Webhooks server

Webhooks Server

Webhooks are a foundation for MLOps-related features. They allow you to listen for new changes on specific repos or to all repos belonging to particular users/organizations you’re interested in following. To learn more about webhooks on the Huggingface Hub, you can read the Webhooks .

Check out this for a step-by-step tutorial on how to setup your webhooks server and deploy it as a Space.

This is an experimental feature. This means that we are still working on improving the API. Breaking changes might be introduced in the future without prior notice. Make sure to pin the version of huggingface_hub in your requirements. A warning is triggered when you use an experimental feature. You can disable it by setting HF_HUB_DISABLE_EXPERIMENTAL_WARNING=1 as an environment variable.

Server

The server is a app. It has a UI to display instructions for you or your users and an API to listen to webhooks. Implementing a webhook endpoint is as simple as decorating a function. You can then debug it by redirecting the Webhooks to your machine (using a Gradio tunnel) before deploying it to a Space.

WebhooksServer

huggingface_hub.WebhooksServer

( *args**kwargs )

Parameters

ui (gradio.Blocks, optional) — A Gradio UI instance to be used as the Space landing page. If None, a UI displaying instructions about the configured webhooks is created.
webhook_secret (str, optional) — A secret key to verify incoming webhook requests. You can set this value to any secret you want as long as you also configure it in your . You can also set this value as the WEBHOOK_SECRET environment variable. If no secret is provided, the webhook endpoints are opened without any security.

The class lets you create an instance of a Gradio app that can receive Huggingface webhooks. These webhooks can be registered using the add_webhook() decorator. Webhook endpoints are added to the app as a POST endpoint to the FastAPI router. Once all the webhooks are registered, the run method has to be called to start the app.

It is recommended to accept as the first argument of the webhook function. It is a Pydantic model that contains all the information about the webhook event. The data will be parsed automatically for you.

WebhooksServer is experimental. Its API is subject to change in the future.

You must have gradio installed to use WebhooksServer (pip install --upgrade gradio).

Example:

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import gradio as gr
from huggingface_hub import WebhooksServer, WebhookPayload

with gr.Blocks() as ui:
    ...

app = WebhooksServer(ui=ui, webhook_secret="my_secret_key")

@app.add_webhook("/say_hello")
async def hello(payload: WebhookPayload):
    return {"message": "hello"}

app.run()

@webhook_endpoint

huggingface_hub.webhook_endpoint

( path: typing.Optional[str] = None )

Parameters

path (str, optional) — The URL path to register the webhook function. If not provided, the function name will be used as the path. In any case, all webhooks are registered under /webhooks.

webhook_endpoint is experimental. Its API is subject to change in the future.

You must have gradio installed to use webhook_endpoint (pip install --upgrade gradio).

Examples: The default usage is to register a function as a webhook endpoint. The function name will be used as the path. The server will be started automatically at exit (i.e. at the end of the script).

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from huggingface_hub import webhook_endpoint, WebhookPayload

@webhook_endpoint
async def trigger_training(payload: WebhookPayload):
    if payload.repo.type == "dataset" and payload.event.action == "update":
        # Trigger a training job if a dataset is updated
        ...

# Server is automatically started at the end of the script.

Advanced usage: register a function as a webhook endpoint and start the server manually. This is useful if you are running it in a notebook.

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from huggingface_hub import webhook_endpoint, WebhookPayload

@webhook_endpoint
async def trigger_training(payload: WebhookPayload):
    if payload.repo.type == "dataset" and payload.event.action == "update":
        # Trigger a training job if a dataset is updated
        ...

# Start the server manually
trigger_training.run()

Payload

class huggingface_hub.WebhookPayload

( event: WebhookPayloadEventrepo: WebhookPayloadRepodiscussion: typing.Optional[huggingface_hub._webhooks_payload.WebhookPayloadDiscussion] = Nonecomment: typing.Optional[huggingface_hub._webhooks_payload.WebhookPayloadComment] = Nonewebhook: WebhookPayloadWebhookmovedTo: typing.Optional[huggingface_hub._webhooks_payload.WebhookPayloadMovedTo] = None )

WebhookPayload

class huggingface_hub.WebhookPayload

WebhookPayloadComment

class huggingface_hub.WebhookPayloadComment

( id: strauthor: ObjectIdhidden: boolcontent: typing.Optional[str] = Noneurl: WebhookPayloadUrl )

WebhookPayloadDiscussion

class huggingface_hub.WebhookPayloadDiscussion

( id: strnum: intauthor: ObjectIdurl: WebhookPayloadUrltitle: strisPullRequest: boolstatus: typing.Literal['closed', 'draft', 'open', 'merged']changes: typing.Optional[huggingface_hub._webhooks_payload.WebhookPayloadDiscussionChanges] = Nonepinned: typing.Optional[bool] = None )

WebhookPayloadDiscussionChanges

class huggingface_hub.WebhookPayloadDiscussionChanges

( base: strmergeCommitId: typing.Optional[str] = None )

WebhookPayloadEvent

class huggingface_hub.WebhookPayloadEvent

( action: typing.Literal['create', 'delete', 'move', 'update']scope: str )

WebhookPayloadMovedTo

class huggingface_hub.WebhookPayloadMovedTo

( name: strowner: ObjectId )

WebhookPayloadRepo

class huggingface_hub.WebhookPayloadRepo

( id: strowner: ObjectIdhead_sha: typing.Optional[str] = Nonename: strprivate: boolsubdomain: typing.Optional[str] = Nonetags: typing.Optional[typing.List[str]] = Nonetype: typing.Literal['dataset', 'model', 'space']url: WebhookPayloadUrl )

WebhookPayloadUrl

class huggingface_hub.WebhookPayloadUrl

( web: strapi: typing.Optional[str] = None )

WebhookPayloadWebhook

class huggingface_hub.WebhookPayloadWebhook

( id: strversion: typing.Literal[3] )

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