| import gradio as gr | |
| import numpy as np | |
| import tensorflow as tf | |
| import json | |
| from os.path import dirname, realpath, join | |
| import matplotlib.pyplot as plt | |
| current_dir = dirname(realpath(__file__)) | |
| with open(join(current_dir), 'image_labels.json') as labels_file: | |
| labels=json.load(labels_file) | |
| mobile_net = tf.keras.applications.MobileNetV2() | |
| def image_classifier(img): | |
| arr = np.expand_dims(img, axis=0) | |
| arr = tf.keras.applications.mobilenet.preprocess_input(arr) | |
| prediction = mobile_net.prediction(arr).flatten() | |
| return {labels[i]:float(prediction[i]) for i in range(1000)} | |
| iface = gr.Interface( | |
| image_classifier, | |
| inputs=gr.Image(height=224, width=224), | |
| outputs=gr.Label(num_top_classes=3), | |
| capture_session=True, | |
| interpretation='default', | |
| ) | |
| if __name__ == '__main__': | |
| iface.launch(share=True) |