Upload app.py

app.py

@@ -1,26 +1,12 @@
-__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)
+#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 = gr.load(src='models',name='flobbit/ohbugger2k/',
+               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()