Implement Wizard for Creating Classification Models (#20622)

* Implement extraction of images for classification state models

* Add object classification dataset preparation

* Add first step wizard

* Update i18n

* Add state classification image selection step

* Improve box handling

* Add object selector

* Improve object cropping implementation

* Fix state classification selection

* Finalize training and image selection step

* Cleanup

* Design optimizations

* Cleanup mobile styling

* Update no models screen

* Cleanups and fixes

* Fix bugs

* Improve model training and creation process

* Cleanup

* Dynamically add metrics for new model

* Add loading when hitting continue

* Improve image selection mechanism

* Remove unused translation keys

* Adjust wording

* Add retry button for image generation

* Make no models view more specific

* Adjust plus icon

* Adjust form label

* Start with correct type selected

* Cleanup sizing and more font colors

* Small tweaks

* Add tips and more info

* Cleanup dialog sizing

* Add cursor rule for frontend

* Cleanup

* remove underline

* Lazy loading

frigate/embeddings/maintainer.py

@@ -9,6 +9,7 @@ from typing import Any
 
 from peewee import DoesNotExist
 
+from frigate.comms.config_updater import ConfigSubscriber
 from frigate.comms.detections_updater import DetectionSubscriber, DetectionTypeEnum
 from frigate.comms.embeddings_updater import (
     EmbeddingsRequestEnum,
@@ -95,6 +96,9 @@ class EmbeddingMaintainer(threading.Thread):
                 CameraConfigUpdateEnum.semantic_search,
             ],
         )
+        self.classification_config_subscriber = ConfigSubscriber(
+            "config/classification/custom/"
+        )
 
         # Configure Frigate DB
         db = SqliteVecQueueDatabase(
@@ -255,6 +259,7 @@ class EmbeddingMaintainer(threading.Thread):
         """Maintain a SQLite-vec database for semantic search."""
         while not self.stop_event.is_set():
             self.config_updater.check_for_updates()
+            self._check_classification_config_updates()
             self._process_requests()
             self._process_updates()
             self._process_recordings_updates()
@@ -265,6 +270,7 @@ class EmbeddingMaintainer(threading.Thread):
             self._process_event_metadata()
 
         self.config_updater.stop()
+        self.classification_config_subscriber.stop()
         self.event_subscriber.stop()
         self.event_end_subscriber.stop()
         self.recordings_subscriber.stop()
@@ -275,6 +281,46 @@ class EmbeddingMaintainer(threading.Thread):
         self.requestor.stop()
         logger.info("Exiting embeddings maintenance...")
 
+    def _check_classification_config_updates(self) -> None:
+        """Check for classification config updates and add new processors."""
+        topic, model_config = self.classification_config_subscriber.check_for_update()
+
+        if topic and model_config:
+            model_name = topic.split("/")[-1]
+            self.config.classification.custom[model_name] = model_config
+
+            # Check if processor already exists
+            for processor in self.realtime_processors:
+                if isinstance(
+                    processor,
+                    (
+                        CustomStateClassificationProcessor,
+                        CustomObjectClassificationProcessor,
+                    ),
+                ):
+                    if processor.model_config.name == model_name:
+                        logger.debug(
+                            f"Classification processor for model {model_name} already exists, skipping"
+                        )
+                        return
+
+            if model_config.state_config is not None:
+                processor = CustomStateClassificationProcessor(
+                    self.config, model_config, self.requestor, self.metrics
+                )
+            else:
+                processor = CustomObjectClassificationProcessor(
+                    self.config,
+                    model_config,
+                    self.event_metadata_publisher,
+                    self.metrics,
+                )
+
+            self.realtime_processors.append(processor)
+            logger.info(
+                f"Added classification processor for model: {model_name} (type: {type(processor).__name__})"
+            )
+
     def _process_requests(self) -> None:
         """Process embeddings requests"""