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from fastai.vision.all import *
import gradio as gr
def is_cat(x): return x[0].isupper()
path = untar_data(URLs.PETS)/'images'
dls = ImageDataLoaders.from_name_func('.',
get_image_files(path), valid_pct=0.2, seed=42,
label_func=is_cat,
item_tfms=Resize(192))
learn = load_learner('model.pkl')
labels = learn.dls.vocab
def predict(img):
img = PILImage.create(img)
pred,pred_idx,probs = learn.predict(img)
return {labels[i]: float(probs[i]) for i in range(len(labels))}
gr.Interface(fn=predict, inputs=gr.inputs.Image(shape=(512, 512)), outputs=gr.outputs.Label(num_top_classes=3)).launch()
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