gradio-sync3dcompare 0.0.7

Synchronized side-by-side 3D comparison viewer for Gradio — supports PLY and GLB files with shared camera control.

gradio_sync3dcompare

Synchronized side-by-side 3D comparison viewer for Gradio — supports PLY and GLB files with shared camera control.

Installation

pip install gradio_sync3dcompare

Usage

from pathlib import Path

import gradio as gr

from gradio_sync3dcompare import Sync3DCompare


ROOT = Path(__file__).resolve().parent
SAMPLE_DIR = (ROOT / "demo" / "sample_data").resolve()
MESH_A = SAMPLE_DIR / "mesh_a.glb"
MESH_B = SAMPLE_DIR / "mesh_b.glb"
POINT_SIZE = 0.5
DEFAULT_ZOOM = 1
MIN_ZOOM = 0.5
MAX_ZOOM = 16.0


def load_comparison():
    return [
        {"name": "Mesh A", "path": str(MESH_A)},
        {"name": "Mesh B", "path": str(MESH_B)},
    ]


with gr.Blocks(title="Synchronized GLB Comparison") as demo:
    gr.Markdown("# Synchronized GLB Comparison")
    gr.Markdown(
        "Click the button to load two GLB files into a side-by-side viewer with synchronized orbit, zoom, and pan."
    )

    load_button = gr.Button("Load Comparison")
    viewer = Sync3DCompare(
        value=[],
        label="3D Comparison",
        render_mode="points",
        sync_camera=True,
        point_size=POINT_SIZE,
        default_zoom=DEFAULT_ZOOM,
        min_zoom=MIN_ZOOM,
        max_zoom=MAX_ZOOM,
        height=500,
        num_views=2,
    )

    load_button.click(fn=load_comparison, outputs=viewer)


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

Sync3DCompare

Initialization

name	type	default	description
value	list[dict]| None	value = None	None
label	str| None	value = "3D Comparison"	None
render_mode	"points"| "native"	value = "points"	None
sync_camera	bool	value = True	None
point_size	float	value = 2.0	None
height	int	value = 500	None
num_views	int	value = 2	None
interactive	bool	value = True	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.

• As input: Should return, the output data received by the component from the user's function in the backend.

def predict(
    value: Unknown
) -> list[dict]| None:
    return value