lint fixes

api/onnx_web/chain/result.py

@@ -217,24 +217,31 @@ class ImageMetadata:
 
         if self.inversions is not None:
             for name, weight in self.inversions:
-                hash = self.get_network_hash(server, name, "inversion")[1]
+                model_hash = self.get_network_hash(server, name, "inversion")[1]
                 json["inversions"].append(
-                    {"name": name, "weight": weight, "hash": hash}
+                    {"name": name, "weight": weight, "hash": model_hash}
                 )
 
         if self.loras is not None:
             for name, weight in self.loras:
-                hash = self.get_network_hash(server, name, "lora")[1]
+                model_hash = self.get_network_hash(server, name, "lora")[1]
-                json["loras"].append({"name": name, "weight": weight, "hash": hash})
+                json["loras"].append(
+                    {"name": name, "weight": weight, "hash": model_hash}
+                )
 
         if self.models is not None:
             for name, weight in self.models:
-                name, hash = self.get_model_hash(server)
+                name, model_hash = self.get_model_hash(server)
-                json["models"].append({"name": name, "weight": weight, "hash": hash})
+                json["models"].append(
+                    {"name": name, "weight": weight, "hash": model_hash}
+                )
 
         return json
 
 
+ERROR_NO_METADATA = "metadata must be provided"
+
+
 class StageResult:
     """
     Chain pipeline stage result.
@@ -318,7 +325,7 @@ class StageResult:
         if metadata is not None:
             self.metadata.append(metadata)
         else:
-            raise ValueError("metadata must be provided")
+            raise ValueError(ERROR_NO_METADATA)
 
     def push_image(self, image: Image.Image, metadata: ImageMetadata):
         if self.images is not None:
@@ -331,7 +338,7 @@ class StageResult:
         if metadata is not None:
             self.metadata.append(metadata)
         else:
-            raise ValueError("metadata must be provided")
+            raise ValueError(ERROR_NO_METADATA)
 
     def insert_array(self, index: int, array: np.ndarray, metadata: ImageMetadata):
         if self.arrays is not None:
@@ -346,7 +353,7 @@ class StageResult:
         if metadata is not None:
             self.metadata.insert(index, metadata)
         else:
-            raise ValueError("metadata must be provided")
+            raise ValueError(ERROR_NO_METADATA)
 
     def insert_image(self, index: int, image: Image.Image, metadata: ImageMetadata):
         if self.images is not None:
@@ -359,7 +366,7 @@ class StageResult:
         if metadata is not None:
             self.metadata.insert(index, metadata)
         else:
-            raise ValueError("metadata must be provided")
+            raise ValueError(ERROR_NO_METADATA)
 
     def size(self) -> Size:
         if self.images is not None:

api/onnx_web/server/api.py

@@ -161,7 +161,6 @@ def image_reply(
 
 
 def multi_image_reply(results: Dict[str, Any]):
-    # TODO: not that
     return jsonify(
         {
             "results": results,
@@ -654,7 +653,7 @@ def job_cancel(server: ServerContext, pool: DevicePoolExecutor):
     if len(job_list) == 0:
         return error_reply("at least one job name is required")
 
-    results = []
+    results: List[Dict[str, str]] = []
     for job_name in job_list:
         job_name = sanitize_name(job_name)
         cancelled = pool.cancel(job_name)

api/tests/convert/test_utils.py

@@ -452,10 +452,10 @@ class BuildCachePathsTests(unittest.TestCase):
         name = "model"
         client = "client2"
         cache = "/path/to/cache"
-        format = "onnx"
+        model_format = "onnx"
 
         conversion = ConversionContext(cache_path=cache)
-        result = build_cache_paths(conversion, name, client, cache, format)
+        result = build_cache_paths(conversion, name, client, cache, model_format)
 
         expected_paths = [
             path.join("/path/to/cache", ONNX_MODEL),
@@ -466,10 +466,10 @@ class BuildCachePathsTests(unittest.TestCase):
     def test_build_cache_paths_with_existing_extension(self):
         client = "client3"
         cache = "/path/to/cache"
-        format = "onnx"
+        model_format = "onnx"
 
         conversion = ConversionContext(cache_path=cache)
-        result = build_cache_paths(conversion, TORCH_MODEL, client, cache, format)
+        result = build_cache_paths(conversion, TORCH_MODEL, client, cache, model_format)
 
         expected_paths = [
             path.join("/path/to/cache", TORCH_MODEL),
@@ -481,10 +481,10 @@ class BuildCachePathsTests(unittest.TestCase):
         name = "model"
         client = "client4"
         cache = "/path/to/cache"
-        format = "onnx"
+        model_format = "onnx"
 
         conversion = ConversionContext(cache_path=cache)
-        result = build_cache_paths(conversion, name, client, cache, format)
+        result = build_cache_paths(conversion, name, client, cache, model_format)
 
         expected_paths = [
             path.join("/path/to/cache", ONNX_MODEL),

api/tests/image/test_laion_face.py

@@ -6,7 +6,7 @@ from onnx_web.image.laion_face import draw_pupils, generate_annotation, reverse_
 
 
 class TestLaionFace(unittest.TestCase):
-    @unittest.skip
+    @unittest.skip("need to prepare a good input image")
     def test_draw_pupils(self):
         # Create a dummy image
         image = np.zeros((100, 100, 3), dtype=np.uint8)
@@ -34,7 +34,7 @@ class TestLaionFace(unittest.TestCase):
 
         self.assertNotEqual(np.sum(image), 0, "Image should be modified")
 
-    @unittest.skip
+    @unittest.skip("need to prepare a good input image")
     def test_generate_annotation(self):
         # Create a dummy image
         image = np.zeros((100, 100, 3), dtype=np.uint8)