app.py

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

#learn =  load_learner('model.pkl')
learn =  load_learner('model_convnext.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 = "Pet Breed Classifier"
description = "A pet breed classifier trained on the Oxford Pets dataset with fastai. Created as a demo for Gradio and HuggingFace Spaces."
examples = ['shiba.jpeg','ragdoll.jpeg']
#interpretation='default'
#enable_queue=True

gr.Interface(fn=predict,inputs=gr.Image(type="pil"),outputs=gr.Label(num_top_classes=3)
             ,title=title,description=description,examples=examples).launch()