frigate/config/camera/camera.py

@@ -177,12 +177,6 @@ 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

@@ -56,12 +56,6 @@ 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/post/review_descriptions.py

@@ -16,7 +16,6 @@ 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
@@ -31,7 +30,6 @@ from ..types import DataProcessorMetrics
 logger = logging.getLogger(__name__)
 
 RECORDING_BUFFER_EXTENSION_PERCENT = 0.10
-MIN_RECORDING_DURATION = 10
 
 
 class ReviewDescriptionProcessor(PostProcessorApi):
@@ -132,17 +130,7 @@ class ReviewDescriptionProcessor(PostProcessorApi):
 
             if image_source == ImageSourceEnum.recordings:
                 duration = final_data["end_time"] - final_data["start_time"]
-                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)
+                buffer_extension = duration * RECORDING_BUFFER_EXTENSION_PERCENT
 
                 thumbs = self.get_recording_frames(
                     camera,
@@ -194,7 +182,7 @@ class ReviewDescriptionProcessor(PostProcessorApi):
                     self.requestor,
                     self.genai_client,
                     self.review_desc_speed,
-                    camera_config,
+                    camera,
                     final_data,
                     thumbs,
                     camera_config.review.genai,
@@ -423,7 +411,7 @@ def run_analysis(
     requestor: InterProcessRequestor,
     genai_client: GenAIClient,
     review_inference_speed: InferenceSpeed,
-    camera_config: CameraConfig,
+    camera: str,
     final_data: dict[str, str],
     thumbs: list[bytes],
     genai_config: GenAIReviewConfig,
@@ -431,19 +419,10 @@ 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_config.get_formatted_name(),
-        "zones": formatted_zones,
+        "camera": camera,
+        "zones": final_data["data"]["zones"],
         "start": datetime.datetime.fromtimestamp(final_data["start_time"]).strftime(
             "%A, %I:%M %p"
         ),

frigate/genai/__init__.py

@@ -51,7 +51,8 @@ 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 ""
@@ -69,7 +70,7 @@ 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"]} 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"].replace("_", " ")} security camera.
 
 ## Normal Activity Patterns for This Property
 
@@ -109,7 +110,7 @@ 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(review_data["zones"]) if review_data["zones"] else "None"}
+- Zones involved: {", ".join(z.replace("_", " ").title() for z in review_data["zones"]) or "None"}
 
 ## Objects in Scene
 

web/src/components/classification/ClassificationModelEditDialog.tsx

@@ -28,7 +28,6 @@ import {
   CustomClassificationModelConfig,
   FrigateConfig,
 } from "@/types/frigateConfig";
-import { ClassificationDatasetResponse } from "@/types/classification";
 import { getTranslatedLabel } from "@/utils/i18n";
 import { zodResolver } from "@hookform/resolvers/zod";
 import axios from "axios";
@@ -141,19 +140,16 @@ export default function ClassificationModelEditDialog({
   });
 
   // Fetch dataset to get current classes for state models
-  const { data: dataset } = useSWR<ClassificationDatasetResponse>(
-    isStateModel ? `classification/${model.name}/dataset` : null,
-    {
-      revalidateOnFocus: false,
-    },
-  );
+  const { data: dataset } = useSWR<{
+    [id: string]: string[];
+  }>(isStateModel ? `classification/${model.name}/dataset` : null, {
+    revalidateOnFocus: false,
+  });
 
   // Update form with classes from dataset when loaded
   useEffect(() => {
-    if (isStateModel && dataset?.categories) {
-      const classes = Object.keys(dataset.categories).filter(
-        (key) => key !== "none",
-      );
+    if (isStateModel && dataset) {
+      const classes = Object.keys(dataset).filter((key) => key !== "none");
       if (classes.length > 0) {
         (form as ReturnType<typeof useForm<StateFormData>>).setValue(
           "classes",

web/src/types/classification.ts

@@ -20,17 +20,3 @@ export type ClassificationThreshold = {
   recognition: number;
   unknown: number;
 };
-
-export type ClassificationDatasetResponse = {
-  categories: {
-    [id: string]: string[];
-  };
-  training_metadata: {
-    has_trained: boolean;
-    last_training_date: string | null;
-    last_training_image_count: number;
-    current_image_count: number;
-    new_images_count: number;
-    dataset_changed: boolean;
-  } | null;
-};

web/src/views/classification/ModelSelectionView.tsx

@@ -11,7 +11,6 @@ import {
   CustomClassificationModelConfig,
   FrigateConfig,
 } from "@/types/frigateConfig";
-import { ClassificationDatasetResponse } from "@/types/classification";
 import { useCallback, useEffect, useMemo, useState } from "react";
 import { useTranslation } from "react-i18next";
 import { FaFolderPlus } from "react-icons/fa";
@@ -210,10 +209,9 @@ type ModelCardProps = {
 function ModelCard({ config, onClick, onUpdate, onDelete }: ModelCardProps) {
   const { t } = useTranslation(["views/classificationModel"]);
 
-  const { data: dataset } = useSWR<ClassificationDatasetResponse>(
-    `classification/${config.name}/dataset`,
-    { revalidateOnFocus: false },
-  );
+  const { data: dataset } = useSWR<{
+    [id: string]: string[];
+  }>(`classification/${config.name}/dataset`, { revalidateOnFocus: false });
 
   const [deleteDialogOpen, setDeleteDialogOpen] = useState(false);
   const [editDialogOpen, setEditDialogOpen] = useState(false);
@@ -262,25 +260,20 @@ function ModelCard({ config, onClick, onUpdate, onDelete }: ModelCardProps) {
   }, []);
 
   const coverImage = useMemo(() => {
-    if (!dataset || !dataset.categories) {
-      return undefined;
-    }
-
-    const keys = Object.keys(dataset.categories).filter((key) => key != "none");
-    if (keys.length === 0) {
+    if (!dataset) {
       return undefined;
     }
 
+    const keys = Object.keys(dataset).filter((key) => key != "none");
     const selectedKey = keys[0];
-    const images = dataset.categories[selectedKey];
 
-    if (!images || images.length === 0) {
+    if (!dataset[selectedKey]) {
       return undefined;
     }
 
     return {
       name: selectedKey,
-      img: images[0],
+      img: dataset[selectedKey][0],
     };
   }, [dataset]);
 
@@ -324,19 +317,11 @@ function ModelCard({ config, onClick, onUpdate, onDelete }: ModelCardProps) {
         )}
         onClick={onClick}
       >
-        {coverImage ? (
-          <>
-            <img
-              className="size-full"
-              src={`${baseUrl}clips/${config.name}/dataset/${coverImage.name}/${coverImage.img}`}
-            />
-            <ImageShadowOverlay lowerClassName="h-[30%] z-0" />
-          </>
-        ) : (
-          <div className="flex size-full items-center justify-center bg-background_alt">
-            <MdModelTraining className="size-16 text-muted-foreground" />
-          </div>
-        )}
+        <img
+          className="size-full"
+          src={`${baseUrl}clips/${config.name}/dataset/${coverImage?.name}/${coverImage?.img}`}
+        />
+        <ImageShadowOverlay lowerClassName="h-[30%] z-0" />
         <div className="absolute bottom-2 left-3 text-lg text-white smart-capitalize">
           {config.name}
         </div>

web/src/views/classification/ModelTrainingView.tsx

@@ -59,11 +59,7 @@ import { useNavigate } from "react-router-dom";
 import { IoMdArrowRoundBack } from "react-icons/io";
 import TrainFilterDialog from "@/components/overlay/dialog/TrainFilterDialog";
 import useApiFilter from "@/hooks/use-api-filter";
-import {
-  ClassificationDatasetResponse,
-  ClassificationItemData,
-  TrainFilter,
-} from "@/types/classification";
+import { ClassificationItemData, TrainFilter } from "@/types/classification";
 import {
   ClassificationCard,
   GroupedClassificationCard,
@@ -122,10 +118,17 @@ export default function ModelTrainingView({ model }: ModelTrainingViewProps) {
   const { data: trainImages, mutate: refreshTrain } = useSWR<string[]>(
     `classification/${model.name}/train`,
   );
-  const { data: datasetResponse, mutate: refreshDataset } =
-    useSWR<ClassificationDatasetResponse>(
-      `classification/${model.name}/dataset`,
-    );
+  const { data: datasetResponse, mutate: refreshDataset } = useSWR<{
+    categories: { [id: string]: string[] };
+    training_metadata: {
+      has_trained: boolean;
+      last_training_date: string | null;
+      last_training_image_count: number;
+      current_image_count: number;
+      new_images_count: number;
+      dataset_changed: boolean;
+    } | null;
+  }>(`classification/${model.name}/dataset`);
 
   const dataset = datasetResponse?.categories || {};
   const trainingMetadata = datasetResponse?.training_metadata;