gradio-imageslider

gradio_imageslider

A Gradio component for comparing two images.

This component can be used in several ways:

• as a unified input / output where users will upload a single image and an inference function will generate an image it can be compared to (see demo),
• as a manual upload input allowing users to compare two of their own images (which can then be passed along elsewhere, e.g. to a model),
• as static output component allowing users to compare two images generated by an inference function.

Installation

pip install gradio_imageslider

Usage

import gradio as gr
from gradio_imageslider import ImageSlider
from PIL import ImageFilter


def fn(im):
    if not im or not im[0]:
        return im
    return (im[0], im[0].filter(filter=ImageFilter.GaussianBlur(radius=10)))


with gr.Blocks() as demo:
    with gr.Group():
        img1 = ImageSlider(label="Blur image", type="pil", slider_color="pink")
        img1.upload(fn, inputs=img1, outputs=img1)

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

ImageSlider

Initialization

name	type	default	description
value	tuple[str, str] | tuple[PIL.Image.Image, PIL.Image.Image] | tuple[numpy.ndarray, numpy.ndarray] | None	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.
position	int	0.5	The position of the slider, between 0 and 1.
upload_count	int	1	The number of images that can be uploaded to the component. 1 or 2.
height	int | None	None	Height of the displayed image in pixels.
width	int | None	None	Width of the displayed image in pixels.
type	"numpy" | "pil" | "filepath"	"numpy"	The format the image is converted to 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.
label	str | None	None	component name in interface.
every	float | None	None	If `value` is a callable, run the function 'every' number of seconds while the client connection is open. Has no effect otherwise. Queue must be enabled. The event can be accessed (e.g. to cancel it) via this component's .load_event attribute.
show_label	bool | None	None	if True, will display label.
show_download_button	bool	True	If True, will display button to download image.
container	bool	True	If True, will place the component in a container - providing some extra padding around the border.
scale	int | None	None	relative width compared to adjacent Components in a Row. For example, if Component A has scale=2, and Component B has scale=1, A will be twice as wide as B. Should be an integer.
min_width	int	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	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	True	If False, component will be hidden.
elem_id	str | None	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	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.
show_share_button	bool | None	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.
slider_color	str | None	None	The color of the slider separator.

Events

name	description
change	Triggered when the value of the ImageSlider 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.
upload	This listener is triggered when the user uploads a file into the ImageSlider.

User function

The impact on the users predict function varies depending on whether the component is used as an input or output for an event (or both).

• When used as an Input, the component only impacts the input signature of the user function.
• When used as an output, the component only impacts the return signature of the user function.

The code snippet below is accurate in cases where the component is used as both an input and an output.

• As output: Is passed, tuple of images in the requested format.
• As input: Should return, image as a numpy array, PIL Image, string/Path filepath, or string URL.

def predict(
    value: tuple[str, str]
   | tuple[PIL.Image.Image, PIL.Image.Image]
   | tuple[numpy.ndarray, numpy.ndarray]
   | None
) -> tuple[str, str]
   | tuple[PIL.Image.Image, PIL.Image.Image]
   | tuple[numpy.ndarray, numpy.ndarray]
   | None:
    return value