fix: guard json.loads() in JSON tensor parsing — prevent hard crash on None or malformed tensor values

src/opengradient/client/_conversions.py

@@ -112,59 +112,67 @@
     return number_tensors, string_tensors
 
 
 def convert_to_model_output(event_data: AttributeDict) -> Dict[str, np.ndarray]:
     """
     Converts inference output into a user-readable output.
     Expects the inference node to return a dict with the format:
         key: output_name (str)
         value: (output_array (list), shape (list)) (tuple)
 
     We need to reshape each output array using the shape parameter in order to get the array
     back into its original shape.
     """
     output_dict = {}
     output = event_data.get("output", {})
 
     if isinstance(output, (AttributeDict, dict)):
         for tensor in output.get("numbers", []):
             if isinstance(tensor, (AttributeDict, dict)):
                 name = tensor.get("name")
                 shape = tensor.get("shape")
                 values = []
                 # Convert from fixed point back into np.float32
                 for v in tensor.get("values", []):
                     if isinstance(v, (AttributeDict, dict)):
                         values.append(convert_to_float32(value=int(v.get("value")), decimals=int(v.get("decimals"))))
                     else:
                         logging.warning(f"Unexpected number type: {type(v)}")
                 output_dict[name] = np.array(values).reshape(shape)
             else:
                 logging.warning(f"Unexpected tensor type: {type(tensor)}")
 
         # Parse strings
         for tensor in output.get("strings", []):
             if isinstance(tensor, (AttributeDict, dict)):
                 name = tensor.get("name")
                 shape = tensor.get("shape")
                 values = tensor.get("values", [])
                 output_dict[name] = np.array(values).reshape(shape)
             else:
                 logging.warning(f"Unexpected tensor type: {type(tensor)}")
 
         # Parse JSON dicts
         for tensor in output.get("jsons", []):
             if isinstance(tensor, (AttributeDict, dict)):
                 name = tensor.get("name")
                 value = tensor.get("value")
-                output_dict[name] = np.array(json.loads(value))
+                if name is None:
+                    logging.warning("JSON tensor is missing 'name' field; skipping.")
+                    continue
+                try:
+                    output_dict[name] = np.array(json.loads(value))
+                except (json.JSONDecodeError, TypeError) as e:
+                    logging.warning(
+                        f"Failed to parse JSON tensor '{name}' — value={value!r}: {e}. Skipping."
+                    )
             else:
                 logging.warning(f"Unexpected tensor type: {type(tensor)}")
 
     else:
         logging.warning(f"Unexpected output type: {type(output)}")
 
     logging.debug(f"Parsed output: {output_dict}")
     return output_dict
 
 
 def convert_array_to_model_output(array_data: List) -> ModelOutput:
@@ -203,7 +211,15 @@
     for tensor in array_data[2]:
         name = tensor[0]
         value = tensor[1]
-        json_data[name] = np.array(json.loads(value))
+        if name is None:
+            logging.warning("JSON tensor entry is missing a name; skipping.")
+            continue
+        try:
+            json_data[name] = np.array(json.loads(value))
+        except (json.JSONDecodeError, TypeError) as e:
+            logging.warning(
+                f"Failed to parse JSON tensor '{name}' — value={value!r}: {e}. Skipping."
+            )
 
     return ModelOutput(
         numbers=number_data,