app.py

import os
import gradio as gr

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

title = "Talk To Me Morty"
description = """
<p>
<center>
The bot was trained on Rick and Morty dialogues Kaggle Dataset using DialoGPT.
<img src="https://huggingface.co/spaces/kingabzpro/Rick_and_Morty_Bot/resolve/main/img/rick.png" alt="rick" width="200"/>
</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><center><img src='https://visitor-badge.glitch.me/badge?page_id=kingabzpro/Rick_and_Morty_Bot' alt='visitor badge'></center></p>"
examples = [["How are you Rick?"]]
from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

tokenizer = AutoTokenizer.from_pretrained("ericzhou/DialoGPT-Medium-Rick_v2")
model = AutoModelForCausalLM.from_pretrained("ericzhou/DialoGPT-Medium-Rick_v2")

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=4000, 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]).split("<|endoftext|>")
    #print('decoded_response-->>'+str(response))
    response = [(response[i], response[i+1]) for i in range(0, len(response)-1, 2)]  # convert to tuples of list
    #print('response-->>'+str(response))
    return response, history

gr.Interface(fn=predict,
             title=title,
             description=description,
             examples=examples,
             flagging_callback = hf_writer,
             allow_flagging = "manual",
             inputs=["text", "state"],
             outputs=["chatbot", "state"],
             theme='gradio/seafoam').launch()

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