import os
import cv2
import paddlehub as hub
import gradio as gr
import torch
from PIL import Image
import numpy as np

# Images
torch.hub.download_url_to_file('https://cdn.pixabay.com/photo/2018/08/12/16/59/ara-3601194_1280.jpg', 'parrot.jpg')
torch.hub.download_url_to_file('https://cdn.pixabay.com/photo/2016/10/21/14/46/fox-1758183_1280.jpg', 'fox.jpg')
model = hub.Module(name='U2Net')
def infer(img):
  img.save("./data/data.png")
  result = model.Segmentation(
      images=[cv2.cvtColor(np.array(img), cv2.COLOR_RGB2BGR)],
      paths=None,
      batch_size=1,
      input_size=320,
      output_dir='output',
      visualization=True)
  im = Image.fromarray(result[0]['mask'])
  im.save("./data/data_mask.png")
  os.system('python predict.py model.path=./big-lama indir=./data outdir=./dataout device=cpu')
  return "./dataout/data_mask.png"
inputs = gr.inputs.Image(type='file', label="Original Image")
outputs = gr.outputs.Image(type="numpy",label="output")
title = "U^2-Net"
description = "demo for U^2-Net. To use it, simply upload your image, or click one of the examples to load them. Read more at the links below."
article = "<p style='text-align: center'><a href='https://arxiv.org/abs/2005.09007'>U^2-Net: Going Deeper with Nested U-Structure for Salient Object Detection</a> | <a href='https://github.com/xuebinqin/U-2-Net'>Github Repo</a></p>"
examples = [
  ['fox.jpg'],
  ['parrot.jpg']
]
gr.Interface(infer, inputs, outputs, title=title, description=description, article=article, examples=examples).launch()