Miscellaneous fixes (#21373)

* Send preferred language for report service

* make object lifecycle scrollable in tracking details

* fix info popovers in live camera drawer

* ensure metrics are initialized if genai is enabled

* docs

* ollama cloud model docs

* Ensure object descriptions get claened up

---------

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

docs/docs/configuration/custom_classification/object_classification.md

@@ -33,9 +33,9 @@ For object classification:
   - Example: `cat` → `Leo`, `Charlie`, `None`.
 
 - **Attribute**:
-  - Added as metadata to the object (visible in /events): `<model_name>: <predicted_value>`.
+  - Added as metadata to the object, visible in the Tracked Object Details pane in Explore, `frigate/events` MQTT messages, and the HTTP API response as `<model_name>: <predicted_value>`.
   - Ideal when multiple attributes can coexist independently.
-  - Example: Detecting if a `person` in a construction yard is wearing a helmet or not.
+  - Example: Detecting if a `person` in a construction yard is wearing a helmet or not, and if they are wearing a yellow vest or not.
 
 :::note
 
@@ -81,6 +81,8 @@ classification:
         classification_type: sub_label # or: attribute
 ```
 
+An optional config, `save_attempts`, can be set as a key under the model name. This defines the number of classification attempts to save in the Recent Classifications tab. For object classification models, the default is 200.
+
 ## Training the model
 
 Creating and training the model is done within the Frigate UI using the `Classification` page. The process consists of two steps:
@@ -89,6 +91,8 @@ Creating and training the model is done within the Frigate UI using the `Classif
 
 Enter a name for your model, select the object label to classify (e.g., `person`, `dog`, `car`), choose the classification type (sub label or attribute), and define your classes. Include a `none` class for objects that don't fit any specific category.
 
+For example: To classify your two cats, create a model named "Our Cats" and create two classes, "Charlie" and "Leo". Create a third class, "none", for other neighborhood cats that are not your own.
+
 ### Step 2: Assign Training Examples
 
 The system will automatically generate example images from detected objects matching your selected label. You'll be guided through each class one at a time to select which images represent that class. Any images not assigned to a specific class will automatically be assigned to `none` when you complete the last class. Once all images are processed, training will begin automatically.

docs/docs/configuration/custom_classification/state_classification.md

@@ -48,6 +48,8 @@ classification:
             crop: [0, 180, 220, 400]
 ```
 
+An optional config, `save_attempts`, can be set as a key under the model name. This defines the number of classification attempts to save in the Recent Classifications tab. For state classification models, the default is 100.
+
 ## Training the model
 
 Creating and training the model is done within the Frigate UI using the `Classification` page. The process consists of three steps:

docs/docs/configuration/genai.md

@@ -56,7 +56,7 @@ Parallel requests also come with some caveats. You will need to set `OLLAMA_NUM_
 
 ### Supported Models
 
-You must use a vision capable model with Frigate. Current model variants can be found [in their model library](https://ollama.com/library). At the time of writing, this includes `llava`, `llava-llama3`, `llava-phi3`, and `moondream`. Note that Frigate will not automatically download the model you specify in your config, you must download the model to your local instance of Ollama first i.e. by running `ollama pull llava:7b` on your Ollama server/Docker container. Note that the model specified in Frigate's config must match the downloaded model tag.
+You must use a vision capable model with Frigate. Current model variants can be found [in their model library](https://ollama.com/library). Note that Frigate will not automatically download the model you specify in your config, you must download the model to your local instance of Ollama first i.e. by running `ollama pull llava:7b` on your Ollama server/Docker container. Note that the model specified in Frigate's config must match the downloaded model tag.
 
 :::note
 
@@ -64,6 +64,10 @@ You should have at least 8 GB of RAM available (or VRAM if running on GPU) to ru
 
 :::
 
+#### Ollama Cloud models
+
+Ollama also supports [cloud models](https://ollama.com/cloud), where your local Ollama instance handles requests from Frigate, but model inference is performed in the cloud. Set up Ollama locally, sign in with your Ollama account, and specify the cloud model name in your Frigate config. For more details, see the Ollama cloud model [docs](https://docs.ollama.com/cloud).
+
 ### Configuration
 
 ```yaml