Create app.py

app.py

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+__all__ = ['learn', 'classify_image', 'categories', 'image', 'label', 'examples', 'intf']
+
+from fastai.vision.all import *
+import gradio as gr
+
+learn = load_learner('/kaggle/input/beach-pkl-test/export.pkl')
+
+categories = ('Barnacles', 'Flowers', 'Grass', 'Kelp', 'Mussels', 'Primrose', 'Sage_Bush', 'SeaGull', 'Seashells')
+def classify_image(img):
+    pred, idx, probs = learn.predict(img)
+    return dict(zip(categories, map(float, probs)))
+
+image=gr.Image(height = 192, width = 192)
+label = gr.Label()
+examples = ['105a9dad-f329-4cfc-b3e3-52ef9334111f.jpg','095f0070-b680-4dbb-aca9-8451129a41bc.jpg', '0ecf9374-9120-4e75-9fc1-6633e61f6115.jpg', '22332b64-118f-48f5-b92b-e2f997d1a8cc.jpg', '270ab7a9-1c17-49b1-b1fe-9827b59aca9c.jpg', '061b7222-e979-4fcf-8454-78b6c879f5f0.jpg', '141747ba-db2a-4d67-af59-dccfa844ba51.jpg', '0cf94408-5399-4754-aba5-5f8bafbd71fd.jpg', '06dc143b-21b9-4dc0-979c-7b6801252cc7.jpg' ]
+intf = gr.Interface(fn=classify_image, inputs=image, outputs=label, examples=examples)
+intf.launch(inline=False)