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("cuda")

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

# Processing function
def fn(image):
    im = load_img(image, output_type="pil")
    im = im.convert("RGB")
    origin = im.copy()
    image = process(im)
    return (image, origin)

@spaces.GPU
def process(image):
    image_size = image.size
    input_images = transform_image(image).unsqueeze(0).to("cuda")
    # 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)
    image.putalpha(mask)
    return image

# Process file upload
def process_file(f):
    name_path = f.rsplit(".", 1)[0] + ".png"
    im = load_img(f, output_type="pil")
    im = im.convert("RGB")
    transparent = process(im)
    transparent.save(name_path)
    return name_path

# Custom CSS Styling
css_style = """
.gradio-container {
    background-color: #1b1b1b !important;
    color: #f5f5f5 !important;
}

input, textarea, button {
    border-radius: 8px !important;
    background-color: #2b2b2b !important;
    color: #f5f5f5 !important;
    border: 1px solid #3c3c3c !important;
}

button {
    background-color: #9146ff !important;
    padding: 10px 24px !important;
    color: white !important;
}

button:hover {
    background-color: #732eb5 !important;
}

.gr-button-primary {
    background-color: #9146ff !important;
}

.gr-image-preview {
    background-color: #2b2b2b !important;
    border-radius: 8px !important;
}

.gr-file-preview {
    background-color: #2b2b2b !important;
    color: white !important;
}

#output-image img {
    border-radius: 8px;
    max-width: 100%;
}
"""

# Image sliders
slider1 = ImageSlider(label="birefnet", type="pil")
slider2 = ImageSlider(label="birefnet", type="pil")

# Image upload areas
image = gr.Image(label="Upload an image", live=True)  # Automatically submits on upload
image2 = gr.Image(label="Upload an image", type="filepath", live=True)

# Other inputs and file outputs
text = gr.Textbox(label="Paste an image URL")
png_file = gr.File(label="Output PNG file")

# Sample images and URLs
chameleon = load_img("butterfly.jpg", output_type="pil")
url = "https://hips.hearstapps.com/hmg-prod/images/gettyimages-1229892983-square.jpg"

# Define the tabs for each functionality
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")

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

# Create the Gradio app with custom CSS and tabs
with gr.Blocks(css=css_style) as demo:
    gr.TabbedInterface([tab1, tab2, tab3], ["image", "text", "png"], title="birefnet for background removal")

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