app.py

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

learn = load_learner('fast-ai/OxfordPetAI/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))}

# Create and launch the Gradio interface
title = "Pet Breed Classifier"
examples = ['fast-ai/OxfordPetAI/beagle.jpeg']
description = "A pet breed classifier trained on the Oxford Pets dataset with fastai. Created as a demo for Gradio and HuggingFace Spaces."
gr.Interface(fn=predict, inputs="image", outputs="label", title=title, description=description, examples=examples).launch(share=True)