app.py

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

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 = "Car or Bike Classifier"
description = "A Car or Bike or not classifier trained with downloaded data from internet. Created as a demo for Gradio and HuggingFace Spaces."

examples = ["cars.jpg", "bike.jpg"]
interpretation = "default"
enable_queue = True

gr.Interface(
    fn=predict,
    inputs=gr.inputs.Image(shape=(512, 512)),
    outputs=gr.outputs.Label(num_top_classes=2),
    title=title,
    description=description,
    examples=examples,
    interpretation=interpretation,
    enable_queue=enable_queue,
).launch(share=False)