try to fix image error

handler.py

@@ -7,7 +7,7 @@ class EndpointHandler:
     def __init__(self, model_dir):
         self.model = torch.jit.load(f"{model_dir}/model_scripted_efficientnet.pt")
         self.model.eval()
-        
+
         self.transform = transforms.Compose([
             transforms.Resize((224, 224)),
             transforms.ToTensor(),
@@ -16,7 +16,7 @@ class EndpointHandler:
                 std=[0.229, 0.224, 0.225]
             )
         ])
-        
+
         self.supported_issues = [
             "Dark Spots",
             "Dry Lips",
@@ -31,36 +31,26 @@ class EndpointHandler:
         ]
 
     def __call__(self, data):
-        """
-        Args:
-            data (:obj:`Dict[str, Any]`): Dictionary with the following key:
-                - "inputs" (str): Base64 encoded string of the image
-        Return:
-            A :obj:`Dict[str, Any]` with the following keys:
-                - "predictions" (List[str]): List of predicted issues
-        """
-        if isinstance(data, bytes):
+        if isinstance(data, bytes):  # Correctly handle bytes input
             image = Image.open(io.BytesIO(data)).convert("RGB")
         elif isinstance(data, dict) and "inputs" in data:
             if isinstance(data["inputs"], bytes):
                 image = Image.open(io.BytesIO(data["inputs"])).convert("RGB")
-            else:
-                image = Image.open(io.BytesIO(data["inputs"].encode())).convert("RGB")
+            # REMOVE the else block below. data["inputs"] is already bytes.
+            # else:  # This branch is unnecessary and causes the error
+            #     image = Image.open(io.BytesIO(data["inputs"].encode())).convert("RGB")  # Remove .encode()
         else:
             raise ValueError("Unsupported input format")
 
-        # Preprocess image
         image_tensor = self.transform(image).unsqueeze(0)
-        
-        # Run inference
+
         with torch.no_grad():
             outputs = self.model(image_tensor)
             probabilities = torch.nn.functional.softmax(outputs, dim=1)
-        
-        # Post-process results
+
         predictions = probabilities.squeeze().tolist()
         output = [issue for issue, prob in zip(self.supported_issues, predictions) if prob > 0.5]
-        
+
         return {"predictions": output}
 
-EndpointHandler = EndpointHandler
+EndpointHandler = EndpointHandler  # Crucial for import