def main()

in searcher/main.py [0:0]

• 15 lines of code
• 2 McCabe index (conditional complexity)

def main(index_endpoint_name: str, deployed_index_id: str, image_path: str) -> None:
    print("===== Started making vector =====")

    model = tf.keras.applications.EfficientNetB0(include_top=False, pooling="avg")

    raw = tf.io.read_file(image_path)
    image = tf.image.decode_jpeg(raw, channels=3)
    image = tf.image.resize(image, [224, 224])

    vector = model.predict(np.array([image.numpy()]))[0].tolist()

    # https://github.com/googleapis/python-aiplatform/blob/v1.22.0/google/cloud/aiplatform/matching_engine/matching_engine_index_endpoint.py#L85
    endpoint = MatchingEngineIndexEndpoint(index_endpoint_name=index_endpoint_name)

    print("===== Started query =====")
    # https://github.com/googleapis/python-aiplatform/blob/v1.22.0/google/cloud/aiplatform/matching_engine/matching_engine_index_endpoint.py#L902
    res = endpoint.match(
        deployed_index_id=deployed_index_id, queries=[vector], num_neighbors=5
    )
    print("===== Finished query =====")

    for neighbor in res[0]:
        print(f"{neighbor.id}: distance={neighbor.distance}")