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()


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()