app.py

from typing import Tuple
import gradio as gr
import os
from PIL import Image
from datasets import load_dataset
from prepare_samples import prepare_samples

DIR_PATH = os.path.dirname(__file__)


def inference(rgb: Image.Image, depth: Image.Image) -> Tuple[Image.Image]:
    return (rgb,)


dataset = load_dataset("RGBD-SOD/test", "v1", split="train", cache_dir="data")

# with gr.Blocks() as demo:
#     with gr.Row(elem_id="center"):
#         gr.Markdown("# BBS-Net Demo")

TITLE = "BBS-Net Demo"
DESCRIPTION = "Gradio demo for BBS-Net: RGB-D salient object detection with a bifurcated backbone strategy network."
examples = prepare_samples()

demo = gr.Interface(
    fn=inference,
    inputs=[
        gr.inputs.Image(label="RGB", type="pil"),
        gr.inputs.Image(label="Depth", type="pil"),
    ],
    outputs=[
        gr.outputs.Image(label="Prediction", type="pil"),
    ],
    title=TITLE,
    examples=examples,
    description=DESCRIPTION,
)


if __name__ == "__main__":
    demo.launch(enable_queue=True, cache_examples=False)