Update app.py

app.py

@@ -1,34 +1,47 @@
 import malaya
-from fastapi import FastAPI
+from fastapi import FastAPI, HTTPException
 from pydantic import BaseModel
 import uvicorn
+import os
 
-app = FastAPI()
+app = FastAPI(title="Jawi Transliteration API")
 
-# Note: Loading the model can take a minute on the first run 
-# as it downloads the pre-trained weights.
+# Load the modern Jawi model (T5-based)
+# On first run, this downloads ~200MB of weights
 try:
-    # We specify the model='small' to save memory on free tier Spaces
-    model = malaya.jawi.transformer(model = 'small')
-except AttributeError:
-    # Fallback for certain versions if the attribute name differs
-    model = malaya.jawi.deep_model()
+    print("Loading Jawi model... this may take a moment.")
+    model = malaya.jawi.huggingface()
+    print("Model loaded successfully!")
+except Exception as e:
+    print(f"Failed to load model: {e}")
+    model = None
 
 class RequestData(BaseModel):
     text: str
 
 @app.get("/")
-def read_root():
-    return {"status": "API is running"}
+def health_check():
+    return {"status": "ready" if model else "loading/error"}
 
 @app.post("/convert")
-async def convert(data: RequestData):
+async def convert_to_jawi(data: RequestData):
+    if not model:
+        raise HTTPException(status_code=503, detail="Model not loaded")
+    
+    if not data.text.strip():
+        return {"jawi": ""}
+
     try:
-        # Malaya's transformer returns a list of results
+        # result is a list, we take the first item
         result = model.generate([data.text], to_lang='jawi')
-        return {"jawi": result[0]}
+        return {
+            "rumi": data.text,
+            "jawi": result[0]
+        }
     except Exception as e:
         return {"error": str(e)}
 
 if __name__ == "__main__":
-    uvicorn.run(app, host="0.0.0.0", port=7860)
+    # HF Spaces uses port 7860 by default
+    port = int(os.environ.get("PORT", 7860))
+    uvicorn.run(app, host="0.0.0.0", port=port)