app.py

import gradio as gr
import numpy as np
from PIL import Image
import tensorflow as tf
import logging


#load label
labels = open("labels.txt", "r")
labels = labels.read().splitlines()

# load model
model = tf.keras.models.load_model('mobilenet_v3_large_final.h5')

def predict(img):

    img = np.expand_dims(img, axis=0)/255
    pred = model.predict(img)
    return {labels[i]: float(pred[0][i]) for i in range(len(labels))}
 
title = "Shazam for Food"
description = "A food classifier trained on MobileNetV3Large."
article="<p style='text-align: center'><a href='https://kwangjong.github.io/https://kwangjong.github.io/2022/07/28/shazam-for-food/' target='_blank'>Blog post</a></p>"
examples = ['img/waffle.jpg', "img/lasagna.jpg", "img/taco.jpg", "img/bibimbap.jpg", "img/pad-thai.jpg"]
interpretation='default'
enable_queue=True

gr.Interface(fn=predict,inputs=gr.inputs.Image(shape=(224,224)),outputs=gr.outputs.Label(num_top_classes=5),title=title,description=description,article=article,examples=examples,interpretation=interpretation,enable_queue=enable_queue).launch()