Update app.py

app.py

@@ -1,5 +1,7 @@
 import gradio as gr
 from fastai.vision.all import *
+import requests
+from PIL import Image
 
 # Re-define the GrayscaleTransform class (same as during training)
 class GrayscaleTransform(Transform):
@@ -14,8 +16,28 @@ def classify_waterfowl(img):
     pred, pred_idx, probs = learn.predict(img)
     return {learn.dls.vocab[i]: float(probs[i]) for i in range(len(probs))}
 
+# Provide some example images
+examples = [
+    ['https://upload.wikimedia.org/wikipedia/commons/7/74/White_domesticated_duck,_stretching.jpg'],
+    ['https://upload.wikimedia.org/wikipedia/commons/3/39/Domestic_Goose.jpg'],
+    ['https://upload.wikimedia.org/wikipedia/commons/a/a2/CygneVaires.jpg']
+]
+
+# Description and instructions for users
+description = """
+This is an AI model trained to differentiate between ducks, geese, and swans. You can upload your own image or use one of the example images below.
+Try uploading an image of a duck, goose, or swan and see if the model can correctly classify it.
+"""
+
 # Create a Gradio interface
-interface = gr.Interface(fn=classify_waterfowl, inputs=gr.Image(type="pil"), outputs=gr.Label())
+interface = gr.Interface(
+    fn=classify_waterfowl,
+    inputs=gr.Image(type="pil"),
+    outputs=gr.Label(),
+    examples=examples,  # Add example images
+    description=description,  # Add instructions for users
+    title="Duck, Goose, or Swan Classifier"
+)
 
 # Launch the Gradio app
-interface.launch()
+interface.launch()