# https://www.gradio.app/guides/using-hugging-face-integrations

from transformers import pipeline
import gradio as gr

pipe = pipeline("translation", model="Helsinki-NLP/opus-mt-en-es")

demo = gr.Interface.from_pipeline(pipe)
demo.launch()

"""
from transformers import AutoModelForCausalLM, AutoTokenizer
import gradio as gr
import torch

model = "mistralai/Mistral-7B-Instruct-v0.1"
model = "TinyLlama/TinyLlama-1.1B-Chat-v0.3"

# Gradio
title = "Shisa 7B"
description = "Test out Shisa 7B in either English or Japanese."
placeholder = "Type Here / ここに入力してください" 
examples = [
    "Hello, how are you?", 
    "こんにちは、元気ですか？",
    "おっす、元気？",
    "こんにちは、いかがお過ごしですか？",
]

tokenizer = AutoTokenizer.from_pretrained(model)
model = AutoModelForCausalLM.from_pretrained(model)

def chat(input, history=[]):
    input_ids = tokenizer.encode(input + tokenizer.eos_token, return_tensors="pt")
    history = model.generate(
        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|>")
    '''
    # 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.ChatInterface(
    chat,
    chatbot=gr.Chatbot(height=400),
    textbox=gr.Textbox(placeholder=placeholder, container=False, scale=7),
    title=title,
    description=description,
    theme="soft",
    examples=examples,
    cache_examples=False,
    undo_btn="Delete Previous",
    clear_btn="Clear",
).queue().launch()
"""