Adds title, description, and examples.

app.py

@@ -3,15 +3,39 @@ from fastai.learner import load_learner
 from fastai.vision.core import PILImage
 from huggingface_hub import hf_hub_download
 
-learner = load_learner(hf_hub_download("pinkpekoe/lesson2-bear-classifier", "export.pkl"))
+learner = load_learner(
+    hf_hub_download("pinkpekoe/lesson2-bear-classifier", "export.pkl")
+)
 
 
-learner = load_learner('export.pkl')
+learner = load_learner("export.pkl")
+
 
 def predict_bear_type(img_path):
     img = PILImage.create(img_path)
     pred, pred_idx, probs = learner.predict(img)
-    return f"Prediction: {pred}; Probability: {probs[pred_idx]:.02f}"
+    probabilities = [
+        f"{probs[i]:.02f}*" if i == pred_idx else f"{probs[i]:.02f}"
+        for i in range(len(probs))
+    ]
+    return f"Prediction: {pred}; Probabilities: " + ", ".join(probabilities)
+
+
+title = "Pet Breed Classifier"
+description = "A pet breed classifier trained on the Oxford Pets dataset with fastai. Created as a demo for Gradio and HuggingFace Spaces."
+article = "<p style='text-align: center'><a href='https://tmabraham.github.io/blog/gradio_hf_spaces_tutorial' target='_blank'>Blog post</a></p>"
+examples = ["black.jpeg", "brown.jpeg", "teddy.jpeg"]
+interpretation = "default"
+enable_queue = True
 
-iface = gr.Interface(fn=predict_bear_type, inputs="image", outputs="text")
-iface.launch()
+iface = gr.Interface(
+    fn=predict_bear_type,
+    inputs="image",
+    outputs="text",
+    title=title,
+    description=description,
+    article=article,
+    examples=examples,
+    interpretation=interpretation,
+)
+iface.launch(enable_queue=enable_queue)