Introducing Gradio 5.0
Read MoreIntroducing Gradio 5.0
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gradio.Number(···)
float
or int
into the function, depending on precision
.def predict(
value: float | int | None
)
...
int
or float
returned from the function and sets field value to it.def predict(···) -> float | int | None
...
return value
value: float | Callable | None
= None
default value. If callable, the function will be called whenever the app loads to set the initial value of the component.
label: str | None
= None
the label for this component, displayed above the component if `show_label` is `True` 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 corresponds to.
info: str | None
= None
additional component description, appears below the label in smaller font. Supports markdown / HTML syntax.
every: Timer | float | None
= 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
= 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
= None
if True, will display label.
container: bool
= True
If True, will place the component in a container - providing some extra padding around the border.
scale: int | None
= 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
= 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 be editable; 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
= 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.
render: bool
= 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
= 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.
precision: int | None
= None
Precision to round input/output to. If set to 0, will round to nearest integer and convert type to int. If None, no rounding happens.
minimum: float | None
= None
Minimum value. Only applied when component is used as an input. If a user provides a smaller value, a gr.Error exception is raised by the backend.
maximum: float | None
= None
Maximum value. Only applied when component is used as an input. If a user provides a larger value, a gr.Error exception is raised by the backend.
step: float
= 1
The interval between allowed numbers in the component. Can be used along with optional parameters `minimum` and `maximum` to create a range of legal values starting from `minimum` and incrementing according to this parameter.
Class | Interface String Shortcut | Initialization |
---|---|---|
| "number" | Uses default values |
import gradio as gr
def tax_calculator(income, marital_status, assets):
tax_brackets = [(10, 0), (25, 8), (60, 12), (120, 20), (250, 30)]
total_deductible = sum(assets["Cost"])
taxable_income = income - total_deductible
total_tax = 0
for bracket, rate in tax_brackets:
if taxable_income > bracket:
total_tax += (taxable_income - bracket) * rate / 100
if marital_status == "Married":
total_tax *= 0.75
elif marital_status == "Divorced":
total_tax *= 0.8
return round(total_tax)
demo = gr.Interface(
tax_calculator,
[
"number",
gr.Radio(["Single", "Married", "Divorced"]),
gr.Dataframe(
headers=["Item", "Cost"],
datatype=["str", "number"],
label="Assets Purchased this Year",
),
],
"number",
examples=[
[10000, "Married", [["Suit", 5000], ["Laptop", 800], ["Car", 1800]]],
[80000, "Single", [["Suit", 800], ["Watch", 1800], ["Car", 800]]],
],
)
demo.launch()
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.
The Number component supports the following event listeners. Each event listener takes the same parameters, which are listed in the Event Parameters table below.
Listener | Description |
---|---|
| Triggered when the value of the Number 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 |
| This listener is triggered when the user changes the value of the Number. |
| This listener is triggered when the user presses the Enter key while the Number is focused. |
| This listener is triggered when the Number is focused. |
fn: Callable | None | Literal['decorator']
= "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
= 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
= 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]
= 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
= False
If True, will scroll to output component on completion
show_progress: Literal['full', 'minimal', 'hidden']
= "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
= 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
= 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
= 4
Maximum number of inputs to batch together if this is called from the queue (only relevant if batch=True)
preprocess: bool
= 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
= 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
= 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
= 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
= 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"
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
= 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
= 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
= None
stream_every: float
= 0.5
like_user_message: bool
= False