import gradio as gr
import torch
from transformers import T5Tokenizer, AutoModelForCausalLM
from utils import translate_from_jp_to_en

tokenizer = T5Tokenizer.from_pretrained("rinna/japanese-gpt-1b")
model = AutoModelForCausalLM.from_pretrained("rinna/japanese-gpt-1b")


def generate(text, max_length=512):

    token_ids = tokenizer.encode(
        text, add_special_tokens=False, return_tensors="pt")

    with torch.no_grad():
        output_ids = model.generate(
            token_ids,
            max_length=max_length,
            do_sample=True,
            top_k=500,
            top_p=0.95,
            #pad_token_id=tokenizer.pad_token_id,
            #bos_token_id=tokenizer.bos_token_id,
            #eos_token_id=tokenizer.eos_token_id,
            #bad_word_ids=[[tokenizer.unk_token_id]]
            early_stopping=False,
        )

    output = tokenizer.decode(output_ids.tolist()[0], skip_specual_tokens=True)
    return output, translate_from_jp_to_en(output)


title = "JP GPT Demo"
description = "Demo for generating text in Japanase using a GPT model"
examples = [['日本のeスポーツ障害者がステレオタイプを撃ち落とす', 128]]
gr.Interface(fn=generate, inputs=[gr.inputs.Textbox(lines=4, label="Prompt"),
                                  gr.inputs.Slider(minimum=8, maximum=4096, step=8, default=64, label="Numbers of tokens")],
             outputs=["text", "text"],
             title=title, description=description,
             # article= article,
             examples=examples).launch(enable_queue=True)