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`. For SVGs, the `type` parameter is ignored and the filepath of the SVG is returned.

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

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

default = None

A PIL Image, numpy array, path or URL for the default value that Image component is going to take. If callable, the function will be called whenever the app loads to set the initial value of the component.

format: str

default = "webp"

File format (e.g. "png" or "gif") 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. Applies both when this component is used as an input or output. This parameter has no effect on SVG files.

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 file or numpy array, but will affect the displayed image.

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 file or numpy array, but will affect the displayed image.

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

default = "RGB"

The pixel format and color depth that the image should be loaded and preprocessed as. "RGB" will load the image as a color image, or "L" as black-and-white. See https://pillow.readthedocs.io/en/stable/handbook/concepts.html for other supported image modes and their meaning. This parameter has no effect on SVG or GIF files. If set to None, the image_mode will be inferred from the image file type (e.g. "RGBA" for a .png image, "RGB" in most other cases).

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

default = None

List of sources for the 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. If None, defaults to ["upload", "webcam", "clipboard"] if streaming is False, otherwise defaults to ["webcam"].

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

default = "numpy"

The format the image is converted before being passed into the prediction function. "numpy" converts the image to a numpy array with shape (height, width, 3) and values from 0 to 255, "pil" converts the image to a PIL image object, "filepath" passes a str path to a temporary file containing the image. If the image is SVG, the `type` is ignored and the filepath of the SVG is returned. To support animated GIFs in input, the `type` should be set to "filepath" or "pil".

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.

streaming: bool

default = False

If True when used in a `live` interface, will automatically stream webcam feed. Only valid is source is 'webcam'.

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.

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.

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.

show_fullscreen_button: bool

default = True

If True, will show a fullscreen icon in the corner of the component that allows user to view the image in fullscreen mode. If False, icon does not appear.

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 X 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

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

Guides

Image Classification In Pytorch

Image Classification In Tensorflow

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. For SVGs, the type parameter is ignored and the filepath of the SVG is returned.

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

Guides

As input component: Passes the uploaded image as a `numpy.array`, `PIL.Image` or `str` filepath depending on `type`. For SVGs, the `type` parameter is ignored and the filepath of the SVG is returned.

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