Update app.py

app.py

@@ -14,13 +14,13 @@ def inference(img):
     #img = img.resize((basewidth, hsize), Image.ANTIALIAS)
     img.save("test/1.png", "PNG")
     os.system(
-        'python main_test_SRMNet.py --input_dir test --weights experiments/pretrained_models/real_denoising_SRMNet.pth')
+        'python main_test_HWMNet.py --input_dir test --weights experiments/pretrained_models/LOL_enhancement_HWMNet.pth')
     return 'result/1.png'
 
 
-title = "Selective Residual M-Net for Real-world Image Denoising"
-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. See the paper and project page for detailed results below. 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. Reference from: https://huggingface.co/akhaliq"
-article = "<p style='text-align: center'><a href='https://' target='_blank'>Selective Residual M-Net</a> | <a href='https://github.com/FanChiMao/SRMNet' target='_blank'>Github Repo</a></p>"
+title = "Half Wavelet Attention on M-Net+ for Low-light Image Enhancement"
+description = "Gradio demo for HWMNet. HWMNet has competitive performance results on two real-world low-light datasets in terms of quantitative metrics and visual quality. See the paper and project page for detailed results below. Here, we provide a demo for low-light image enhancement. To use it, simply upload your image, or click one of the examples to load them. Reference from: https://huggingface.co/akhaliq"
+article = "<p style='text-align: center'><a href='https://' target='_blank'>Half Wavelet Attention on M-Net+ for Low-light Image Enhancement</a> | <a href='https://github.com/FanChiMao/HWMNet' target='_blank'>Github Repo</a></p>"
 
 examples = [['Noise.png'], ['Noise2.png']]
 gr.Interface(