Create app.py

app.py

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+import platform
+import pathlib
+
+plt = platform.system()
+if plt == 'Windows': 
+    pathlib.WindowsPath = pathlib.PosixPath
+
+import gradio as gr
+from fastai.vision.all import *
+from PIL import Image
+
+# Load your trained/saved model
+learn = load_learner('model.pkl')
+
+def classify_bear(image):
+    # convert PIL image to fastai image object
+    img = PILImage.create(image)
+    
+    # get predictions
+    pred, idx, probs = learn.predict(img)
+    
+    return {learn.dls.vocab[i]: float(probs[i]) for i in range(len(probs))}
+
+example_images = [
+    'images/black.jpg',
+    'images/teddy.jpg',
+    'images/grizzly.jpg',
+    'images/black2.jpg',
+    'images/grizzly2.jpg'
+]
+
+iface = gr.Interface(
+    fn=classify_bear, 
+    inputs=gr.Image(type='pil'), 
+    outputs=gr.Label(num_top_classes=3),
+    examples=example_images,
+    description="Classify bear images as grizzly, black or teddy:"
+)
+iface.launch()