Update app.py

app.py

@@ -105,41 +105,24 @@ def process_image(img):
     return (lowres, preds)
 
 image = gr.inputs.Image()
-image_out = gr.outputs.Image()
+#image_out = gr.outputs.Image()
 
 markdown_part = """
 
-This space is the demo for the EDSR (Enhanced Deep Residual Networks for Single Image Super-Resolution) model. This model surpassed the performace of the current available SOTA models.
+Model Link - https://huggingface.co/keras-io/EDSR
 
-Paper Link - https://arxiv.org/pdf/1707.02921
-
-Keras Example link - https://keras.io/examples/vision/edsr/
-
-
-TODO:
-
-Hack to make this work for any image size. Currently the model takes input of image size 150 x 150.
-
-We pad the input image with transparent pixels so that it is a square image, which is a multiple of 150 x 150
-
-Then we chop the image into multiple 150 x 150 sub images
-
-Upscale it and stitch it together.
-
-The output image might look a bit off, because each sub-image dosent have data about other sub-images.
-
-This approach assumes that the subimage has enough data about its surroundings
 """
 
-
+examples = [["./examples/1.png"]]
 
 gr.Interface(
     process_image, 
     title="EDSR - Enhanced Deep Residual Networks for Single Image Super-Resolution",
     description="SuperResolution",
     inputs = image,
-    outputs = gr.Gallery(label="Outputs, First image is low res, next one is High Res",visible=True),
+    examples = examples,
+    outputs = gr.Gallery(label="Outputs, First image is low res, next one is High Res",visible=True).style(grid=[2], height="auto"),
     article = markdown_part,
     interpretation='default',
     allow_flagging='never'
-            ).launch()
+            ).launch(debug=True)