update

app.py

@@ -1,15 +1,13 @@
-# app.py (potongan penting saja)
-
-import os
 import gradio as gr
-from inference import predict, predict_batch  
+from inference import predict, predict_batch
 
 APP_TITLE = "# Face Shape Classification — EfficientNetB4 (300×300)"
 APP_DESC = """
-**Model:** EfficientNetB4 (ImageNet) fine-tuned pada 5 kelas: **Heart, Oblong, Oval, Round, Square**.  
-**Val Acc (best): ~96%** · **Input:** Foto wajah **frontal** RGB (1 orang), auto-resize **300×300**.  
-**Output:** Prediksi + confidence (Top-5).  
-**Disclaimer:** Untuk penelitian/edukasi.
+Model EfficientNetB4 (ImageNet) fine-tuned pada 5 kelas: Heart, Oblong, Oval, Round, Square.
+
+• Input: Foto wajah frontal RGB (1 orang), auto-resize 300×300.
+• Output: Prediksi + confidence (Top-5).
+• Disclaimer: Untuk penelitian/edukasi.
 """
 
 with gr.Blocks(theme=gr.themes.Soft()) as demo:
@@ -24,13 +22,15 @@ with gr.Blocks(theme=gr.themes.Soft()) as demo:
         btn = gr.Button("Predict", variant="primary")
         gr.ClearButton([inp, out])
 
-    btn.click(predict, inputs=inp, outputs=out)
+    # Expose stable API names for @gradio/client consumers
+    btn.click(predict, inputs=inp, outputs=out, api_name="predict")
 
     with gr.Tab("Batch (optional)"):
-        gal = gr.Gallery(label="Images", columns=4, height="auto")   
+        gal = gr.Gallery(label="Images", columns=4, height="auto")
         out_gal = gr.JSON(label="Batch outputs")
         runb = gr.Button("Run batch")
-        runb.click(predict_batch, inputs=gal, outputs=out_gal)
+        runb.click(predict_batch, inputs=gal, outputs=out_gal, api_name="predict_batch")
 
 if __name__ == "__main__":
     demo.launch()
+