Miscellaneous Fixes (#20866)

* Don't warn when event ids have expired for trigger sync

* Import faster_whisper conditinally to avoid illegal instruction

* Catch OpenVINO runtime error

* fix race condition in detail stream context

navigating between tracked objects in Explore would sometimes prevent the object track from appearing

* Handle case where classification images are deleted

* Adjust default rounded corners on larger screens

* Improve flow handling for classification state

* Remove images when wizard is cancelled

* Improve deletion handling for classes

* Set constraints on review buffers

* Update to support correct data format

* Set minimum duration for recording based review items

* Use friendly name in review genai prompt

---------

Co-authored-by: Nicolas Mowen <nickmowen213@gmail.com>

frigate/api/classification.py

@@ -595,9 +595,13 @@ def get_classification_dataset(name: str):
             "last_training_image_count": 0,
             "current_image_count": current_image_count,
             "new_images_count": current_image_count,
+            "dataset_changed": current_image_count > 0,
         }
     else:
         last_training_count = metadata.get("last_training_image_count", 0)
+        # Dataset has changed if count is different (either added or deleted images)
+        dataset_changed = current_image_count != last_training_count
+        # Only show positive count for new images (ignore deletions in the count display)
         new_images_count = max(0, current_image_count - last_training_count)
         training_metadata = {
             "has_trained": True,
@@ -605,6 +609,7 @@ def get_classification_dataset(name: str):
             "last_training_image_count": last_training_count,
             "current_image_count": current_image_count,
             "new_images_count": new_images_count,
+            "dataset_changed": dataset_changed,
         }
 
     return JSONResponse(
@@ -948,31 +953,29 @@ async def generate_object_examples(request: Request, body: GenerateObjectExample
     dependencies=[Depends(require_role(["admin"]))],
     summary="Delete a classification model",
     description="""Deletes a specific classification model and all its associated data.
-    The name must exist in the classification models. Returns a success message or an error if the name is invalid.""",
+    Works even if the model is not in the config (e.g., partially created during wizard).
+    Returns a success message.""",
 )
 def delete_classification_model(request: Request, name: str):
-    config: FrigateConfig = request.app.frigate_config
-
-    if name not in config.classification.custom:
-        return JSONResponse(
-            content=(
-                {
-                    "success": False,
-                    "message": f"{name} is not a known classification model.",
-                }
-            ),
-            status_code=404,
-        )
+    sanitized_name = sanitize_filename(name)
 
     # Delete the classification model's data directory in clips
-    data_dir = os.path.join(CLIPS_DIR, sanitize_filename(name))
+    data_dir = os.path.join(CLIPS_DIR, sanitized_name)
     if os.path.exists(data_dir):
-        shutil.rmtree(data_dir)
+        try:
+            shutil.rmtree(data_dir)
+            logger.info(f"Deleted classification data directory for {name}")
+        except Exception as e:
+            logger.debug(f"Failed to delete data directory for {name}: {e}")
 
     # Delete the classification model's files in model_cache
-    model_dir = os.path.join(MODEL_CACHE_DIR, sanitize_filename(name))
+    model_dir = os.path.join(MODEL_CACHE_DIR, sanitized_name)
     if os.path.exists(model_dir):
-        shutil.rmtree(model_dir)
+        try:
+            shutil.rmtree(model_dir)
+            logger.info(f"Deleted classification model directory for {name}")
+        except Exception as e:
+            logger.debug(f"Failed to delete model directory for {name}: {e}")
 
     return JSONResponse(
         content=(

frigate/config/camera/camera.py

@@ -177,6 +177,12 @@ class CameraConfig(FrigateBaseModel):
     def ffmpeg_cmds(self) -> list[dict[str, list[str]]]:
         return self._ffmpeg_cmds
 
+    def get_formatted_name(self) -> str:
+        """Return the friendly name if set, otherwise return a formatted version of the camera name."""
+        if self.friendly_name:
+            return self.friendly_name
+        return self.name.replace("_", " ").title() if self.name else ""
+
     def create_ffmpeg_cmds(self):
         if "_ffmpeg_cmds" in self:
             return

frigate/config/camera/zone.py

@@ -56,6 +56,12 @@ class ZoneConfig(BaseModel):
     def contour(self) -> np.ndarray:
         return self._contour
 
+    def get_formatted_name(self, zone_name: str) -> str:
+        """Return the friendly name if set, otherwise return a formatted version of the zone name."""
+        if self.friendly_name:
+            return self.friendly_name
+        return zone_name.replace("_", " ").title()
+
     @field_validator("objects", mode="before")
     @classmethod
     def validate_objects(cls, v):

frigate/data_processing/common/audio_transcription/model.py

@@ -4,7 +4,6 @@ import logging
 import os
 
 import sherpa_onnx
-from faster_whisper.utils import download_model
 
 from frigate.comms.inter_process import InterProcessRequestor
 from frigate.const import MODEL_CACHE_DIR
@@ -25,6 +24,9 @@ class AudioTranscriptionModelRunner:
 
         if model_size == "large":
             # use the Whisper download function instead of our own
+            # Import dynamically to avoid crashes on systems without AVX support
+            from faster_whisper.utils import download_model
+
             logger.debug("Downloading Whisper audio transcription model")
             download_model(
                 size_or_id="small" if device == "cuda" else "tiny",

frigate/data_processing/post/audio_transcription.py

@@ -6,7 +6,6 @@ import threading
 import time
 from typing import Optional
 
-from faster_whisper import WhisperModel
 from peewee import DoesNotExist
 
 from frigate.comms.inter_process import InterProcessRequestor
@@ -51,6 +50,9 @@ class AudioTranscriptionPostProcessor(PostProcessorApi):
 
     def __build_recognizer(self) -> None:
         try:
+            # Import dynamically to avoid crashes on systems without AVX support
+            from faster_whisper import WhisperModel
+
             self.recognizer = WhisperModel(
                 model_size_or_path="small",
                 device="cuda"

frigate/data_processing/post/review_descriptions.py

@@ -16,6 +16,7 @@ from peewee import DoesNotExist
 from frigate.comms.embeddings_updater import EmbeddingsRequestEnum
 from frigate.comms.inter_process import InterProcessRequestor
 from frigate.config import FrigateConfig
+from frigate.config.camera import CameraConfig
 from frigate.config.camera.review import GenAIReviewConfig, ImageSourceEnum
 from frigate.const import CACHE_DIR, CLIPS_DIR, UPDATE_REVIEW_DESCRIPTION
 from frigate.data_processing.types import PostProcessDataEnum
@@ -30,6 +31,7 @@ from ..types import DataProcessorMetrics
 logger = logging.getLogger(__name__)
 
 RECORDING_BUFFER_EXTENSION_PERCENT = 0.10
+MIN_RECORDING_DURATION = 10
 
 
 class ReviewDescriptionProcessor(PostProcessorApi):
@@ -130,7 +132,17 @@ class ReviewDescriptionProcessor(PostProcessorApi):
 
             if image_source == ImageSourceEnum.recordings:
                 duration = final_data["end_time"] - final_data["start_time"]
-                buffer_extension = duration * RECORDING_BUFFER_EXTENSION_PERCENT
+                buffer_extension = min(
+                    10, max(2, duration * RECORDING_BUFFER_EXTENSION_PERCENT)
+                )
+
+                # Ensure minimum total duration for short review items
+                # This provides better context for brief events
+                total_duration = duration + (2 * buffer_extension)
+                if total_duration < MIN_RECORDING_DURATION:
+                    # Expand buffer to reach minimum duration, still respecting max of 10s per side
+                    additional_buffer_per_side = (MIN_RECORDING_DURATION - duration) / 2
+                    buffer_extension = min(10, additional_buffer_per_side)
 
                 thumbs = self.get_recording_frames(
                     camera,
@@ -182,7 +194,7 @@ class ReviewDescriptionProcessor(PostProcessorApi):
                     self.requestor,
                     self.genai_client,
                     self.review_desc_speed,
-                    camera,
+                    camera_config,
                     final_data,
                     thumbs,
                     camera_config.review.genai,
@@ -411,7 +423,7 @@ def run_analysis(
     requestor: InterProcessRequestor,
     genai_client: GenAIClient,
     review_inference_speed: InferenceSpeed,
-    camera: str,
+    camera_config: CameraConfig,
     final_data: dict[str, str],
     thumbs: list[bytes],
     genai_config: GenAIReviewConfig,
@@ -419,10 +431,19 @@ def run_analysis(
     attribute_labels: list[str],
 ) -> None:
     start = datetime.datetime.now().timestamp()
+
+    # Format zone names using zone config friendly names if available
+    formatted_zones = []
+    for zone_name in final_data["data"]["zones"]:
+        if zone_name in camera_config.zones:
+            formatted_zones.append(
+                camera_config.zones[zone_name].get_formatted_name(zone_name)
+            )
+
     analytics_data = {
         "id": final_data["id"],
-        "camera": camera,
-        "zones": final_data["data"]["zones"],
+        "camera": camera_config.get_formatted_name(),
+        "zones": formatted_zones,
         "start": datetime.datetime.fromtimestamp(final_data["start_time"]).strftime(
             "%A, %I:%M %p"
         ),

frigate/detectors/detection_runners.py

@@ -394,7 +394,11 @@ class OpenVINOModelRunner(BaseModelRunner):
                     self.infer_request.set_input_tensor(input_index, input_tensor)
 
                 # Run inference
-                self.infer_request.infer()
+                try:
+                    self.infer_request.infer()
+                except Exception as e:
+                    logger.error(f"Error during OpenVINO inference: {e}")
+                    return []
 
             # Get all output tensors
             outputs = []

frigate/embeddings/embeddings.py

@@ -472,7 +472,7 @@ class Embeddings:
                                 )
                                 thumbnail_missing = True
                         except DoesNotExist:
-                            logger.warning(
+                            logger.debug(
                                 f"Event ID {trigger.data} for trigger {trigger_name} does not exist."
                             )
                             continue

frigate/genai/__init__.py

@@ -51,8 +51,7 @@ class GenAIClient:
         def get_concern_prompt() -> str:
             if concerns:
                 concern_list = "\n    - ".join(concerns)
-                return f"""
-- `other_concerns` (list of strings): Include a list of any of the following concerns that are occurring:
+                return f"""- `other_concerns` (list of strings): Include a list of any of the following concerns that are occurring:
     - {concern_list}"""
             else:
                 return ""
@@ -70,7 +69,7 @@ class GenAIClient:
                 return "\n- (No objects detected)"
 
         context_prompt = f"""
-Your task is to analyze the sequence of images ({len(thumbnails)} total) taken in chronological order from the perspective of the {review_data["camera"].replace("_", " ")} security camera.
+Your task is to analyze the sequence of images ({len(thumbnails)} total) taken in chronological order from the perspective of the {review_data["camera"]} security camera.
 
 ## Normal Activity Patterns for This Property
 
@@ -110,7 +109,7 @@ Your response MUST be a flat JSON object with:
 
 - Frame 1 = earliest, Frame {len(thumbnails)} = latest
 - Activity started at {review_data["start"]} and lasted {review_data["duration"]} seconds
-- Zones involved: {", ".join(z.replace("_", " ").title() for z in review_data["zones"]) or "None"}
+- Zones involved: {", ".join(review_data["zones"]) if review_data["zones"] else "None"}
 
 ## Objects in Scene