import os 
import gradio as gr
os.system("gdown https://drive.google.com/uc?id=12ElLliRlgGZqPOhUcqJtNVsa7rmzSI5L")
os.system("gdown https://drive.google.com/uc?id=1-79oBWGFQXrKYw9oxX7t468Zrp87NoWn")

def inference(content, style):
    os.system("""python style_transfer_folder.py --size 1024 --ckpt ./blendgan.pt --psp_encoder_ckpt ./psp_encoder.pt --style_img_path 
 """+style.name+""" --input_img_path """+content.name)
    return "out.jpg"
  
title = "BlendGAN"
description = "Gradio Demo for BlendGAN. To use it, simply upload your image, or click one of the examples to load them. Read more at the links below. Please use a cropped portrait picture for best results similar to the examples below."
article = "<p style='text-align: center'><a href='https://github.com/bryandlee/animegan2-pytorch' target='_blank'>Github Repo Pytorch</a> | <a href='https://github.com/Kazuhito00/AnimeGANv2-ONNX-Sample' target='_blank'>Github Repo ONNX</a></p><p style='text-align: center'>samples from repo: <img src='https://user-images.githubusercontent.com/26464535/129888683-98bb6283-7bb8-4d1a-a04a-e795f5858dcf.gif' alt='animation'/> <img src='https://user-images.githubusercontent.com/26464535/137619176-59620b59-4e20-4d98-9559-a424f86b7f24.jpg' alt='animation'/></p>"

gr.Interface(inference, [gr.inputs.Image(type="file"),gr.inputs.Image(type="file")], gr.outputs.Image(type="file"),title=title,description=description,article=article,enable_queue=True,examples=examples,allow_flagging=False).launch()