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

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 process(image):
    image_size = image.size
    input_images = transform_image(image).unsqueeze(0).to("cpu")
    # Prediction
    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)

    white_background = Image.new("RGBA", image_size, (255, 255, 255, 255))
    image.putalpha(mask)
    combined = Image.alpha_composite(white_background, image)

    return combined
  
def process_file(f):
    name_path = f.rsplit(".",1)[0]+".jpeg"
    im = load_img(f, output_type="pil")
    im = im.convert("RGB")
    transparent = process(im)
    rgb_image = transparent.convert("RGB")  # Ensure the final image is in RGB mode for JPEG
    rgb_image.save(name_path)
    return name_path

slider1 = gr.Image()
slider2 = ImageSlider(label="birefnet", type="pil")
image = gr.Image(label="Upload an image")
image2 = gr.Image(label="Upload an image",type="filepath")
text = gr.Textbox(label="Paste an image URL")
png_file = gr.File(label="output jpeg file")


chameleon = load_img("butterfly.jpg", output_type="pil")

url = "https://hips.hearstapps.com/hmg-prod/images/gettyimages-1229892983-square.jpg"

tab3 = gr.Interface(process_file, inputs=image2, outputs=png_file, examples=["butterfly.jpg"], api_name="png")


demo = gr.TabbedInterface(
    [tab3], ["jpeg"], title="Na Na"
)

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