Add merged model + processor

handler.py

@@ -94,16 +94,17 @@ class EndpointHandler:
         pixel_values = self.transform(image).unsqueeze(0)   # [1, C, H, W]
 
         with torch.no_grad():
-            logits = self.model(pixel_values).logits
-            probs  = logits.softmax(dim=-1)[0]
-
-        top_idx = int(probs.argmax())
-        top_label = self.id2label[top_idx]
-
-        return {
-            "predicted_label": top_label,
-            "scores": {
-                self.id2label[i]: float(p)
-                for i, p in enumerate(probs)
-            }
-        }
+            logits = self.model(pixel_values).logits[0]        # tensor [num_labels]
+            probs  = logits.softmax(dim=-1)
+
+        # convert to the required wire format (top‑k or all classes)
+        k = min(5, probs.numel())                              # send top‑5
+        topk = torch.topk(probs, k)
+
+        response = [
+            {"label": self.id2label[idx.item()], "score": prob.item()}
+            for prob, idx in zip(topk.values, topk.indices)
+        ]
+
+        return response               # <‑‑ must be a *list* of dicts
+