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 = """
The bot is trained on Rick and Morty dialogues Kaggle Dataset using DialoGPT.
"""
article = "Complete Tutorial
Project is Available at DAGsHub
"
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=1000, pad_token_id=tokenizer.eos_token_id).tolist()
# convert the tokens to text, and then split the responses into the right format
response = tokenizer.decode(history[0]).split("<|endoftext|>")
response = [(response[i], response[i+1]) for i in range(0, len(response)-1, 2)] # convert to tuples of list
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