# app.py
import gradio as gr
from functools import lru_cache
from transformers import pipeline

# Ganti sesuai nama repo-mu di HF
ID2MD = "hasmar03/mt5_id2md"
MD2ID = "hasmar03/mt5_md2id"   # <- model baru hasil training md→id

@lru_cache(maxsize=2)
def get_pipe(model_id: str):
    """
    Cache per-model supaya load cuma sekali (jalan di CPU Spaces).
    """
    return pipeline(
        task="text2text-generation",
        model=model_id,
        tokenizer=model_id,
        device=-1,   # CPU (gratis)
    )

def translate(direction: str, text: str, max_new_tokens: int = 64) -> str:
    text = (text or "").strip()
    if not text:
        return ""

    # pilih model berdasar arah
    model_id = ID2MD if direction == "id2md" else MD2ID

    # prefix sesuai format training
    prompt = f"translate {direction}: {text}"

    out = get_pipe(model_id)(
        prompt,
        max_new_tokens=int(max_new_tokens),
        num_beams=5,
        do_sample=False,
        no_repeat_ngram_size=3,
        early_stopping=True,
    )[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)")

    with gr.Row():
        direction = gr.Dropdown(["id2md", "md2id"], value="id2md", label="Arah")
        max_tok   = gr.Slider(16, 128, value=64, step=1, label="max_new_tokens")

    inp = gr.Textbox(label="Teks sumber", lines=3, placeholder="Ketik kalimat...")
    out = gr.Textbox(label="Terjemahan", lines=3)
    btn = gr.Button("Terjemahkan", variant="primary")

    btn.click(translate, [direction, inp, max_tok], [out], api_name="translate")

    gr.Examples(
        examples=[
            ["id2md", "ia terus pulang begitu saja", 64],
            ["md2id", "tarrus i pole tia", 64],
        ],
        inputs=[direction, inp, max_tok],
        outputs=[out],
        label="Contoh cepat",
    )

# kompatibel Gradio 4.x/5.x
try:
    demo.queue()
except TypeError:
    try:
        demo.queue(max_size=12)
    except TypeError:
        demo.queue(concurrency_count=1, max_size=12)

if __name__ == "__main__":
    demo.launch()