Add meta information

.gitattributes

@@ -29,3 +29,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+*.jpg filter=lfs diff=lfs merge=lfs -text

README.md

@@ -1,7 +1,7 @@
 ---
-title: Chicken Breeds
-emoji: 💩
-colorFrom: yellow
+title: Chicken Breed Classifier
+emoji: 🐓
+colorFrom: red
 colorTo: pink
 sdk: gradio
 sdk_version: 3.1.7

app.py

@@ -1,5 +1,13 @@
-import gradio as gr
-from fastai.vision.all import *
+from fastai.vision.all import PILImage, load_learner
+from gradio import Interface
+from gradio.components import Image, Label
+
+TITLE = "Chicken Breed Classifier"
+DESCRIPTION = """A chicken breed classifier trained using the dataset here: https://www.kaggle.com/datasets/abdalnassir/chicken-breeds.\n
+Due to the limitations of the data, only the following breeds are currently recognised: American Gamefowl, Sapphire Gem, Speckled Sussex, Wyandotte, chick (all chicks recognised as 'Chick').
+"""
+EXAMPLES = ["wyandotte.jpg"]
+
 
 learn = load_learner("export.pkl")
 labels = learn.dls.vocab
@@ -11,10 +19,13 @@ def predict(img):
     return {labels[i]: float(probs[i]) for i in range(len(labels))}
 
 
-iface = gr.Interface(
+iface = Interface(
     fn=predict,
-    inputs=gr.inputs.Image(shape=(512, 512)),
-    outputs=gr.outputs.Label(num_top_classes=3),
+    inputs=Image(shape=(512, 512)),
+    outputs=Label(num_top_classes=3),
+    title=TITLE,
+    description=DESCRIPTION,
+    examples=EXAMPLES,
 )
 
-iface.launch()
+iface.launch(enable_queue=True)