import requests
import tensorflow as tf

import gradio as gr

inception_net = tf.keras.applications.MobileNetV2()  # load the model

# Download human-readable labels for ImageNet.
response = requests.get("https://git.io/JJkYN")
labels = response.text.split("\n")


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()
    return {labels[i]: float(prediction[i]) for i in range(1000)}


image = gr.inputs.Image(shape=(224, 224))
label = gr.outputs.Label(num_top_classes=3)

gr.Interface(
    fn=classify_image,
    inputs=image,
    outputs=label,
    examples=[["images/cheetah1.jpg"], ["images/lion.jpg"]],
).launch()