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import os
os.system("pip install gradio==2.4.6")
import torch
import gradio as gr
import numpy as np
import torchvision.utils as vutils
import torchvision.transforms as transforms
from PIL import Image
from torch.autograd import Variable
from network.Transformer import Transformer
LOAD_SIZE = 1280
STYLE = "shinkai_makoto"
MODEL_PATH = "models"
COLOUR_MODEL = "RGB"
# model = Transformer()
# model.load_state_dict(torch.load(os.path.join(MODEL_PATH, f"{STYLE}.pth")))
# model.eval()
# disable_gpu = torch.cuda.is_available()
def inference(img):
# # load image
# input_image = img.convert(COLOUR_MODEL)
# input_image = np.asarray(input_image)
# # RGB -> BGR
# input_image = input_image[:, :, [2, 1, 0]]
# input_image = transforms.ToTensor()(input_image).unsqueeze(0)
# # preprocess, (-1, 1)
# input_image = -1 + 2 * input_image
# if disable_gpu:
# input_image = Variable(input_image).float()
# else:
# input_image = Variable(input_image).cuda()
# # forward
# output_image = model(input_image)
# output_image = output_image[0]
# # BGR -> RGB
# output_image = output_image[[2, 1, 0], :, :]
# output_image = output_image.data.cpu().float() * 0.5 + 0.5
# return output_image
return ""
title = "AnimeBackgroundGAN"
description = "CartoonGAN from [Chen et.al](http://openaccess.thecvf.com/content_cvpr_2018/CameraReady/2205.pdf) based on [Yijunmaverick's implementation](https://github.com/Yijunmaverick/CartoonGAN-Test-Pytorch-Torch)"
article = "<p style='text-align: center'><a href='https://github.com/venture-anime/cartoongan-pytorch' target='_blank'>Github Repo</a></p> <center><img src='https://visitor-badge.glitch.me/badge?page_id=akiyamasho' alt='visitor badge'></center></p>"
examples = [
["examples/garden_in.jpeg", "examples/garden_out.jpg"],
["examples/library_in.jpeg", "examples/library_out.jpg"],
]
gr.Interface(
fn=inference,
inputs=gr.inputs.Textbox(
lines=1, placeholder=None, default="", label=None
),
outputs=gr.outputs.Textbox(type="auto", label=None),
title=title,
description=description,
article=None,
examples=None,
allow_flagging=False,
allow_screenshot=False,
enable_queue=True,
).launch()
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