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("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")

grad.Interface(fn=converse,
               theme="default",
               inputs=[text, "state"],
               outputs=["html", "state"],
               css=css).launch()