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from fastai.vision.all import *

learn = load_learner('export2.pkl')

labels = learn.dls.vocab
def predict(img):
    img = PILImage.create(img).resize((132, 132))
    pred,pred_idx,probs = learn.predict(img)
    return {labels[i]: float(probs[i]) for i in range(len(labels))}

#hide_output
import gradio as gr

title = "Pet Breed Classifier"
description = "A pet breed classifier trained on the Oxford Pets dataset with fastai. Created as a demo for Gradio and HuggingFace Spaces."

examples = ['cat2.jpg','dog1.jpg','cat3.jpg','gat1.jpeg']

#hide_output
gr.Interface(fn=predict,inputs=gr.inputs.Image(shape=(512, 512)),outputs=gr.outputs.Label(num_top_classes=3),title=title,description=description,examples=examples).launch()