app.py

import gradio as gr
from fastai.vision.all import *

learn = load_learner("export_5cats.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))}

title = "Bear classifier"
description = "It can only do panda, polar or spectacled, black, grizzly and teddy bears so far... Trained using resnet18 and the fastai library"
interpretation= "default"
examples = ["panda.jpg","polar.jpg","spectacled.jpg","black.jpg","grizzly.jpg","teddy.jpg"]

gr.Interface(fn = predict,
             inputs = gr.inputs.Image(shape = (512, 512)),
             outputs = gr.outputs.Label(num_top_classes=5),
             title = title,
             description = description,
             examples = examples,
             interpretation = interpretation).launch()