app.py

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

learn = load_learner('model.pkl')

labels = learn.dls.vocab

labels = list(map(lambda x: x.replace('food recipe', 'Food Plate'), labels))
labels = list(map(lambda x: x.replace('empty plate', 'Empty Plate'), labels))

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 = "Empty Plate Classifier"
description = "We are passionate about the emptiness of plates, but it is often rather challenging to determine whether a plate is empty or not. This model will help you in that task!"
article = "<p style='text-align: center'><a href='https://foodand.eu/' target='_blank'>Food& Magazine</a></p>"
examples = ['empty_plate.jpeg', 'full_plate.jpeg']
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()