Update app.py

app.py

@@ -1,112 +1,32 @@
-# app.py — Gradio dengan decoding yang konsisten seperti di Colab
-import os
 import gradio as gr
-import torch
-from transformers import (
-    AutoTokenizer, AutoModelForSeq2SeqLM, pipeline, GenerationConfig
-)
-
-MODEL_ID = os.getenv("MODEL_ID", "hasmar03/mt5_id2md")
+from transformers import pipeline
 
-# >>>>>> UBAH SESUAI DATA TRAINING KAMU <<<<<<
-ID2MD_PREFIX = "translate Indonesian to Mandar: "
-MD2ID_PREFIX = "translate Mandar to Indonesian: "
-# Jika kamu melatih pakai token lain (mis. "id2md: " / "md2id: " atau ">>md<< "),
-# ganti string di atas agar 100% sama.
+MODEL_ID = "hasmar03/mt5_id2md"
 
-# Default decoding (samakan dengan Colab)
-DEFAULT_DECODE = dict(
-    num_beams=5,
-    length_penalty=1.0,
-    no_repeat_ngram_size=3,
-    early_stopping=True,
-    max_new_tokens=128,
+pipe = pipeline(
+    "text2text-generation",
+    model=MODEL_ID,
+    tokenizer=MODEL_ID,
+    device=-1  # CPU
 )
 
-pipe = None
-gen_cfg = None
-
-def get_pipe():
-    global pipe, gen_cfg
-    if pipe is None:
-        tok = AutoTokenizer.from_pretrained(MODEL_ID)
-        mdl = AutoModelForSeq2SeqLM.from_pretrained(MODEL_ID)
-
-        # Muat generation_config dari repo (jika ada)
-        try:
-            gen_cfg = GenerationConfig.from_pretrained(MODEL_ID)
-            mdl.generation_config = gen_cfg
-        except Exception:
-            gen_cfg = mdl.generation_config  # fallback
-
-        pipe = pipeline(
-            "text2text-generation",
-            model=mdl,
-            tokenizer=tok,
-            device=0 if torch.cuda.is_available() else -1,
-        )
-    return pipe
-
-def build_prompt(text: str, direction: str):
-    if direction == "Indonesia → Mandar":
-        return f"{ID2MD_PREFIX}{text}"
-    else:
-        return f"{MD2ID_PREFIX}{text}"
-
-def translate(text: str, direction: str,
-              num_beams: int, max_new_tokens: int,
-              no_repeat_ngram_size: int, length_penalty: float,
-              do_sample: bool, temperature: float, top_p: float, top_k: int):
-    p = get_pipe()
-    prompt = build_prompt(text, direction)
-
-    # Susun argumen generate; mulai dari DEFAULT_DECODE lalu override dari UI
-    gen_args = dict(DEFAULT_DECODE)
-    gen_args.update(
-        num_beams=int(num_beams),
-        max_new_tokens=int(max_new_tokens),
-        no_repeat_ngram_size=int(no_repeat_ngram_size),
-        length_penalty=float(length_penalty),
-    )
-    if do_sample:
-        gen_args.update(do_sample=True, temperature=float(temperature),
-                        top_p=float(top_p), top_k=int(top_k))
-    else:
-        gen_args.update(do_sample=False)
-
-    out = p(prompt, **gen_args)[0]["generated_text"]
+def translate(direction, text, max_new_tokens=64):
+    if not text.strip():
+        return ""
+    prompt = f"translate {direction}: {text}"
+    out = pipe(prompt, num_beams=4, do_sample=False, max_new_tokens=int(max_new_tokens))[0]["generated_text"]
     return out
 
-with gr.Blocks(title="Mandar ↔ Indonesia Translator") as demo:
-    gr.Markdown("### Mandar ↔ Indonesia Translator")
-    with gr.Row():
-        direction = gr.Radio(
-            ["Indonesia → Mandar", "Mandar → Indonesia"],
-            value="Indonesia → Mandar", label="Arah"
-        )
-    src = gr.Textbox(label="Teks sumber", lines=3, placeholder="Ketik teks…")
-    btn = gr.Button("Terjemahkan")
-    out = gr.Textbox(label="Hasil", lines=3)
-
-    with gr.Accordion("Advanced decoding", open=False):
-        num_beams = gr.Slider(1, 10, value=DEFAULT_DECODE["num_beams"], step=1, label="num_beams")
-        max_new_tokens = gr.Slider(16, 512, value=DEFAULT_DECODE["max_new_tokens"], step=8, label="max_new_tokens")
-        no_repeat_ngram_size = gr.Slider(0, 10, value=DEFAULT_DECODE["no_repeat_ngram_size"], step=1, label="no_repeat_ngram_size")
-        length_penalty = gr.Slider(0.0, 2.0, value=DEFAULT_DECODE["length_penalty"], step=0.1, label="length_penalty")
-        do_sample = gr.Checkbox(False, label="Sampling (non-deterministic)")
-        temperature = gr.Slider(0.1, 2.0, value=1.0, step=0.1, label="temperature")
-        top_p = gr.Slider(0.1, 1.0, value=0.95, step=0.05, label="top_p")
-        top_k = gr.Slider(0, 100, value=50, step=5, label="top_k")
-
-    btn.click(
-        translate,
-        inputs=[src, direction, num_beams, max_new_tokens, no_repeat_ngram_size,
-                length_penalty, do_sample, temperature, top_p, top_k],
-        outputs=out,
-        api_name="translate"
-    )
-
-demo.queue()
+with gr.Blocks() 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])
 
 if __name__ == "__main__":
     demo.launch()