Source code for tests.system.google.cloud.natural_language.example_natural_language

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"""
Example Airflow DAG for Google Cloud Natural Language service
"""

from __future__ import annotations

import os
from datetime import datetime

from google.cloud.language_v1 import Document

from airflow.models.dag import DAG
from airflow.providers.google.cloud.operators.natural_language import (
    CloudNaturalLanguageAnalyzeEntitiesOperator,
    CloudNaturalLanguageAnalyzeEntitySentimentOperator,
    CloudNaturalLanguageAnalyzeSentimentOperator,
    CloudNaturalLanguageClassifyTextOperator,
)
from airflow.providers.standard.operators.bash import BashOperator


[docs]ENV_ID = os.environ.get("SYSTEM_TESTS_ENV_ID", "default")


[docs]DAG_ID = "gcp_natural_language"


# [START howto_operator_gcp_natural_language_document_text]

[docs]TEXT = """Airflow is a platform to programmatically author, schedule and monitor workflows.

Use Airflow to author workflows as Directed Acyclic Graphs (DAGs) of tasks. The Airflow scheduler executes
 your tasks on an array of workers while following the specified dependencies. Rich command line utilities
 make performing complex surgeries on DAGs a snap. The rich user interface makes it easy to visualize
 pipelines running in production, monitor progress, and troubleshoot issues when needed.
"""


[docs]document = Document(content=TEXT, type="PLAIN_TEXT")

# [END howto_operator_gcp_natural_language_document_text]

# [START howto_operator_gcp_natural_language_document_gcs]

[docs]GCS_CONTENT_URI = "gs://INVALID BUCKET NAME/sentiment-me.txt"


[docs]document_gcs = Document(gcs_content_uri=GCS_CONTENT_URI, type="PLAIN_TEXT")

# [END howto_operator_gcp_natural_language_document_gcs]


with DAG(
    DAG_ID,
    schedule="@once",  # Override to match your needs
    start_date=datetime(2021, 1, 1),
    catchup=False,
    tags=["example", "natural-language"],
) as dag:
    # [START howto_operator_gcp_natural_language_analyze_entities]

[docs]    analyze_entities = CloudNaturalLanguageAnalyzeEntitiesOperator(
        document=document, task_id="analyze_entities"
    )

    # [END howto_operator_gcp_natural_language_analyze_entities]

    # [START howto_operator_gcp_natural_language_analyze_entities_result]
    analyze_entities_result = BashOperator(
        bash_command=f"echo {analyze_entities.output}",
        task_id="analyze_entities_result",
    )
    # [END howto_operator_gcp_natural_language_analyze_entities_result]

    # [START howto_operator_gcp_natural_language_analyze_entity_sentiment]
    analyze_entity_sentiment = CloudNaturalLanguageAnalyzeEntitySentimentOperator(
        document=document, task_id="analyze_entity_sentiment"
    )
    # [END howto_operator_gcp_natural_language_analyze_entity_sentiment]

    # [START howto_operator_gcp_natural_language_analyze_entity_sentiment_result]
    analyze_entity_sentiment_result = BashOperator(
        bash_command=f"echo {analyze_entity_sentiment.output}",
        task_id="analyze_entity_sentiment_result",
    )
    # [END howto_operator_gcp_natural_language_analyze_entity_sentiment_result]

    # [START howto_operator_gcp_natural_language_analyze_sentiment]
    analyze_sentiment = CloudNaturalLanguageAnalyzeSentimentOperator(
        document=document, task_id="analyze_sentiment"
    )
    # [END howto_operator_gcp_natural_language_analyze_sentiment]

    # [START howto_operator_gcp_natural_language_analyze_sentiment_result]
    analyze_sentiment_result = BashOperator(
        bash_command=f"echo {analyze_sentiment.output}",
        task_id="analyze_sentiment_result",
    )
    # [END howto_operator_gcp_natural_language_analyze_sentiment_result]

    # [START howto_operator_gcp_natural_language_analyze_classify_text]
    analyze_classify_text = CloudNaturalLanguageClassifyTextOperator(
        document=document, task_id="analyze_classify_text"
    )
    # [END howto_operator_gcp_natural_language_analyze_classify_text]

    # [START howto_operator_gcp_natural_language_analyze_classify_text_result]
    analyze_classify_text_result = BashOperator(
        bash_command=f"echo {analyze_classify_text.output}",
        task_id="analyze_classify_text_result",
    )
    # [END howto_operator_gcp_natural_language_analyze_classify_text_result]

    analyze_entities >> analyze_entities_result
    analyze_entity_sentiment >> analyze_entity_sentiment_result
    analyze_sentiment >> analyze_sentiment_result
    analyze_classify_text >> analyze_classify_text_result

    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: tests/system/README.md#run_via_pytest)

[docs]test_run = get_test_run(dag)

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