import gradio as gr
import os
import cv2 
import shutil
import sys
from subprocess import call
import torch
import numpy as np
from skimage import color
import torchvision.transforms as transforms
from PIL import Image
import torch

os.system("pip install dlib")
os.system('bash setup.sh')

def lab2rgb(L, AB):
    """Convert an Lab tensor image to a RGB numpy output
    Parameters:
        L  (1-channel tensor array): L channel images (range: [-1, 1], torch tensor array)
        AB (2-channel tensor array):  ab channel images (range: [-1, 1], torch tensor array)

    Returns:
        rgb (RGB numpy image): rgb output images  (range: [0, 255], numpy array)
    """
    AB2 = AB * 110.0
    L2 = (L + 1.0) * 50.0
    Lab = torch.cat([L2, AB2], dim=1)
    Lab = Lab[0].data.cpu().float().numpy()
    Lab = np.transpose(Lab.astype(np.float64), (1, 2, 0))
    rgb = color.lab2rgb(Lab) * 255
    return rgb

def get_transform(params=None, grayscale=False, method=Image.BICUBIC):
    #params
    preprocess = 'resize_and_crop'
    load_size = 256
    crop_size = 256
    transform_list = []
    if grayscale:
        transform_list.append(transforms.Grayscale(1))
    if 'resize' in preprocess:
        osize = [load_size, load_size]
        transform_list.append(transforms.Resize(osize, method))
    if 'crop' in preprocess:
        if params is None:
            transform_list.append(transforms.RandomCrop(crop_size))

    return transforms.Compose(transform_list)

def inferColorization(img,model_name):
    print(model_name)
    if model_name == "Pix2Pix_resnet9b":
        model = torch.hub.load('manhkhanhad/ImageRestorationInfer', 'pix2pixColorization_resnet9b')
    elif model_name == "Pix2Pix_unet256":
        model = torch.hub.load('manhkhanhad/ImageRestorationInfer', 'pix2pixColorization_unet256')
    elif model_name == "Deoldify":
        model = torch.hub.load('manhkhanhad/ImageRestorationInfer', 'DeOldifyColorization')
        transform_list = [
                  transforms.ToTensor(),
                  transforms.Normalize((0.5,), (0.5,))
                    ]
        transform = transforms.Compose(transform_list)
        #a = transforms.ToTensor()(a)
        img = img.convert('L')
        img = transform(img)
        img = torch.unsqueeze(img, 0)
        result = model(img)
        
        result = result[0].detach()
        result = (result +1)/2.0
        
        #img = transforms.Grayscale(3)(img)
        #img = transforms.ToTensor()(img)
        #img = torch.unsqueeze(img, 0)
        #result = model(img)
        #result = torch.clip(result, min=0, max=1)
        image_pil = transforms.ToPILImage()(result)
        return image_pil
    
    transform_seq = get_transform()
    im = transform_seq(img)
    im = np.array(img)
    lab = color.rgb2lab(im).astype(np.float32)
    lab_t = transforms.ToTensor()(lab)
    A = lab_t[[0], ...] / 50.0 - 1.0
    B = lab_t[[1, 2], ...] / 110.0
    #data = {'A': A, 'B': B, 'A_paths': "", 'B_paths': ""}
    L = torch.unsqueeze(A, 0)
    #print(L.shape)
    ab = model(L)
    Lab = lab2rgb(L, ab).astype(np.uint8)
    image_pil = Image.fromarray(Lab)
    #image_pil.save('test.png')
    #print(Lab.shape)
    return image_pil
    
def colorizaition(image,model_name):
    image = Image.fromarray(image)
    result = inferColorization(image,model_name)
    return result


def run_cmd(command):
    try:
        call(command, shell=True)
    except KeyboardInterrupt:
        print("Process interrupted")
        sys.exit(1)

def run(image):
    os.makedirs("Temp")
    os.makedirs("Temp/input")
    print(type(image))
    cv2.imwrite("Temp/input/input_img.png", image)

    command = ("python run.py --input_folder "
            + "Temp/input"
            + " --output_folder "
            + "Temp"
            + " --GPU "
            + "-1"
            + " --with_scratch")
    run_cmd(command)

    result_restoration = Image.open("Temp/final_output/input_img.png")
    shutil.rmtree("Temp")

    result_colorization = inferColorization(result_restoration,"Deoldify")

    return result_colorization
    
iface = gr.Interface(fn=run, inputs="image", outputs="image").launch(debug=True,share=True)