gradio-variableslider 0.0.5

Python library for easily interacting with trained machine learning models

gradio_variableslider

Python library for easily interacting with trained machine learning models

Installation

pip install gradio_variableslider

Usage

import gradio as gr
from gradio_variableslider import VariableSlider

example = VariableSlider().example_value()

with gr.Blocks() as demo:
    sub_slider = gr.Slider(label="Sub-Divisions", minimum=5, maximum=1000, step=5, value=1000, info="This is the amount of values that will occur between each integer.", interactive=True)
    def slider_function(slider_val):
        step = round(1/slider_val, 3)
        slider_final = VariableSlider(minimum=step, maximum=3.00001, step=step, interactive=True)
        return slider_final
    alpha_slider = VariableSlider(label="Alpha", minimum=0, maximum=3.00001, step=0.01, interactive=True)
    sub_slider.release(fn=slider_function, inputs=sub_slider, outputs=alpha_slider)


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

VariableSlider

Initialization

name	type	default	description
minimum	float	0	minimum value for slider.
maximum	float	100	maximum value for slider.
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. Ignored if randomized=True.
step	float | None	None	increment between slider values.
label	str | None	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.
info	str | None	None	additional component description.
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. 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.
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, slider will be adjustable; if False, adjusting 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.

Events

name	description
change	Triggered when the value of the VariableSlider 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.
input	This listener is triggered when the user changes the value of the VariableSlider.
release	This listener is triggered when the user releases the mouse on this VariableSlider.

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, passes slider value as a {float} into the function.
• As input: Should return, expects an {int} or {float} returned from function and sets slider value to it as long as it is within range (otherwise, sets to minimum value).

def predict(
    value: float
) -> float | None:
    return value