import os
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(
    "briaai/RMBG-2.0", 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]),
    ]
)

output_folder = 'output_images'
if not os.path.exists(output_folder):
    os.makedirs(output_folder)

def fn(image):
    im = load_img(image, output_type="pil")
    im = im.convert("RGB")
    origin = im.copy()
    image = process(im)    
    image_path = os.path.join(output_folder, "no_bg_image.png")
    image.save(image_path)
    return (image, origin), image_path

@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
  
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

slider1 = ImageSlider(label="RMBG-2.0", type="pil")
slider2 = ImageSlider(label="RMBG-2.0", 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 png file")


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

url = "http://farm9.staticflickr.com/8488/8228323072_76eeddfea3_z.jpg"

tab1 = gr.Interface(
    fn, inputs=image, outputs=[slider1, gr.File(label="output png file")], examples=[chameleon], api_name="image"
)

tab2 = gr.Interface(fn, inputs=text, outputs=[slider2, gr.File(label="output png file")], examples=[url], api_name="text")
tab3 = gr.Interface(process_file, inputs=image2, outputs=png_file, examples=["giraffe.jpg"], api_name="png")


import gradio as gr

hyperlinks = {
    "BRIA.AI": "https://bria.ai",
    "Commercial use license": "https://go.bria.ai/3ZCBTLH",
    "Model card": "https://huggingface.co/briaai/RMBG-2.0",
    "Blog": "https://blog.bria.ai/brias-new-state-of-the-art-remove-background-2.0-outperforms-the-competition"
}

# Create a formatted description with hyperlinks
description = f"""
Background removal model developed by <a href='{hyperlinks["BRIA.AI"]}' target='_blank'>BRIA.AI</a>, trained on a carefully selected dataset, and is available as an open-source model for non-commercial use. 
For testing upload your image and wait. 
<a href='{hyperlinks["Commercial use license"]}' target='_blank'>Commercial use license</a> | 
<a href='{hyperlinks["Model card"]}' target='_blank'>Model card</a> | 
<a href='{hyperlinks["Blog"]}' target='_blank'>Blog</a>
"""

# Define tabs for the interface
tab1 = gr.inputs.Image(label="Upload Image")
tab2 = gr.inputs.Textbox(label="Enter URL")

# Create the Gradio interface
demo = gr.TabbedInterface(
    [tab1, tab2],
    ["input image", "input url"],
    title="RMBG-2.0 for background removal",
    description=description
)

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