fixes

Author: skonto

.github/workflows/validation.yml

@@ -27,8 +27,10 @@ jobs:
       - name: Sync all dependencies (including dev)
         run: uv sync --dev --extra torch --extra onnx
 
-      - name: Lint with Ruff
-        run: uv run ruff src/
+      - name: Lint with Ruff (GitHub annotations)
+        uses: astral-sh/ruff-action@v3
+        with:
+          src: "./src ./app"
 
       - name: Check formatting with Black
         run: uv run black --check src/

src/data/data.py

@@ -1,6 +1,8 @@
-from pydantic import BaseModel, field_validator
 from typing import List
 
+from pydantic import BaseModel, field_validator
+
+
 class BatchInput(BaseModel):
     features: List[List[float]]  # Batch of input samples
 

src/inference_onnx/__init__.py

@@ -1,4 +1,4 @@
-from .onnx_engine import ONNXInferenceEngine
 from .config import EngineParams
+from .onnx_engine import ONNXInferenceEngine
 
 __all__ = ["ONNXInferenceEngine", "EngineParams"] 

src/inference_onnx/config.py

@@ -1,6 +1,8 @@
-from pydantic_settings import BaseSettings
 from typing import Literal
+
 import onnxruntime as ort
+from pydantic_settings import BaseSettings
+
 
 class EngineParams(BaseSettings):
     device: Literal["cpu", "cuda"] = "cuda"

src/inference_onnx/onnx_engine.py

@@ -1,8 +1,10 @@
-import onnxruntime as ort
-import numpy as np
-from concurrent.futures import ThreadPoolExecutor
 import asyncio
-from typing import List, Dict, Any
+from concurrent.futures import ThreadPoolExecutor
+from typing import Any, Dict, List
+
+import numpy as np
+import onnxruntime as ort
+
 
 class ONNXInferenceEngine:
     def __init__(self, onnx_path: str, providers: List[str], num_threads: int = 4):

src/inference_torch/__init__.py

@@ -1,4 +1,4 @@
-from .torch_engine import TorchInferenceEngine
 from .config import EngineParams
+from .torch_engine import TorchInferenceEngine
 
 __all__ = ["TorchInferenceEngine", "EngineParams"]

src/inference_torch/config.py

@@ -1,7 +1,9 @@
-from pydantic_settings import BaseSettings
 from typing import Literal
+
 import torch
 from loguru import logger
+from pydantic_settings import BaseSettings
+
 
 class EngineParams(BaseSettings):
     device: Literal["cpu", "cuda"] = "cuda"

src/inference_torch/torch_engine.py

@@ -1,9 +1,11 @@
-import torch
-from torch import nn
-from concurrent.futures import ThreadPoolExecutor
 import asyncio
-from typing import List, Dict, Any
+from concurrent.futures import ThreadPoolExecutor
+from typing import Any, Dict, List
+
 import numpy as np
+import torch
+from torch import nn
+
 
 class TorchInferenceEngine:
     def __init__(self, model: nn.Module, device: torch.device, num_threads: int = 4):

src/log_config/log_config.py

@@ -1,6 +1,7 @@
 from loguru import logger
 from pydantic_settings import BaseSettings
 
+
 def setup_logging(
     log_file: str = "app.log",
     level: str = "DEBUG",

src/models/iris.py

@@ -1,5 +1,6 @@
-import torch.nn as nn
 import torch
+import torch.nn as nn
+
 
 class IrisDL(nn.Module):
     def __init__(