import gradio as gr
import torch
from torchvision import transforms
from PIL import Image
import numpy as np
import os
from u2net import U2NET
import data_transforms
import torch.nn.functional as F
from skimage import io
from torchvision.transforms.functional import normalize

# Load the model
model = U2NET(3, 1)
model_path = "u2net.pth"
model.load_state_dict(torch.load(model_path, map_location="cpu"))
model.eval()

# Preprocess the image
def preprocess(image):
    label_3 = np.zeros(image.shape)
    label = np.zeros(label_3.shape[0:2])

    if 3 == len(label_3.shape):
        label = label_3[:, :, 0]
    elif 2 == len(label_3.shape):
        label = label_3

    if 3 == len(image.shape) and 2 == len(label.shape):
        label = label[:, :, np.newaxis]
    elif 2 == len(image.shape) and 2 == len(label.shape):
        image = image[:, :, np.newaxis]
        label = label[:, :, np.newaxis]

    transform = transforms.Compose([data_transforms.RescaleT(320), data_transforms.ToTensorLab(flag=0)])
    sample = transform({"imidx": np.array([0]), "image": image, "label": label})

    return sample

# Define the function to generate the mask
def generate_mask(image):
    # Preprocess the image
    image = np.array(image.convert("RGB"))
    img = preprocess(image)
    
    input_size = [1024, 1024]
    im_shp = image.shape[0:2]
    im_tensor = torch.tensor(image, dtype=torch.float32).permute(2, 0, 1)
    im_tensor = F.upsample(torch.unsqueeze(im_tensor, 0), input_size, mode="bilinear").type(torch.uint8)
    image = torch.divide(im_tensor, 255.0)
    image = normalize(image, [0.5, 0.5, 0.5], [1.0, 1.0, 1.0])

    with torch.no_grad():
        result = model(image)
        result = torch.squeeze(F.upsample(result[0][0], im_shp, mode='bilinear'), 0)
        ma = torch.max(result)
        mi = torch.min(result)
        result = (result - mi) / (ma - mi)
        result = result.numpy()

    output_mask = result[0]
    output_mask = (output_mask - output_mask.min()) / (output_mask.max() - output_mask.min()) * 255
    output_mask = output_mask.astype(np.uint8)
    output_image = Image.fromarray(output_mask)

    return output_image

# Create the Gradio interface
iface = gr.Interface(
    fn=generate_mask,
    inputs=gr.inputs.Image(type="pil"),
    outputs=gr.outputs.Image(type="pil"),
    title="U2NET Background Removal",
    description="Upload an image and get the background mask"
)

if __name__ == "__main__":
    iface.launch()