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gradio-categoricalslider
Advanced tools
Python library for easily interacting with trained machine learning models
gradio_categoricalslider
Python library for easily interacting with trained machine learning models
pip install gradio_categoricalslider
import gradio as gr
from gradio_categoricalslider import CategoricalSlider
demo = gr.Interface(
lambda x: x,
# interactive version of your component
CategoricalSlider(categories=[("A", 1), ("B", 2), ("C", 3)]),
None, # static version of your component
# examples=[[example]], # uncomment this line to view the "example version" of your component
)
if __name__ == "__main__":
demo.launch()
CategoricalSlider
name | type | default | description |
---|---|---|---|
categories |
| [] | List of tuples containing category and value. |
value |
| 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. |
label |
| 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 |
| None | additional component description. |
every |
| 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 |
| None | if True, will display label. |
container |
| True | If True, will place the component in a container - providing some extra padding around the border. |
scale |
| 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 |
| 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 |
| True | 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 |
| True | If False, component will be hidden. |
elem_id |
| 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 |
| 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 |
| 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 |
| 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. |
randomize |
| False | If True, the value of the slider when the app loads is taken uniformly at random from the list of categories. |
name | description |
---|---|
change | Triggered when the value of the CategoricalSlider 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 CategoricalSlider. |
release | This listener is triggered when the user releases the mouse on this CategoricalSlider. |
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).
The code snippet below is accurate in cases where the component is used as both an input and an output.
def predict(
value: str
) -> float | None:
return value
FAQs
Python library for easily interacting with trained machine learning models
We found that gradio-categoricalslider demonstrated a healthy version release cadence and project activity because the last version was released less than a year ago. It has 1 open source maintainer collaborating on the project.
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