Components
Image

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ImageEditor

Image

gradio.Image(···)

import gradio as gr with gr.Blocks() as demo: gr.Image() demo.launch()

Description

Creates an image component that can be used to upload images (as an input) or display images (as an output).

Behavior

As input component: Passes the uploaded image as a `numpy.array`, `PIL.Image` or `str` filepath depending on `type`.

Your function should accept one of these types:

def predict(
	value: np.ndarray | PIL.Image.Image | str | None
)
	...

As output component: Expects a `numpy.array`, `PIL.Image`, or `str` or `pathlib.Path` filepath to an image which is displayed.

Your function should return one of these types:

def predict(···) -> np.ndarray | PIL.Image.Image | str | Path | None
	...	
	return value

Initialization

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value: str | PIL.Image.Image | np.ndarray | Callable | None

default = None

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format: str

default = "webp"

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height: int | str | None

default = None

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width: int | str | None

default = None

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image_mode: Literal['1', 'L', 'P', 'RGB', 'RGBA', 'CMYK', 'YCbCr', 'LAB', 'HSV', 'I', 'F'] | None

default = "RGB"

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sources: list[Literal['upload', 'webcam', 'clipboard']] | Literal['upload', 'webcam', 'clipboard'] | None

default = None

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type: Literal['numpy', 'pil', 'filepath']

default = "numpy"

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label: str | I18nData | None

default = None

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every: Timer | float | None

default = None

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inputs: Component | list[Component] | set[Component] | None

default = None

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show_label: bool | None

default = None

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buttons: list[Literal['download', 'share', 'fullscreen']] | None

default = None

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container: bool

default = True

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scale: int | None

default = None

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min_width: int

default = 160

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interactive: bool | None

default = None

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visible: bool | Literal['hidden']

default = True

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streaming: bool

default = False

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elem_id: str | None

default = None

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elem_classes: list[str] | str | None

default = None

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render: bool

default = True

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key: int | str | tuple[int | str, ...] | None

default = None

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preserved_by_key: list[str] | str | None

default = "value"

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webcam_options: WebcamOptions | None

default = None

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placeholder: str | None

default = None

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watermark: WatermarkOptions | None

default = None

Shortcuts

Class	Interface String Shortcut	Initialization
`gradio.Image`	"image"	Uses default values

`GIF` and `SVG` Image Formats

The gr.Image component can process or display any image format that is supported by the PIL library, including animated GIFs. In addition, it also supports the SVG image format.

When the gr.Image component is used as an input component, the image is converted into a str filepath, a PIL.Image object, or a numpy.array, depending on the type parameter. However, animated GIF and SVG images are treated differently:

Animated GIF images can only be converted to str filepaths or PIL.Image objects. If they are converted to a numpy.array (which is the default behavior), only the first frame will be used. So if your demo expects an input GIF image, make sure to set the type parameter accordingly, e.g.

import gradio as gr

demo = gr.Interface(
    fn=lambda x:x, 
    inputs=gr.Image(type="filepath"), 
    outputs=gr.Image()
)
    
demo.launch()

For SVG images, the type parameter is ignored altogether and the image is always returned as an image filepath. This is because SVG images cannot be processed as PIL.Image or numpy.array objects.

Demos

import numpy as np import gradio as gr def sepia(input_img): sepia_filter = np.array([ [0.393, 0.769, 0.189], [0.349, 0.686, 0.168], [0.272, 0.534, 0.131] ]) sepia_img = input_img.dot(sepia_filter.T) sepia_img /= sepia_img.max() return sepia_img demo = gr.Interface(sepia, gr.Image(), "image") if __name__ == "__main__": demo.launch()

import numpy as np
import gradio as gr

def sepia(input_img):
    sepia_filter = np.array([
        [0.393, 0.769, 0.189],
        [0.349, 0.686, 0.168],
        [0.272, 0.534, 0.131]
    ])
    sepia_img = input_img.dot(sepia_filter.T)
    sepia_img /= sepia_img.max()
    return sepia_img

demo = gr.Interface(sepia, gr.Image(), "image")
if __name__ == "__main__":
    demo.launch()

import gradio as gr import numpy as np import time def fake_diffusion(steps): rng = np.random.default_rng() for i in range(steps): time.sleep(1) image = rng.random(size=(600, 600, 3)) yield image image = np.ones((1000,1000,3), np.uint8) image[:] = [255, 124, 0] yield image demo = gr.Interface(fake_diffusion, inputs=gr.Slider(1, 10, 3, step=1), outputs="image") if __name__ == "__main__": demo.launch()

import gradio as gr
import numpy as np
import time

def fake_diffusion(steps):
    rng = np.random.default_rng()
    for i in range(steps):
        time.sleep(1)
        image = rng.random(size=(600, 600, 3))
        yield image
    image = np.ones((1000,1000,3), np.uint8)
    image[:] = [255, 124, 0]
    yield image

demo = gr.Interface(fake_diffusion,
                    inputs=gr.Slider(1, 10, 3, step=1),
                    outputs="image")

if __name__ == "__main__":
    demo.launch()

Event Listeners

Description

Event listeners allow you to respond to user interactions with the UI components you've defined in a Gradio Blocks app. When a user interacts with an element, such as changing a slider value or uploading an image, a function is called.

Supported Event Listeners

The Image component supports the following event listeners. Each event listener takes the same parameters, which are listed in the Event Parameters table below.

Listener	Description
`Image.clear(fn, ···)`	This listener is triggered when the user clears the Image using the clear button for the component.
`Image.change(fn, ···)`	Triggered when the value of the Image changes either because of user input (e.g. a user types in a textbox) OR because of a function update (e.g. an image receives a value from the output of an event trigger). See `.input()` for a listener that is only triggered by user input.
`Image.stream(fn, ···)`	This listener is triggered when the user streams the Image.
`Image.select(fn, ···)`	Event listener for when the user selects or deselects the Image. Uses event data gradio.SelectData to carry `value` referring to the label of the Image, and `selected` to refer to state of the Image. See EventData documentation on how to use this event data
`Image.upload(fn, ···)`	This listener is triggered when the user uploads a file into the Image.
`Image.input(fn, ···)`	This listener is triggered when the user changes the value of the Image.

Event Parameters

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fn: Callable | None | Literal['decorator']

default = "decorator"

the function to call when this event is triggered. Often a machine learning model's prediction function. Each parameter of the function corresponds to one input component, and the function should return a single value or a tuple of values, with each element in the tuple corresponding to one output component.

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inputs: Component | BlockContext | list[Component | BlockContext] | Set[Component | BlockContext] | None

default = None

List of gradio.components to use as inputs. If the function takes no inputs, this should be an empty list.

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outputs: Component | BlockContext | list[Component | BlockContext] | Set[Component | BlockContext] | None

default = None

List of gradio.components to use as outputs. If the function returns no outputs, this should be an empty list.

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api_name: str | None | Literal[False]

default = None

defines how the endpoint appears in the API docs. Can be a string, None, or False. If set to a string, the endpoint will be exposed in the API docs with the given name. If None (default), the name of the function will be used as the API endpoint. If False, the endpoint will not be exposed in the API docs and downstream apps (including those that `gr.load` this app) will not be able to use this event.

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api_description: str | None | Literal[False]

default = None

Description of the API endpoint. Can be a string, None, or False. If set to a string, the endpoint will be exposed in the API docs with the given description. If None, the function's docstring will be used as the API endpoint description. If False, then no description will be displayed in the API docs.

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scroll_to_output: bool

default = False

If True, will scroll to output component on completion

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show_progress: Literal['full', 'minimal', 'hidden']

default = "full"

how to show the progress animation while event is running: "full" shows a spinner which covers the output component area as well as a runtime display in the upper right corner, "minimal" only shows the runtime display, "hidden" shows no progress animation at all

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show_progress_on: Component | list[Component] | None

default = None

Component or list of components to show the progress animation on. If None, will show the progress animation on all of the output components.

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queue: bool

default = True

If True, will place the request on the queue, if the queue has been enabled. If False, will not put this event on the queue, even if the queue has been enabled. If None, will use the queue setting of the gradio app.

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batch: bool

default = False

If True, then the function should process a batch of inputs, meaning that it should accept a list of input values for each parameter. The lists should be of equal length (and be up to length `max_batch_size`). The function is then *required* to return a tuple of lists (even if there is only 1 output component), with each list in the tuple corresponding to one output component.

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max_batch_size: int

default = 4

Maximum number of inputs to batch together if this is called from the queue (only relevant if batch=True)

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preprocess: bool

default = True

If False, will not run preprocessing of component data before running 'fn' (e.g. leaving it as a base64 string if this method is called with the `Image` component).

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postprocess: bool

default = True

If False, will not run postprocessing of component data before returning 'fn' output to the browser.

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cancels: dict[str, Any] | list[dict[str, Any]] | None

default = None

A list of other events to cancel when this listener is triggered. For example, setting cancels=[click_event] will cancel the click_event, where click_event is the return value of another components .click method. Functions that have not yet run (or generators that are iterating) will be cancelled, but functions that are currently running will be allowed to finish.

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trigger_mode: Literal['once', 'multiple', 'always_last'] | None

default = None

If "once" (default for all events except `.change()`) would not allow any submissions while an event is pending. If set to "multiple", unlimited submissions are allowed while pending, and "always_last" (default for `.change()` and `.key_up()` events) would allow a second submission after the pending event is complete.

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js: str | Literal[True] | None

default = None

Optional frontend js method to run before running 'fn'. Input arguments for js method are values of 'inputs' and 'outputs', return should be a list of values for output components.

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concurrency_limit: int | None | Literal['default']

default = "default"

If set, this is the maximum number of this event that can be running simultaneously. Can be set to None to mean no concurrency_limit (any number of this event can be running simultaneously). Set to "default" to use the default concurrency limit (defined by the `default_concurrency_limit` parameter in `Blocks.queue()`, which itself is 1 by default).

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concurrency_id: str | None

default = None

If set, this is the id of the concurrency group. Events with the same concurrency_id will be limited by the lowest set concurrency_limit.

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show_api: bool

default = True

whether to show this event in the "view API" page of the Gradio app, or in the ".view_api()" method of the Gradio clients. Unlike setting api_name to False, setting show_api to False will still allow downstream apps as well as the Clients to use this event. If fn is None, show_api will automatically be set to False.

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time_limit: int | None

default = None

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stream_every: float

default = 0.5

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like_user_message: bool

default = False

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key: int | str | tuple[int | str, ...] | None

default = None

A unique key for this event listener to be used in @gr.render(). If set, this value identifies an event as identical across re-renders when the key is identical.

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validator: Callable | None

default = None

Optional validation function to run before the main function. If provided, this function will be executed first with queue=False, and only if it completes successfully will the main function be called. The validator receives the same inputs as the main function and should return a `gr.validate()` for each input value.

Helper Classes

Webcam Options

gradio.WebcamOptions(···)

Description

A dataclass for specifying options for the webcam tool in the ImageEditor component. An instance of this class can be passed to the `webcam_options` parameter of `gr.ImageEditor`.

Initialization

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mirror: bool

default = True

If True, the webcam will be mirrored.

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constraints: dict[str, Any] | None

default = None

A dictionary of constraints for the webcam.

Guides

Image Classification In Pytorch

Image Classification With Vision Transformers

Create Your Own Friends With A Gan

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ImageEditor

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Image

Description

Creates an image component that can be used to upload images (as an input) or display images (as an output).

Behavior

As input component: Passes the uploaded image as a numpy.array, PIL.Image or str filepath depending on type.

Your function should accept one of these types:

As output component: Expects a numpy.array, PIL.Image, or str or pathlib.Path filepath to an image which is displayed.

Your function should return one of these types:

Initialization

Shortcuts

GIF and SVG Image Formats

Demos

Event Listeners

Description

Supported Event Listeners

Event Parameters

Helper Classes

Webcam Options

Description

A dataclass for specifying options for the webcam tool in the ImageEditor component. An instance of this class can be passed to the webcam_options parameter of gr.ImageEditor.

Initialization

Guides

As input component: Passes the uploaded image as a `numpy.array`, `PIL.Image` or `str` filepath depending on `type`.

As output component: Expects a `numpy.array`, `PIL.Image`, or `str` or `pathlib.Path` filepath to an image which is displayed.

`GIF` and `SVG` Image Formats

A dataclass for specifying options for the webcam tool in the ImageEditor component. An instance of this class can be passed to the `webcam_options` parameter of `gr.ImageEditor`.