chore(imports): reduce startup time

.github/workflows/geti-inspect.yaml

@@ -39,10 +39,13 @@ jobs:
         with:
           version: "0.9.18"
 
-      - name: Install OpenCV dependencies
+      - name: Install system dependencies
         run: |
           sudo apt-get update
-          sudo apt-get install -y libgl1 libglib2.0-0
+          sudo apt-get install -y libgl1 libglib2.0-0 libavformat-dev libavcodec-dev libavdevice-dev libavutil-dev libswscale-dev libswresample-dev libavfilter-dev pkg-config
+
+      - name: Install build tools
+        run: sudo apt-get install -y build-essential pkg-config
 
       - name: Prepare venv and install Python dependencies
         working-directory: application/backend
@@ -82,6 +85,14 @@ jobs:
         with:
           version: "0.9.18"
 
+      - name: Install system dependencies
+        run: |
+          sudo apt-get update
+          sudo apt-get install -y libavformat-dev libavcodec-dev libavdevice-dev libavutil-dev libswscale-dev libswresample-dev libavfilter-dev pkg-config
+
+      - name: Install build tools
+        run: sudo apt-get install -y build-essential pkg-config
+
       - name: Prepare venv and install Python dependencies
         working-directory: application/backend
         run: |

application/backend/pyproject.toml

@@ -16,7 +16,8 @@ dependencies = [
     "openvino==2025.2.0",
     "pydantic-settings~=2.10.1",
     "aiortc~=1.13.0",
-    "anomalib[full]",
+    "anomalib[openvino]",
+    "tensorboard",
     "torchmetrics>=1.8.2",
     "opencv-python-headless~=4.12",
     "watchdog==6.0.0",

application/backend/src/entities/stream_data.py

@@ -1,11 +1,14 @@
-# Copyright (C) 2025 Intel Corporation
+# Copyright (C) 2025-2026 Intel Corporation
 # SPDX-License-Identifier: Apache-2.0
 
+from __future__ import annotations
+
 from dataclasses import dataclass
-from typing import Any
+from typing import TYPE_CHECKING, Any
 
-import numpy as np
-from anomalib.data import NumpyImageBatch as PredictionResult
+if TYPE_CHECKING:
+    import numpy as np
+    from anomalib.data import NumpyImageBatch as PredictionResult
 
 
 @dataclass(kw_only=True)

application/backend/src/services/dispatchers/base.py

@@ -1,18 +1,22 @@
-# Copyright (C) 2025 Intel Corporation
+# Copyright (C) 2025-2026 Intel Corporation
 # SPDX-License-Identifier: Apache-2.0
+from __future__ import annotations
+
 import asyncio
 import base64
 import time
 from abc import ABCMeta, abstractmethod
 from datetime import datetime
-from typing import Any
+from typing import TYPE_CHECKING, Any
 
 import cv2
-import numpy as np
-from anomalib.data import NumpyImageBatch as PredictionResult
 
 from pydantic_models import OutputFormat, Sink
 
+if TYPE_CHECKING:
+    import numpy as np
+    from anomalib.data import NumpyImageBatch as PredictionResult
+
 
 def numpy_to_base64(image: np.ndarray, fmt: str = ".jpg") -> str:
     """Convert a numpy array image to a base64 string."""

application/backend/src/services/dispatchers/filesystem.py

@@ -1,17 +1,22 @@
-# Copyright (C) 2025 Intel Corporation
+# Copyright (C) 2025-2026 Intel Corporation
 # SPDX-License-Identifier: Apache-2.0
 
+from __future__ import annotations
+
 import os
 from datetime import datetime
+from typing import TYPE_CHECKING
 
 import cv2
-import numpy as np
-from anomalib.data import NumpyImageBatch as PredictionResult
 from loguru import logger
 
 from pydantic_models.sink import FolderSinkConfig, OutputFormat
 from services.dispatchers.base import BaseDispatcher
 
+if TYPE_CHECKING:
+    import numpy as np
+    from anomalib.data import NumpyImageBatch as PredictionResult
+
 
 class FolderDispatcher(BaseDispatcher):
     """FolderDispatcher allows outputting to a folder in the local filesystem."""

application/backend/src/services/dispatchers/mqtt.py

@@ -1,13 +1,13 @@
-# Copyright (C) 2025 Intel Corporation
+# Copyright (C) 2025-2026 Intel Corporation
 # SPDX-License-Identifier: Apache-2.0
 
+from __future__ import annotations
+
 import json
 import threading
 import time
-from typing import Any
+from typing import TYPE_CHECKING, Any
 
-import numpy as np
-from anomalib.data import NumpyImageBatch as PredictionResult
 from loguru import logger
 
 from pydantic_models.sink import MqttSinkConfig
@@ -18,6 +18,10 @@
 except ImportError:
     mqtt = None  # type: ignore[assignment]
 
+if TYPE_CHECKING:
+    import numpy as np
+    from anomalib.data import NumpyImageBatch as PredictionResult
+
 
 MAX_RETRIES = 3
 RETRY_DELAY = 1
@@ -28,7 +32,7 @@ class MqttDispatcher(BaseDispatcher):
     def __init__(
         self,
         output_config: MqttSinkConfig,
-        mqtt_client: "mqtt.Client | None" = None,
+        mqtt_client: mqtt.Client | None = None,
         track_messages: bool | None = False,
     ) -> None:
         """
@@ -61,7 +65,7 @@ def __init__(
         self.client = mqtt_client or self._create_default_client()
         self._connect()
 
-    def _create_default_client(self) -> "mqtt.Client":
+    def _create_default_client(self) -> mqtt.Client:
         client_id = f"dispatcher_{int(time.time())}"
         client = mqtt.Client(client_id=client_id)
         client.on_connect = self._on_connect
@@ -86,15 +90,15 @@ def _connect(self) -> None:
                 time.sleep(RETRY_DELAY * (attempt + 1))
         raise ConnectionError("Failed to connect to MQTT broker")
 
-    def _on_connect(self, _client: "mqtt.Client", _userdata: Any, _flags: dict[str, int], rc: int):
+    def _on_connect(self, _client: mqtt.Client, _userdata: Any, _flags: dict[str, int], rc: int):
         if rc == 0:
             self._connected = True
             self._connection_event.set()
             logger.info("Connected to MQTT broker")
         else:
             logger.error(f"MQTT connect failed with code {rc}")
 
-    def _on_disconnect(self, _client: "mqtt.Client", _userdata: Any, rc: int):
+    def _on_disconnect(self, _client: mqtt.Client, _userdata: Any, rc: int):
         self._connected = False
         self._connection_event.clear()
         logger.warning(f"MQTT disconnected (rc={rc})")

application/backend/src/services/dispatchers/webhook.py

@@ -1,20 +1,24 @@
-# Copyright (C) 2025 Intel Corporation
+# Copyright (C) 2025-2026 Intel Corporation
 # SPDX-License-Identifier: Apache-2.0
 
 """This module contains the WebhookDispatcher class for dispatching images and predictions to a webhook endpoint."""
 
-from typing import Any
+from __future__ import annotations
+
+from typing import TYPE_CHECKING, Any
 
-import numpy as np
 import requests
-from anomalib.data import NumpyImageBatch as PredictionResult
 from loguru import logger
 from requests.adapters import HTTPAdapter
 from urllib3.util.retry import Retry
 
 from pydantic_models.sink import WebhookSinkConfig
 from services.dispatchers.base import BaseDispatcher
 
+if TYPE_CHECKING:
+    import numpy as np
+    from anomalib.data import NumpyImageBatch as PredictionResult
+
 MAX_RETRIES = 3
 BACKOFF_FACTOR = 0.3
 RETRY_ON_STATUS = [500, 502, 503, 504]

application/backend/src/services/training_service.py

@@ -68,10 +68,9 @@ async def _run_training_job(cls, job: Job, job_service: JobService) -> Model | N
         project_id = job.project_id
         model_name = job.payload.get("model_name")
         device = job.payload.get("device")
-        snapshot_id_ = job.payload.get("dataset_snapshot_id")
-        snapshot_id = UUID(snapshot_id_) if snapshot_id_ else None
-        max_epochs = job.payload.get("max_epochs", 200)
-
+        snapshot_id = UUID(snapshot_id_str) if (snapshot_id_str := job.payload.get("dataset_snapshot_id")) else None
+        # UI can return None
+        max_epochs: int = payload_epochs if (payload_epochs := job.payload.get("max_epochs")) is not None else 200
         if model_name is None:
             raise ValueError(f"Job {job.id} payload must contain 'model_name'")