import gradio as gr
import torch
import torch.nn as nn
import torchvision.transforms as transforms
from PIL import Image

norm_layer = nn.InstanceNorm2d


class ResidualBlock(nn.Module):
    def __init__(self, in_features):
        super(ResidualBlock, self).__init__()

        conv_block = [
            nn.ReflectionPad2d(1),
            nn.Conv2d(in_features, in_features, 3),
            norm_layer(in_features),
            nn.ReLU(inplace=True),
            nn.ReflectionPad2d(1),
            nn.Conv2d(in_features, in_features, 3),
            norm_layer(in_features),
        ]

        self.conv_block = nn.Sequential(*conv_block)

    def forward(self, x):
        return x + self.conv_block(x)


class Generator(nn.Module):
    def __init__(self, input_nc, output_nc, n_residual_blocks=9, sigmoid=True):
        super(Generator, self).__init__()

        # Initial convolution block
        model0 = [
            nn.ReflectionPad2d(3),
            nn.Conv2d(input_nc, 64, 7),
            norm_layer(64),
            nn.ReLU(inplace=True),
        ]
        self.model0 = nn.Sequential(*model0)

        # Downsampling
        model1 = []
        in_features = 64
        out_features = in_features * 2
        for _ in range(2):
            model1 += [
                nn.Conv2d(in_features, out_features, 3, stride=2, padding=1),
                norm_layer(out_features),
                nn.ReLU(inplace=True),
            ]
            in_features = out_features
            out_features = in_features * 2
        self.model1 = nn.Sequential(*model1)

        model2 = []
        # Residual blocks
        for _ in range(n_residual_blocks):
            model2 += [ResidualBlock(in_features)]
        self.model2 = nn.Sequential(*model2)

        # Upsampling
        model3 = []
        out_features = in_features // 2
        for _ in range(2):
            model3 += [
                nn.ConvTranspose2d(
                    in_features, out_features, 3, stride=2, padding=1, output_padding=1
                ),
                norm_layer(out_features),
                nn.ReLU(inplace=True),
            ]
            in_features = out_features
            out_features = in_features // 2
        self.model3 = nn.Sequential(*model3)

        # Output layer
        model4 = [nn.ReflectionPad2d(3), nn.Conv2d(256, output_nc, 7)]
        if sigmoid:
            model4 += [nn.Sigmoid()]

        self.model4 = nn.Sequential(*model4)

    def forward(self, x, cond=None):
        out = self.model0(x)
        out = self.model1(out)
        out = self.model2(out)
        out = self.model3(out)
        out = self.model4(out)

        return out


model1 = Generator(3, 1, 3)
model1.load_state_dict(torch.load("model.pth", map_location=torch.device("cpu")))
model1.eval()

model2 = Generator(3, 1, 3)
model2.load_state_dict(torch.load("model2.pth", map_location=torch.device("cpu")))
model2.eval()


def predict(input_img, ver):
    input_img = Image.open(input_img)
    transform = transforms.Compose(
        [transforms.Resize(1080, Image.BICUBIC), transforms.ToTensor()]
    )
    input_img = transform(input_img)
    input_img = torch.unsqueeze(input_img, 0)

    drawing = 0
    with torch.no_grad():
        if ver == "Simple Lines":
            drawing = model2(input_img)[0].detach()
        else:
            drawing = model1(input_img)[0].detach()

    drawing = transforms.ToPILImage()(drawing)
    return drawing


title = "Image to Line Drawings - Complex and Simple Portraits and Landscapes"
examples = [
    ["01.jpg", "Complex Lines"],
    ["02.jpg", "Simple Lines"],
    ["03.jpg", "Simple Lines"],
    ["04.jpg", "Simple Lines"],
    ["05.jpg", "Simple Lines"],
]

iface = gr.Interface(
    predict,
    [
        gr.inputs.Image(type="filepath"),
        gr.inputs.Radio(
            ["Complex Lines", "Simple Lines"],
            type="value",
            default="Simple Lines",
            label="version",
        ),
    ],
    gr.outputs.Image(type="pil"),
    title=title,
    examples=examples,
)

iface.launch()