import numpy as np
import torch
import torch.nn.functional as F
from torchvision.transforms.functional import normalize
import gradio as gr
from briarmbg import BriaRMBG
import PIL
from PIL import Image
import requests
from io import BytesIO

net = BriaRMBG.from_pretrained("briaai/RMBG-1.4")
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
net.to(device)
    
def resize_image(image):
    image = image.convert('RGB')
    model_input_size = (1024, 1024)
    image = image.resize(model_input_size, Image.BILINEAR)
    return image

def process(image=None, url=None):
    if url:
        response = requests.get(url)
        image = Image.open(BytesIO(response.content))
    else:
        image = Image.fromarray(image)

    w, h = orig_im_size = image.size
    image = resize_image(image)
    im_np = np.array(image)
    im_tensor = torch.tensor(im_np, dtype=torch.float32).permute(2, 0, 1)
    im_tensor = torch.unsqueeze(im_tensor, 0)
    im_tensor = torch.divide(im_tensor, 255.0)
    im_tensor = normalize(im_tensor, [0.5, 0.5, 0.5], [1.0, 1.0, 1.0])
    if torch.cuda.is_available():
        im_tensor = im_tensor.cuda()

    result = net(im_tensor)
    result = torch.squeeze(F.interpolate(result[0][0], size=(h, w), mode='bilinear'), 0)
    ma = torch.max(result)
    mi = torch.min(result)
    result = (result - mi) / (ma - mi)
    im_array = (result * 255).cpu().data.numpy().astype(np.uint8)
    pil_im = Image.fromarray(np.squeeze(im_array))
    new_im = Image.new("RGBA", pil_im.size, (0, 0, 0, 0))
    new_im.paste(image, mask=pil_im)
    return new_im

title = "Background Removal"
description = r"""Background removal model developed by <a href='https://BRIA.AI' target='_blank'><b>BRIA.AI</b></a>, trained on a carefully selected dataset and is available as an open-source model for non-commercial use.<br> 
For test upload your image and wait. Read more at model card <a href='https://huggingface.co/briaai/RMBG-1.4' target='_blank'><b>briaai/RMBG-1.4</b></a>.<br>
"""
examples = [['./input.jpg'],]

inputs = [
    gr.Image(source="upload", tool="editor", type="numpy", label="Upload Image"),
    gr.Textbox(label="Image URL", placeholder="Enter the URL of an image")
]
output = gr.Image(type="pil", label="Image without background", show_download_button=True)

demo = gr.Interface(fn=process, inputs=inputs, outputs=output, examples=examples, title=title, description=description)

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