Added title and description, removed unneeded itemgetter funcs

app.py

@@ -17,14 +17,9 @@ if not exists(model_fn):
 else:
   print('file exists')
 
-# Load the model
-def open_img(fn:Path): return Image.open(fn).convert('RGB').copy()
-def get_images(path):
-  return get_image_files(path/'train') + get_image_files(path/'test')
-def get_x(item):
-  return np.array(open_img(item).crop((0, 0, 512, 512)))
-def get_y(item):
-  return np.array(open_img(item).crop((512, 0, 1024, 512)))
+# Load the model (requires dummy itemgetters)
+def get_x(item):return None
+def get_y(item):return None
 sketch_model = load_learner(model_fn)
 
 
@@ -33,10 +28,19 @@ def sketchify(image_path):
    np_im = pred[0].permute(1, 2, 0).numpy()
    return np_im
     
+title = "Sketchy Unet Demo"
+description = """
+<center>
+A resnet34-based unet model trained (briefly) to sketchify faces.
+</center>
+"""
+
+article = "Blog post on this with more details coming soon on https://datasciencecastnet.home.blog/"
     
 
 iface = gr.Interface(fn=sketchify, 
-  inputs=[gr.inputs.Image(shape=(512, 512), type="filepath")], 
-  outputs=[gr.outputs.Image(type="numpy", label="Output Image")]
+  inputs=[gr.inputs.Image(label="Input Image", shape=(512, 512), type="filepath")], 
+  outputs=[gr.outputs.Image(type="numpy", label="Model Output")],
+  title = title, description = description, article = article
 )
 iface.launch()