Add merged model + processor

handler.py

@@ -61,12 +61,34 @@ class EndpointHandler:
         Expects {"inputs": "<base64‑encoded image>"}.
         Returns the top prediction + per‑class probabilities.
         """
-        if "inputs" not in data:
-            raise ValueError("Request JSON must contain an 'inputs' field.")
-
-        # Decode base64 → PIL
-        img_bytes = base64.b64decode(data["inputs"])
-        image = Image.open(io.BytesIO(img_bytes))
+        # case 1 ─ raw bytes (default HF client / curl -T)
+        if isinstance(data, (bytes, bytearray)):
+            img_bytes = data
+
+        # case 2 ─ JSON with "inputs": <something>
+        elif isinstance(data, dict) and "inputs" in data:
+            inp = data["inputs"]
+
+            # Base‑64 string
+            if isinstance(inp, str):
+                img_bytes = base64.b64decode(inp.split(",")[-1])  # drop "data:..." if present
+
+            # Already‑bytes
+            elif isinstance(inp, (bytes, bytearray)):
+                img_bytes = inp
+
+            # Already a PIL Image object
+            elif hasattr(inp, "convert"):
+                image = inp                                    # PIL.Image
+            else:
+                raise ValueError("Unsupported 'inputs' format")
+
+        else:
+            raise ValueError("Unsupported request body type")
+
+        # If we didn’t get a ready‑made PIL Image above, decode bytes → PIL
+        if "image" not in locals():
+            image = Image.open(io.BytesIO(img_bytes))
 
         # Preprocess with your own transform
         pixel_values = self.transform(image).unsqueeze(0)   # [1, C, H, W]