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

# 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)

    # Replace F.upsample with F.interpolate
    im_tensor = F.interpolate(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.interpolate(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)

    return output_mask

def predict(image):
    # Generate the mask
    mask = generate_mask(image)

    # Convert the image to RGBA (to support transparency)
    image_rgba = image.convert("RGBA")

    # Create a binary mask from the generated mask and resize it to the image size
    mask = Image.fromarray(mask).resize(image.size).convert("L")  # Convert to grayscale

    # Create a new image with transparency (RGBA) for the output with transparent background
    transparent_image = Image.new("RGBA", image.size)
    transparent_image.paste(image_rgba, mask=mask)

    # Create foreground and background masks
    red_foreground = Image.new("RGBA", image.size, (255, 0, 0, 128))  # Red foreground with 50% opacity
    blue_background = Image.new("RGBA", image.size, (0, 0, 255, 128))  # Blue background with 50% opacity

    # Create an empty overlay image
    overlay_image = Image.new("RGBA", image.size)

    # Overlay the red and blue masks based on the mask
    overlay_image.paste(blue_background, (0, 0))  # Fill the entire overlay with blue
    overlay_image.paste(red_foreground, (0, 0), mask=mask)  # Paste red where mask is white

    # Combine the original image with the overlay at 50% opacity
    combined_image = Image.blend(image_rgba, overlay_image, alpha=0.5)

    return transparent_image, combined_image

# Create the Gradio interface with two outputs
iface = gr.Interface(
    fn=predict,
    inputs=gr.Image(type="pil"),
    outputs=[
        gr.Image(type="pil", label="Transparent Background", image_mode="RGBA", format="png"),  # Transparent output
        gr.Image(type="pil", label="Overlay with Colors", image_mode="RGBA", format="png"),  # Colored overlay output
    ],
    title="Background Removal with U2NET",
    description="Upload an image to remove the background and visualize it with an overlay."
)

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