app.py

import requests
import tensorflow as tf

import gradio as gr

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

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)}


title = "Image Classifiction + Interpretation"
description = """
Task: Image Classification\n
Dataset: COCO 2017, 1,000 classes\n
Model: https://huggingface.co/google/mobilenet_v2_1.0_224\n
Developer: Google \n
"""
image = gr.Image(shape=(224, 224))
label = gr.Label(num_top_classes=3)
examples = [
    ["buger.jpg"],
    ["goldfish.jpg"],
    ["lake-house.jpg"],
    ["truck.jpg"],
]

demo = gr.Interface(
    fn=classify_image, 
    inputs=image, 
    outputs=label, 
    interpretation="default", 
    title=title,
    description=description,
    examples=examples,
    theme="freddyaboulton/dracula_revamped",
    )

demo.launch()