Create app.py

app.py

@@ -0,0 +1,30 @@
+import os
+import gradio as gr
+from PIL import Image
+import torch
+
+ 
+os.system('wget https://github.com/JingyunLiang/SwinIR/releases/download/v0.0/003_realSR_BSRGAN_DFO_s64w8_SwinIR-M_x4_GAN.pth -P experiments/pretrained_models')
+
+def inference(img):
+ os.system('mkdir test')
+ basewidth = 256
+ wpercent = (basewidth/float(img.size[0]))
+ hsize = int((float(img.size[1])*float(wpercent)))
+ img = img.resize((basewidth,hsize), Image.ANTIALIAS)
+ img.save("test/1.jpg", "JPEG")
+ os.system('python main_test_swinir.py --task real_sr --model_path experiments/pretrained_models/003_realSR_BSRGAN_DFO_s64w8_SwinIR-M_x4_GAN.pth --folder_lq test --scale 4')
+ return 'results/swinir_real_sr_x4/1_SwinIR.jpg'
+ 
+title = "Real-ESRGAN"
+description = "Gradio demo for Real-ESRGAN. To use it, simply upload your image, or click one of the examples to load them. Read more at the links below. Please click submit only once"
+article = "<p style='text-align: center'><a href='https://arxiv.org/abs/2107.10833'>Real-ESRGAN: Training Real-World Blind Super-Resolution with Pure Synthetic Data</a> | <a href='https://github.com/xinntao/Real-ESRGAN'>Github Repo</a></p>"
+gr.Interface(
+ inference, 
+ [gr.inputs.Image(type="pil", label="Input")], 
+ gr.outputs.Image(type="file", label="Output"),
+ title=title,
+ description=description,
+ article=article,
+ enable_queue=True
+ ).launch(debug=True)