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from fastai.vision.all import *
import gradio as gr
learn = load_learner('model.pkl')
image = gr.inputs.Image(shape=(128, 128))
label = gr.outputs.Label()
examples = ['coral.jpg', 'crabs.jpg', 'dolphin.jpg', 'sea_rays.jpg', 'seahorse.jpg', 'turtle_tortoise.jpg']
categories = ('corals','crabs','dolphin','eel','jelly fish','lobster','nudibranchs','octopus','penguin','puffers','sea rays','sea urchins','seahorse','seal','sharks','squid','starfish','turtle_tortoise','whale')
def classify_img(img):
pred,idx,probs = learn.predict(img)
return dict(zip(categories, map(float, probs)))
interface = gr.Interface(fn=classify_img,
inputs=image,
outputs=label,
examples=examples)
interface.launch(inline=False)
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