Don't remove tensorflow on trt (#20743)

* Pull context size from openai models

* Adjust wording based on type of model

* Instruct to not use parenthesis

* Simplify genai config

* Don't use GPU for training

docker/tensorrt/Dockerfile.amd64

@@ -21,7 +21,7 @@ FROM deps AS frigate-tensorrt
 ARG PIP_BREAK_SYSTEM_PACKAGES
 
 RUN --mount=type=bind,from=trt-wheels,source=/trt-wheels,target=/deps/trt-wheels \
-    pip3 uninstall -y onnxruntime tensorflow-cpu \
+    pip3 uninstall -y onnxruntime \
     && pip3 install -U /deps/trt-wheels/*.whl
 
 COPY --from=rootfs / /

docker/tensorrt/requirements-amd64.txt

@@ -13,7 +13,6 @@ nvidia_cusolver_cu12==11.6.3.*; platform_machine == 'x86_64'
 nvidia_cusparse_cu12==12.5.1.*; platform_machine == 'x86_64'
 nvidia_nccl_cu12==2.23.4; platform_machine == 'x86_64'
 nvidia_nvjitlink_cu12==12.5.82; platform_machine == 'x86_64'
-tensorflow==2.19.*; platform_machine == 'x86_64'
 onnx==1.16.*; platform_machine == 'x86_64'
 onnxruntime-gpu==1.22.*; platform_machine == 'x86_64'
 protobuf==3.20.3; platform_machine == 'x86_64'

docs/docs/configuration/custom_classification/object_classification.md

@@ -10,7 +10,6 @@ Object classification allows you to train a custom MobileNetV2 classification mo
 Object classification models are lightweight and run very fast on CPU. Inference should be usable on virtually any machine that can run Frigate.
 
 Training the model does briefly use a high amount of system resources for about 1–3 minutes per training run. On lower-power devices, training may take longer.
-When running the `-tensorrt` image, Nvidia GPUs will automatically be used to accelerate training.
 
 ## Classes
 

docs/docs/configuration/custom_classification/state_classification.md

@@ -10,7 +10,6 @@ State classification allows you to train a custom MobileNetV2 classification mod
 State classification models are lightweight and run very fast on CPU. Inference should be usable on virtually any machine that can run Frigate.
 
 Training the model does briefly use a high amount of system resources for about 1–3 minutes per training run. On lower-power devices, training may take longer.
-When running the `-tensorrt` image, Nvidia GPUs will automatically be used to accelerate training.
 
 ## Classes
 

frigate/genai/__init__.py

@@ -114,7 +114,7 @@ Your response MUST be a flat JSON object with:
 
 ## Objects in Scene
 
-Each line represents a detection state, not necessarily unique individuals. Objects with names in parentheses (e.g., "Name (person)") are verified identities. Objects without names (e.g., "Person") are detected but not identified.
+Each line represents a detection state, not necessarily unique individuals. Parentheses indicate object type or category, use only the name/label in your response, not the parentheses.
 
 **CRITICAL: When you see both recognized and unrecognized entries of the same type (e.g., "Joe (person)" and "Person"), visually count how many distinct people/objects you actually see based on appearance and clothing. If you observe only ONE person throughout the sequence, use ONLY the recognized name (e.g., "Joe"). The same person may be recognized in some frames but not others. Only describe both if you visually see MULTIPLE distinct people with clearly different appearances.**
 

frigate/genai/openai.py

@@ -18,6 +18,7 @@ class OpenAIClient(GenAIClient):
     """Generative AI client for Frigate using OpenAI."""
 
     provider: OpenAI
+    context_size: Optional[int] = None
 
     def _init_provider(self):
         """Initialize the client."""
@@ -69,5 +70,33 @@ class OpenAIClient(GenAIClient):
 
     def get_context_size(self) -> int:
         """Get the context window size for OpenAI."""
-        # OpenAI GPT-4 Vision models have 128K token context window
-        return 128000
+        if self.context_size is not None:
+            return self.context_size
+
+        try:
+            models = self.provider.models.list()
+            for model in models.data:
+                if model.id == self.genai_config.model:
+                    if hasattr(model, "max_model_len") and model.max_model_len:
+                        self.context_size = model.max_model_len
+                        logger.debug(
+                            f"Retrieved context size {self.context_size} for model {self.genai_config.model}"
+                        )
+                        return self.context_size
+
+        except Exception as e:
+            logger.debug(
+                f"Failed to fetch model context size from API: {e}, using default"
+            )
+
+        # Default to 128K for ChatGPT models, 8K for others
+        model_name = self.genai_config.model.lower()
+        if "gpt-4o" in model_name:
+            self.context_size = 128000
+        else:
+            self.context_size = 8192
+
+        logger.debug(
+            f"Using default context size {self.context_size} for model {self.genai_config.model}"
+        )
+        return self.context_size

frigate/util/config.py

@@ -384,10 +384,10 @@ def migrate_017_0(config: dict[str, dict[str, Any]]) -> dict[str, dict[str, Any]
         new_object_config["genai"] = {}
 
         for key in global_genai.keys():
-            if key not in ["enabled", "model", "provider", "base_url", "api_key"]:
-                new_object_config["genai"][key] = global_genai[key]
-            else:
+            if key in ["model", "provider", "base_url", "api_key"]:
                 new_genai_config[key] = global_genai[key]
+            else:
+                new_object_config["genai"][key] = global_genai[key]
 
         config["genai"] = new_genai_config
 

web/public/locales/en/views/classificationModel.json

@@ -86,6 +86,7 @@
       "classificationSubLabel": "Sub Label",
       "classificationAttribute": "Attribute",
       "classes": "Classes",
+      "states": "States",
       "classesTip": "Learn about classes",
       "classesStateDesc": "Define the different states your camera area can be in. For example: 'open' and 'closed' for a garage door.",
       "classesObjectDesc": "Define the different categories to classify detected objects into. For example: 'delivery_person', 'resident', 'stranger' for person classification.",

web/src/components/classification/wizard/Step1NameAndDefine.tsx

@@ -394,7 +394,9 @@ export default function Step1NameAndDefine({
             <div className="flex items-center justify-between">
               <div className="flex items-center gap-1">
                 <FormLabel className="text-primary-variant">
-                  {t("wizard.step1.classes")}
+                  {watchedModelType === "state"
+                    ? t("wizard.step1.states")
+                    : t("wizard.step1.classes")}
                 </FormLabel>
                 <Popover>
                   <PopoverTrigger asChild>