)
dku_config.add_param(
name='text_direction',
value=config.get('text_direction'),
checks=[{
'type': 'in',
'op': ['rtl', 'ltr']
}],
required=(dku_config.language == "none")
)
dku_config.add_param(
name='tokenization_engine',
value=config.get('tokenization_engine'),
checks=[{
'type': 'in',
'op': ['white_space', 'char']
}],
required=(dku_config.language == "none")
)
return dku_config
config = get_webapp_config()
dku_config = create_dku_config(config)
prepare_datasets(dku_config)
initial_df = dataiku.Dataset(config["unlabeled"]).get_dataframe()
queries_df = None
define_endpoints(app, DataikuLALHandler(TextClassifier(initial_df, queries_df, dku_config), dku_config))
import dataiku
from dataiku.customwebapp import get_webapp_config
from lal.api import define_endpoints
from lal.app_configuration import prepare_datasets
from lal.classifiers.image_object_classifier import ImageObjectClassifier
from lal.handlers.dataiku_lal_handler import DataikuLALHandler
config = get_webapp_config()
labels_schema = [{"name": "path", "type": "string"}]
prepare_datasets(config, labels_schema)
unlabeled_mf = dataiku.Folder(config["unlabeled"])
queries_df = dataiku.Dataset(
config["queries_ds"]).get_dataframe() if "queries_ds" in config else None
define_endpoints(
app,
DataikuLALHandler(ImageObjectClassifier(unlabeled_mf, queries_df, config),
config))
import dataiku
from dataiku.customwebapp import get_webapp_config
from lal.api import define_endpoints
from lal.app_configuration import prepare_datasets
from lal.classifiers.tabular_classifier import TabularClassifier
from lal.handlers.dataiku_lal_handler import DataikuLALHandler
config = get_webapp_config()
prepare_datasets(config)
initial_df = dataiku.Dataset(config["unlabeled"]).get_dataframe()
queries_df = dataiku.Dataset(
config["queries_ds"]).get_dataframe() if "queries_ds" in config else None
define_endpoints(
app,
DataikuLALHandler(TabularClassifier(initial_df, queries_df, config),
config))