Components
JSON

New to Gradio? Start here: Getting Started

To install Gradio from main, run the following command:

pip install https://gradio-builds.s3.amazonaws.com/ac9bf5ec9208b92579f36ee94a247ae3e676c02f/gradio-5.6.0-py3-none-any.whl

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

←

ImageEditor

Label

→

JSON

gradio.JSON(···)

import gradio as gr import numpy as np with gr.Blocks() as demo: out = gr.JSON(value={ "Key 1": "Value 1", "Key 2": {"Key 3": "Value 2", "Key 4": "Value 3"}, "Key 5": ["Item 1", "Item 2", "Item 3"], "Key 6": 123, "Key 7": 123.456, "Key 8": True, "Key 9": False, "Key 10": None, "Key 11": np.array([1, 2, 3]), }) demo.launch() numpy

Description

Used to display arbitrary JSON output prettily. As this component does not accept user input, it is rarely used as an input component.

Behavior

As input component: Passes the JSON value as a `dict` or `list` depending on the value.

Your function should accept one of these types:

def predict(
	value: dict | list | None
)
	...

As output component: Expects a valid JSON `str` -- or a `list` or `dict` that can be serialized to a JSON string. The `list` or `dict` value can contain numpy arrays.

Your function should return one of these types:

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

Initialization

value: str | dict | list | Callable | None

default = None

Default value as a valid JSON `str` -- or a `list` or `dict` that can be serialized to a JSON string. 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.

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.

open: bool

default = False

If True, all JSON nodes will be expanded when rendered. By default, node levels deeper than 3 are collapsed.

show_indices: bool

default = False

Whether to show numerical indices when displaying the elements of a list within the JSON object.

height: int | str | None

default = None

Height of the JSON component in pixels if a number is passed, or in CSS units if a string is passed. Overflow will be scrollable. If None, the height will be automatically adjusted to fit the content.

max_height: int | str | None

default = 500

min_height: int | str | None

default = None

Shortcuts

Class	Interface String Shortcut	Initialization
`gradio.JSON`	"json"	Uses default values

Demos

from zipfile import ZipFile import gradio as gr def zip_to_json(file_obj): files = [] with ZipFile(file_obj.name) as zfile: for zinfo in zfile.infolist(): files.append( { "name": zinfo.filename, "file_size": zinfo.file_size, "compressed_size": zinfo.compress_size, } ) return files demo = gr.Interface(zip_to_json, "file", "json") if __name__ == "__main__": demo.launch()

from zipfile import ZipFile

import gradio as gr

def zip_to_json(file_obj):
    files = []
    with ZipFile(file_obj.name) as zfile:
        for zinfo in zfile.infolist():
            files.append(
                {
                    "name": zinfo.filename,
                    "file_size": zinfo.file_size,
                    "compressed_size": zinfo.compress_size,
                }
            )
    return files

demo = gr.Interface(zip_to_json, "file", "json")

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

import gradio as gr disease_values = [0.25, 0.5, 0.75] def xray_model(diseases, img): return [{disease: disease_values[idx] for idx,disease in enumerate(diseases)}] def ct_model(diseases, img): return [{disease: 0.1 for disease in diseases}] with gr.Blocks(fill_width=True) as demo: gr.Markdown( """ # Detect Disease From Scan With this model you can lorem ipsum - ipsum 1 - ipsum 2 """ ) gr.DuplicateButton() disease = gr.CheckboxGroup( info="Select the diseases you want to scan for.", choices=["Covid", "Malaria", "Lung Cancer"], label="Disease to Scan For" ) slider = gr.Slider(0, 100) with gr.Tab("X-ray") as x_tab: with gr.Row(): xray_scan = gr.Image() xray_results = gr.JSON() xray_run = gr.Button("Run") xray_run.click( xray_model, inputs=[disease, xray_scan], outputs=xray_results, api_name="xray_model" ) with gr.Tab("CT Scan"): with gr.Row(): ct_scan = gr.Image() ct_results = gr.JSON() ct_run = gr.Button("Run") ct_run.click( ct_model, inputs=[disease, ct_scan], outputs=ct_results, api_name="ct_model" ) upload_btn = gr.Button("Upload Results", variant="primary") upload_btn.click( lambda ct, xr: None, inputs=[ct_results, xray_results], outputs=[], ) if __name__ == "__main__": demo.launch()

import gradio as gr

disease_values = [0.25, 0.5, 0.75]

def xray_model(diseases, img):
    return [{disease: disease_values[idx] for idx,disease in enumerate(diseases)}]

def ct_model(diseases, img):
    return [{disease: 0.1 for disease in diseases}]

with gr.Blocks(fill_width=True) as demo:
    gr.Markdown(
        """
# Detect Disease From Scan
With this model you can lorem ipsum
- ipsum 1
- ipsum 2
"""
    )
    gr.DuplicateButton()
    disease = gr.CheckboxGroup(
        info="Select the diseases you want to scan for.",
        choices=["Covid", "Malaria", "Lung Cancer"], label="Disease to Scan For"
    )
    slider = gr.Slider(0, 100)

    with gr.Tab("X-ray") as x_tab:
        with gr.Row():
            xray_scan = gr.Image()
            xray_results = gr.JSON()
        xray_run = gr.Button("Run")
        xray_run.click(
            xray_model,
            inputs=[disease, xray_scan],
            outputs=xray_results,
            api_name="xray_model"
        )

    with gr.Tab("CT Scan"):
        with gr.Row():
            ct_scan = gr.Image()
            ct_results = gr.JSON()
        ct_run = gr.Button("Run")
        ct_run.click(
            ct_model,
            inputs=[disease, ct_scan],
            outputs=ct_results,
            api_name="ct_model"
        )

    upload_btn = gr.Button("Upload Results", variant="primary")
    upload_btn.click(
        lambda ct, xr: None,
        inputs=[ct_results, xray_results],
        outputs=[],
    )

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

Listener	Description
`JSON.change(fn, ···)`	Triggered when the value of the JSON 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

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

←

ImageEditor

Label

→

JSON

Description

Used to display arbitrary JSON output prettily. As this component does not accept user input, it is rarely used as an input component.

Behavior

As input component: Passes the JSON value as a dict or list depending on the value.

Your function should accept one of these types:

As output component: Expects a valid JSON str -- or a list or dict that can be serialized to a JSON string. The list or dict value can contain numpy arrays.

Your function should return one of these types:

Initialization

Shortcuts

Demos

Event Listeners

Description

Supported Event Listeners

Event Parameters

As input component: Passes the JSON value as a `dict` or `list` depending on the value.

As output component: Expects a valid JSON `str` -- or a `list` or `dict` that can be serialized to a JSON string. The `list` or `dict` value can contain numpy arrays.