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"""
Example Airflow DAG that translates text in Google Cloud Translate using V3 API version
service in the Google Cloud.
"""
from __future__ import annotations
import os
from datetime import datetime
from airflow.models.dag import DAG
from airflow.providers.google.cloud.operators.gcs import GCSCreateBucketOperator, GCSDeleteBucketOperator
from airflow.providers.google.cloud.operators.translate import (
TranslateCreateDatasetOperator,
TranslateCreateModelOperator,
TranslateDeleteDatasetOperator,
TranslateDeleteModelOperator,
TranslateImportDataOperator,
TranslateModelsListOperator,
TranslateTextOperator,
)
from airflow.providers.google.cloud.transfers.gcs_to_gcs import GCSToGCSOperator
try:
from airflow.sdk import TriggerRule
except ImportError:
# Compatibility for Airflow < 3.1
from airflow.utils.trigger_rule import TriggerRule # type: ignore[no-redef,attr-defined]
DAG_ID = "gcp_translate_automl_native_model"
PROJECT_ID = os.environ.get("SYSTEM_TESTS_GCP_PROJECT", "default")
ENV_ID = os.environ.get("SYSTEM_TESTS_ENV_ID", "default")
REGION = "us-central1"
RESOURCE_DATA_BUCKET = "airflow-system-tests-resources"
DATA_SAMPLE_GCS_BUCKET_NAME = f"bucket_{DAG_ID}_{ENV_ID}".replace("_", "-")
DATA_FILE_NAME = "import_en-es.tsv"
RESOURCE_PATH = f"V3_translate/create_ds/import_data/{DATA_FILE_NAME}"
COPY_DATA_PATH = f"gs://{RESOURCE_DATA_BUCKET}/V3_translate/create_ds/import_data/{DATA_FILE_NAME}"
DST_PATH = f"translate/import/{DATA_FILE_NAME}"
DATASET_DATA_PATH = f"gs://{DATA_SAMPLE_GCS_BUCKET_NAME}/{DST_PATH}"
DATASET = {
"display_name": f"ds_native_{DAG_ID}_{ENV_ID}",
"source_language_code": "en",
"target_language_code": "es",
}
with DAG(
DAG_ID,
schedule="@once", # Override to match your needs
start_date=datetime(2025, 1, 1),
catchup=False,
tags=[
"example",
"translate_native_model",
],
) as dag:
create_bucket = GCSCreateBucketOperator(
task_id="create_bucket",
bucket_name=DATA_SAMPLE_GCS_BUCKET_NAME,
storage_class="REGIONAL",
location=REGION,
)
copy_dataset_source_tsv = GCSToGCSOperator(
task_id="copy_dataset_file",
source_bucket=RESOURCE_DATA_BUCKET,
source_object=RESOURCE_PATH,
destination_bucket=DATA_SAMPLE_GCS_BUCKET_NAME,
destination_object=DST_PATH,
)
create_dataset_op = TranslateCreateDatasetOperator(
task_id="translate_v3_ds_create",
dataset=DATASET,
project_id=PROJECT_ID,
location=REGION,
)
import_ds_data_op = TranslateImportDataOperator(
task_id="translate_v3_ds_import_data",
dataset_id=create_dataset_op.output["dataset_id"],
input_config={
"input_files": [{"usage": "UNASSIGNED", "gcs_source": {"input_uri": DATASET_DATA_PATH}}]
},
project_id=PROJECT_ID,
location=REGION,
)
# [START howto_operator_translate_automl_create_model]
create_model = TranslateCreateModelOperator(
task_id="translate_v3_model_create",
display_name=f"native_model_{ENV_ID}"[:32].replace("-", "_"),
dataset_id=create_dataset_op.output["dataset_id"],
project_id=PROJECT_ID,
location=REGION,
)
# [END howto_operator_translate_automl_create_model]
# [START howto_operator_translate_automl_list_models]
list_models = TranslateModelsListOperator(
task_id="translate_v3_list_models",
project_id=PROJECT_ID,
location=REGION,
)
# [END howto_operator_translate_automl_list_models]
model_id = create_model.output["model_id"]
translate_text_with_model = TranslateTextOperator(
task_id="translate_v3_op",
contents=["Hello!", "Can I have a cup of coffee, please?"],
# AutoML model format
model=f"projects/{PROJECT_ID}/locations/{REGION}/models/{model_id}",
source_language_code="en",
target_language_code="es",
location=REGION,
)
# [START howto_operator_translate_automl_delete_model]
delete_model = TranslateDeleteModelOperator(
task_id="translate_v3_automl_delete_model",
model_id=model_id,
project_id=PROJECT_ID,
location=REGION,
)
# [END howto_operator_translate_automl_delete_model]
delete_ds_op = TranslateDeleteDatasetOperator(
task_id="translate_v3_ds_delete",
dataset_id=create_dataset_op.output["dataset_id"],
project_id=PROJECT_ID,
location=REGION,
)
# [END howto_operator_translate_automl_delete_dataset]
delete_bucket = GCSDeleteBucketOperator(
task_id="delete_bucket",
bucket_name=DATA_SAMPLE_GCS_BUCKET_NAME,
trigger_rule=TriggerRule.ALL_DONE,
)
(
# TEST SETUP
[create_bucket >> copy_dataset_source_tsv]
>> create_dataset_op
>> import_ds_data_op
# TEST BODY
>> create_model
>> list_models
>> translate_text_with_model
>> delete_model
# TEST TEARDOWN
>> delete_ds_op
>> delete_bucket
)
from tests_common.test_utils.watcher import watcher
# This test needs watcher in order to properly mark success/failure
# when "tearDown" task with trigger rule is part of the DAG
list(dag.tasks) >> watcher()
from tests_common.test_utils.system_tests import get_test_run # noqa: E402
# Needed to run the example DAG with pytest (see: contributing-docs/testing/system_tests.rst)
test_run = get_test_run(dag)