Components
Dataset

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Dataframe

DateTime

Dataset

gradio.Dataset(···)

import gradio as gr with gr.Blocks() as demo: gr.Dataset(components=[gr.Textbox(visible=False)], label="Text Dataset", samples=[ ["The quick brown fox jumps over the lazy dog"], ["Build & share delightful machine learning apps"], ["She sells seashells by the seashore"], ["Supercalifragilisticexpialidocious"], ["Lorem ipsum"], ["That's all folks!"] ], ) demo.launch()

Description

Creates a gallery or table to display data samples. This component is primarily designed for internal use to display examples. However, it can also be used directly to display a dataset and let users select examples.

Behavior

As input component: Passes the selected sample either as a `list` of data corresponding to each input component (if `type` is "value") or as an `int` index (if `type` is "index"), or as a `tuple` of the index and the data (if `type` is "tuple").

Your function should accept one of these types:

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

As output component: Expects an `int` index or `list` of sample data. Returns the index of the sample in the dataset or `None` if the sample is not found.

Your function should return one of these types:

def predict(···) -> list[list]
	...	
	return value

Initialization

label: str | None

default = None

the label for this component, appears above the component.

components: list[Component] | list[str] | None

default = None

Which component types to show in this dataset widget, can be passed in as a list of string names or Components instances. The following components are supported in a Dataset: Audio, Checkbox, CheckboxGroup, ColorPicker, Dataframe, Dropdown, File, HTML, Image, Markdown, Model3D, Number, Radio, Slider, Textbox, TimeSeries, Video

component_props: list[dict[str, Any]] | None

default = None

samples: list[list[Any]] | None

default = None

a nested list of samples. Each sublist within the outer list represents a data sample, and each element within the sublist represents an value for each component

headers: list[str] | None

default = None

Column headers in the Dataset widget, should be the same len as components. If not provided, inferred from component labels

type: Literal['values', 'index', 'tuple']

default = "values"

"values" if clicking on a sample should pass the value of the sample, "index" if it should pass the index of the sample, or "tuple" if it should pass both the index and the value of the sample.

samples_per_page: int

default = 10

how many examples to show per page.

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.

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.

proxy_url: str | None

default = None

The URL of the external Space used to load this component. Set automatically when using `gr.load()`. This should not be set manually.

sample_labels: list[str] | None

default = None

A list of labels for each sample. If provided, the length of this list should be the same as the number of samples, and these labels will be used in the UI instead of rendering the sample values.

Shortcuts

Class	Interface String Shortcut	Initialization
`gradio.Dataset`	"dataset"	Uses default values

Examples

Updating a Dataset

In this example, we display a text dataset using gr.Dataset and then update it when the user clicks a button:

import gradio as gr

philosophy_quotes = [
    ["I think therefore I am."],
    ["The unexamined life is not worth living."]
]

startup_quotes = [
    ["Ideas are easy. Implementation is hard"],
    ["Make mistakes faster."]
]

def show_startup_quotes():
    return gr.Dataset(samples=startup_quotes)

with gr.Blocks() as demo:
    textbox = gr.Textbox()
    dataset = gr.Dataset(components=[textbox], samples=philosophy_quotes)
    button = gr.Button()

    button.click(show_startup_quotes, None, dataset)

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

Listener	Description
`Dataset.click(fn, ···)`	Triggered when the Dataset is clicked.
`Dataset.select(fn, ···)`	Event listener for when the user selects or deselects the Dataset. Uses event data gradio.SelectData to carry `value` referring to the label of the Dataset, and `selected` to refer to state of the Dataset. See EventData documentation on how to use this event data

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

←

Dataframe

DateTime

→

Dataset

Description

Creates a gallery or table to display data samples. This component is primarily designed for internal use to display examples. However, it can also be used directly to display a dataset and let users select examples.

Behavior

As input component: Passes the selected sample either as a list of data corresponding to each input component (if type is "value") or as an int index (if type is "index"), or as a tuple of the index and the data (if type is "tuple").

Your function should accept one of these types:

As output component: Expects an int index or list of sample data. Returns the index of the sample in the dataset or None if the sample is not found.

Your function should return one of these types:

Initialization

Shortcuts

Examples

Event Listeners

Description

Supported Event Listeners

Event Parameters

As input component: Passes the selected sample either as a `list` of data corresponding to each input component (if `type` is "value") or as an `int` index (if `type` is "index"), or as a `tuple` of the index and the data (if `type` is "tuple").

As output component: Expects an `int` index or `list` of sample data. Returns the index of the sample in the dataset or `None` if the sample is not found.