floorplan/README.md

gradio_floorplan

A Gradio custom component for interactive SVG floor plan editing

Installation

pip install gradio_floorplan

Usage

import gradio as gr
from gradio_floorplan import FloorPlan

DEFAULT_VALUE = {
    "corners": [[50, 50], [550, 50], [550, 450], [50, 450]],
    "furnitures": [
        {
            "object": "Sofa",
            "localisation": [150, 100, 250, 300],
            "description": "3-seat sofa",
        },
        {
            "object": "Table",
            "localisation": [300, 200, 380, 400],
            "description": "Coffee table",
        },
    ],
}


def on_furniture_moved(value):
    """Receives updated floor plan after user moves a furniture item."""
    return value


with gr.Blocks() as demo:
    gr.Markdown("## LaMaison — Floor Plan")
    floor_plan = FloorPlan(value=DEFAULT_VALUE, label="Floor Plan", interactive=True)
    output = gr.JSON(label="Updated positions")
    floor_plan.change(on_furniture_moved, inputs=floor_plan, outputs=output)

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

FloorPlan

Initialization

name	type	default	description
value	dict | None	value = None	None
label	str | None	value = None	None
info	str | None	value = None	None
every	'Timer | float | None'	value = None	None
show_label	bool | None	value = None	None
container	bool	value = True	None
scale	int | None	value = None	None
min_width	int	value = 160	None
interactive	bool | None	value = None	None
visible	bool	value = True	None
elem_id	str | None	value = None	None
elem_classes	list[str] | str | None	value = None	None
render	bool	value = True	None

Events

name	description
change

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.

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