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

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)
    image = image.resize(origin.size) 
    return (image, origin)

@spaces.GPU
def process(image):
    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
  
slider1 = ImageSlider(label="Birefnet", type="pil")
slider2 = ImageSlider(label="Birefnet", type="pil")
image = gr.Image(label="Bir resim yükleyin")
text = gr.Textbox(label="Bir resim URL'si yapıştırın")

chameleon = load_img("elon.webp", output_type="pil")

url= "https://dadanizm.com/wp-content/uploads/2022/11/elon-musk-992x2656.jpeg"
tab1 = gr.Interface(
    fn, inputs=image, outputs=slider1, examples=[chameleon], api_name="image"
)

tab2 = gr.Interface(fn, inputs=text, outputs=slider2, examples=[url], api_name="text")

demo = gr.TabbedInterface(
    [tab1, tab2], ["Resim", "URL"], title="Birefnet ile Arka Plan Kaldırma"
)

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