app.py

import numpy as np
import tensorflow as tf
import requests
import gradio as gr

inception_net = tf.keras.applications.MobileNetV2()
labels = np.load('labels.npz')['labels']

def classify_image(inp):
    inp = inp.reshape((-1, 224, 224, 3))
    inp = tf.keras.applications.mobilenet_v2.preprocess_input(inp)
    prediction = inception_net.predict(inp).flatten()
    confidences = {labels[i]: float(prediction[i]) for i in range(1000)}
    return confidences


gradio_interface = gr.Interface(fn=classify_image, 
                         inputs=gr.Image(shape=(224, 224)),
                         outputs=gr.Label(num_top_classes=3),
                         examples=["example_images/banana.jpg", 
                                   "example_images/auto.jpg",
                                   "example_images/gatto.jpg"])

gradio_interface.launch()