Gradio example for image classification

gradio_example.py

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

# Load a classification model
model = tf.keras.applications.MobileNetV3Large(
    input_shape=None,
    alpha=1.0,
    minimalistic=False,
    include_top=True,
    weights="imagenet",
    input_tensor=None,
    classes=1000,
    pooling=None,
    dropout_rate=0.2,
    classifier_activation="softmax",
    include_preprocessing=True,
)

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

def pred(inp):
    predictions = model.predict(inp[np.newaxis, ...]).flatten().tolist()
    confidences = dict(zip(labels, predictions))
    return confidences


demo = gr.Interface(
    fn=pred,
    inputs=gr.inputs.Image(shape=(224, 224)),
    outputs=gr.outputs.Label(num_top_classes=15),
)

demo.launch(share=False)

demo.close()