Miscellaneous fixes (0.17 beta) (#21655)

* Fix jetson stats reading

* Return result

* Avoid unknown class for cover image

* fix double encoding of passwords in camera wizard

* formatting

* empty homekit config fixes

* add locks to jina v1 embeddings

protect tokenizer and feature extractor in jina_v1_embedding with per-instance thread lock to avoid the "Already borrowed" RuntimeError during concurrent tokenization

* Capitalize correctly

* replace deprecated google-generativeai with google-genai

update gemini genai provider with new calls from SDK
provider_options specifies any http options
suppress unneeded info logging

* fix attribute area on detail stream hover

---------

Co-authored-by: Josh Hawkins <32435876+hawkeye217@users.noreply.github.com>

docker/main/requirements-wheels.txt

@@ -47,7 +47,7 @@ onnxruntime == 1.22.*
 # Embeddings
 transformers == 4.45.*
 # Generative AI
-google-generativeai == 0.8.*
+google-genai == 1.58.*
 ollama == 0.6.*
 openai == 1.65.*
 # push notifications

docker/main/rootfs/etc/s6-overlay/s6-rc.d/go2rtc/run

@@ -69,15 +69,15 @@ function setup_homekit_config() {
     local cleaned_json="/tmp/cache/homekit_cleaned.json"
     jq '
         # Keep only the homekit section if it exists, otherwise empty object
-        if has("homekit") then {homekit: .homekit} else {homekit: {}} end
+        if has("homekit") then {homekit: .homekit} else {} end
     ' "${temp_json}" > "${cleaned_json}" 2>/dev/null || {
-        echo '{"homekit": {}}' > "${cleaned_json}"
+        echo '{}' > "${cleaned_json}"
     }
 
     # Convert back to YAML and write to the config file
     yq eval -P "${cleaned_json}" > "${config_path}" 2>/dev/null || {
         echo "[WARNING] Failed to convert cleaned config to YAML, creating minimal config"
-        echo 'homekit: {}' > "${config_path}"
+        echo '{}' > "${config_path}"
     }
 
     # Clean up temp files

frigate/api/camera.py

@@ -848,9 +848,10 @@ async def onvif_probe(
                             try:
                                 if isinstance(uri, str) and uri.startswith("rtsp://"):
                                     if username and password and "@" not in uri:
-                                        # Inject URL-encoded credentials and add only the
+                                        # Inject raw credentials and add only the
-                                        # authenticated version.
+                                        # authenticated version. The credentials will be encoded
-                                        cred = f"{quote_plus(username)}:{quote_plus(password)}@"
+                                        # later by ffprobe_stream or the config system.
+                                        cred = f"{username}:{password}@"
                                         injected = uri.replace(
                                             "rtsp://", f"rtsp://{cred}", 1
                                         )
@@ -903,12 +904,8 @@ async def onvif_probe(
                     "/cam/realmonitor?channel=1&subtype=0",
                     "/11",
                 ]
-                # Use URL-encoded credentials for pattern fallback URIs when provided
+                # Use raw credentials for pattern fallback URIs when provided
-                auth_str = (
+                auth_str = f"{username}:{password}@" if username and password else ""
-                    f"{quote_plus(username)}:{quote_plus(password)}@"
-                    if username and password
-                    else ""
-                )
                 rtsp_port = 554
                 for path in common_paths:
                     uri = f"rtsp://{auth_str}{host}:{rtsp_port}{path}"
@@ -930,7 +927,7 @@ async def onvif_probe(
                         and uri.startswith("rtsp://")
                         and "@" not in uri
                     ):
-                        cred = f"{quote_plus(username)}:{quote_plus(password)}@"
+                        cred = f"{username}:{password}@"
                         cred_uri = uri.replace("rtsp://", f"rtsp://{cred}", 1)
                         if cred_uri not in to_test:
                             to_test.append(cred_uri)

frigate/embeddings/onnx/jina_v1_embedding.py

@@ -2,6 +2,7 @@
 
 import logging
 import os
+import threading
 import warnings
 
 from transformers import AutoFeatureExtractor, AutoTokenizer
@@ -54,6 +55,7 @@ class JinaV1TextEmbedding(BaseEmbedding):
         self.tokenizer = None
         self.feature_extractor = None
         self.runner = None
+        self._lock = threading.Lock()
         files_names = list(self.download_urls.keys()) + [self.tokenizer_file]
 
         if not all(
@@ -134,17 +136,18 @@ class JinaV1TextEmbedding(BaseEmbedding):
             )
 
     def _preprocess_inputs(self, raw_inputs):
-        max_length = max(len(self.tokenizer.encode(text)) for text in raw_inputs)
+        with self._lock:
-        return [
+            max_length = max(len(self.tokenizer.encode(text)) for text in raw_inputs)
-            self.tokenizer(
+            return [
-                text,
+                self.tokenizer(
-                padding="max_length",
+                    text,
-                truncation=True,
+                    padding="max_length",
-                max_length=max_length,
+                    truncation=True,
-                return_tensors="np",
+                    max_length=max_length,
-            )
+                    return_tensors="np",
-            for text in raw_inputs
+                )
-        ]
+                for text in raw_inputs
+            ]
 
 
 class JinaV1ImageEmbedding(BaseEmbedding):
@@ -174,6 +177,7 @@ class JinaV1ImageEmbedding(BaseEmbedding):
         self.download_path = os.path.join(MODEL_CACHE_DIR, self.model_name)
         self.feature_extractor = None
         self.runner: BaseModelRunner | None = None
+        self._lock = threading.Lock()
         files_names = list(self.download_urls.keys())
         if not all(
             os.path.exists(os.path.join(self.download_path, n)) for n in files_names
@@ -216,8 +220,9 @@ class JinaV1ImageEmbedding(BaseEmbedding):
             )
 
     def _preprocess_inputs(self, raw_inputs):
-        processed_images = [self._process_image(img) for img in raw_inputs]
+        with self._lock:
-        return [
+            processed_images = [self._process_image(img) for img in raw_inputs]
-            self.feature_extractor(images=image, return_tensors="np")
+            return [
-            for image in processed_images
+                self.feature_extractor(images=image, return_tensors="np")
-        ]
+                for image in processed_images
+            ]

frigate/genai/gemini.py

@@ -3,8 +3,8 @@
 import logging
 from typing import Optional
 
-import google.generativeai as genai
+from google import genai
-from google.api_core.exceptions import GoogleAPICallError
+from google.genai import errors, types
 
 from frigate.config import GenAIProviderEnum
 from frigate.genai import GenAIClient, register_genai_provider
@@ -16,44 +16,51 @@ logger = logging.getLogger(__name__)
 class GeminiClient(GenAIClient):
     """Generative AI client for Frigate using Gemini."""
 
-    provider: genai.GenerativeModel
+    provider: genai.Client
 
     def _init_provider(self):
         """Initialize the client."""
-        genai.configure(api_key=self.genai_config.api_key)
+        # Merge provider_options into HttpOptions
-        return genai.GenerativeModel(
+        http_options_dict = {
-            self.genai_config.model, **self.genai_config.provider_options
+            "api_version": "v1",
+            "timeout": int(self.timeout * 1000),  # requires milliseconds
+        }
+
+        if isinstance(self.genai_config.provider_options, dict):
+            http_options_dict.update(self.genai_config.provider_options)
+
+        return genai.Client(
+            api_key=self.genai_config.api_key,
+            http_options=types.HttpOptions(**http_options_dict),
         )
 
     def _send(self, prompt: str, images: list[bytes]) -> Optional[str]:
         """Submit a request to Gemini."""
-        data = [
+        contents = [
-            {
+            types.Part.from_bytes(data=img, mime_type="image/jpeg") for img in images
-                "mime_type": "image/jpeg",
-                "data": img,
-            }
-            for img in images
         ] + [prompt]
         try:
             # Merge runtime_options into generation_config if provided
             generation_config_dict = {"candidate_count": 1}
             generation_config_dict.update(self.genai_config.runtime_options)
 
-            response = self.provider.generate_content(
+            response = self.provider.models.generate_content(
-                data,
+                model=self.genai_config.model,
-                generation_config=genai.types.GenerationConfig(
+                contents=contents,
-                    **generation_config_dict
+                config=types.GenerateContentConfig(
-                ),
+                    **generation_config_dict,
-                request_options=genai.types.RequestOptions(
-                    timeout=self.timeout,
                 ),
             )
-        except GoogleAPICallError as e:
+        except errors.APIError as e:
             logger.warning("Gemini returned an error: %s", str(e))
             return None
+        except Exception as e:
+            logger.warning("An unexpected error occurred with Gemini: %s", str(e))
+            return None
+
         try:
             description = response.text.strip()
-        except ValueError:
+        except (ValueError, AttributeError):
             # No description was generated
             return None
         return description

frigate/log.py

@@ -89,6 +89,7 @@ def apply_log_levels(default: str, log_levels: dict[str, LogLevel]) -> None:
         "ws4py": LogLevel.error,
         "PIL": LogLevel.warning,
         "numba": LogLevel.warning,
+        "google_genai.models": LogLevel.warning,
         **log_levels,
     }
 

frigate/util/services.py

@@ -540,9 +540,16 @@ def get_jetson_stats() -> Optional[dict[int, dict]]:
     try:
         results["mem"] = "-"  # no discrete gpu memory
 
-        with open("/sys/devices/gpu.0/load", "r") as f:
+        if os.path.exists("/sys/devices/gpu.0/load"):
-            gpuload = float(f.readline()) / 10
+            with open("/sys/devices/gpu.0/load", "r") as f:
-            results["gpu"] = f"{gpuload}%"
+                gpuload = float(f.readline()) / 10
+                results["gpu"] = f"{gpuload}%"
+        elif os.path.exists("/sys/devices/platform/gpu.0/load"):
+            with open("/sys/devices/platform/gpu.0/load", "r") as f:
+                gpuload = float(f.readline()) / 10
+                results["gpu"] = f"{gpuload}%"
+        else:
+            results["gpu"] = "-"
     except Exception:
         return None
 

web/src/components/timeline/DetailStream.tsx

@@ -887,7 +887,10 @@ function LifecycleItem({
                       </span>
                       <span className="font-medium text-foreground">
                         {attributeAreaPx}{" "}
-                        {t("information.pixels", { ns: "common" })}{" "}
+                        {t("information.pixels", {
+                          ns: "common",
+                          area: attributeAreaPx,
+                        })}{" "}
                         <span className="text-secondary-foreground">·</span>{" "}
                         {attributeAreaPct}%
                       </span>

web/src/utils/cameraUtil.ts

@@ -81,7 +81,8 @@ export async function detectReolinkCamera(
 export function maskUri(uri: string): string {
   try {
     // Handle RTSP URLs with user:pass@host format
-    const rtspMatch = uri.match(/rtsp:\/\/([^:]+):([^@]+)@(.+)/);
+    // Use greedy match for password to handle passwords with @
+    const rtspMatch = uri.match(/rtsp:\/\/([^:]+):(.+)@(.+)/);
     if (rtspMatch) {
       return `rtsp://${rtspMatch[1]}:${"*".repeat(4)}@${rtspMatch[3]}`;
     }

web/src/views/classification/ModelSelectionView.tsx

@@ -266,7 +266,10 @@ function ModelCard({ config, onClick, onUpdate, onDelete }: ModelCardProps) {
       return undefined;
     }
 
-    const keys = Object.keys(dataset.categories).filter((key) => key != "none");
+    const keys = Object.keys(dataset.categories).filter(
+      (key) => key != "none" && key.toLowerCase() != "unknown",
+    );
+
     if (keys.length === 0) {
       return undefined;
     }

web/src/views/classification/ModelTrainingView.tsx

@@ -75,6 +75,7 @@ import SearchDetailDialog, {
 } from "@/components/overlay/detail/SearchDetailDialog";
 import { SearchResult } from "@/types/search";
 import { HiSparkles } from "react-icons/hi";
+import { capitalizeFirstLetter } from "@/utils/stringUtil";
 
 type ModelTrainingViewProps = {
   model: CustomClassificationModelConfig;
@@ -88,7 +89,7 @@ export default function ModelTrainingView({ model }: ModelTrainingViewProps) {
   // title
 
   useEffect(() => {
-    document.title = `${model.name.toUpperCase()} - ${t("documentTitle")}`;
+    document.title = `${capitalizeFirstLetter(model.name)} - ${t("documentTitle")}`;
   }, [model.name, t]);
 
   // model state