Skip H5 files with no model_config (#96)

modelscan/scanners/h5/scan.py

@@ -41,11 +41,17 @@ def scan(
             )
             return None
 
-        return self.label_results(self._scan_keras_h5_file(source))
+        results = self._scan_keras_h5_file(source)
+        if results:
+            return self.label_results(results)
+        else:
+            return None
 
-    def _scan_keras_h5_file(self, source: Union[str, Path]) -> ScanResults:
+    def _scan_keras_h5_file(self, source: Union[str, Path]) -> Optional[ScanResults]:
         machine_learning_library_name = "Keras"
         operators_in_model = self._get_keras_h5_operator_names(source)
+        if not operators_in_model:
+            return None
         return H5LambdaDetectScan._check_for_unsafe_tf_keras_operator(
             module_name=machine_learning_library_name,
             raw_operator=operators_in_model,
@@ -55,11 +61,15 @@ def _scan_keras_h5_file(self, source: Union[str, Path]) -> ScanResults:
             ]["unsafe_keras_operators"],
         )
 
-    def _get_keras_h5_operator_names(self, source: Union[str, Path]) -> List[str]:
+    def _get_keras_h5_operator_names(
+        self, source: Union[str, Path]
+    ) -> Optional[List[str]]:
         # Todo: source isn't guaranteed to be a file
 
         with h5py.File(source, "r") as model_hdf5:
             try:
+                if not "model_config" in model_hdf5.attrs.keys():
+                    return None
                 model_config = json.loads(model_hdf5.attrs.get("model_config", {}))
                 layers = model_config.get("config", {}).get("layers", {})
                 lambda_layers = []