Given a Google Cloud Storage folder location, generate a automl image classification training data label file using the folders as labels. (https://cloud.google.com/ai-platform-unified/docs/datasets/prepare-image)

script.py

from google.cloud import storage

folder_location = 'automl-ui-dataset/x-ray-dataset'

parts = folder_location.split('/')
prefix = '/'.join(parts[1:]) if len(parts) > 1 else ''

storage_client = storage.Client()
blobs = storage_client.list_blobs(parts[0], prefix=prefix)

f = open("labels.csv", "w")

for blob in list(blobs)[1:]:
    path = 'gs://' + blob.name
    label = blob.name.split(prefix)[1].split('/')[1]

    print(path, label)
    f.write(path+','+label+'\n')
f.close()

print(parts, prefix)