Update app text

README.md

@@ -1,5 +1,5 @@
 ---
-title: Ugly Duckling Magic Mirror
+title: Magic Mirror
 emoji: 🪞🦢
 colorFrom: purple
 colorTo: blue

app.py

@@ -11,12 +11,12 @@ def classify_image(image):
     _, _, probs = learn.predict(image)
     return dict(zip(categories, map(float, probs)))
 
-title = 'Mirror, mirror on the wall, am I a duckling or a cygnet after all?'
-description = """Hans Christian Andersen's tale of the ugly duckling tells us about the sad youth of a cygnet which is accidentally brought up in a family of ducks and is ostrized on the account of it being different. But what if the cygnet had a magic mirror to tell it that it was in fact a young swan after all? Machine learning to the rescue!"""
+title = 'Mirror, Mirror on the Wall, am I a Duckling or a Cygnet after all?'
+description = """Hans Christian Andersen's tale of the ugly duckling tells us about the sad youth of a cygnet which is accidentally brought up in a family of ducks and is ostrized on the account of it being different. But what if the cygnet had had a magic mirror to tell it that it had been a young swan all along? Machine learning to the rescue!"""
 
 examples = ['duckling.jpg', 'cygnet.jpg', 'sunflower.jpg', 'whiteclouds.jpg', 'yellowclouds.jpg']
 
-article = 'This model was build using a resnet-18 architecture, pretrained on ImageNet and finetuned using 100 images of ducklings and cygnets each.'
+article = 'This model was build using a resnet-18 architecture with weights pretrained on the ImageNet data set and fine-tuned using about 80 images of ducklings and cygnets each.\nNote that it is binary classifier and can therefore only output cygnet or duckling, "other" is not an option. As a fun exercise, I included some non-waterfowl pictures in the example. Can you guess what the model will classify them as?\nOn a final note, whilst this classifier claims to be able to detect ducklings, it really only detects mallard ducklings (aka the yellow ones) and has a hard time recognizing ducklings of other species. To see this in action, compare its performance for a mallard duckling with its performance when given the image of a black cayuga duckling for example.'
 
 app = gr.Interface(fn=classify_image,
                    inputs=gr.components.Image(),