Update app.py

app.py

@@ -1,43 +1,53 @@
 import gradio as gr
 import os
 
-
+# Cloning and setting up the model
 os.system("git clone https://github.com/megvii-research/NAFNet")
 os.system("mv NAFNet/* ./")
 os.system("mv *.pth experiments/pretrained_models/")
 os.system("python3 setup.py develop --no_cuda_ext --user")
 
-
+# Inference function
 def inference(image, task):
     if not os.path.exists('tmp'):
-      os.system('mkdir tmp')
+        os.makedirs('tmp')
     image.save("tmp/lq_image.png", "PNG")
     
     if task == 'Denoising':
-      os.system("python basicsr/demo.py -opt options/test/SIDD/NAFNet-width64.yml --input_path ./tmp/lq_image.png --output_path ./tmp/image.png")
-  
-    if task == 'Deblurring':
-      os.system("python basicsr/demo.py -opt options/test/REDS/NAFNet-width64.yml --input_path ./tmp/lq_image.png --output_path ./tmp/image.png")
-  
-    return 'tmp/image.png'
-   
+        os.system("python basicsr/demo.py -opt options/test/SIDD/NAFNet-width64.yml --input_path ./tmp/lq_image.png --output_path ./tmp/image.png")
+    elif task == 'Deblurring':
+        os.system("python basicsr/demo.py -opt options/test/REDS/NAFNet-width64.yml --input_path ./tmp/lq_image.png --output_path ./tmp/image.png")
+    
+    return "tmp/image.png"
+
+# Title and description
 title = "NAFNet"
-description = "Gradio demo for <b>NAFNet: Nonlinear Activation Free Network for Image Restoration</b>. NAFNet achieves state-of-the-art performance on three tasks: image denoising, image debluring and stereo image super-resolution (SR). See the paper and project page for detailed results below. Here, we provide a demo for image denoise and deblur. To use it, simply upload your image, or click one of the examples to load them. Inference needs some time since this demo uses CPU."
-article = "<p style='text-align: center'><a href='https://arxiv.org/abs/2204.04676' target='_blank'>Simple Baselines for Image Restoration</a> | <a href='https://arxiv.org/abs/2204.08714' target='_blank'>NAFSSR: Stereo Image Super-Resolution Using NAFNet</a>  | <a href='https://github.com/megvii-research/NAFNet' target='_blank'> Github Repo</a></p>"
+description = """Gradio demo for <b>NAFNet: Nonlinear Activation Free Network for Image Restoration</b>.
+NAFNet achieves state-of-the-art performance on three tasks: image denoising, image deblurring, and stereo image super-resolution (SR).
+See the paper and project page for detailed results below. Here, we provide a demo for image denoise and deblur.
+To use it, simply upload your image, or click one of the examples to load them. Inference needs some time since this demo uses CPU."""
+
+article = """<p style='text-align: center'>
+<a href='https://arxiv.org/abs/2204.04676' target='_blank'>Simple Baselines for Image Restoration</a> |
+<a href='https://arxiv.org/abs/2204.08714' target='_blank'>NAFSSR: Stereo Image Super-Resolution Using NAFNet</a> |
+<a href='https://github.com/megvii-research/NAFNet' target='_blank'>Github Repo</a>
+</p>"""
 
+examples = [['demo/noisy.png', 'Denoising'], ['demo/blurry.jpg', 'Deblurring']]
 
-examples = [['demo/noisy.png', 'Denoising'],
-            ['demo/blurry.jpg', 'Deblurring']]
-            
+# Updated Gradio Interface
 iface = gr.Interface(
-    inference, 
-    [gr.inputs.Image(type="pil", label="Input"),
-    gr.inputs.Radio(["Denoising", "Deblurring"], default="Denoising", label='task'),], 
-    gr.outputs.Image(type="file", label="Output"),
+    fn=inference,  # Updated function syntax
+    inputs=[
+        gr.Image(type="pil", label="Input Image"),  # Replaced gr.inputs.Image
+        gr.Radio(["Denoising", "Deblurring"], value="Denoising", label="Task")  # Replaced gr.inputs.Radio
+    ],
+    outputs=gr.Image(type="file", label="Output Image"),  # Replaced gr.outputs.Image
     title=title,
     description=description,
     article=article,
-    enable_queue=True,
     examples=examples
-    )
-iface.launch(debug=True,enable_queue=True)
+)
+
+# Launch interface
+iface.launch(debug=True)