import gradio as gr import tensorflow as tf # from vis.utils import utils # from vis.visualization import visualize_cam import numpy as np from PIL import Image import requests from urllib.request import urlretrieve # # Download human-readable labels for ImageNet. response = requests.get("https://git.io/JJkYN") labels = response.text.split("\n") mobile_net = tf.keras.applications.MobileNetV2() def image_classifier(im): arr = np.expand_dims(im, axis=0) arr = tf.keras.applications.mobilenet.preprocess_input(arr) prediction = mobile_net.predict(arr).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) io = gr.Interface(image_classifier, image, label, capture_session=True, interpretation="default", examples=[ ["images/cheetah1.jpg"], ["images/lion.jpg"] ]) io.launch()