Update app.py

app.py

@@ -1,7 +1,7 @@
 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')
@@ -13,17 +13,17 @@ def inference(img):
     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")
+    img.save("test/1.png", "PNG")
     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'
+        'python main_test_SRMNet.py --weights experiments/pretrained_models/AWGN_denoising_SRMNet.pth')
+    return 'result/1.png'
 
 
 title = "Selective Residual M-Net (SRMNet)"
-description = "Gradio demo for SRMNet. SRMNet has competitive performance results on two synthetic and two realworld noisy datasets in terms of quantitative metrics and visual quality. Here, we provide a demo for real-world image denoising. To use it, simply upload your image, or click one of the examples to load them."
+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']]
+examples = [['Noise.png']]
 gr.Interface(
     inference,
     [gr.inputs.Image(type="pil", label="Input")],