app.py

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

# Load the model from Hugging Face
birefnet = AutoModelForImageSegmentation.from_pretrained('zhengpeng7/BiRefNet_lite', trust_remote_code=True)
device = 'cuda' if torch.cuda.is_available() else 'cpu'
birefnet.to(device)
birefnet.eval()

# Define the transform to preprocess the input image
image_size = (1024, 1024)
transform_image = transforms.Compose([
    transforms.Resize(image_size),
    transforms.ToTensor(),
    transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
])

def extract_object(image):
    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_with_alpha = image.convert("RGBA")
    image_with_alpha.putalpha(mask)
    return image_with_alpha

iface = gr.Interface(
    fn=extract_object,
    inputs=gr.Image(type="pil", label="Upload Image"),
    outputs=gr.Image(type="pil", label="Segmented Image"),
    title="BiRefNet Background Removal",
    description="Upload an image and get the foreground object extracted."
)

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