app.py

__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()