"""
Created By: ishwor subedi
Date: 2024-05-19
"""

from PIL import Image
import torch
import torch.nn.functional as F
from torchvision.transforms.functional import normalize
import numpy as np
from architecture import BackgroundEnhancer
import gradio as gr
import os


def preprocess_image(im: np.ndarray, model_input_size: list) -> torch.Tensor:
    if len(im.shape) < 3:
        im = im[:, :, np.newaxis]
    im_tensor = torch.tensor(im, dtype=torch.float32).permute(2, 0, 1)
    im_tensor = F.interpolate(torch.unsqueeze(im_tensor, 0), size=model_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])
    return image


def postprocess_image(result: torch.Tensor, im_size: list) -> np.ndarray:
    result = torch.squeeze(F.interpolate(result, size=im_size, mode='bilinear'), 0)
    ma = torch.max(result)
    mi = torch.min(result)
    result = (result - mi) / (ma - mi)
    im_array = (result * 255).permute(1, 2, 0).cpu().data.numpy().astype(np.uint8)
    im_array = np.squeeze(im_array)
    return im_array


def example_inference(image):
    orig_im = image.copy()
    orig_image = image.copy()

    model_path = "model.pth"

    net = BackgroundEnhancer()
    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
    net.load_state_dict(torch.load(model_path, map_location=device))
    net.to(device)
    net.eval()

    # prepare input
    model_input_size = [1024, 1024]
    orig_im_size = orig_im.size
    orig_im_size = (orig_im_size[1], orig_im_size[0])
    orig_im = np.array(orig_im)
    image = preprocess_image(orig_im, model_input_size).to(device)

    # inference
    result = net(image)

    # post process
    result_image = postprocess_image(result[0][0], orig_im_size)

    # save result
    pil_im = Image.fromarray(result_image)
    no_bg_image = Image.new("RGBA", pil_im.size, (0, 0, 0, 0))
    no_bg_image.paste(orig_image, mask=pil_im)

    return no_bg_image


original_image, binary_image = None, None
colors = [Image.open(path) for path in
          [os.path.join("bg_images/color", file) for file in os.listdir("bg_images/color")]]
houses = [Image.open(path) for path in
          [os.path.join("bg_images/house", file) for file in os.listdir("bg_images/house")]]
natures = [Image.open(path) for path in
           [os.path.join("bg_images/nature", file) for file in os.listdir("bg_images/nature")]]
studios = [Image.open(path) for path in
           [os.path.join("bg_images/studio", file) for file in os.listdir("bg_images/studio")]]
walls = [Image.open(path) for path in [os.path.join("bg_images/wall", file) for file in os.listdir("bg_images/wall")]]
woods = [Image.open(path) for path in [os.path.join("bg_images/wood", file) for file in os.listdir("bg_images/wood")]]

with gr.Blocks(
        theme=gr.themes.Default(primary_hue=gr.themes.colors.red, secondary_hue=gr.themes.colors.indigo)) as demo:
    with gr.Row():
        input_img = gr.Image(label="Input", interactive=True, type='pil')
        hidden_img = gr.Image(label="Chosen Background", visible=False)
        output_img = gr.Image(label="Output", interactive=False, type='pil')


    def clearFunc():
        global original_image
        global binary_image


    def update_visibility():
        return gr.Image(visible=True)

        torch.cuda.empty_cache()
        gc.collect()
        return gr.Image(visible=False, value=None)


    with gr.Row():
        examples = gr.Examples(examples=studios, inputs=[hidden_img], label="Studio Backgrounds")

    with gr.Row():
        examples6 = gr.Examples(examples=colors, inputs=[hidden_img], label="Color Backgrounds")

    with gr.Row():
        examples2 = gr.Examples(examples=walls, inputs=[hidden_img], label="Wall Backgrounds")
        examples3 = gr.Examples(examples=natures, inputs=[hidden_img], label="Nature Backgrounds")

    with gr.Row():
        examples4 = gr.Examples(examples=houses, inputs=[hidden_img], label="House Backgrounds")
        examples5 = gr.Examples(examples=woods, inputs=[hidden_img], label="Wood Backgrounds")

    with gr.Row():
        submit = gr.Button("Submit")
        clear = gr.ClearButton(components=[input_img, output_img, hidden_img], value="Reset", variant="stop")


    def generate_img(image, background):
        orig_img = example_inference(image)
        width, height = orig_img.size

        background = Image.fromarray(background).resize((width, height))
        orig_img = Image.fromarray(np.array(orig_img)).resize((width, height))
        background.paste(orig_img, (0, 0), mask=orig_img)
        return background


    hidden_img.change(fn=update_visibility, inputs=[], outputs=[hidden_img])

    submit.click(generate_img, inputs=[input_img, hidden_img], outputs=[output_img])
    clear.click(fn=clearFunc, inputs=[], outputs=[hidden_img])

demo.launch(share=True, debug=True)