Update app.py

app.py

@@ -1,70 +1,57 @@
+# app.py  — Gradio Blocks + REST API bawaan (api_name), lazy-load model
 import os
 import gradio as gr
-from fastapi import FastAPI
-from pydantic import BaseModel
 from transformers import AutoTokenizer, AutoModelForSeq2SeqLM, pipeline
 
-# ====== KONFIG ======
-MODEL_ID = os.getenv("MODEL_ID", "hasmar03/mt5_id2md")  # ganti kalau perlu
+# ===== Konfigurasi =====
+MODEL_ID = os.getenv("MODEL_ID", "hasmar03/mt5_id2md")
 MAX_LEN  = int(os.getenv("MAX_LEN", "128"))
 
-# ====== LOAD MODEL SEKALI SAJA (startup) ======
-tokenizer = AutoTokenizer.from_pretrained(MODEL_ID)
-model     = AutoModelForSeq2SeqLM.from_pretrained(MODEL_ID)
-pipe = pipeline(
-    "text2text-generation",
-    model=model,
-    tokenizer=tokenizer,
-    max_length=MAX_LEN,
-)
+# ===== Lazy loader =====
+pipe = None
+def get_pipe():
+    global pipe
+    if pipe is None:
+        tok = AutoTokenizer.from_pretrained(MODEL_ID)
+        mdl = AutoModelForSeq2SeqLM.from_pretrained(MODEL_ID)
+        pipe = pipeline(
+            "text2text-generation",
+            model=mdl,
+            tokenizer=tok,
+            max_length=MAX_LEN,
+        )
+    return pipe
 
-# ====== LOGIKA TERJEMAH ======
 def _build_prompt(text: str, direction: str):
-    # sesuaikan dengan cara Anda melatih prompt-nya
-    if direction == "id2md":
+    # Sesuaikan dengan skema training Anda
+    if direction == "id2md" or direction == "Indonesia → Mandar":
         return f"translate Indonesian to Mandar: {text}"
-    elif direction == "md2id":
+    elif direction == "md2id" or direction == "Mandar → Indonesia":
         return f"translate Mandar to Indonesian: {text}"
-    else:
-        return text
+    return text
 
 def translate_fn(text: str, arah: str):
-    prompt = _build_prompt(text, "id2md" if "Indonesia" in arah else "md2id")
-    out = pipe(prompt)[0]["generated_text"]
+    p = get_pipe()
+    prompt = _build_prompt(text, arah)
+    out = p(prompt)[0]["generated_text"]
     return out
 
-# ====== UI GRADIO (untuk testing manual di browser) ======
 with gr.Blocks(title="Mandar ↔ Indonesia Translator") as demo:
     gr.Markdown("### Mandar ↔ Indonesia Translator")
     arah = gr.Radio(
         ["Indonesia → Mandar", "Mandar → Indonesia"],
         value="Indonesia → Mandar",
-        label="Arah"
+        label="Arah",
     )
-    src = gr.Textbox(label="Teks sumber", lines=3)
+    src = gr.Textbox(label="Teks sumber", lines=3, placeholder="Ketik teks…")
     btn = gr.Button("Terjemahkan")
     out = gr.Textbox(label="Hasil", lines=3)
-    btn.click(translate_fn, inputs=[src, arah], outputs=out)
+    # api_name membuat REST endpoint: /api/predict/translate
+    btn.click(translate_fn, inputs=[src, arah], outputs=out, api_name="translate")
 
-# ====== API FASTAPI (untuk Android / cURL) ======
-class TranslateReq(BaseModel):
-    text: str
-    direction: str = "id2md"   # "id2md" atau "md2id"
+# Antrian (aman untuk Space)
+demo.queue()
 
-class TranslateResp(BaseModel):
-    translation: str
-
-fastapi_app = FastAPI()
-
-@fastapi_app.get("/health")
-def health():
-    return {"ok": True}
-
-@fastapi_app.post("/translate", response_model=TranslateResp)
-def translate_api(body: TranslateReq):
-    prompt = _build_prompt(body.text, body.direction)
-    out = pipe(prompt)[0]["generated_text"]
-    return {"translation": out}
-
-# Mount: root (/) tetap UI Gradio, endpoint /translate adalah FastAPI
-app = gr.mount_gradio_app(fastapi_app, demo, path="/")
+# Opsional: saat run lokal
+if __name__ == "__main__":
+    demo.launch()