app.py

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

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))}

# Create the gradio interface with title, description, default interpretation, and examples (car.jpg and scooter.png) that shows only the top 1 prediction

gr.Interface(fn=predict,
        title="Car or Scooter",
        description="Upload an image of a car or scooter and we'll tell you which one it is.",
        inputs=gr.inputs.Image(shape=(192, 192)),
        outputs=gr.outputs.Label(num_top_classes=1),
        interpretation="default",
        examples=[["car.jpg"], ["scooter.png"]],
        share=True
    ).launch()