# 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 = """
#
#
# The bot was trained on Rick and Morty dialogues Kaggle Dataset using DialoGPT.
#
#
#
# """
# article = "Complete Tutorial
Project is Available at DAGsHub
"
# 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()
import gradio as gr
with gr.Blocks() as demo:
with gr.Tab("Translate to Spanish"):
gr.load("gradio/en2es", src="spaces")
with gr.Tab("Translate to French"):
gr.load("abidlabs/en2fr", src="spaces")
demo.launch()