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()