app.py

import gradio as gr
from functools import lru_cache
from transformers import pipeline

MODEL_ID = "hasmar03/mt5_id2md"

@lru_cache(maxsize=1)
def get_pipe():
    return pipeline(
        "text2text-generation",
        model=MODEL_ID,
        tokenizer=MODEL_ID,
        device=-1,           # CPU
    )

def translate(direction, text, max_new_tokens=64):
    if not text.strip():
        return ""
    prompt = f"translate {direction}: {text}"
    out = get_pipe()(
        prompt,
        num_beams=4,
        do_sample=False,
        max_new_tokens=int(max_new_tokens),
    )[0]["generated_text"]
    return out

with gr.Blocks(title="mT5 id↔md Translator (HF Space API)") as demo:
    gr.Markdown("# mT5 id↔md Translator (HF Space API)")
    direction = gr.Dropdown(["id2md", "md2id"], value="id2md", label="Arah")
    inp = gr.Textbox(label="Teks sumber", lines=3)
    max_tok = gr.Slider(16, 128, value=64, step=1, label="max_new_tokens")
    out = gr.Textbox(label="Terjemahan", lines=3)
    btn = gr.Button("Terjemahkan")
    btn.click(translate, [direction, inp, max_tok], [out], api_name="translate")

    gr.Examples(
        [["id2md", "ia terus pulang begitu saja", 64],
         ["md2id", "tarrus i pole tia", 64]],
        [direction, inp, max_tok],
        [out]
    )

demo.queue(concurrency_count=1, max_size=12)
if __name__ == "__main__":
    demo.launch()