app.py

# AUTOGENERATED! DO NOT EDIT! File to edit: app.ipynb.

# %% auto 0
__all__ = ['learner', 'labels', 'title', 'description', 'article', 'image', 'label', 'examples', 'interpretation', 'enable_queue',
           'intf', 'classify_image']

# %% app.ipynb 1
from fastai.vision.all import *
import scipy
import gradio as gr

# %% app.ipynb 2
learner = load_learner('pet_learner.pkl')
labels = learner.dls.vocab

def classify_image(img):
    """Gradio need a f-n that returns a dict of each class and its probability.
    It also does not accept tensors.
    """
    img = PILImage.create(img)
    pred, pred_idx, probs = learner.predict(img)
    return dict(zip(labels, map(float, probs)))

# %% app.ipynb 3
title = "Cat & Dog 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>"

image = gr.components.Image(shape=(512,512))
label = gr.components.Label(num_top_classes=3)
examples = ['shiba.jpeg', 'yorkshire_terrier.jpeg']
interpretation = 'default'
enable_queue=True

intf = gr.Interface(classify_image,
                    inputs = image,
                    outputs=label,
                    examples=examples,
                    title=title,
                    description=description,
                    interpretation=interpretation)

intf.launch(enable_queue=enable_queue)