import os
import gradio as gr

HF_TOKEN = os.getenv('HF_TOKEN')
hf_writer = gr.HuggingFaceDatasetSaver(HF_TOKEN, "Rick-bot-flags")

title = "Have Fun With RickBot"
description = """
<p>
<center>
The bot is trained on Rick and Morty dialogues Kaggle Dataset using DialoGPT.
<img src="https://gradio.app/assets/img/rick.png">
</center>
</p>
"""
article = "<p style='text-align: center'><a href='https://medium.com/geekculture/discord-bot-using-dailogpt-and-huggingface-api-c71983422701' target='_blank'>Complete Tutorial</a></p><p style='text-align: center'><a href='https://dagshub.com/kingabzpro/DailoGPT-RickBot' target='_blank'>Project is Available at DAGsHub</a></p></center></p>"

from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

tokenizer = AutoTokenizer.from_pretrained("kingabzpro/DialoGPT-small-Rick-Bot")
model = AutoModelForCausalLM.from_pretrained("kingabzpro/DialoGPT-small-Rick-Bot")

def predict(input, history=[]):
    # tokenize the new input sentence
    new_user_input_ids = tokenizer.encode(input + tokenizer.eos_token, return_tensors='pt')

    # append the new user input tokens to the chat history
    bot_input_ids = torch.cat([torch.LongTensor(history), new_user_input_ids], dim=-1)

    # generate a response 
    history = model.generate(bot_input_ids, max_length=1000, pad_token_id=tokenizer.eos_token_id).tolist()

    # convert the tokens to text, and then split the responses into lines
    response = tokenizer.decode(history[0]).replace("<|endoftext|>", "\n")
    
    return response, history

gr.Interface(fn = predict, inputs = ["textbox","state"], outputs = ["chatbot","state"],allow_flagging = "manual",theme ="grass",title = title, flagging_callback = hf_writer, description = description, article = article ).launch(enable_queue=True)  # customizes the input component

 #theme ="grass", 
 #title = title, 
 #flagging_callback=hf_writer,
 #description = description, 
 #article = article