Upload app.py

app.py

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+__all__ = ['learn', 'classify_image', 'categories', 'image', 'label', 'examples', 'intf']
+
+# Cell
+from fastai.vision.all import *
+import gradio as gr
+
+# Cell
+#learn = load_learner('ohbugger2k.pkl')
+learn = load_learner('flobbit/ohbugger2k')
+
+# Cell
+categories = learn.dls.vocab
+
+def classify_image(img):
+    pred,idx,probs = learn.predict(img)
+    return dict(zip(categories, map(float,probs)))
+
+# Cell
+image = gr.inputs.Image(shape=(192, 192))
+label = gr.outputs.Label(num_top_classes=5)
+examples = ['carolina.jpg','abb.jpg','lady.jpg','mantis.jpg','monarch.jpg','western striped cucumber.jpg','carolina2.jpg', 'large milkweed bug.jpg', 'twice-stabbed.jpg', 'viceroy.jpg', 'lubber.jpg', 'GrashopperAulocaraElliotti958.webp']
+
+# Cell
+intf = gr.Interface(fn=classify_image, inputs=image, outputs=label, examples=examples,
+    title="Oh! Bugger! 2k", description="Trained on 130133 images over 2000 species using ResNet18. Provide an image or select from one below.")
+intf.launch()