Update app.py

app.py

@@ -4,7 +4,7 @@ from PIL import Image
 import torch
 
 os.system(
-    'wget https://github.com/TentativeGitHub/CMFNet/releases/download/0.0/dehaze_model.pth -P experiments/pretrained_models')
+    'wget https://github.com/TentativeGitHub/CMFNet/releases/download/0.0/deraindrop_model.pth -P experiments/pretrained_models')
 
 
 def inference(img):
@@ -15,15 +15,15 @@ def inference(img):
     img = img.resize((basewidth, hsize), Image.BILINEAR)
     img.save("test/1.png", "PNG")
     os.system(
-        'python main_test_CMFNet.py --input_dir test --weights experiments/pretrained_models/dehaze_model.pth')
+        'python main_test_CMFNet.py --input_dir test --weights experiments/pretrained_models/deraindrop_model.pth')
     return 'results/1.png'
 
 
-title = "Compound Multi-branch Feature Fusion (Dehaze)"
-description = "Gradio demo for CMFNet. CMFNet 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><a href='https://huggingface.co/akhaliq' target='_blank'>Reference from</a></p>"
+title = "Compound Multi-branch Feature Fusion (Deraindrop)"
+description = "Gradio demo for CMFNet. CMFNet achieves competitive performance on three tasks: image deblurring, image dehazing and image deraindrop. Here, we provide a demo for image deraindrop. 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://' target='_blank'>Compound Multi-branch Feature Fusion for Real Image Restoration</a> | <a href='https://github.com/FanChiMao/CMFNet' target='_blank'>Github Repo</a></p>"
 
-examples = [['Haze.png']]
+examples = [['Rain.png']]
 gr.Interface(
     inference,
     [gr.inputs.Image(type="pil", label="Input")],