Marmelator/image-classification-service
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switch logger & only return 1 value in process_request()

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Francisco Schulz 2023-02-07 15:59:23 +01:00
parent 734f530231
commit 41d4934cc1
1 changed files with 21 additions and 7 deletions
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28
src/serve.py
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@ -2,6 +2,7 @@ import gzip
import io
import json
import logging
import sys
from image_prediction.config import Config
from image_prediction.locations import CONFIG_FILE
@ -15,9 +16,21 @@ from pyinfra.storage.storage import get_storage
PYINFRA_CONFIG = config.get_config()
IMAGE_CONFIG = Config(CONFIG_FILE)
logging.getLogger().addHandler(logging.StreamHandler())
logger = logging.getLogger("main")
logger.setLevel(PYINFRA_CONFIG.logging_level_root)
# logging.getLogger().addHandler(logging.StreamHandler())
# logger = logging.getLogger("main")
# logger.setLevel(PYINFRA_CONFIG.logging_level_root)
LOG_FORMAT = "%(asctime)s [%(levelname)s] - [%(filename)s -> %(funcName)s() -> %(lineno)s] : %(message)s"
DATE_FORMAT = "%Y-%m-%d %H:%M:%S"
logger = logging.getLogger()
logger.setLevel("DEBUG")
stream_handler = logging.StreamHandler(sys.stdout)
stream_handler_format = logging.Formatter(LOG_FORMAT, datefmt=DATE_FORMAT)
stream_handler.setFormatter(stream_handler_format)
logger.addHandler(stream_handler)
# A component of the callback (probably tensorflow) does not release allocated memory (see RED-4206).
@ -39,7 +52,7 @@ def process_request(request_message):
pipeline = load_pipeline(verbose=IMAGE_CONFIG.service.verbose, batch_size=IMAGE_CONFIG.service.batch_size)
if storage.exists(bucket, target_file_name):
should_publish_result = True
# should_publish_result = True
object_bytes = storage.get_object(bucket, target_file_name)
object_bytes = gzip.decompress(object_bytes)
classifications = list(pipeline(pdf=object_bytes))
@ -55,10 +68,11 @@ def process_request(request_message):
result = {**request_message, "data": classifications, "dataCV": classifications_cv}
storage_bytes = gzip.compress(json.dumps(result).encode("utf-8"))
storage.put_object(bucket, response_file_name, storage_bytes)
else:
should_publish_result = False
return should_publish_result, {"dossierId": dossier_id, "fileId": file_id}
return {"dossierId": dossier_id, "fileId": file_id}
# else:
# should_publish_result = False
# return None
def main():