app.py

import gradio as gr
from loadimg import load_img
import spaces
from transformers import AutoModelForImageSegmentation
import torch
from torchvision import transforms
import uuid
import os

# Select device
device = "cuda" if torch.cuda.is_available() else "cpu"
print(f"Using device: {device}")

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

# Load BiRefNet model
birefnet = AutoModelForImageSegmentation.from_pretrained(
    "ZhengPeng7/BiRefNet", trust_remote_code=True
)
birefnet.to(device)

# Preprocessing
transform_image = transforms.Compose(
    [
        transforms.Resize((1024, 1024)),
        transforms.ToTensor(),
        transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]),
    ]
)

@spaces.GPU
def process(image):
    image_size = image.size
    input_images = transform_image(image).unsqueeze(0).to(device)
    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

# Main function: image upload → preview + downloadable PNG
def fn(image):
    im = load_img(image, output_type="pil").convert("RGB")
    processed_image = process(im)

    filename = f"/tmp/processed_{uuid.uuid4().hex}.png"
    processed_image.save(filename)

    return processed_image, filename

# Gradio interface
demo = gr.Interface(
    fn,
    inputs=gr.Image(label="Upload an image", sources=["upload"]),
    outputs=[
        gr.Image(label="Processed Preview"),
        gr.File(label="Download PNG")
    ],
    title="Background Removal Tool"
)

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