app.py

import gradio as gr
from gradio_imageslider import ImageSlider
from loadimg import load_img
import spaces
from transformers import AutoModelForImageSegmentation
import torch
from torchvision import transforms
from PIL import Image
import os
import tempfile

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

birefnet = AutoModelForImageSegmentation.from_pretrained(
    "ZhengPeng7/BiRefNet", trust_remote_code=True
)
birefnet.to("cuda")

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 fn(image):
    if image is None:
        return None, None
    im = load_img(image, output_type="pil")
    im = im.convert("RGB")
    image_size = im.size
    origin = im.copy()
    input_images = transform_image(im).unsqueeze(0).to("cuda")
    # 예측
    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)
    im.putalpha(mask)
    return im, origin

def save_image(image):
    if image is None:
        return None
    with tempfile.NamedTemporaryFile(delete=False, suffix=".png") as temp_file:
        image.save(temp_file.name, format="PNG")
    return temp_file.name

def process_and_download(input_image):
    result, original = fn(input_image)
    if result is None:
        return None, None
    result_path = save_image(result)
    original_path = save_image(original)
    return [result_path, original_path], result_path

# 예제 이미지를 직접 PIL 객체로 로드
example_image1 = Image.open("example_images/example1.png")
example_image2 = Image.open("example_images/example2.png")
example_image3 = Image.open("example_images/example3.png")

# 인터페이스 컴포넌트 정의
image = gr.Image(label="이미지 업로드")
slider = ImageSlider(label="배경 제거 결과", type="filepath")
png_output = gr.File(label="PNG 다운로드")

# Gradio 인터페이스 구성
demo = gr.Interface(
    process_and_download,
    inputs=image,
    outputs=[slider, png_output],
    examples=[example_image1, example_image2, example_image3],
    title="배경 제거",
    description="이미지를 업로드하면 BiRefNet 모델을 사용하여 배경을 제거합니다. 결과를 PNG 파일로 다운로드할 수 있습니다."
)

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