# demo source from: https://github.com/MarcoForte/FBA_Matting
import gradio as gr
from FBA_Matting import inference
from huggingface_hub import hf_hub_download

from networks.models import build_model

REPO_ID = "leonelhs/FBA-Matting"

weights = hf_hub_download(repo_id=REPO_ID, filename="FBA.pth")
model = build_model(weights)
model.eval().cpu()


def predict(image, trimap):
    return inference(model, image, trimap)


footer = r"""
<center>
<b>
Demo for <a href='https://github.com/MarcoForte/FBA_Matting'>FBA Matting</a>
</b>
</center>
"""

with gr.Blocks(title="FBA Matting") as app:
    gr.HTML("<center><h1>FBA Matting</h1></center>")
    gr.HTML("<center><h3>Foreground, Background, Alpha Matting Generator.</h3></center>")
    with gr.Row(equal_height=False):
        with gr.Column():
            input_img = gr.Image(type="filepath", label="Input image")
            input_trimap = gr.Image(type="filepath", label="Trimap image")
            run_btn = gr.Button(variant="primary")
        with gr.Column():
            fg = gr.Image(type="numpy", label="Foreground")
            bg = gr.Image(type="numpy", label="Background")
            alpha = gr.Image(type="numpy", label="Alpha")
            composite = gr.Image(type="numpy", label="Composite")
            gr.ClearButton(components=[input_img, input_trimap, fg, bg, alpha, composite], variant="stop")

    run_btn.click(predict, [input_img, input_trimap], [fg, bg, alpha, composite])

    with gr.Row():
        blobs = [[
            f"./images/{x:02d}.png",
            f"./trimaps/{x:02d}.png"] for x in range(1, 7)]
        examples = gr.Dataset(components=[input_img, input_trimap], samples=blobs)
        examples.click(lambda x: (x[0], x[1]), [examples], [input_img, input_trimap])

    with gr.Row():
        gr.HTML(footer)

app.queue()
app.launch(share=False, debug=True, show_error=True)