Sharing Your App

In this Guide, we dive more deeply into the various aspects of sharing a Gradio app with others. We will cover:

Sharing demos with the share parameter
Hosting on HF Spaces
Embedding hosted spaces
Using the API page
Accessing network requests
Mounting within FastAPI
Authentication
Analytics

Sharing Demos

Gradio demos can be easily shared publicly by setting share=True in the launch() method. Like this:

import gradio as gr

def greet(name):
    return "Hello " + name + "!"

demo = gr.Interface(fn=greet, inputs="textbox", outputs="textbox")
    
demo.launch(share=True)  # Share your demo with just 1 extra parameter 🚀

This generates a public, shareable link that you can send to anybody! When you send this link, the user on the other side can try out the model in their browser. Because the processing happens on your device (as long as your device stays on), you don't have to worry about any packaging any dependencies.

A share link usually looks something like this: https://07ff8706ab.gradio.live. Although the link is served through the Gradio Share Servers, these servers are only a proxy for your local server, and do not store any data sent through your app. Share links expire after 72 hours. (it is also possible to set up your own Share Server on your own cloud server to overcome this restriction.)

Tip: Keep in mind that share links are publicly accessible, meaning that anyone can use your model for prediction! Therefore, make sure not to expose any sensitive information through the functions you write, or allow any critical changes to occur on your device. Or you can add authentication to your Gradio app as discussed below.

Note that by default, share=False, which means that your server is only running locally. (This is the default, except in Google Colab notebooks, where share links are automatically created). As an alternative to using share links, you can use use SSH port-forwarding to share your local server with specific users.

Hosting on HF Spaces

If you'd like to have a permanent link to your Gradio demo on the internet, use Hugging Face Spaces. Hugging Face Spaces provides the infrastructure to permanently host your machine learning model for free!

After you have created a free Hugging Face account, you have two methods to deploy your Gradio app to Hugging Face Spaces:

From terminal: run gradio deploy in your app directory. The CLI will gather some basic metadata and then launch your app. To update your space, you can re-run this command or enable the Github Actions option to automatically update the Spaces on git push.
From your browser: Drag and drop a folder containing your Gradio model and all related files here. See this guide how to host on Hugging Face Spaces for more information, or watch the embedded video:

Embedding Hosted Spaces

Once you have hosted your app on Hugging Face Spaces (or on your own server), you may want to embed the demo on a different website, such as your blog or your portfolio. Embedding an interactive demo allows people to try out the machine learning model that you have built, without needing to download or install anything — right in their browser! The best part is that you can embed interactive demos even in static websites, such as GitHub pages.

There are two ways to embed your Gradio demos. You can find quick links to both options directly on the Hugging Face Space page, in the "Embed this Space" dropdown option:

Embedding with Web Components

Web components typically offer a better experience to users than IFrames. Web components load lazily, meaning that they won't slow down the loading time of your website, and they automatically adjust their height based on the size of the Gradio app.

To embed with Web Components:

Import the gradio JS library into into your site by adding the script below in your site (replace {GRADIO_VERSION} in the URL with the library version of Gradio you are using).

<script
    type="module"
    src="https://gradio.s3-us-west-2.amazonaws.com/{GRADIO_VERSION}/gradio.js"
></script>

<gradio-app src="https://$your_space_host.hf.space"></gradio-app>

element where you want to place the app. Set the src= attribute to your Space's embed URL, which you can find in the "Embed this Space" button. For example:

<gradio-app
    src="https://abidlabs-pytorch-image-classifier.hf.space"
></gradio-app>

You can see examples of how web components look on the Gradio landing page.

You can also customize the appearance and behavior of your web component with attributes that you pass into the <gradio-app> tag:

src: as we've seen, the src attributes links to the URL of the hosted Gradio demo that you would like to embed
space: an optional shorthand if your Gradio demo is hosted on Hugging Face Space. Accepts a username/space_name instead of a full URL. Example: gradio/Echocardiogram-Segmentation. If this attribute attribute is provided, then src does not need to be provided.
control_page_title: a boolean designating whether the html title of the page should be set to the title of the Gradio app (by default "false")
initial_height: the initial height of the web component while it is loading the Gradio app, (by default "300px"). Note that the final height is set based on the size of the Gradio app.
container: whether to show the border frame and information about where the Space is hosted (by default "true")
info: whether to show just the information about where the Space is hosted underneath the embedded app (by default "true")
autoscroll: whether to autoscroll to the output when prediction has finished (by default "false")
eager: whether to load the Gradio app as soon as the page loads (by default "false")
theme_mode: whether to use the dark, light, or default system theme mode (by default "system")
render: an event that is triggered once the embedded space has finished rendering.

Here's an example of how to use these attributes to create a Gradio app that does not lazy load and has an initial height of 0px.

<gradio-app
    space="gradio/Echocardiogram-Segmentation"
    eager="true"
    initial_height="0px"
></gradio-app>

Here's another example of how to use the render event. An event listener is used to capture the render event and will call the handleLoadComplete() function once rendering is complete.

<script>
    function handleLoadComplete() {
        console.log("Embedded space has finished rendering");
    }

    const gradioApp = document.querySelector("gradio-app");
    gradioApp.addEventListener("render", handleLoadComplete);
</script>

Note: While Gradio's CSS will never impact the embedding page, the embedding page can affect the style of the embedded Gradio app. Make sure that any CSS in the parent page isn't so general that it could also apply to the embedded Gradio app and cause the styling to break. Element selectors such as header { ... } and footer { ... } will be the most likely to cause issues.

Embedding with IFrames

To embed with IFrames instead (if you cannot add javascript to your website, for example), add this element:

<iframe src="https://$your_space_host.hf.space"></iframe>

Again, you can find the src= attribute to your Space's embed URL, which you can find in the "Embed this Space" button.

Note: if you use IFrames, you'll probably want to add a fixed height attribute and set style="border:0;" to remove the boreder. In addition, if your app requires permissions such as access to the webcam or the microphone, you'll need to provide that as well using the allow attribute.

API Page

You can use almost any Gradio app as an API! In the footer of a Gradio app like this one, you'll see a "Use via API" link.

This is a page that lists the endpoints that can be used to query the Gradio app, via our supported clients: either the Python client, or the JavaScript client. For each endpoint, Gradio automatically generates the parameters and their types, as well as example inputs, like this.

The endpoints are automatically created when you launch a Gradio Interface. If you are using Gradio Blocks, you can also set up a Gradio API page, though we recommend that you explicitly name each event listener, such as

btn.click(add, [num1, num2], output, api_name="addition")

This will add and document the endpoint /api/addition/ to the automatically generated API page. Otherwise, your API endpoints will appear as "unnamed" endpoints.

Accessing the Network Request Directly

When a user makes a prediction to your app, you may need the underlying network request, in order to get the request headers (e.g. for advanced authentication), log the client's IP address, getting the query parameters, or for other reasons. Gradio supports this in a similar manner to FastAPI: simply add a function parameter whose type hint is gr.Request and Gradio will pass in the network request as that parameter. Here is an example:

import gradio as gr

def echo(text, request: gr.Request):
    if request:
        print("Request headers dictionary:", request.headers)
        print("IP address:", request.client.host)
        print("Query parameters:", dict(request.query_params))
    return text

io = gr.Interface(echo, "textbox", "textbox").launch()

Note: if your function is called directly instead of through the UI (this happens, for example, when examples are cached, or when the Gradio app is called via API), then request will be None. You should handle this case explicitly to ensure that your app does not throw any errors. That is why we have the explicit check if request.

Mounting Within Another FastAPI App

In some cases, you might have an existing FastAPI app, and you'd like to add a path for a Gradio demo. You can easily do this with gradio.mount_gradio_app().

Here's a complete example:

$code_custom_path

Note that this approach also allows you run your Gradio apps on custom paths (http://localhost:8000/gradio in the example above).

Authentication

Password-protected app

You may wish to put an authentication page in front of your app to limit who can open your app. With the auth= keyword argument in the launch() method, you can provide a tuple with a username and password, or a list of acceptable username/password tuples; Here's an example that provides password-based authentication for a single user named "admin":

demo.launch(auth=("admin", "pass1234"))

For more complex authentication handling, you can even pass a function that takes a username and password as arguments, and returns True to allow access, False otherwise.

Here's an example of a function that accepts any login where the username and password are the same:

def same_auth(username, password):
    return username == password
demo.launch(auth=same_auth)

If you have multiple users, you may wish to customize the content that is shown depending on the user that is logged in. You can retrieve the logged in user by accessing the network request directly as discussed above, and then reading the .username attribute of the request. Here's an example:

import gradio as gr

def update_message(request: gr.Request):
    return f"Welcome, {request.username}"

with gr.Blocks() as demo:
    m = gr.Markdown()
    demo.load(update_message, None, m)
    
demo.launch(auth=[("Abubakar", "Abubakar"), ("Ali", "Ali")])

Note: For authentication to work properly, third party cookies must be enabled in your browser. This is not the case by default for Safari or for Chrome Incognito Mode.

If users visit the /logout page of your Gradio app, they will automatically be logged out and session cookies deleted. This allows you to add logout functionality to your Gradio app as well. Let's update the previous example to include a log out button:

import gradio as gr

def update_message(request: gr.Request):
    return f"Welcome, {request.username}"

with gr.Blocks() as demo:
    m = gr.Markdown()
    logout_button = gr.Button("Logout", link="/logout")
    demo.load(update_message, None, m)
    
demo.launch(auth=[("Pete", "Pete"), ("Dawood", "Dawood")])

Note: Gradio's built-in authentication provides a straightforward and basic layer of access control but does not offer robust security features for applications that require stringent access controls (e.g. multi-factor authentication, rate limiting, or automatic lockout policies).

OAuth (Login via Hugging Face)

Gradio natively supports OAuth login via Hugging Face. In other words, you can easily add a "Sign in with Hugging Face" button to your demo, which allows you to get a user's HF username as well as other information from their HF profile. Check out this Space for a live demo.

To enable OAuth, you must set hf_oauth: true as a Space metadata in your README.md file. This will register your Space as an OAuth application on Hugging Face. Next, you can use gr.LoginButton to add a login button to your Gradio app. Once a user is logged in with their HF account, you can retrieve their profile by adding a parameter of type gr.OAuthProfile to any Gradio function. The user profile will be automatically injected as a parameter value. If you want to perform actions on behalf of the user (e.g. list user's private repos, create repo, etc.), you can retrieve the user token by adding a parameter of type gr.OAuthToken. You must define which scopes you will use in your Space metadata (see documentation for more details).

Here is a short example:

$code_login_with_huggingface

When the user clicks on the login button, they get redirected in a new page to authorize your Space.

Users can revoke access to their profile at any time in their settings.

As seen above, OAuth features are available only when your app runs in a Space. However, you often need to test your app locally before deploying it. To test OAuth features locally, your machine must be logged in to Hugging Face. Please run huggingface-cli login or set HF_TOKEN as environment variable with one of your access token. You can generate a new token in your settings page (https://huggingface.co/settings/tokens). Then, clicking on the gr.LoginButton will login your local Hugging Face profile, allowing you to debug your app with your Hugging Face account before deploying it to a Space.

Security Note: It is important to note that adding a gr.LoginButton does not restrict users from using your app, in the same way that adding username-password authentication does. This means that users of your app who have not logged in with Hugging Face can still access and run events in your Gradio app -- the difference is that the gr.OAuthProfile or gr.OAuthToken will be None in the corresponding functions.

OAuth (with external providers)

It is also possible to authenticate with external OAuth providers (e.g. Google OAuth) in your Gradio apps. To do this, first mount your Gradio app within a FastAPI app (as discussed above). Then, you must write an authentication function, which gets the user's username from the OAuth provider and returns it. This function should be passed to the auth_dependency parameter in gr.mount_gradio_app.

Similar to FastAPI dependency functions, the function specified by auth_dependency will run before any Gradio-related route in your FastAPI app. The function should accept a single parameter: the FastAPI Request and return either a string (representing a user's username) or None. If a string is returned, the user will be able to access the Gradio-related routes in your FastAPI app.

First, let's show a simplistic example to illustrate the auth_dependency parameter:

from fastapi import FastAPI, Request
import gradio as gr

app = FastAPI()

def get_user(request: Request):
    return request.headers.get("user")

demo = gr.Interface(lambda s: f"Hello {s}!", "textbox", "textbox")

app = gr.mount_gradio_app(app, demo, path="/demo", auth_dependency=get_user)

if __name__ == '__main__':
    uvicorn.run(app)

In this example, only requests that include a "user" header will be allowed to access the Gradio app. Of course, this does not add much security, since any user can add this header in their request.

Here's a more complete example showing how to add Google OAuth to a Gradio app (assuming you've already created OAuth Credentials on the Google Developer Console):

import os
from authlib.integrations.starlette_client import OAuth, OAuthError
from fastapi import FastAPI, Depends, Request
from starlette.config import Config
from starlette.responses import RedirectResponse
from starlette.middleware.sessions import SessionMiddleware
import uvicorn
import gradio as gr

app = FastAPI()

# Replace these with your own OAuth settings
GOOGLE_CLIENT_ID = "..."
GOOGLE_CLIENT_SECRET = "..."
SECRET_KEY = "..."

config_data = {'GOOGLE_CLIENT_ID': GOOGLE_CLIENT_ID, 'GOOGLE_CLIENT_SECRET': GOOGLE_CLIENT_SECRET}
starlette_config = Config(environ=config_data)
oauth = OAuth(starlette_config)
oauth.register(
    name='google',
    server_metadata_url='https://accounts.google.com/.well-known/openid-configuration',
    client_kwargs={'scope': 'openid email profile'},
)

SECRET_KEY = os.environ.get('SECRET_KEY') or "a_very_secret_key"
app.add_middleware(SessionMiddleware, secret_key=SECRET_KEY)

# Dependency to get the current user
def get_user(request: Request):
    user = request.session.get('user')
    if user:
        return user['name']
    return None

@app.get('/')
def public(user: dict = Depends(get_user)):
    if user:
        return RedirectResponse(url='/gradio')
    else:
        return RedirectResponse(url='/login-demo')

@app.route('/logout')
async def logout(request: Request):
    request.session.pop('user', None)
    return RedirectResponse(url='/')

@app.route('/login')
async def login(request: Request):
    redirect_uri = request.url_for('auth')
    # If your app is running on https, you should ensure that the
    # `redirect_uri` is https, e.g. uncomment the following lines:
    # 
    # from urllib.parse import urlparse, urlunparse
    # redirect_uri = urlunparse(urlparse(str(redirect_uri))._replace(scheme='https'))
    return await oauth.google.authorize_redirect(request, redirect_uri)

@app.route('/auth')
async def auth(request: Request):
    try:
        access_token = await oauth.google.authorize_access_token(request)
    except OAuthError:
        return RedirectResponse(url='/')
    request.session['user'] = dict(access_token)["userinfo"]
    return RedirectResponse(url='/')

with gr.Blocks() as login_demo:
    gr.Button("Login", link="/login")

app = gr.mount_gradio_app(app, login_demo, path="/login-demo")

def greet(request: gr.Request):
    return f"Welcome to Gradio, {request.username}"

with gr.Blocks() as main_demo:
    m = gr.Markdown("Welcome to Gradio!")
    gr.Button("Logout", link="/logout")
    main_demo.load(greet, None, m)

app = gr.mount_gradio_app(app, main_demo, path="/gradio", auth_dependency=get_user)

if __name__ == '__main__':
    uvicorn.run(app)

There are actually two separate Gradio apps in this example! One that simply displays a log in button (this demo is accessible to any user), while the other main demo is only accessible to users that are logged in. You can try this example out on this Space.

Analytics

By default, Gradio collects certain analytics to help us better understand the usage of the gradio library. This includes the following information:

What environment the Gradio app is running on (e.g. Colab Notebook, Hugging Face Spaces)
What input/output components are being used in the Gradio app
Whether the Gradio app is utilizing certain advanced features, such as auth or show_error
The IP address which is used solely to measure the number of unique developers using Gradio
The version of Gradio that is running

No information is collected from users of your Gradio app. If you'd like to diable analytics altogether, you can do so by setting the analytics_enabled parameter to False in gr.Blocks, gr.Interface, or gr.ChatInterface. Or, you can set the GRADIO_ANALYTICS_ENABLED environment variable to "False" to apply this to all Gradio apps created across your system.

Note: this reflects the analytics policy as of gradio>=4.32.0.