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# Demo: (Image) -> (Label)
import gradio as gr
import tensorflow as tf
import numpy as np
import json
from os.path import dirname, realpath, join
# Load human-readable labels for ImageNet.
current_dir = dirname(realpath(__file__))
with open(join(current_dir, "imagenet_labels.json")) as labels_file:
labels = json.load(labels_file)
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)}
iface = gr.Interface(
image_classifier,
gr.inputs.Image(shape=(224, 224)),
gr.outputs.Label(num_top_classes=3),
capture_session=True,
interpretation="default",
examples=[
["images/cheetah1.jpg"],
["images/lion.jpg"]
])
if __name__ == "__main__":
iface.launch(share=True)
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