Upload app.py

app.py

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+import os
+import gradio as gr
+from PIL import Image
+import torch
+
+os.system(
+    'wget https://github.com/TentativeGitHub/SRMNet/releases/download/0.0/AWGN_denoising_SRMNet.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.png'
+
+
+title = "Selective Residual M-Net (SRMNet)"
+description = "Gradio demo for SwinIR. SwinIR achieves state-of-the-art performance on six tasks: image super-resolution (including classical, lightweight and real-world image super-resolution), image denoising (including grayscale and color image denoising) and JPEG compression artifact reduction. See the paper and project page for detailed results below. Here, we provide a demo for real-world image SR.To use it, simply upload your image, or click one of the examples to load them."
+article = "<p style='text-align: center'><a href='https://arxiv.org/abs/2108.10257' target='_blank'>SwinIR: Image Restoration Using Swin Transformer</a> | <a href='https://github.com/JingyunLiang/SwinIR' target='_blank'>Github Repo</a></p>"
+
+examples = [['ETH_LR.png']]
+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,
+    examples=examples
+).launch(debug=True)