from huggingface_hub import hf_hub_download


Rain_Princess = hf_hub_download(repo_id="maze/FastStyleTransfer", filename="Rain_Princess_512.pth")
The_Scream = hf_hub_download(repo_id="maze/FastStyleTransfer", filename="Scream_512.pth")
The_Mosaic = hf_hub_download(repo_id="maze/FastStyleTransfer", filename="Mosaic_512.pth")
Starry_Night = hf_hub_download(repo_id="maze/FastStyleTransfer", filename="Starry_Night_512.pth")


import numpy as np
from PIL import Image
import gradio as gr

import torch
import torch.nn as nn

import torchvision.transforms as transforms
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")


class TransformerNetwork(nn.Module):
    def __init__(self, tanh_multiplier=None):
        super(TransformerNetwork, self).__init__()
        self.ConvBlock = nn.Sequential(
            ConvLayer(3, 32, 9, 1),
            nn.ReLU(),
            ConvLayer(32, 64, 3, 2),
            nn.ReLU(),
            ConvLayer(64, 128, 3, 2),
            nn.ReLU()
        )
        self.ResidualBlock = nn.Sequential(
            ResidualLayer(128, 3), 
            ResidualLayer(128, 3), 
            ResidualLayer(128, 3), 
            ResidualLayer(128, 3), 
            ResidualLayer(128, 3)
        )
        self.DeconvBlock = nn.Sequential(
            DeconvLayer(128, 64, 3, 2, 1),
            nn.ReLU(),
            DeconvLayer(64, 32, 3, 2, 1),
            nn.ReLU(),
            ConvLayer(32, 3, 9, 1, norm="None")
        )
        self.tanh_multiplier = tanh_multiplier

    def forward(self, x):
        x = self.ConvBlock(x)
        x = self.ResidualBlock(x)
        x = self.DeconvBlock(x)
        if isinstance(self.tanh_multiplier, int):
            x = self.tanh_multiplier * F.tanh(x)
        return x

class ConvLayer(nn.Module):
    def __init__(self, in_channels, out_channels, kernel_size, stride, norm="instance"):
        super(ConvLayer, self).__init__()
        padding_size = kernel_size // 2
        self.pad = nn.ReflectionPad2d(padding_size)
        self.conv = nn.Conv2d(in_channels, out_channels, kernel_size, stride)
        if norm == "instance":
            self.norm = nn.InstanceNorm2d(out_channels, affine=True)
        elif norm == "batch":
            self.norm = nn.BatchNorm2d(out_channels, affine=True)
        else:
            self.norm = nn.Identity()

    def forward(self, x):
        x = self.pad(x)
        x = self.conv(x)
        x = self.norm(x)
        return x

class ResidualLayer(nn.Module):
    def __init__(self, channels=128, kernel_size=3):
        super(ResidualLayer, self).__init__()
        self.conv1 = ConvLayer(channels, channels, kernel_size, stride=1)
        self.relu = nn.ReLU()
        self.conv2 = ConvLayer(channels, channels, kernel_size, stride=1)

    def forward(self, x):
        identity = x
        out = self.relu(self.conv1(x))
        out = self.conv2(out)
        out = out + identity
        return out

class DeconvLayer(nn.Module):
    def __init__(self, in_channels, out_channels, kernel_size, stride, output_padding, norm="instance"):
        super(DeconvLayer, self).__init__()

        padding_size = kernel_size // 2
        self.conv_transpose = nn.ConvTranspose2d(in_channels, out_channels, kernel_size, stride, padding_size, output_padding)
        if norm == "instance":
            self.norm = nn.InstanceNorm2d(out_channels, affine=True)
        elif norm == "batch":
            self.norm = nn.BatchNorm2d(out_channels, affine=True)
        else:
            self.norm = nn.Identity()

    def forward(self, x):
        x = self.conv_transpose(x)
        out = self.norm(x)
        return out


mean = np.array([0.485, 0.456, 0.406])
std = np.array([0.229, 0.224, 0.225])

transformer = TransformerNetwork().to(device)

transformer.eval()

transform = transforms.Compose([
    transforms.Resize(512),
    transforms.ToTensor(),
    transforms.Normalize(mean, std),
])

denormalize = transforms.Normalize(
   mean= [-m/s for m, s in zip(mean, std)],
   std= [1/s for s in std]
)
tensor2Image = transforms.ToPILImage()

@torch.no_grad()
def process(image, model):
    image = transform(image).to(device)
    image = image.unsqueeze(dim=0)

    image = denormalize(model(image)).cpu()
    image = torch.clamp(image.squeeze(dim=0), 0, 1)
    image = tensor2Image(image)

    return image
    

def main(image, backbone, style):
    if style == "The Scream":
        transformer.load_state_dict(torch.load(The_Scream, map_location=torch.device('cpu')))
    elif style == "Rain Princess":
        transformer.load_state_dict(torch.load(Rain_Princess, map_location=torch.device('cpu')))
    elif style == "The Mosaic":
        transformer.load_state_dict(torch.load(The_Mosaic, map_location=torch.device('cpu')))
    elif style == "Starry Night":
        transformer.load_state_dict(torch.load(Starry_Night, map_location=torch.device('cpu')))
    else:
        transformer.load_state_dict(torch.load(Rain_Princess, map_location=torch.device('cpu')))
    image = Image.fromarray(image)
    isize = image.size
    image = process(image, transformer)
    s = f"The output image {str(image.size)} is processed by {backbone} based on input image {str(isize)}. <br> Please <b>rate</b> the generated image through the <b>Flag</b> button below!"
    print(s)
    return image, s

# "Standard ResNet50"， "VGG19"
gr.Interface(
    title = "Stylize", 
    description = "Image generated based on Fast Style Transfer",
    fn = main, 
    inputs = [
        gr.inputs.Image(), 
        gr.inputs.Radio(["Robust ResNet50"], label="Backbone"),
        gr.inputs.Dropdown(["The Scream", "Rain Princess", "Starry Night", "The Mosaic"], type="value", default="Rain Princess", label="style")
    ], 
    outputs = [gr.outputs.Image(label="Stylized"), gr.outputs.HTML(label="Comment")],
    # examples = [
    #     []
    # ],
    # live = True,    # the interface will recalculate as soon as the user input changes.
    allow_flagging = "manual",
    flagging_options = ["Excellect", "Moderate", "Bad"],
    flagging_dir = "flagged",
    allow_screenshot = False,
).launch()
# iface.launch(enable_queue=True, cache_examples=True, debug=True)