def execute_workflow(generate_config, component=None):
progress_show = None
if component:
progress_show = component.empty()
progress_show.code("🏄🏄🏄 Processing 🐢🐢🐢")
try:
obsei_configuration = ObseiConfiguration(configuration=generate_config)
source_config = obsei_configuration.initialize_instance("source_config")
source = obsei_configuration.initialize_instance("source")
analyzer = obsei_configuration.initialize_instance("analyzer")
analyzer_config = obsei_configuration.initialize_instance("analyzer_config")
sink_config = obsei_configuration.initialize_instance("sink_config")
sink = obsei_configuration.initialize_instance("sink")
source_response_list = source.lookup(source_config)
analyzer_response_list = analyzer.analyze_input(
source_response_list=source_response_list, analyzer_config=analyzer_config
)
sink_response_list = sink.send_data(analyzer_response_list, sink_config)
if progress_show:
progress_show.code("🎉🎉🎉 Processing Complete!! 🍾🍾🍾")
except Exception as ex:
if progress_show:
progress_show.code(f"❗❗❗ Processing Failed!! 😞😞😞 \n 👉 ({str(ex)})")
raise ex
def execute_workflow(generate_config, component=None, log_components=None):
progress_show = None
if component:
progress_show = component.empty()
progress_show.code("🏄🏄🏄 Processing 🐢🐢🐢")
try:
obsei_configuration = ObseiConfiguration(configuration=generate_config)
source_config = obsei_configuration.initialize_instance(
"source_config")
source = obsei_configuration.initialize_instance("source")
analyzer = obsei_configuration.initialize_instance("analyzer")
analyzer_config = obsei_configuration.initialize_instance(
"analyzer_config")
sink_config = obsei_configuration.initialize_instance("sink_config")
sink = obsei_configuration.initialize_instance("sink")
source_response_list = source.lookup(source_config)
log_components["source"].write(
[vars(response) for response in source_response_list])
analyzer_response_list = analyzer.analyze_input(
source_response_list=source_response_list,
analyzer_config=analyzer_config)
log_components["analyzer"].write(
[vars(response) for response in analyzer_response_list])
sink_response_list = sink.send_data(analyzer_response_list,
sink_config)
if sink.TYPE == 'Pandas':
log_components["sink"].write(sink_response_list)
elif sink_response_list is not None:
log_components["sink"].write(
[vars(response) for response in sink_response_list])
else:
log_components["sink"].write("No Data")
if progress_show:
progress_show.code("🎉🎉🎉 Processing Complete!! 🍾🍾🍾")
except Exception as ex:
if progress_show:
progress_show.code(f"❗❗❗ Processing Failed!! 😞😞😞 \n 👉 ({str(ex)})")
raise ex
def get_obsei_config(current_path, file_name):
return ObseiConfiguration(
config_path=current_path,
config_filename=file_name,
).configuration
import logging
import sys
from obsei.configuration import ObseiConfiguration
logger = logging.getLogger(__name__)
logging.basicConfig(stream=sys.stdout, level=logging.INFO)
obsei_configuration = ObseiConfiguration(
config_path="../example",
config_filename="sdk.yaml",
)
play_store_source_config = obsei_configuration.get_play_store_source_config()
twitter_source_config = obsei_configuration.get_twitter_source_config()
http_sink_config = obsei_configuration.get_http_sink_config()
daily_get_sink_config = obsei_configuration.get_daily_get_sink_config()
text_analyzer = obsei_configuration.get_analyzer()
analyzer_config = obsei_configuration.get_analyzer_config()
# docker run -d --name elasticsearch -p 9200:9200 -e "discovery.type=single-node" elasticsearch:7.9.2
elasticsearch_sink_config = obsei_configuration.get_elasticsearch_sink_config()
# Start jira server locally `atlas-run-standalone --product jira`
jira_sink_config = obsei_configuration.get_jira_sink_config()
def config_init() -> None:
global obsei_config
obsei_config = ObseiConfiguration(
config_path=os.getenv('OBSEI_CONFIG_PATH', "../config"),
config_filename=os.getenv('OBSEI_CONFIG_FILENAME', "rest.yaml"))
import logging
import sys
from obsei.configuration import ObseiConfiguration
logger = logging.getLogger(__name__)
logging.basicConfig(stream=sys.stdout, level=logging.INFO)
obsei_configuration = ObseiConfiguration(
config_path="../example",
config_filename="sdk.yaml",
)
text_analyzer = obsei_configuration.initialize_instance("analyzer")
analyzer_config = obsei_configuration.initialize_instance("analyzer_config")
slack_source_config = obsei_configuration.initialize_instance("slack_sink_config")
slack_sink = obsei_configuration.initialize_instance("slack_sink")
play_store_source_config = obsei_configuration.initialize_instance("play_store_source")
twitter_source_config = obsei_configuration.initialize_instance("twitter_source")
http_sink_config = obsei_configuration.initialize_instance("http_sink")
daily_get_sink_config = obsei_configuration.initialize_instance("daily_get_sink")
# docker run -d --name elasticsearch -p 9200:9200 -e "discovery.type=single-node" elasticsearch:7.9.2
elasticsearch_sink_config = obsei_configuration.initialize_instance(
"elasticsearch_sink"
)
# Start jira server locally `atlas-run-standalone --product jira`
jira_sink_config = obsei_configuration.initialize_instance("jira_sink")
import logging
from obsei.configuration import ObseiConfiguration
logger = logging.getLogger(__name__)
# Extract config via yaml file using `config_path` and `config_filename`
obsei_configuration = ObseiConfiguration()
# Initialize objects using configuration
source_config = obsei_configuration.initialize_instance("source_config")
source = obsei_configuration.initialize_instance("source")
analyzer = obsei_configuration.initialize_instance("analyzer")
analyzer_config = obsei_configuration.initialize_instance("analyzer_config")
sink_config = obsei_configuration.initialize_instance("sink_config")
sink = obsei_configuration.initialize_instance("sink")
# This will fetch information from configured source ie twitter, app store etc
source_response_list = source.lookup(source_config)
for idx, source_response in enumerate(source_response_list):
logger.info(f"source_response#'{idx}'='{vars(source_response)}'")
# This will execute analyzer (Sentiment, classification etc) on source data with provided analyzer_config
# Analyzer will it's output to `segmented_data` inside `analyzer_response`
analyzer_response_list = analyzer.analyze_input(
source_response_list=source_response_list,
analyzer_config=analyzer_config
)
for idx, analyzer_response in enumerate(analyzer_response_list):
logger.info(f"source_response#'{idx}'='{vars(analyzer_response)}'")