Gradio ColorPicker Docs

New to Gradio? Start here: Getting Started

To install Gradio from main, run the following command:

pip install https://gradio-builds.s3.amazonaws.com/98c1cbcd0e3b66a8d2463820d3ce157efe0a032d/gradio-4.31.4-py3-none-any.whl

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

← Code

Dataframe →

ColorPicker

gradio.ColorPicker(···)

Description

Creates a color picker for user to select a color as string input. Can be used as an input to pass a color value to a function or as an output to display a color value.

Behavior

As input component: Passes selected color value as a hex str into the function.

Your function should accept one of these types:

def predict(
	value: str | None
)
	...

As output component: Expects a hex str returned from function and sets color picker value to it.

Your function should return one of these types:

def predict(···) -> str | None
	...	
	return value

Initialization

Parameter	Description
`value` str \| Callable \| None default: None	default text to provide in color picker. If callable, the function will be called whenever the app loads to set the initial value of the component.
`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.
`info` str \| None default: None	additional component description.
`every` float \| None default: None	If `value` is a callable, run the function 'every' number of seconds while the client connection is open. Has no effect otherwise.sed (e.g. to cancel it) via this component's .load_event attribute.
`show_label` bool \| None default: None	if True, will display label.
`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 be rendered as an editable color picker; if False, editing will be disabled. 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.

Shortcuts

Class	Interface String Shortcut	Initialization
`gradio.ColorPicker`	"colorpicker"	Uses default values

Demos

import gradio as gr import numpy as np import os from PIL import Image, ImageColor def change_color(icon, color): """ Function that given an icon in .png format changes its color Args: icon: Icon whose color needs to be changed. color: Chosen color with which to edit the input icon. Returns: edited_image: Edited icon. """ img = icon.convert("LA") img = img.convert("RGBA") image_np = np.array(icon) _, _, _, alpha = image_np.T mask = alpha > 0 image_np[..., :-1][mask.T] = ImageColor.getcolor(color, "RGB") edited_image = Image.fromarray(image_np) return edited_image inputs = [ gr.Image(label="icon", type="pil", image_mode="RGBA"), gr.ColorPicker(label="color"), ] outputs = gr.Image(label="colored icon") demo = gr.Interface( fn=change_color, inputs=inputs, outputs=outputs ) if __name__ == "__main__": demo.launch()

Event Listeners

Description

Event listeners allow you to capture and 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 ColorPicker component supports the following event listeners. Each event listener takes the same parameters, which are listed in the Event Arguments table below.

Listener	Description
`gradio.ColorPicker.change(fn, ···)`	Triggered when the value of the ColorPicker 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.
`gradio.ColorPicker.input(fn, ···)`	This listener is triggered when the user changes the value of the ColorPicker.
`gradio.ColorPicker.submit(fn, ···)`	This listener is triggered when the user presses the Enter key while the ColorPicker is focused.
`gradio.ColorPicker.focus(fn, ···)`	This listener is triggered when the ColorPicker is focused.
`gradio.ColorPicker.blur(fn, ···)`	This listener is triggered when the ColorPicker is unfocused/blurred.

Event Arguments

Parameter	Description
`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 \| list[Component] \| set[Component] \| 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 \| list[Component] \| 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"	If True, will show progress animation while pending
`queue` bool \| None default: None	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.
`every` float \| None default: None	Run this event 'every' number of seconds while the client connection is open. Interpreted in seconds.
`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.

← Code

Dataframe →

ColorPicker

Description
Behavior
Initialization
Shortcuts
Demos
Event Listeners