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from fastai.vision import *
import gradio as gr
from pathlib import Path
from fastbook import load_learner
model_path = Path('models')
image_path = Path('images')
learn = load_learner(model_path/'cloudmodel.pk1')
categories = (
'Cirrus',
'Cirrostratus',
'Cirrocumulus',
'Altostratus',
'Altocumulus',
'Stratus',
'Stratocumulus',
'Nimbostratus',
'Cumulus',
'Cumulonimbus',
'Lenticular'
)
image = gr.inputs.Image(shape=(192,192))
label = gr.outputs.Label()
learn = load_learner(model_path/'cloudmodel.pkl')
examples = [image_path / f"{c}.jpg" for c in categories]
def classify_image(img):
pred,idx,probs = learn.predict(img)
return dict(zip(categories, map(float, probs)))
iface = gr.Interface(fn=classify_image, inputs=image, outputs=label,
examples=examples)
iface.launch()
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