app.py

# created in vim editor
import gradio as gr
import skimage
from fastai.vision.all import *
def is_cat(x): return x[0].isupper() 
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))}

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>"
examples = ['siamese.jpg']
interpretation='default'
enable_queue=True
gr.interface(fn=predict,inputs=gr.inputs.Image(shape=(512,512)), outputs=gr.outputs.Label(num_top_classes=3),title=title,
        description=description,
        article=article,
        examples=examples,
        interpretation=interpretation,
        enable_queue=enable_queue).launch()