app.py

import gradio as gr
import numpy as np


import torch
from super_image import EdsrModel, ImageLoader
from PIL import Image
import requests
import torchvision
import torchvision.transforms as T


def greet(name):
    return "Hello " + name + "!!"

def transformation(image):
 #   print(image)
  #  print( type(image) )
  #  url = 'https://paperswithcode.com/media/datasets/Set5-0000002728-07a9793f_zA3bDjj.jpg'
  #  imagee = Image.open(requests.get(url, stream=True).raw)
  #  model = EdsrModel.from_pretrained('eugenesiow/edsr-base', scale=4)
  #  inputs = ImageLoader.load_image(imagee)
 #   preds = model(inputs)
 #   print("1  :",preds)
 #   print( type(preds) )
 #   prednumpy=preds.detach().numpy()
    

    #preds=np.array(preds)
 #   print("2 :",prednumpy)
 #   ImageLoader.save_image(preds, './scaled_2x.png')
 #   ImageLoader.save_compare(inputs, preds, './scaled_2x_compare.png')

    url = 'https://paperswithcode.com/media/datasets/Set5-0000002728-07a9793f_zA3bDjj.jpg'
    url='photofloue.jpg'
   # image = Image.open(requests.get(url, stream=True).raw)
    image = Image.open(url)
    model = EdsrModel.from_pretrained('eugenesiow/edsr-base', scale=4)
    print('ok')
    inputs = ImageLoader.load_image(image)
    preds = model(inputs)
    print('ok1')
  #  ImageLoader.save_image(preds, './scaled_2x.png')
    ImageLoader.save_compare(inputs, preds, 'scaleed_2x_compare.png')
    print("ok2")
    #prednumpy=preds.detach().numpy()
    preds = preds.data.cpu().numpy()
    pred = preds[0].transpose((1, 2, 0)) * 255.0
      #  return Image.fromarray(pred.astype('uint8'), 'RGB')
  #  print('pnump',type(prednumpy))
    print('predtype',type(preds))
    print('ok3')
   # prednumpy = np.squeeze(prednumpy)

    return Image.fromarray(pred.astype('uint8'), 'RGB')

    


    
  #  large_image = cartoon_upsampling_8x(image, 'a_8x_larger_output_image.png' )
 #   return prednumpy

with gr.Blocks() as demo:
    image1=gr.Image(type='filepath')
    
    button=gr.Button("LE BOUTON")
    image2=gr.Image(type='pil')
    button.click(fn=transformation,inputs=image1,outputs=image2,api_name="upscale")


iface = gr.Interface(fn=greet, inputs="text", outputs="text")
demo.launch()