app.py

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

# create function to distinguish dogs from cats
def label_func(f): return f[0].isupper()

learn = load_learner('export.pkl')

# labels = learn.dls.vocab
def predict(img):
    img = PILImage.create(img)
    pred,pred_idx,probs = learn.predict(img)
    return pred
    # return {labels[i]: float(probs[i]) for i in range(len(labels))}

# ('False', TensorBase(0), TensorBase([9.9999e-01, 7.5253e-06]))

title = "Dog Cat Classifier"
description = "A dog cat classifier. Created as a demo for Gradio and HuggingFace Spaces."
article=""
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()