Introducing Gradio 5.0

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  1. Components
  2. ImageEditor

New to Gradio? Start here: Getting Started

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To install Gradio from main, run the following command:

pip install https://gradio-builds.s3.amazonaws.com/69acfebffd0d3479a40352de19c8763863557428/gradio-5.4.0-py3-none-any.whl

*Note: Setting share=True in launch() will not work.

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ImageEditor

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

Description

Creates an image component that, as an input, can be used to upload and edit images using simple editing tools such as brushes, strokes, cropping, and layers. Or, as an output, this component can be used to display images.

Behavior

As input component: Passes the uploaded images as an instance of EditorValue, which is just a dict with keys: 'background', 'layers', and 'composite'. The values corresponding to 'background' and 'composite' are images, while 'layers' is a list of images. The images are of type PIL.Image, np.array, or str filepath, depending on the type parameter.

Your function should accept one of these types:
def predict(
	value: EditorValue | None
)
	...

As output component: Expects a EditorValue, which is just a dictionary with keys: 'background', 'layers', and 'composite'. The values corresponding to 'background' and 'composite' should be images or None, while layers should be a list of images. Images can be of type PIL.Image, np.array, or str filepath/URL. Or, the value can be simply a single image (ImageType), in which case it will be used as the background.

Your function should return one of these types:
def predict(···) -> EditorValue | ImageType | None
	...	
	return value

Initialization

Parameters 
value: EditorValue | ImageType | None
default = None

Optional initial image(s) to populate the image editor. Should be a dictionary with keys: `background`, `layers`, and `composite`. The values corresponding to `background` and `composite` should be images or None, while `layers` should be a list of images. Images can be of type PIL.Image, np.array, or str filepath/URL. Or, the value can be a callable, in which case the function will be called whenever the app loads to set the initial value of the component.

height: int | str | None
default = None

The height of the component, specified in pixels if a number is passed, or in CSS units if a string is passed. This has no effect on the preprocessed image files or numpy arrays, but will affect the displayed images.

width: int | str | None
default = None

The width of the component, specified in pixels if a number is passed, or in CSS units if a string is passed. This has no effect on the preprocessed image files or numpy arrays, but will affect the displayed images.

image_mode: Literal['1', 'L', 'P', 'RGB', 'RGBA', 'CMYK', 'YCbCr', 'LAB', 'HSV', 'I', 'F']
default = "RGBA"

"RGB" if color, or "L" if black and white. See https://pillow.readthedocs.io/en/stable/handbook/concepts.html for other supported image modes and their meaning.

sources: Iterable[Literal['upload', 'webcam', 'clipboard']] | Literal['upload', 'webcam', 'clipboard'] | None
default = ('upload', 'webcam', 'clipboard')

List of sources that can be used to set the background image. "upload" creates a box where user can drop an image file, "webcam" allows user to take snapshot from their webcam, "clipboard" allows users to paste an image from the clipboard.

type: Literal['numpy', 'pil', 'filepath']
default = "numpy"

The format the images are converted to before being passed into the prediction function. "numpy" converts the images to numpy arrays with shape (height, width, 3) and values from 0 to 255, "pil" converts the images to PIL image objects, "filepath" passes images as str filepaths to temporary copies of the images.

label: str | None
default = None

the label for this component. Appears above the component and is also used as the header if there are a table of examples for this component. If None and used in a `gr.Interface`, the label will be the name of the parameter this component is assigned to.

every: Timer | float | None
default = None

Continously calls `value` to recalculate it if `value` is a function (has no effect otherwise). Can provide a Timer whose tick resets `value`, or a float that provides the regular interval for the reset Timer.

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

Components that are used as inputs to calculate `value` if `value` is a function (has no effect otherwise). `value` is recalculated any time the inputs change.

show_label: bool | None
default = None

if True, will display label.

show_download_button: bool
default = True

If True, will display button to download image.

container: bool
default = True

If True, will place the component in a container - providing some extra padding around the border.

scale: int | None
default = None

relative size compared to adjacent Components. For example if Components A and B are in a Row, and A has scale=2, and B has scale=1, A will be twice as wide as B. Should be an integer. scale applies in Rows, and to top-level Components in Blocks where fill_height=True.

min_width: int
default = 160

minimum pixel width, will wrap if not sufficient screen space to satisfy this value. If a certain scale value results in this Component being narrower than min_width, the min_width parameter will be respected first.

interactive: bool | None
default = None

if True, will allow users to upload and edit an image; if False, can only be used to display images. If not provided, this is inferred based on whether the component is used as an input or output.

visible: bool
default = True

If False, component will be hidden.

elem_id: str | None
default = None

An optional string that is assigned as the id of this component in the HTML DOM. Can be used for targeting CSS styles.

elem_classes: list[str] | str | None
default = None

An optional list of strings that are assigned as the classes of this component in the HTML DOM. Can be used for targeting CSS styles.

render: bool
default = True

If False, component will not render be rendered in the Blocks context. Should be used if the intention is to assign event listeners now but render the component later.

key: int | str | None
default = None

if assigned, will be used to assume identity across a re-render. Components that have the same key across a re-render will have their value preserved.

placeholder: str | None
default = None

Custom text for the upload area. Overrides default upload messages when provided. Accepts new lines and `#` to designate a heading.

mirror_webcam: bool
default = True

If True webcam will be mirrored. Default is True.

show_share_button: bool | None
default = None

If True, will show a share icon in the corner of the component that allows user to share outputs to Hugging Face Spaces Discussions. If False, icon does not appear. If set to None (default behavior), then the icon appears if this Gradio app is launched on Spaces, but not otherwise.

crop_size: tuple[int | float, int | float] | str | None
default = None

The size of the crop box in pixels. If a tuple, the first value is the width and the second value is the height. If a string, the value must be a ratio in the form `width:height` (e.g. "16:9").

transforms: Iterable[Literal['crop']]
default = ('crop',)

The transforms tools to make available to users. "crop" allows the user to crop the image.

eraser: Eraser | None | Literal[False]
default = None

The options for the eraser tool in the image editor. Should be an instance of the `gr.Eraser` class, or None to use the default settings. Can also be False to hide the eraser tool. [See `gr.Eraser` docs](#eraser).

brush: Brush | None | Literal[False]
default = None

The options for the brush tool in the image editor. Should be an instance of the `gr.Brush` class, or None to use the default settings. Can also be False to hide the brush tool, which will also hide the eraser tool. [See `gr.Brush` docs](#brush).

format: str
default = "webp"

Format to save image if it does not already have a valid format (e.g. if the image is being returned to the frontend as a numpy array or PIL Image). The format should be supported by the PIL library. This parameter has no effect on SVG files.

layers: bool
default = True

If True, will allow users to add layers to the image. If False, the layers option will be hidden.

canvas_size: tuple[int, int] | None
default = None

The size of the default canvas in pixels. If a tuple, the first value is the width and the second value is the height. If None, the canvas size will be the same as the background image or 800 x 600 if no background image is provided.

show_fullscreen_button: bool
default = True

If True, will display button to view image in fullscreen mode.

Shortcuts

Class Interface String Shortcut Initialization

gradio.ImageEditor

"imageeditor"

 Uses default values

gradio.Sketchpad

"sketchpad"

 Uses sources=(), brush=Brush(colors=["#000000"], color_mode="fixed")

gradio.Paint

"paint"

 Uses sources=()

gradio.ImageMask

"imagemask"

 Uses brush=Brush(colors=["#000000"], color_mode="fixed")

Demos

import gradio as gr
import time

def sleep(im):
    time.sleep(5)
    return [im["background"], im["layers"][0], im["layers"][1], im["composite"]]

def predict(im):
    return im["composite"]

with gr.Blocks() as demo:
    with gr.Row():
        im = gr.ImageEditor(
            type="numpy",
            crop_size="1:1",
        )
        im_preview = gr.Image()
    n_upload = gr.Number(0, label="Number of upload events", step=1)
    n_change = gr.Number(0, label="Number of change events", step=1)
    n_input = gr.Number(0, label="Number of input events", step=1)

    im.upload(lambda x: x + 1, outputs=n_upload, inputs=n_upload)
    im.change(lambda x: x + 1, outputs=n_change, inputs=n_change)
    im.input(lambda x: x + 1, outputs=n_input, inputs=n_input)
    im.change(predict, outputs=im_preview, inputs=im, show_progress="hidden")

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

Listener Description

ImageEditor.clear(fn, ···)

This listener is triggered when the user clears the ImageEditor using the clear button for the component.

ImageEditor.change(fn, ···)

Triggered when the value of the ImageEditor 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.

ImageEditor.input(fn, ···)

This listener is triggered when the user changes the value of the ImageEditor.

ImageEditor.select(fn, ···)

Event listener for when the user selects or deselects the ImageEditor. Uses event data gradio.SelectData to carry value referring to the label of the ImageEditor, and selected to refer to state of the ImageEditor. See EventData documentation on how to use this event data

ImageEditor.upload(fn, ···)

This listener is triggered when the user uploads a file into the ImageEditor.

ImageEditor.apply(fn, ···)

This listener is triggered when the user applies changes to the ImageEditor through an integrated UI action.

Event Parameters

Parameters 
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.

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.

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.

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.

scroll_to_output: bool
default = False

If True, will scroll to output component on completion

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

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.

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.

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)

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).

postprocess: bool
default = True

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

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.

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.

js: str | 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.

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).

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.

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.

time_limit: int | None
default = None 
stream_every: float
default = 0.5 
like_user_message: bool
default = False

Helper Classes

Brush

gradio.Brush(···)

Description

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

Initialization

Parameters 
default_size: int | Literal['auto']
default = "auto"

The default radius, in pixels, of the brush tool. Defaults to "auto" in which case the radius is automatically determined based on the size of the image (generally 1/50th of smaller dimension).

colors: Union[list[str], str, None]
default = None

A list of colors to make available to the user when using the brush. Defaults to a list of 5 colors.

default_color: Union[str, Literal['auto']]
default = "auto"

The default color of the brush. Defaults to the first color in the `colors` list.

color_mode: Literal['fixed', 'defaults']
default = "defaults"

If set to "fixed", user can only select from among the colors in `colors`. If "defaults", the colors in `colors` are provided as a default palette, but the user can also select any color using a color picker.

Eraser

gradio.Eraser(···)

Description

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

Initialization

Parameters 
default_size: int | Literal['auto']
default = "auto"

The default radius, in pixels, of the eraser tool. Defaults to "auto" in which case the radius is automatically determined based on the size of the image (generally 1/50th of smaller dimension).

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