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https://github.com/blakeblackshear/frigate.git
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Merge bfe46d9f4a into cfeb86646f
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commit
c8d288e8d1
5
.github/copilot-instructions.md
vendored
5
.github/copilot-instructions.md
vendored
@ -1,2 +1,3 @@
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Never write strings in the frontend directly, always write to and reference the relevant translations file.
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Always conform new and refactored code to the existing coding style in the project.
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- For Frigate NVR, never write strings in the frontend directly. Since the project uses `react-i18next`, use `t()` and write the English string in the relevant translations file in `web/public/locales/en`.
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- Always conform new and refactored code to the existing coding style in the project.
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- Always have a way to test your work and confirm your changes. When running backend tests, use `python3 -u -m unittest`.
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@ -68,8 +68,8 @@ Fine-tune the LPR feature using these optional parameters at the global level of
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- Default: `1000` pixels. Note: this is intentionally set very low as it is an _area_ measurement (length x width). For reference, 1000 pixels represents a ~32x32 pixel square in your camera image.
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- Depending on the resolution of your camera's `detect` stream, you can increase this value to ignore small or distant plates.
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- **`device`**: Device to use to run license plate detection _and_ recognition models.
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- Default: `CPU`
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- This can be `CPU`, `GPU`, or the GPU's device number. For users without a model that detects license plates natively, using a GPU may increase performance of the YOLOv9 license plate detector model. See the [Hardware Accelerated Enrichments](/configuration/hardware_acceleration_enrichments.md) documentation. However, for users who run a model that detects `license_plate` natively, there is little to no performance gain reported with running LPR on GPU compared to the CPU.
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- Default: `None`
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- This is auto-selected by Frigate and can be `CPU`, `GPU`, or the GPU's device number. For users without a model that detects license plates natively, using a GPU may increase performance of the YOLOv9 license plate detector model. See the [Hardware Accelerated Enrichments](/configuration/hardware_acceleration_enrichments.md) documentation. However, for users who run a model that detects `license_plate` natively, there is little to no performance gain reported with running LPR on GPU compared to the CPU.
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- **`model_size`**: The size of the model used to identify regions of text on plates.
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- Default: `small`
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- This can be `small` or `large`.
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@ -432,6 +432,6 @@ If you are using a model that natively detects `license_plate`, add an _object m
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If you are not using a model that natively detects `license_plate` or you are using dedicated LPR camera mode, only a _motion mask_ over your text is required.
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### I see "Error running ... model" in my logs. How can I fix this?
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### I see "Error running ... model" in my logs, or my inference time is very high. How can I fix this?
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This usually happens when your GPU is unable to compile or use one of the LPR models. Set your `device` to `CPU` and try again. GPU acceleration only provides a slight performance increase, and the models are lightweight enough to run without issue on most CPUs.
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@ -23,7 +23,12 @@ from markupsafe import escape
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from peewee import SQL, fn, operator
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from pydantic import ValidationError
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from frigate.api.auth import allow_any_authenticated, allow_public, require_role
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from frigate.api.auth import (
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allow_any_authenticated,
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allow_public,
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get_allowed_cameras_for_filter,
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require_role,
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)
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from frigate.api.defs.query.app_query_parameters import AppTimelineHourlyQueryParameters
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from frigate.api.defs.request.app_body import AppConfigSetBody
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from frigate.api.defs.tags import Tags
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@ -687,13 +692,19 @@ def plusModels(request: Request, filterByCurrentModelDetector: bool = False):
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@router.get(
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"/recognized_license_plates", dependencies=[Depends(allow_any_authenticated())]
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)
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def get_recognized_license_plates(split_joined: Optional[int] = None):
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def get_recognized_license_plates(
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split_joined: Optional[int] = None,
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allowed_cameras: List[str] = Depends(get_allowed_cameras_for_filter),
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):
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try:
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query = (
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Event.select(
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SQL("json_extract(data, '$.recognized_license_plate') AS plate")
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)
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.where(SQL("json_extract(data, '$.recognized_license_plate') IS NOT NULL"))
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.where(
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(SQL("json_extract(data, '$.recognized_license_plate') IS NOT NULL"))
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& (Event.camera << allowed_cameras)
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)
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.distinct()
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)
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recognized_license_plates = [row[0] for row in query.tuples()]
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@ -662,6 +662,13 @@ class FrigateConfig(FrigateBaseModel):
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# generate zone contours
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if len(camera_config.zones) > 0:
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for zone in camera_config.zones.values():
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if zone.filters:
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for object_name, filter_config in zone.filters.items():
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zone.filters[object_name] = RuntimeFilterConfig(
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frame_shape=camera_config.frame_shape,
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**filter_config.model_dump(exclude_unset=True),
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)
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zone.generate_contour(camera_config.frame_shape)
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# Set live view stream if none is set
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@ -24,6 +24,14 @@ class GeminiClient(GenAIClient):
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http_options_dict = {
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"api_version": "v1",
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"timeout": int(self.timeout * 1000), # requires milliseconds
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"retry_options": types.HttpRetryOptions(
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attempts=3,
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initial_delay=1.0,
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max_delay=60.0,
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exp_base=2.0,
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jitter=1.0,
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http_status_codes=[429, 500, 502, 503, 504],
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),
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}
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if isinstance(self.genai_config.provider_options, dict):
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@ -632,6 +632,49 @@ class TestConfig(unittest.TestCase):
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)
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assert frigate_config.cameras["back"].zones["test"].color != (0, 0, 0)
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def test_zone_filter_area_percent_converts_to_pixels(self):
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config = {
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"mqtt": {"host": "mqtt"},
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"record": {
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"alerts": {
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"retain": {
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"days": 20,
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}
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}
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},
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"cameras": {
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"back": {
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"ffmpeg": {
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"inputs": [
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{"path": "rtsp://10.0.0.1:554/video", "roles": ["detect"]}
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]
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},
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"detect": {
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"height": 1080,
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"width": 1920,
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"fps": 5,
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},
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"zones": {
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"notification": {
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"coordinates": "0.03,1,0.025,0,0.626,0,0.643,1",
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"objects": ["person"],
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"filters": {"person": {"min_area": 0.1}},
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}
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},
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}
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},
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}
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frigate_config = FrigateConfig(**config)
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expected_min_area = int(1080 * 1920 * 0.1)
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assert (
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frigate_config.cameras["back"]
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.zones["notification"]
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.filters["person"]
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.min_area
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== expected_min_area
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)
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def test_zone_relative_matches_explicit(self):
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config = {
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"mqtt": {"host": "mqtt"},
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@ -3,6 +3,7 @@
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"untilForTime": "Until {{time}}",
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"untilForRestart": "Until Frigate restarts.",
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"untilRestart": "Until restart",
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"never": "Never",
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"ago": "{{timeAgo}} ago",
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"justNow": "Just now",
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"today": "Today",
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@ -268,7 +268,7 @@ export default function CreateTriggerDialog({
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<FormItem className="flex flex-row items-center justify-between">
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<div className="space-y-0.5">
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<FormLabel className="text-base">
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{t("enabled", { ns: "common" })}
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{t("button.enabled", { ns: "common" })}
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</FormLabel>
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<div className="text-sm text-muted-foreground">
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{t("triggers.dialog.form.enabled.description")}
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@ -394,7 +394,10 @@ export default function CreateTriggerDialog({
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</FormLabel>
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<div className="space-y-2">
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{availableActions.map((action) => (
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<div key={action} className="flex items-center space-x-2">
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<label
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key={action}
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className="flex cursor-pointer items-center space-x-2"
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>
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<FormControl>
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<Checkbox
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checked={form
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@ -416,10 +419,10 @@ export default function CreateTriggerDialog({
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}}
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/>
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</FormControl>
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<FormLabel className="text-sm font-normal">
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<span className="text-sm font-normal">
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{t(`triggers.actions.${action}`)}
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</FormLabel>
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</div>
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</span>
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</label>
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))}
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</div>
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<FormDescription>
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@ -142,7 +142,10 @@ export default function Step3ThresholdAndActions({
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<FormLabel>{t("triggers.dialog.form.actions.title")}</FormLabel>
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<div className="space-y-2">
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{availableActions.map((action) => (
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<div key={action} className="flex items-center space-x-2">
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<label
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key={action}
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className="flex cursor-pointer items-center space-x-2"
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>
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<FormControl>
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<Checkbox
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checked={form
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@ -164,10 +167,10 @@ export default function Step3ThresholdAndActions({
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}}
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/>
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</FormControl>
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<FormLabel className="text-sm font-normal">
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<span className="text-sm font-normal">
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{t(`triggers.actions.${action}`)}
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</FormLabel>
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</div>
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</span>
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</label>
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))}
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</div>
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<FormDescription>
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@ -197,9 +200,7 @@ export default function Step3ThresholdAndActions({
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{isLoading && <ActivityIndicator className="mr-2 size-5" />}
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{isLoading
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? t("button.saving", { ns: "common" })
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: t("triggers.dialog.form.save", {
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defaultValue: "Save Trigger",
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})}
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: t("button.save", { ns: "common" })}
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</Button>
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</div>
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</form>
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@ -206,7 +206,7 @@ function Exports() {
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>
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{Object.values(exports).map((item) => (
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<ExportCard
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key={item.name}
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key={item.id}
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className={
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search == "" || filteredExports.includes(item) ? "" : "hidden"
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}
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@ -1,6 +1,7 @@
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import { useCallback, useEffect, useMemo, useState } from "react";
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import { Trans, useTranslation } from "react-i18next";
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import { Toaster, toast } from "sonner";
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import { toast } from "sonner";
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import { Toaster } from "@/components/ui/sonner";
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import useSWR from "swr";
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import axios from "axios";
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import { Button } from "@/components/ui/button";
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@ -598,7 +599,7 @@ export default function TriggerView({
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date_style: "medium",
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},
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)
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: "Never"}
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: t("never", { ns: "common" })}
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</span>
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{trigger_status?.triggers[trigger.name]
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?.triggering_event_id && (
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@ -663,7 +664,9 @@ export default function TriggerView({
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<TableHeader className="sticky top-0 bg-muted/50">
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<TableRow>
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<TableHead className="w-4"></TableHead>
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<TableHead>{t("name", { ns: "common" })}</TableHead>
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<TableHead>
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{t("name", { ns: "triggers.table.name" })}
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</TableHead>
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<TableHead>{t("triggers.table.type")}</TableHead>
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<TableHead>
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{t("triggers.table.lastTriggered")}
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@ -759,7 +762,7 @@ export default function TriggerView({
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date_style: "medium",
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},
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)
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: "Never"}
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: t("time.never", { ns: "common" })}
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</span>
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{trigger_status?.triggers[trigger.name]
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?.triggering_event_id && (
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