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Running
on
CPU Upgrade
Commit
·
d4e77f2
1
Parent(s):
3701087
make scheduler 30 mins for testing
Browse files
app.py
CHANGED
@@ -134,7 +134,7 @@ def parse_hub_id_entry(hub_id: str) -> Union[str, List[str]]:
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except HFValidationError as e:
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raise gr.Error(f"Invalid format for Hugging Face Hub dataset ID. {e}") from e
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except RepositoryNotFoundError as e:
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raise gr.Error("Invalid Hugging Face Hub dataset ID") from e
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def remove_user_from_tracking_datasets(dataset_id, profile: gr.OAuthProfile | None):
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@@ -399,8 +399,10 @@ This Gradio app allows you to track datasets hosted on the Hugging Face Hub and
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**Tip** *You can use a wildcard `*` to track all datasets for a user or organization on the hub. For example `biglam/*` will create alerts for all the datasets under the biglam Hugging Face Organization*
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**
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"""
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with gr.Blocks() as demo:
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@@ -439,13 +441,13 @@ with gr.Blocks() as demo:
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user_stats_btn.click(user_stats, [], output)
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scheduler = BackgroundScheduler()
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if local:
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else:
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-
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-
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scheduler.start()
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demo.queue(max_size=5)
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demo.launch()
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except HFValidationError as e:
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raise gr.Error(f"Invalid format for Hugging Face Hub dataset ID. {e}") from e
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except RepositoryNotFoundError as e:
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raise gr.Error(f"{hub_id}: Invalid Hugging Face Hub dataset ID") from e
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def remove_user_from_tracking_datasets(dataset_id, profile: gr.OAuthProfile | None):
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**Tip** *You can use a wildcard `*` to track all datasets for a user or organization on the hub. For example `biglam/*` will create alerts for all the datasets under the biglam Hugging Face Organization*
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**You need to be logged in to your Hugging Face account to use this app.** If you don't have a Hugging Face Hub account you can get one <a href="https://huggingface.co/join">here</a>.
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**NOTE** This app is a proof of concept and is intended to validate how much interest there is for a feature like this.
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If you have feedback please add it to this [discussion]()
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"""
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with gr.Blocks() as demo:
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user_stats_btn.click(user_stats, [], output)
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scheduler = BackgroundScheduler()
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# if local:
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scheduler.add_job(notify_about_new_models, "interval", minutes=30)
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# else:
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# scheduler.add_job(
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# notify_about_new_models,
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# CronTrigger.from_crontab("0 */12 * * *"),
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# )
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scheduler.start()
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demo.queue(max_size=5)
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demo.launch()
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