add descriptive text for app

app.py

@@ -3,11 +3,20 @@ from fastai.vision.all import *
 from os import listdir
 import random
 
+title = "Who Am I? (Hockey Edition)"
+
+desc = """This app uses a 'neural network' (a type of machine learning model) to classify 
+an image as containing a hockey player, a hockey goalie or a hockey referee."""
+
+art = """I built this model using about 50 hockey related images found on the web and in my own collection. I started with a pretrained `resnet18` model (resnet18 is trained on `imagenet`, a very large dataset with millions of images)
+and then performed `fine tuning` of this using python and the `fast.ai` library. 
+The total training time for this was about 5 minutes on a basic GPU.  It's impressive how accurate this quick / small model can be!"""
+
 learn = load_learner('hockey_model.pkl')
 
 categories = ('Hockey Goalie', 'Hockey Player', "Hockey Referee")
-image = gr.inputs.Image(shape=(192, 192))
-label = gr.outputs.Label()
+image = gr.Image(shape=(192, 192))
+label = gr.Label()
 skater_example = 'assets/skaters/' + random.choice(listdir('assets/skaters')) 
 ref_example = 'assets/referees/' + random.choice(listdir('assets/referees'))
 goalie_example = 'assets/goalies/' + random.choice(listdir('assets/goalies'))
@@ -17,5 +26,6 @@ def classify_image(img):
     return dict(zip(categories, map(float, prob)))
 
 iface = gr.Interface(fn=classify_image, inputs=image, outputs=label, theme=gr.themes.Glass(), 
-                    examples=[skater_example, ref_example, goalie_example])
+                    examples=[skater_example, ref_example, goalie_example], title=title, 
+                    description=desc, article=art)
 iface.launch(share=False, debug=True)