app.py

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

learn = load_learner('architecture.pkl')

labels = learn.dls.vocab


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


title = "Architecture Classifier"
description = "An architecture classifier trained on DuckDuckGo images.... Created as a demo for Gradio and HuggingFace Spaces."
examples = ['images/baroche.jpg', 'images/byzantin.jpg', 'images/modern.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,
             examples=examples,
             interpretation=interpretation,
             enable_queue=enable_queue).launch()