Introducing Gradio 5.0

Read More
Gradio logo
  1. Components
  2. Gallery

New to Gradio? Start here: Getting Started

See the Release History

To install Gradio from main, run the following command:

pip install https://gradio-builds.s3.amazonaws.com/9285dd9eb842ee05bd8a0fd4f0f9143788096bbc/gradio-5.9.1-py3-none-any.whl

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

←

FileExplorer

HighlightedText

→

Gallery

gradio.Gallery(···)
import gradio as gr with gr.Blocks() as demo: cheetahs = [ "https://upload.wikimedia.org/wikipedia/commons/0/09/TheCheethcat.jpg", "https://nationalzoo.si.edu/sites/default/files/animals/cheetah-003.jpg", "https://img.etimg.com/thumb/msid-50159822,width-650,imgsize-129520,,resizemode-4,quality-100/.jpg", "https://nationalzoo.si.edu/sites/default/files/animals/cheetah-002.jpg", "https://images.theconversation.com/files/375893/original/file-20201218-13-a8h8uq.jpg?ixlib=rb-1.1.0&rect=16%2C407%2C5515%2C2924&q=45&auto=format&w=496&fit=clip", ] gr.Gallery(value=cheetahs, columns=4) demo.launch()

Description

Creates a gallery component that allows displaying a grid of images or videos, and optionally captions. If used as an input, the user can upload images or videos to the gallery. If used as an output, the user can click on individual images or videos to view them at a higher resolution.

Behavior

As input component: Passes the list of images or videos as a list of (media, caption) tuples, or a list of (media, None) tuples if no captions are provided (which is usually the case). Images can be a str file path, a numpy array, or a PIL.Image object depending on type. Videos are always str file path.

Your function should accept one of these types:
def predict(
	value: List[tuple[str, str | None]] | List[tuple[PIL.Image.Image, str | None]] | List[tuple[np.ndarray, str | None]] | None
)
	...

As output component: Expects the function to return a list of images or videos, or list of (media, str caption) tuples. Each image can be a str file path, a numpy array, or a PIL.Image object. Each video can be a str file path.

Your function should return one of these types:
def predict(···) -> list[GalleryImageType | CaptionedGalleryImageType] | None
	...	
	return value

Initialization

Parameters 
value: list[np.ndarray | PIL.Image.Image | str | Path | tuple] | Callable | None
default = None

List of images or videos to display in the gallery by default. If callable, the function will be called whenever the app loads to set the initial value of the component.

format: str
default = "webp"

Format to save images before they are returned to the frontend, such as 'jpeg' or 'png'. This parameter only applies to images that are returned from the prediction function as numpy arrays or PIL Images. The format should be supported by the PIL library.

file_types: list[str] | None
default = None

List of file extensions or types of files to be uploaded (e.g. ['image', '.mp4']), when this is used as an input component. "image" allows only image files to be uploaded, "video" allows only video files to be uploaded, ".mp4" allows only mp4 files to be uploaded, etc. If None, any image and video files types are allowed.

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.

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.

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.

columns: int | list[int] | tuple[int, ...] | None
default = 2

Represents the number of images that should be shown in one row, for each of the six standard screen sizes (<576px, <768px, <992px, <1200px, <1400px, >1400px). If fewer than 6 are given then the last will be used for all subsequent breakpoints

rows: int | list[int] | None
default = None

Represents the number of rows in the image grid, for each of the six standard screen sizes (<576px, <768px, <992px, <1200px, <1400px, >1400px). If fewer than 6 are given then the last will be used for all subsequent breakpoints

height: int | float | str | None
default = None

The height of the gallery component, specified in pixels if a number is passed, or in CSS units if a string is passed. If more images are displayed than can fit in the height, a scrollbar will appear.

allow_preview: bool
default = True

If True, images in the gallery will be enlarged when they are clicked. Default is True.

preview: bool | None
default = None

If True, Gallery will start in preview mode, which shows all of the images as thumbnails and allows the user to click on them to view them in full size. Only works if allow_preview is True.

selected_index: int | None
default = None

The index of the image that should be initially selected. If None, no image will be selected at start. If provided, will set Gallery to preview mode unless allow_preview is set to False.

object_fit: Literal['contain', 'cover', 'fill', 'none', 'scale-down'] | None
default = None

CSS object-fit property for the thumbnail images in the gallery. Can be "contain", "cover", "fill", "none", or "scale-down".

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.

show_download_button: bool | None
default = True

If True, will show a download button in the corner of the selected image. If False, the icon does not appear. Default is True.

interactive: bool | None
default = None

If True, the gallery will be interactive, allowing the user to upload images. If False, the gallery will be static. Default is True.

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

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. If the image is SVG, the `type` is ignored and the filepath of the SVG is returned.

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 gallery in fullscreen mode. 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.

Shortcuts

Class Interface String Shortcut Initialization

gradio.Gallery

"gallery"

 Uses default values

Demos

# This demo needs to be run from the repo folder.
# python demo/fake_gan/run.py
import random

import gradio as gr

def fake_gan():
    images = [
        (random.choice(
            [
                "http://www.marketingtool.online/en/face-generator/img/faces/avatar-1151ce9f4b2043de0d2e3b7826127998.jpg",
                "http://www.marketingtool.online/en/face-generator/img/faces/avatar-116b5e92936b766b7fdfc242649337f7.jpg",
                "http://www.marketingtool.online/en/face-generator/img/faces/avatar-1163530ca19b5cebe1b002b8ec67b6fc.jpg",
                "http://www.marketingtool.online/en/face-generator/img/faces/avatar-1116395d6e6a6581eef8b8038f4c8e55.jpg",
                "http://www.marketingtool.online/en/face-generator/img/faces/avatar-11319be65db395d0e8e6855d18ddcef0.jpg",
            ]
        ), f"label {i}")
        for i in range(3)
    ]
    return images

with gr.Blocks() as demo:
    gallery = gr.Gallery(
        label="Generated images", show_label=False, elem_id="gallery"
    , columns=[3], rows=[1], object_fit="contain", height="auto")
    btn = gr.Button("Generate images", scale=0)

    btn.click(fake_gan, None, gallery)

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

Listener Description

Gallery.select(fn, ···)

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

Gallery.upload(fn, ···)

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

Gallery.change(fn, ···)

Triggered when the value of the Gallery 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.

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
←

FileExplorer

HighlightedText

→