app.py

import gradio as gr
from loadimg import load_img
from transformers import AutoModelForImageSegmentation
import torch
from torchvision import transforms
from PIL import Image
import tempfile

torch.set_float32_matmul_precision(["high", "highest"][0])

birefnet = AutoModelForImageSegmentation.from_pretrained(
    "ZhengPeng7/BiRefNet", trust_remote_code=True
)
birefnet.to("cpu")
transform_image = transforms.Compose(
    [
        transforms.Resize((1024, 1024)),
        transforms.ToTensor(),
        transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]),
    ]
)

def fn(image):
    im = load_img(image, output_type="pil")
    im = im.convert("RGB")
    origin = im.copy()
    image = process(im)
    return image

def parse_color(color):
    if color.startswith('#'):
        hex_color = color.lstrip('#')
        r = int(hex_color[0:2], 16)
        g = int(hex_color[2:4], 16)
        b = int(hex_color[4:6], 16)
    elif color.startswith('rgba'):
        rgba_values = color.replace('rgba(', '').replace(')', '')
        parts = [x.strip() for x in rgba_values.split(',')]
        r, g, b = int(float(parts[0])), int(float(parts[1])), int(float(parts[2]))
    elif color.startswith('rgb'):
        rgb_values = color.replace('rgb(', '').replace(')', '')
        r, g, b = [int(float(x.strip())) for x in rgb_values.split(',')]
    else:
        r, g, b = 255, 255, 255
    return (r, g, b, 255)

def process(image):
    image_size = image.size
    input_images = transform_image(image).unsqueeze(0).to("cpu")
    with torch.no_grad():
        preds = birefnet(input_images)[-1].sigmoid().cpu()
    pred = preds[0].squeeze()
    pred_pil = transforms.ToPILImage()(pred)
    mask = pred_pil.resize(image_size)
    image.putalpha(mask)
    return image, mask

def process_file(f, bg_color):
    im = load_img(f, output_type="pil")
    im = im.convert("RGB")

    transparent_img, mask = process(im)

    # With background color
    rgba_color = parse_color(bg_color)
    background = Image.new("RGBA", im.size, rgba_color)
    with_bg = Image.alpha_composite(background, transparent_img)
    with_bg_rgb = with_bg.convert("RGB")

    bg_png_path = tempfile.mktemp(suffix=".png")
    with_bg.save(bg_png_path, "PNG")

    bg_jpeg_path = tempfile.mktemp(suffix=".jpeg")
    with_bg_rgb.save(bg_jpeg_path, "JPEG")

    # Transparent (no background)
    trans_png_path = tempfile.mktemp(suffix=".png")
    transparent_img.save(trans_png_path, "PNG")

    return (with_bg_rgb, bg_png_path, bg_jpeg_path,
            transparent_img, trans_png_path)

css = """
.gradio-container h1 {
    margin-bottom: 24px;
}
.small-file, .small-file * {
    min-height: 0 !important;
    height: auto !important;
}
.small-file svg {
    display: none !important;
}
"""

with gr.Blocks(css=css, title="Background Remover") as background_remover_app:
    gr.Markdown("<h1 style='text-align: center;'>Background Remover</h1>")

    with gr.Row():
        with gr.Column(scale=2):
            gr.Markdown("### Input")
            input_image = gr.Image(label="Upload an image", type="filepath")
            color_picker = gr.ColorPicker(label="Background Color", value="#ffffff")
            submit_btn = gr.Button("Submit", variant="primary")

        with gr.Column(scale=2):
            with gr.Row():
                with gr.Column(scale=1):
                    gr.Markdown("### Output With Background Color")
                    bg_preview = gr.Image(label="Preview")
                    bg_png = gr.File(label="Download PNG", elem_classes="small-file")
                    bg_jpeg = gr.File(label="Download JPEG", elem_classes="small-file")

                with gr.Column(scale=1):
                    gr.Markdown("### Output With Transparent Background")
                    trans_preview = gr.Image(label="Preview")
                    trans_png = gr.File(label="Download PNG", elem_classes="small-file")

    gr.Examples(
        examples=[["butterfly.jpg", "#ffffff"]],
        inputs=[input_image, color_picker]
    )

    submit_btn.click(
        fn=process_file,
        inputs=[input_image, color_picker],
        outputs=[bg_preview, bg_png, bg_jpeg, trans_preview, trans_png]
    )

if __name__ == "__main__":
    background_remover_app.launch(share=True)