improve model downloading and add status screen

frigate/app.py

@@ -590,13 +590,13 @@ class FrigateApp:
         self.init_onvif()
         self.init_recording_manager()
         self.init_review_segment_manager()
-        self.init_embeddings_manager()
         self.init_go2rtc()
         self.bind_database()
         self.check_db_data_migrations()
-        self.init_embeddings_client()
         self.init_inter_process_communicator()
         self.init_dispatcher()
+        self.init_embeddings_manager()
+        self.init_embeddings_client()
         self.start_detectors()
         self.start_video_output_processor()
         self.start_ptz_autotracker()

frigate/comms/dispatcher.py

@@ -16,10 +16,12 @@ from frigate.const import (
     REQUEST_REGION_GRID,
     UPDATE_CAMERA_ACTIVITY,
     UPDATE_EVENT_DESCRIPTION,
+    UPDATE_MODEL_STATE,
     UPSERT_REVIEW_SEGMENT,
 )
 from frigate.models import Event, Previews, Recordings, ReviewSegment
 from frigate.ptz.onvif import OnvifCommandEnum, OnvifController
+from frigate.types import ModelStatusTypesEnum
 from frigate.util.object import get_camera_regions_grid
 from frigate.util.services import restart_frigate
 
@@ -83,6 +85,7 @@ class Dispatcher:
             comm.subscribe(self._receive)
 
         self.camera_activity = {}
+        self.model_state = {}
 
     def _receive(self, topic: str, payload: str) -> Optional[Any]:
         """Handle receiving of payload from communicators."""
@@ -144,6 +147,14 @@ class Dispatcher:
                 "event_update",
                 json.dumps({"id": event.id, "description": event.data["description"]}),
             )
+        elif topic == UPDATE_MODEL_STATE:
+            model = payload["model"]
+            state = payload["state"]
+            self.model_state[model] = ModelStatusTypesEnum[state]
+            self.publish("model_state", json.dumps(self.model_state))
+        elif topic == "modelState":
+            model_state = self.model_state.copy()
+            self.publish("model_state", json.dumps(model_state))
         elif topic == "onConnect":
             camera_status = self.camera_activity.copy()
 

frigate/const.py

@@ -84,6 +84,7 @@ UPSERT_REVIEW_SEGMENT = "upsert_review_segment"
 CLEAR_ONGOING_REVIEW_SEGMENTS = "clear_ongoing_review_segments"
 UPDATE_CAMERA_ACTIVITY = "update_camera_activity"
 UPDATE_EVENT_DESCRIPTION = "update_event_description"
+UPDATE_MODEL_STATE = "update_model_state"
 
 # Stats Values
 

frigate/embeddings/__init__.py

@@ -71,8 +71,7 @@ def manage_embeddings(config: FrigateConfig) -> None:
 
 class EmbeddingsContext:
     def __init__(self, db: SqliteVecQueueDatabase):
-        self.db = db
-        self.embeddings = Embeddings(self.db)
+        self.embeddings = Embeddings(db)
         self.thumb_stats = ZScoreNormalization()
         self.desc_stats = ZScoreNormalization()
 

frigate/embeddings/embeddings.py

@@ -10,8 +10,11 @@ from typing import List, Tuple, Union
 from PIL import Image
 from playhouse.shortcuts import model_to_dict
 
+from frigate.comms.inter_process import InterProcessRequestor
+from frigate.const import UPDATE_MODEL_STATE
 from frigate.db.sqlitevecq import SqliteVecQueueDatabase
 from frigate.models import Event
+from frigate.types import ModelStatusTypesEnum
 
 from .functions.clip import ClipEmbedding
 from .functions.minilm_l6_v2 import MiniLMEmbedding
@@ -65,11 +68,30 @@ class Embeddings:
 
     def __init__(self, db: SqliteVecQueueDatabase) -> None:
         self.db = db
+        self.requestor = InterProcessRequestor()
 
         # Create tables if they don't exist
         self._create_tables()
 
-        self.clip_embedding = ClipEmbedding(model="ViT-B/32")
+        models = [
+            "sentence-transformers/all-MiniLM-L6-v2-model.onnx",
+            "sentence-transformers/all-MiniLM-L6-v2-tokenizer",
+            "clip-clip_image_model_vitb32.onnx",
+            "clip-clip_text_model_vitb32.onnx",
+        ]
+
+        for model in models:
+            self.requestor.send_data(
+                UPDATE_MODEL_STATE,
+                {
+                    "model": model,
+                    "state": ModelStatusTypesEnum.not_downloaded,
+                },
+            )
+
+        self.clip_embedding = ClipEmbedding(
+            preferred_providers=["CPUExecutionProvider"]
+        )
         self.minilm_embedding = MiniLMEmbedding(
             preferred_providers=["CPUExecutionProvider"],
         )

frigate/embeddings/functions/clip.py

@@ -1,30 +1,59 @@
-"""CLIP Embeddings for Frigate."""
-
-import errno
 import logging
 import os
-from pathlib import Path
-from typing import List, Union
+from typing import List, Optional, Union
 
 import numpy as np
 import onnxruntime as ort
-import requests
-from onnx_clip import OnnxClip
+from onnx_clip import OnnxClip, Preprocessor, Tokenizer
 from PIL import Image
 
-from frigate.const import MODEL_CACHE_DIR
+from frigate.const import MODEL_CACHE_DIR, UPDATE_MODEL_STATE
+from frigate.types import ModelStatusTypesEnum
+from frigate.util.downloader import ModelDownloader
+
+logger = logging.getLogger(__name__)
 
 
 class Clip(OnnxClip):
-    """Override load models to download to cache directory."""
+    """Override load models to use pre-downloaded models from cache directory."""
+
+    def __init__(
+        self,
+        model: str = "ViT-B/32",
+        batch_size: Optional[int] = None,
+        providers: List[str] = ["CPUExecutionProvider"],
+    ):
+        """
+        Instantiates the model and required encoding classes.
+
+        Args:
+            model: The model to utilize. Currently ViT-B/32 and RN50 are
+                allowed.
+            batch_size: If set, splits the lists in `get_image_embeddings`
+                and `get_text_embeddings` into batches of this size before
+                passing them to the model. The embeddings are then concatenated
+                back together before being returned. This is necessary when
+                passing large amounts of data (perhaps ~100 or more).
+        """
+        allowed_models = ["ViT-B/32", "RN50"]
+        if model not in allowed_models:
+            raise ValueError(f"`model` must be in {allowed_models}. Got {model}.")
+        if model == "ViT-B/32":
+            self.embedding_size = 512
+        elif model == "RN50":
+            self.embedding_size = 1024
+        self.image_model, self.text_model = self._load_models(model, providers)
+        self._tokenizer = Tokenizer()
+        self._preprocessor = Preprocessor()
+        self._batch_size = batch_size
 
     @staticmethod
     def _load_models(
         model: str,
-        silent: bool,
+        providers: List[str],
     ) -> tuple[ort.InferenceSession, ort.InferenceSession]:
         """
-        These models are a part of the container. Treat as as such.
+        Load models from cache directory.
         """
         if model == "ViT-B/32":
             IMAGE_MODEL_FILE = "clip_image_model_vitb32.onnx"
@@ -38,58 +67,92 @@ class Clip(OnnxClip):
         models = []
         for model_file in [IMAGE_MODEL_FILE, TEXT_MODEL_FILE]:
             path = os.path.join(MODEL_CACHE_DIR, "clip", model_file)
-            models.append(Clip._load_model(path, silent))
+            models.append(Clip._load_model(path, providers))
 
         return models[0], models[1]
 
     @staticmethod
-    def _load_model(path: str, silent: bool):
-        providers = ["CPUExecutionProvider"]
-
-        try:
-            if os.path.exists(path):
-                return ort.InferenceSession(path, providers=providers)
-            else:
-                raise FileNotFoundError(
-                    errno.ENOENT,
-                    os.strerror(errno.ENOENT),
-                    path,
-                )
-        except Exception:
-            s3_url = f"https://lakera-clip.s3.eu-west-1.amazonaws.com/{os.path.basename(path)}"
-            if not silent:
-                logging.info(
-                    f"The model file ({path}) doesn't exist "
-                    f"or it is invalid. Downloading it from the public S3 "
-                    f"bucket: {s3_url}."  # noqa: E501
-                )
-
-            # Download from S3
-            # Saving to a temporary file first to avoid corrupting the file
-            temporary_filename = Path(path).with_name(os.path.basename(path) + ".part")
-
-            # Create any missing directories in the path
-            temporary_filename.parent.mkdir(parents=True, exist_ok=True)
-
-            with requests.get(s3_url, stream=True) as r:
-                r.raise_for_status()
-                with open(temporary_filename, "wb") as f:
-                    for chunk in r.iter_content(chunk_size=8192):
-                        f.write(chunk)
-                    f.flush()
-            # Finally move the temporary file to the correct location
-            temporary_filename.rename(path)
+    def _load_model(path: str, providers: List[str]):
+        if os.path.exists(path):
             return ort.InferenceSession(path, providers=providers)
+        else:
+            logger.warning(f"CLIP model file {path} not found.")
+            return None
 
 
 class ClipEmbedding:
     """Embedding function for CLIP model."""
 
-    def __init__(self, model: str = "ViT-B/32"):
-        """Initialize CLIP Embedding function."""
-        self.model = Clip(model)
+    def __init__(
+        self,
+        model: str = "ViT-B/32",
+        silent: bool = False,
+        preferred_providers: List[str] = ["CPUExecutionProvider"],
+    ):
+        self.model_name = model
+        self.silent = silent
+        self.preferred_providers = preferred_providers
+        self.model_files = self._get_model_files()
+        self.model = None
+
+        self.downloader = ModelDownloader(
+            model_name="clip",
+            download_path=os.path.join(MODEL_CACHE_DIR, "clip"),
+            file_names=self.model_files,
+            download_func=self._download_model,
+            silent=self.silent,
+        )
+        self.downloader.ensure_model_files()
+
+    def _get_model_files(self):
+        if self.model_name == "ViT-B/32":
+            return ["clip_image_model_vitb32.onnx", "clip_text_model_vitb32.onnx"]
+        elif self.model_name == "RN50":
+            return ["clip_image_model_rn50.onnx", "clip_text_model_rn50.onnx"]
+        else:
+            raise ValueError(
+                f"Unexpected model {self.model_name}. No `.onnx` file found."
+            )
+
+    def _download_model(self, path: str):
+        s3_url = (
+            f"https://lakera-clip.s3.eu-west-1.amazonaws.com/{os.path.basename(path)}"
+        )
+        try:
+            ModelDownloader.download_from_url(s3_url, path, self.silent)
+            self.downloader.requestor.send_data(
+                UPDATE_MODEL_STATE,
+                {
+                    "model": f"{self.model_name}-{os.path.basename(path)}",
+                    "state": ModelStatusTypesEnum.downloaded,
+                },
+            )
+        except Exception:
+            self.downloader.requestor.send_data(
+                UPDATE_MODEL_STATE,
+                {
+                    "model": f"{self.model_name}-{os.path.basename(path)}",
+                    "state": ModelStatusTypesEnum.error,
+                },
+            )
+
+    def _load_model(self):
+        if self.model is None:
+            self.downloader.wait_for_download()
+            self.model = Clip(self.model_name, providers=self.preferred_providers)
 
     def __call__(self, input: Union[List[str], List[Image.Image]]) -> List[np.ndarray]:
+        self._load_model()
+        if (
+            self.model is None
+            or self.model.image_model is None
+            or self.model.text_model is None
+        ):
+            logger.info(
+                "CLIP model is not fully loaded. Please wait for the download to complete."
+            )
+            return []
+
         embeddings = []
         for item in input:
             if isinstance(item, Image.Image):

frigate/embeddings/functions/minilm_l6_v2.py

@@ -1,21 +1,20 @@
-"""Embedding function for ONNX MiniLM-L6 model."""
-
-import errno
 import logging
 import os
-from pathlib import Path
 from typing import List
 
 import numpy as np
 import onnxruntime as ort
-import requests
 
 # importing this without pytorch or others causes a warning
 # https://github.com/huggingface/transformers/issues/27214
 # suppressed by setting env TRANSFORMERS_NO_ADVISORY_WARNINGS=1
 from transformers import AutoTokenizer
 
-from frigate.const import MODEL_CACHE_DIR
+from frigate.const import MODEL_CACHE_DIR, UPDATE_MODEL_STATE
+from frigate.types import ModelStatusTypesEnum
+from frigate.util.downloader import ModelDownloader
+
+logger = logging.getLogger(__name__)
 
 
 class MiniLMEmbedding:
@@ -26,86 +25,83 @@ class MiniLMEmbedding:
     IMAGE_MODEL_FILE = "model.onnx"
     TOKENIZER_FILE = "tokenizer"
 
-    def __init__(self, preferred_providers=None):
-        """Initialize MiniLM Embedding function."""
-        self.tokenizer = self._load_tokenizer()
+    def __init__(self, preferred_providers=["CPUExecutionProvider"]):
+        self.preferred_providers = preferred_providers
+        self.tokenizer = None
+        self.session = None
 
-        model_path = os.path.join(self.DOWNLOAD_PATH, self.IMAGE_MODEL_FILE)
-        if not os.path.exists(model_path):
-            self._download_model()
+        self.downloader = ModelDownloader(
+            model_name=self.MODEL_NAME,
+            download_path=self.DOWNLOAD_PATH,
+            file_names=[self.IMAGE_MODEL_FILE, self.TOKENIZER_FILE],
+            download_func=self._download_model,
+        )
+        self.downloader.ensure_model_files()
 
-        if preferred_providers is None:
-            preferred_providers = ["CPUExecutionProvider"]
+    def _download_model(self, path: str):
+        try:
+            if os.path.basename(path) == self.IMAGE_MODEL_FILE:
+                s3_url = f"https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2/resolve/main/onnx/{self.IMAGE_MODEL_FILE}"
+                ModelDownloader.download_from_url(s3_url, path)
+            elif os.path.basename(path) == self.TOKENIZER_FILE:
+                logger.info("Downloading MiniLM tokenizer")
+                tokenizer = AutoTokenizer.from_pretrained(
+                    self.MODEL_NAME, clean_up_tokenization_spaces=False
+                )
+                tokenizer.save_pretrained(path)
 
-        self.session = self._load_model(model_path)
+            self.downloader.requestor.send_data(
+                UPDATE_MODEL_STATE,
+                {
+                    "model": f"{self.MODEL_NAME}-{os.path.basename(path)}",
+                    "state": ModelStatusTypesEnum.downloaded,
+                },
+            )
+        except Exception:
+            self.downloader.requestor.send_data(
+                UPDATE_MODEL_STATE,
+                {
+                    "model": f"{self.MODEL_NAME}-{os.path.basename(path)}",
+                    "state": ModelStatusTypesEnum.error,
+                },
+            )
+
+    def _load_model_and_tokenizer(self):
+        if self.tokenizer is None or self.session is None:
+            self.downloader.wait_for_download()
+            self.tokenizer = self._load_tokenizer()
+            self.session = self._load_model(
+                os.path.join(self.DOWNLOAD_PATH, self.IMAGE_MODEL_FILE),
+                self.preferred_providers,
+            )
 
     def _load_tokenizer(self):
-        """Load the tokenizer from the local path or download it if not available."""
         tokenizer_path = os.path.join(self.DOWNLOAD_PATH, self.TOKENIZER_FILE)
-        if os.path.exists(tokenizer_path):
-            return AutoTokenizer.from_pretrained(tokenizer_path)
-        else:
-            return AutoTokenizer.from_pretrained(self.MODEL_NAME)
-
-    def _download_model(self):
-        """Download the ONNX model and tokenizer from a remote source if they don't exist."""
-        logging.info(f"Downloading {self.MODEL_NAME} ONNX model and tokenizer...")
-
-        # Download the tokenizer
-        tokenizer = AutoTokenizer.from_pretrained(self.MODEL_NAME)
-        os.makedirs(self.DOWNLOAD_PATH, exist_ok=True)
-        tokenizer.save_pretrained(os.path.join(self.DOWNLOAD_PATH, self.TOKENIZER_FILE))
-
-        # Download the ONNX model
-        s3_url = f"https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2/resolve/main/onnx/{self.IMAGE_MODEL_FILE}"
-        model_path = os.path.join(self.DOWNLOAD_PATH, self.IMAGE_MODEL_FILE)
-        self._download_from_url(s3_url, model_path)
-
-        logging.info(f"Model and tokenizer saved to {self.DOWNLOAD_PATH}")
-
-    def _download_from_url(self, url: str, save_path: str):
-        """Download a file from a URL and save it to a specified path."""
-        temporary_filename = Path(save_path).with_name(
-            os.path.basename(save_path) + ".part"
+        return AutoTokenizer.from_pretrained(
+            tokenizer_path, clean_up_tokenization_spaces=False
         )
-        temporary_filename.parent.mkdir(parents=True, exist_ok=True)
-        with requests.get(url, stream=True, allow_redirects=True) as r:
-            # if the content type is HTML, it's not the actual model file
-            if "text/html" in r.headers.get("Content-Type", ""):
-                raise ValueError(
-                    f"Expected an ONNX file but received HTML from the URL: {url}"
-                )
 
-            # Ensure the download is successful
-            r.raise_for_status()
-
-            # Write the model to a temporary file first
-            with open(temporary_filename, "wb") as f:
-                for chunk in r.iter_content(chunk_size=8192):
-                    f.write(chunk)
-
-        temporary_filename.rename(save_path)
-
-    def _load_model(self, path: str):
-        """Load the ONNX model from a given path."""
-        providers = ["CPUExecutionProvider"]
+    def _load_model(self, path: str, providers: List[str]):
         if os.path.exists(path):
             return ort.InferenceSession(path, providers=providers)
         else:
-            raise FileNotFoundError(errno.ENOENT, os.strerror(errno.ENOENT), path)
+            logger.warning(f"MiniLM model file {path} not found.")
+            return None
 
     def __call__(self, texts: List[str]) -> List[np.ndarray]:
-        """Generate embeddings for the given texts."""
+        self._load_model_and_tokenizer()
+
+        if self.session is None or self.tokenizer is None:
+            logger.error("MiniLM model or tokenizer is not loaded.")
+            return []
+
         inputs = self.tokenizer(
             texts, padding=True, truncation=True, return_tensors="np"
         )
-
         input_names = [input.name for input in self.session.get_inputs()]
         onnx_inputs = {name: inputs[name] for name in input_names if name in inputs}
 
-        # Run inference
         outputs = self.session.run(None, onnx_inputs)
-
         embeddings = outputs[0].mean(axis=1)
 
         return [embedding for embedding in embeddings]

frigate/types.py

@@ -1,3 +1,4 @@
+from enum import Enum
 from typing import TypedDict
 
 from frigate.camera import CameraMetrics
@@ -11,3 +12,10 @@ class StatsTrackingTypes(TypedDict):
     latest_frigate_version: str
     last_updated: int
     processes: dict[str, int]
+
+
+class ModelStatusTypesEnum(str, Enum):
+    not_downloaded = "not_downloaded"
+    downloading = "downloading"
+    downloaded = "downloaded"
+    error = "error"

frigate/util/downloader.py

@@ -0,0 +1,119 @@
+import logging
+import os
+import threading
+import time
+from pathlib import Path
+from typing import Callable, List
+
+import requests
+
+from frigate.comms.inter_process import InterProcessRequestor
+from frigate.const import UPDATE_MODEL_STATE
+from frigate.types import ModelStatusTypesEnum
+
+logger = logging.getLogger(__name__)
+
+
+class FileLock:
+    def __init__(self, path):
+        self.path = path
+        self.lock_file = f"{path}.lock"
+
+    def acquire(self):
+        parent_dir = os.path.dirname(self.lock_file)
+        os.makedirs(parent_dir, exist_ok=True)
+
+        while True:
+            try:
+                with open(self.lock_file, "x"):
+                    return
+            except FileExistsError:
+                time.sleep(0.1)
+
+    def release(self):
+        try:
+            os.remove(self.lock_file)
+        except FileNotFoundError:
+            pass
+
+
+class ModelDownloader:
+    def __init__(
+        self,
+        model_name: str,
+        download_path: str,
+        file_names: List[str],
+        download_func: Callable[[str], None],
+        silent: bool = False,
+    ):
+        self.model_name = model_name
+        self.download_path = download_path
+        self.file_names = file_names
+        self.download_func = download_func
+        self.silent = silent
+        self.requestor = InterProcessRequestor()
+        self.download_thread = None
+        self.download_complete = threading.Event()
+
+    def ensure_model_files(self):
+        for file in self.file_names:
+            self.requestor.send_data(
+                UPDATE_MODEL_STATE,
+                {
+                    "model": f"{self.model_name}-{file}",
+                    "state": ModelStatusTypesEnum.downloading,
+                },
+            )
+        self.download_thread = threading.Thread(target=self._download_models)
+        self.download_thread.start()
+
+    def _download_models(self):
+        for file_name in self.file_names:
+            path = os.path.join(self.download_path, file_name)
+            lock = FileLock(path)
+
+            if not os.path.exists(path):
+                lock.acquire()
+                try:
+                    if not os.path.exists(path):
+                        self.download_func(path)
+                finally:
+                    lock.release()
+
+            self.requestor.send_data(
+                UPDATE_MODEL_STATE,
+                {
+                    "model": f"{self.model_name}-{file_name}",
+                    "state": ModelStatusTypesEnum.downloaded,
+                },
+            )
+
+        self.download_complete.set()
+
+    @staticmethod
+    def download_from_url(url: str, save_path: str, silent: bool = False):
+        temporary_filename = Path(save_path).with_name(
+            os.path.basename(save_path) + ".part"
+        )
+        temporary_filename.parent.mkdir(parents=True, exist_ok=True)
+
+        if not silent:
+            logger.info(f"Downloading model file from: {url}")
+
+        try:
+            with requests.get(url, stream=True, allow_redirects=True) as r:
+                r.raise_for_status()
+                with open(temporary_filename, "wb") as f:
+                    for chunk in r.iter_content(chunk_size=8192):
+                        f.write(chunk)
+
+            temporary_filename.rename(save_path)
+        except Exception as e:
+            logger.error(f"Error downloading model: {str(e)}")
+            raise
+
+        if not silent:
+            logger.info(f"Downloading complete: {url}")
+
+    def wait_for_download(self):
+        self.download_complete.wait()

web/src/api/ws.tsx

@@ -5,6 +5,7 @@ import {
   FrigateCameraState,
   FrigateEvent,
   FrigateReview,
+  ModelState,
   ToggleableSetting,
 } from "@/types/ws";
 import { FrigateStats } from "@/types/stats";
@@ -266,6 +267,41 @@ export function useInitialCameraState(
   return { payload: data ? data[camera] : undefined };
 }
 
+export function useModelState(
+  model: string,
+  revalidateOnFocus: boolean = true,
+): { payload: ModelState } {
+  const {
+    value: { payload },
+    send: sendCommand,
+  } = useWs("model_state", "modelState");
+
+  const data = useDeepMemo(JSON.parse(payload as string));
+
+  useEffect(() => {
+    let listener = undefined;
+    if (revalidateOnFocus) {
+      sendCommand("modelState");
+      listener = () => {
+        if (document.visibilityState == "visible") {
+          sendCommand("modelState");
+        }
+      };
+      addEventListener("visibilitychange", listener);
+    }
+
+    return () => {
+      if (listener) {
+        removeEventListener("visibilitychange", listener);
+      }
+    };
+    // we know that these deps are correct
+    // eslint-disable-next-line react-hooks/exhaustive-deps
+  }, [revalidateOnFocus]);
+
+  return { payload: data ? data[model] : undefined };
+}
+
 export function useMotionActivity(camera: string): { payload: string } {
   const {
     value: { payload },

web/src/pages/Explore.tsx

@@ -1,11 +1,15 @@
-import { useEventUpdate } from "@/api/ws";
+import { useEventUpdate, useModelState } from "@/api/ws";
+import ActivityIndicator from "@/components/indicators/activity-indicator";
 import { useApiFilterArgs } from "@/hooks/use-api-filter";
 import { useTimezone } from "@/hooks/use-date-utils";
 import { FrigateConfig } from "@/types/frigateConfig";
 import { SearchFilter, SearchQuery, SearchResult } from "@/types/search";
+import { ModelState } from "@/types/ws";
 import SearchView from "@/views/search/SearchView";
 import { useCallback, useEffect, useMemo, useState } from "react";
+import { LuCheck, LuExternalLink, LuX } from "react-icons/lu";
 import { TbExclamationCircle } from "react-icons/tb";
+import { Link } from "react-router-dom";
 import useSWR from "swr";
 import useSWRInfinite from "swr/infinite";
 
@@ -111,14 +115,10 @@ export default function Explore() {
 
   // paging
 
-  // usually slow only on first run while downloading models
-  const [isSlowLoading, setIsSlowLoading] = useState(false);
-
   const getKey = (
     pageIndex: number,
     previousPageData: SearchResult[] | null,
   ): SearchQuery => {
-    if (isSlowLoading && !similaritySearch) return null;
     if (previousPageData && !previousPageData.length) return null; // reached the end
     if (!searchQuery) return null;
 
@@ -143,12 +143,6 @@ export default function Explore() {
     revalidateFirstPage: true,
     revalidateOnFocus: true,
     revalidateAll: false,
-    onLoadingSlow: () => {
-      if (!similaritySearch) {
-        setIsSlowLoading(true);
-      }
-    },
-    loadingTimeout: 15000,
   });
 
   const searchResults = useMemo(
@@ -188,17 +182,113 @@ export default function Explore() {
     // eslint-disable-next-line react-hooks/exhaustive-deps
   }, [eventUpdate]);
 
+  // model states
+
+  const { payload: minilmModelState } = useModelState(
+    "sentence-transformers/all-MiniLM-L6-v2-model.onnx",
+  );
+  const { payload: minilmTokenizerState } = useModelState(
+    "sentence-transformers/all-MiniLM-L6-v2-tokenizer",
+  );
+  const { payload: clipImageModelState } = useModelState(
+    "clip-clip_image_model_vitb32.onnx",
+  );
+  const { payload: clipTextModelState } = useModelState(
+    "clip-clip_text_model_vitb32.onnx",
+  );
+
+  const allModelsLoaded = useMemo(() => {
+    return (
+      minilmModelState === "downloaded" &&
+      minilmTokenizerState === "downloaded" &&
+      clipImageModelState === "downloaded" &&
+      clipTextModelState === "downloaded"
+    );
+  }, [
+    minilmModelState,
+    minilmTokenizerState,
+    clipImageModelState,
+    clipTextModelState,
+  ]);
+
+  const renderModelStateIcon = (modelState: ModelState) => {
+    if (modelState === "downloading") {
+      return <ActivityIndicator className="size-5" />;
+    }
+    if (modelState === "downloaded") {
+      return <LuCheck className="size-5 text-success" />;
+    }
+    if (modelState === "not_downloaded" || modelState === "error") {
+      return <LuX className="size-5 text-danger" />;
+    }
+    return null;
+  };
+
+  if (
+    !minilmModelState ||
+    !minilmTokenizerState ||
+    !clipImageModelState ||
+    !clipTextModelState
+  ) {
+    return (
+      <ActivityIndicator className="absolute left-1/2 top-1/2 -translate-x-1/2 -translate-y-1/2" />
+    );
+  }
+
   return (
     <>
-      {isSlowLoading && !similaritySearch ? (
+      {!allModelsLoaded ? (
         <div className="absolute inset-0 left-1/2 top-1/2 flex h-96 w-96 -translate-x-1/2 -translate-y-1/2">
-          <div className="flex flex-col items-center justify-center rounded-lg bg-background/50 p-5">
-            <p className="my-5 text-lg">Search Unavailable</p>
-            <TbExclamationCircle className="mb-3 size-10" />
-            <p className="max-w-96 text-center">
-              If this is your first time using Search, be patient while Frigate
-              downloads the necessary embeddings models. Check Frigate logs.
-            </p>
+          <div className="flex flex-col items-center justify-center space-y-3 rounded-lg bg-background/50 p-5">
+            <div className="my-5 flex flex-col items-center gap-2 text-xl">
+              <TbExclamationCircle className="mb-3 size-10" />
+              <div>Search Unavailable</div>
+            </div>
+            <div className="max-w-96 text-center">
+              Frigate is downloading the necessary embeddings models to support
+              semantic searching. This may take several minutes depending on the
+              speed of your network connection.
+            </div>
+            <div className="flex w-96 flex-col gap-2 py-5">
+              <div className="flex flex-row items-center justify-center gap-2">
+                {renderModelStateIcon(clipImageModelState)}
+                CLIP image model
+              </div>
+              <div className="flex flex-row items-center justify-center gap-2">
+                {renderModelStateIcon(clipTextModelState)}
+                CLIP text model
+              </div>
+              <div className="flex flex-row items-center justify-center gap-2">
+                {renderModelStateIcon(minilmModelState)}
+                MiniLM sentence model
+              </div>
+              <div className="flex flex-row items-center justify-center gap-2">
+                {renderModelStateIcon(minilmTokenizerState)}
+                MiniLM tokenizer
+              </div>
+            </div>
+            {(minilmModelState === "error" ||
+              clipImageModelState === "error" ||
+              clipTextModelState === "error") && (
+              <div className="my-3 max-w-96 text-center text-danger">
+                An error has occurred. Check Frigate logs.
+              </div>
+            )}
+            <div className="max-w-96 text-center">
+              You may want to reindex the embeddings of your tracked objects
+              once the models are downloaded.
+            </div>
+            <div className="flex max-w-96 items-center text-primary-variant">
+              <Link
+                to="https://docs.frigate.video/configuration/semantic_search"
+                target="_blank"
+                rel="noopener noreferrer"
+                className="inline"
+              >
+                Read the documentation{" "}
+                <LuExternalLink className="ml-2 inline-flex size-3" />
+              </Link>
+            </div>
           </div>
         </div>
       ) : (

web/src/types/ws.ts

@@ -56,4 +56,10 @@ export interface FrigateCameraState {
   objects: ObjectType[];
 }
 
+export type ModelState =
+  | "not_downloaded"
+  | "downloading"
+  | "downloaded"
+  | "error";
+
 export type ToggleableSetting = "ON" | "OFF";