder="Lets chat")
grad.Interface(fn=converse, theme="default",inputs=[text,"state"],outputs=["html","state"],css=css).launch()
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