import requests import tensorflow as tf inception_net = tf.keras.applications.MobileNetV2() import requests # 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() confidences = {labels[i]: float(prediction[i]) for i in range(1000)} return confidences import gradio as gr gr.Interface(fn=classify_image, inputs=gr.inputs.Image(shape=(224, 224)), outputs=gr.outputs.Label(num_top_classes=3), examples=["banana.jpg", "car.jpg"], theme="default", css=".footer{display:none !important}").launch()