feat: enhance batch prediction functionality to support multiple image uploads

app.py

@@ -32,7 +32,7 @@ with gr.Blocks(theme=gr.themes.Soft(), css=None, analytics_enabled=False) as ifa
 
     # Endpoint batch (opsional) untuk klien JSON; menjaga compat endpoint default tetap single
     with gr.Accordion("Batch (opsional)", open=False):
-        files_in = gr.Files(filepath="file", label="Unggah beberapa gambar")
+        files_in = gr.Files(file_count="multiple", file_types=["image"], label="Unggah beberapa gambar")
         json_out = gr.JSON(label="Best of batch (JSON)")
         batch_btn = gr.Button("Prediksi Batch")
         batch_btn.click(fn=predict_batch, inputs=files_in, outputs=json_out, api_name="predict_batch")

inference.py

@@ -1,7 +1,7 @@
 import numpy as np
 from PIL import Image
 import tensorflow as tf
-from typing import List, Dict, Tuple
+from typing import List, Dict, Tuple, Any
 import time
 import os
 
@@ -98,13 +98,40 @@ def _dict_to_payload(d: Dict[str, float]) -> Dict[str, object]:
     return {"label": top, "confidences": confs}
 
 
-def predict_batch(images: List[Image.Image]) -> Dict[str, object]:
-    """Prediksi batch: pilih hasil terbaik, kembalikan payload seragam untuk klien JSON."""
+def _to_pil_list(files: List[Any]) -> List[Image.Image]:
+    out: List[Image.Image] = []
+    for f in files:
+        try:
+            if isinstance(f, Image.Image):
+                out.append(f)
+                continue
+            # Gradio >=4: FileData with .path
+            path = None
+            if hasattr(f, "path") and isinstance(getattr(f, "path"), str):
+                path = getattr(f, "path")
+            elif isinstance(f, str):
+                path = f
+            elif hasattr(f, "name") and isinstance(getattr(f, "name"), str):
+                path = getattr(f, "name")
+            if path:
+                img = Image.open(path).convert("RGB")
+                out.append(img)
+        except Exception:
+            continue
+    return out
+
+
+def predict_batch(files: List[Any]) -> Dict[str, object]:
+    """Prediksi batch: menerima FileData/paths atau PIL.Image; pilih hasil terbaik."""
     if not model_instance:
         return {"error": "Instance model tidak tersedia."}
-    if not images:
+    if not files:
         return {"error": "Tidak ada gambar."}
 
+    images = _to_pil_list(files)
+    if not images:
+        return {"error": "Tidak dapat membaca gambar."}
+
     best_payload: Dict[str, object] = {"label": "", "confidences": []}
     best_conf = -1.0
     for img in images: