run.py

import gradio as gr
from pathlib import Path

dir_ = Path(__file__).parent


def predict(im):
    print(im)
    return im, len(im["layers"])


with gr.Blocks() as demo:
    with gr.Row():
        im = gr.ImageEditor(
            type="numpy",
            interactive=True,
        )
        im_preview = gr.ImageEditor(
            interactive=True,
        )

    layer_updates = gr.Textbox(value="", label="Layer Updates")
    num_layers = gr.Number(value=0, label="Num Layers")
    example_ran = gr.Number(value=0, label="Example Ran")

    set_background = gr.Button("Set Background")
    set_background.click(
        lambda: {
            "background": str(dir_ / "cheetah.jpg"),
            "layers": None,
            "composite": None,
        },
        None,
        im,
        show_progress="hidden",
    )
    set_layers = gr.Button("Set Layers")
    set_layers.click(
        lambda: {
            "background": None,
            "layers": [str(dir_ / "cheetah.jpg")],
            "composite": None,
        },
        None,
        im,
        show_progress="hidden",
    )
    im.change(
        lambda x: len(x["layers"]),
        inputs=im,
        outputs=layer_updates,
    )
    set_composite = gr.Button("Set Composite")
    set_composite.click(
        lambda: {
            "background": None,
            "layers": None,
            "composite": "https://huggingface.co/datasets/freddyaboulton/bucket/resolve/main/cheetah-003.jpg",
        },
        None,
        im,
        show_progress="hidden",
    )
    get_layers = gr.Button("Get Layers")

    get_layers.click(
        predict,
        outputs=[im_preview, num_layers],
        inputs=im,
    )

    gr.Examples(
        examples=[
            "https://huggingface.co/datasets/freddyaboulton/bucket/resolve/main/TheCheethcat.jpg",
            {
                "background": str(dir_ / "cheetah.jpg"),
                "layers": [str(dir_ / "layer1.png")],
                "composite": None,
            },
        ],
        inputs=im,
        outputs=[example_ran],
        fn=lambda x: 1,
        run_on_click=True,
    )

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