Update app.py

app.py

@@ -1,22 +1,41 @@
-from zipfile import ZipFile
-
 import gradio as gr
+import os
 
 
-def zip_two_files(file1, file2):
-    with ZipFile("tmp.zip", "w") as zipObj:
-        zipObj.write(file1.name, "file1")
-        zipObj.write(file2.name, "file2")
-    return "tmp.zip", "tmp.zip"
+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")
 
 
-iface = gr.Interface(
-    zip_two_files,
-    ["file", "file"],
-    ["file", "file"],
-    examples=[
-        ["files/titanic.csv", "files/titanic.csv"],
-    ],
-)
+def inference(image_l, image_r):
+    if not os.path.exists('tmp'):
+      os.system('mkdir tmp')
+    image_l.save("tmp/lr_l.png", "PNG")
+    image_r.save("tmp/lr_r.png", "PNG")
+    os.system("python basicsr/demo_ssr.py -opt options/test/NAFSSR/NAFSSR-L_4x.yml"
+             +"--input_l_path ./demo/lr_l.png --input_r_path ./demo/lr_r.png"
+             +"--output_l_path ./demo/image_l.png --output_r_path ./demo/image_r.png")
+ 
+    return 'tmp/image_l.png', 'tmp/image_r.png'
+   
+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 forstereo image super-resolution (SR). To use it, simply upload your left and right view images, or click 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>"
 
-iface.launch()
+
+examples = ['demo/lr_img_l.png', 'demo/lr_img_r.png']
+            
+iface = gr.Interface(
+    inference, 
+    [gr.inputs.Image(type="pil", label="Input (Left View)"),
+        gr.inputs.Image(type="pil", label="Input (Right View)")], 
+    [gr.outputs.Image(type="file", label="Output (Left View)"),
+    gr.outputs.Image(type="file", label="Output (Right View)")]
+    title=title,
+    description=description,
+    article=article,
+    enable_queue=True,
+    examples=examples
+    )
+iface.launch(debug=True,enable_queue=True)