def main(api_endpoint):
email = input('Enter Email: ')
password = getpass.getpass('Enter password for user {}:'.format(email))
kauth = KiliAuth(email, password, api_endpoint=api_endpoint)
playground = Playground(kauth)
project_id = input('Enter project id: ')
# Check and load new predictions
STOP_CONDITION = True
while STOP_CONDITION:
tools = playground.get_tools(project_id=project_id)
assert len(tools) == 1
categories = list(
json.loads(tools[0]['jsonSettings'])['categories'].keys())
print('Export assets and labels...')
assets = playground.export_assets(project_id=project_id)
print('Done.\n')
X, y, X_to_be_predicted, ids_X_to_be_predicted = extract_train_for_autoML(
assets, categories)
if len(X) > 5:
print('Online Learning is on its way...')
predictions = automl_train_and_predict(X, y, X_to_be_predicted)
# Insert pre-annotations
for i, prediction in enumerate(tqdm(predictions)):
json_response = {
'categories': [{
'name': categories[prediction],
'confidence': 100
}]
}
id = ids_X_to_be_predicted[i]
playground.create_prediction(asset_id=id,
json_response=json_response)
print('Done.\n')
time.sleep(SECONDS_BETWEEN_TRAININGS)
for name in files
]
kauth = KiliAuth(email=email, password=password)
playground = Playground(kauth)
for filepath in tqdm(only_files[:MAX_NUMBER_OF_ASSET]):
with open(filepath, 'r') as f:
content = f.read()
external_id = filepath
# Insert asset
playground.append_to_dataset(project_id=project_id,
content=escape_content(content),
external_id=external_id)
asset = playground.get_assets_(project_id=project_id,
external_id_contains=[external_id])
asset_id = asset[0]['id']
# Prioritize assets
playground.update_properties_in_asset(asset_id=asset_id, priority=1)
# Insert pre-annotations
response = analyze_entities(content)
entities = [
e for e in response['entities']
if isinstance(e['type'], str) and e['type'] != 'OTHER'
]
json_response = {'entities': add_id_to_entities(entities)}
playground.create_prediction(asset_id=asset_id,
json_response=json_response)