app.py

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

learn = load_learner('model.pkl')

labels = learn.dls.vocab
def is_cat(x): return x[0].isupper() 

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 Classifier"
description = "A pet classifier trained on the duckduckgo search result 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>"

gr.Interface(fn=predict, inputs=gr.Image(), outputs=gr.Label(num_top_classes=2),
             title=title, description=description, article=article).launch()