webui_gradio, Gradio galleries return data format match.

webui_gradio.py

@@ -316,6 +316,33 @@ def classify(
     return "✅ OK", rows, (face_pil if "face_pil" in locals() else None), (eyes_pil if "eyes_pil" in locals() else None)
 
 
+def _gallery_item_to_pil(item) -> Optional[Image.Image]:
+    """Convert a Gradio gallery item to PIL Image (handles various formats)."""
+    if item is None:
+        return None
+    # Already a PIL Image
+    if isinstance(item, Image.Image):
+        return item
+    # Tuple format: (image, caption)
+    if isinstance(item, (tuple, list)) and len(item) >= 1:
+        return _gallery_item_to_pil(item[0])
+    # Dict format: {"image": ..., "caption": ...} or {"name": filepath, ...}
+    if isinstance(item, dict):
+        if "image" in item:
+            return _gallery_item_to_pil(item["image"])
+        if "name" in item:
+            return Image.open(item["name"]).convert("RGB")
+        if "path" in item:
+            return Image.open(item["path"]).convert("RGB")
+    # String path
+    if isinstance(item, str):
+        return Image.open(item).convert("RGB")
+    # Numpy array
+    if isinstance(item, np.ndarray):
+        return Image.fromarray(item).convert("RGB")
+    return None
+
+
 def add_prototype(
     label_name: str,
     images: List,
@@ -334,9 +361,14 @@ def add_prototype(
         return "❌ Upload at least 1 image."
 
     zs: List[torch.Tensor] = []
-    for x in images:
+    errors: List[str] = []
+    for i, x in enumerate(images):
         try:
-            im = x if isinstance(x, Image.Image) else Image.fromarray(x)
+            im = _gallery_item_to_pil(x)
+            if im is None:
+                errors.append(f"Image {i}: could not parse format {type(x)}")
+                continue
+
             face_pil = None
             eyes_pil = None
             if ex is not None:
@@ -352,11 +384,13 @@ def add_prototype(
             et = _pil_to_tensor(eyes_pil, lm.T_e) if eyes_pil is not None else None
             z = embed_triview(lm, whole=wt, face=ft, eyes=et)
             zs.append(z)
-        except Exception:
+        except Exception as e:
+            errors.append(f"Image {i}: {e}")
             continue
 
     if not zs:
-        return "❌ Could not embed any uploaded images."
+        err_detail = "; ".join(errors[:3]) if errors else "unknown error"
+        return f"❌ Could not embed any uploaded images. Details: {err_detail}"
 
     center = torch.stack(zs, dim=0).mean(dim=0)
     lid = db.add_center(label_name, center)