import onnxruntime
print(onnxruntime.get_device())
import os
os.system("pip install gdown")
os.system("gdown https://drive.google.com/uc?id=1riNxV1BWMAXfmWZ3LrQbEkvzV8f7lOCp")

onnx_session = onnxruntime.InferenceSession("/home/user/app/face_paint_512_v2_0.onnx")

input_name = onnx_session.get_inputs()[0].name
output_name = onnx_session.get_outputs()[0].name

side_length = 512

import cv2 as cv
import numpy as np

def inference(img):
    image = numpy.array(img) 
    image = image[:, :, ::-1].copy() 
    image = cv.resize(image, dsize=(side_length, side_length))
    x = cv.cvtColor(image, cv.COLOR_BGR2RGB)
    
    x = np.array(x, dtype=np.float32)
    x = x.transpose(2, 0, 1)
    x = x * 2 - 1
    x = x.reshape(-1, 3, side_length, side_length)
    
    onnx_result = onnx_session.run([output_name], {input_name: x})
    
    onnx_result = np.array(onnx_result).squeeze()
    onnx_result = (onnx_result * 0.5 + 0.5).clip(0, 1)
    onnx_result = onnx_result * 255
    
    onnx_result = onnx_result.transpose(1, 2, 0).astype('uint8')
    onnx_result = cv.cvtColor(onnx_result, cv.COLOR_RGB2BGR)
    

    img = cv2.cvtColor(onnx_result, cv2.COLOR_BGR2RGB)
    im_pil = Image.fromarray(img)
    return im_pil
      
  
title = "Animeganv2"
description = "Gradio demo for AnimeGanv2 Face Portrait v2. 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</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="pil"), gr.outputs.Image(type="pil"),title=title,description=description,article=article,enable_queue=True).launch()