app.py

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

# def greet(name):
#     return "Hello " + name + "!!"

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."
article="<p style='text-align: center'><a href='https://tmabraham.github.io/blog/gradio_hf_spaces_tutorial' target='_blank'>Blog post</a></p>"
interpretation='default'
enable_queue=True

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)))}

iface = gr.Interface(fn=predict, inputs=gr.inputs.Image(shape=(512, 512)), outputs=gr.outputs.Label(num_top_classes=3)).launch(share=True)
iface.launch()