app.py

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

learn = load_learner('export.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 = "Aircraft Classifier"
description = "An aircraft classifier trained on internet photos with fastai. Created as a demo for Gradio and HuggingFace Spaces."
examples = ['c172-test.jpeg']
interpretation = 'default'
enable_queue = True

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, interpretation=interpretation, enable_queue=enable_queue).launch()