Update app.py

app.py

@@ -1,7 +1,32 @@
 import gradio as gr
+import os
 
-def greet(name):
-    return "Hello " + name + "!!"
+def image_mod(image):
+    return image.rotate(45)
 
-iface = gr.Interface(fn=greet, inputs="text", outputs="text")
-iface.launch()
+os.system("git clone https://github.com/megvii-research/NAFNet")
+os.system("cd NAFNet")
+#os.system("pip install -r requirements.txt")
+os.system("python3 setup.py develop --no_cuda_ext")
+
+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. 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."
+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>"
+
+inference = image_mod
+
+examples = [['demo/noisy.png', 'Denoising'],
+            ['demo/blurry.jpg', 'Deblurring']]
+            
+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"),
+    title=title,
+    description=description,
+    article=article,
+    enable_queue=True,
+    examples=examples
+    )
+iface.launch(debug=True,enable_queue=True)