fix AttributeError: 'Gallery

app.py

@@ -1,29 +1,50 @@
 # app.py
 import os
 import gradio as gr
-from inference import predict, predict_batch  # <-- gunakan fungsi dari inference.py
+from inference import predict, predict_batch  
 
-def _payload(d):
-    # ubah dict {label: prob} -> payload gradio.Label
+APP_TITLE = "# Face Shape Classification — EfficientNetB4 (300×300)"
+APP_DESC = """
+**Model:** EfficientNetB4 (ImageNet) fine-tuned pada 5 kelas: **Heart, Oblong, Oval, Round, Square**.  
+**Input:** Foto wajah **frontal** RGB (1 orang), di-resize otomatis ke **300×300**.  
+**Output:** Prediksi kelas + confidence.  
+**Catatan:** Untuk **penelitian/edukasi**, bukan untuk keputusan sensitif.
+"""
+APP_FAQ = """
+### FAQ
+- **Perlu normalisasi manual?** Tidak. Aplikasi menyesuaikan preprocessing internal model.
+- **Format gambar?** JPG/PNG, RGB.
+- **Privasi:** Gambar diproses saat inferensi dan tidak disimpan.
+"""
+
+def _payload(d: dict):
     items = sorted(d.items(), key=lambda kv: kv[1], reverse=True)
-    return {"label": items[0][0], "confidences": [{"label": k, "confidence": float(v)} for k, v in items]}
+    return {
+        "label": items[0][0],
+        "confidences": [{"label": k, "confidence": float(v)} for k, v in items],
+    }
 
 with gr.Blocks(theme=gr.themes.Soft()) as demo:
-    gr.Markdown("# Face Shape Classification — EfficientNetB4 (300×300)")
+    gr.Markdown(APP_TITLE)
+    gr.Markdown(APP_DESC)
+
     with gr.Row():
         inp = gr.Image(type="pil", label="Upload face (frontal)")
         out = gr.Label(num_top_classes=5, label="Predictions")
-    btn = gr.Button("Predict")
+    with gr.Row():
+        btn = gr.Button("Predict", variant="primary")
+        gr.ClearButton([inp, out])
     btn.click(lambda im: _payload(predict(im)), inputs=inp, outputs=out)
 
-    # Batch tab (opsional)
-    with gr.Tab("Batch"):
-        gal = gr.Gallery(label="Images").style(grid=4)
+    with gr.Accordion("ℹ️ Detail & FAQ", open=False):
+        gr.Markdown(APP_FAQ)
+
+    with gr.Tab("Batch (optional)"):
+        gal = gr.Gallery(label="Images", columns=4, height="auto")
         out_gal = gr.JSON(label="Batch outputs")
         runb = gr.Button("Run batch")
-        runb.click(lambda ims: [predict_batch(ims)], inputs=gal, outputs=out_gal)
+        runb.click(predict_batch, inputs=gal, outputs=out_gal)
 
-    # Examples (jika ada folder examples/)
     if os.path.exists("examples"):
         gr.Examples(
             examples=[os.path.join("examples", f) for f in os.listdir("examples")],