logger refactoring

config.yaml

@@ -5,6 +5,7 @@ webserver:
 
 service:
   logging_level: $LOGGING_LEVEL_ROOT|DEBUG  # Logging level for service logger
+  progressbar: True  # Whether a progress bar over the pages of a document is displayed while processing
   batch_size: $BATCH_SIZE|32  # Number of images in memory simultaneously
   verbose: $VERBOSE|True  # Service prints document processing progress to stdout
   run_id: $RUN_ID|fabfb1f192c745369b88cab34471aba7  # The ID of the mlflow run to load the model from

image_prediction/flask.py

@@ -1,13 +1,10 @@
-import logging
 from typing import Callable
 
 from flask import Flask, request, jsonify
 
-from image_prediction.config import CONFIG
-
-logger = logging.getLogger(__name__)
-logger.setLevel(CONFIG.service.logging_level)
+from image_prediction.utils import get_logger
 
+logger = get_logger()
 
 def make_prediction_server(predict_fn: Callable):
 

image_prediction/predictor.py

@@ -1,4 +1,3 @@
-import logging
 from itertools import chain
 from operator import itemgetter
 from typing import List, Dict, Iterable
@@ -7,12 +6,14 @@ import numpy as np
 
 from image_prediction.config import CONFIG
 from image_prediction.locations import MLRUNS_DIR, BASE_WEIGHTS
-from image_prediction.utils import temporary_pdf_file
+from image_prediction.utils import temporary_pdf_file, get_logger
 from incl.redai_image.redai.redai.backend.model.model_handle import ModelHandle
 from incl.redai_image.redai.redai.backend.pdf.image_extraction import extract_and_stitch
 from incl.redai_image.redai.redai.utils.mlflow_reader import MlflowModelReader
 from incl.redai_image.redai.redai.utils.shared import chunk_iterable
 
+logger = get_logger()
+
 
 class Predictor:
     """`ModelHandle` wrapper. Forwards to wrapped model handle for prediction and produces structured output that is
@@ -37,7 +38,7 @@ class Predictor:
             self.classes_readable = np.array(self.model_handle.classes)
             self.classes_readable_aligned = self.classes_readable[self.classes[list(range(len(self.classes)))]]
         except Exception as e:
-            logging.info(f"Service estimator initialization failed: {e}")
+            logger.info(f"Service estimator initialization failed: {e}")
 
     def __make_predictions_human_readable(self, probs: np.ndarray) -> List[Dict[str, float]]:
         """Translates an n x m matrix of probabilities over classes into an n-element list of mappings from classes to
@@ -89,9 +90,9 @@ class Predictor:
 
         return predictions if probabilities else classes
 
-    def predict_pdf(self, pdf):
+    def predict_pdf(self, pdf, verbose=False):
         with temporary_pdf_file(pdf) as pdf_path:
-            image_metadata_pairs = self.__extract_image_metadata_pairs(pdf_path)
+            image_metadata_pairs = self.__extract_image_metadata_pairs(pdf_path, verbose=verbose)
             return self.__predict_images(image_metadata_pairs)
 
     def __predict_images(self, image_metadata_pairs: Iterable, batch_size: int = CONFIG.service.batch_size):

image_prediction/utils.py

@@ -1,9 +1,35 @@
+import logging
 import tempfile
 from contextlib import contextmanager
 
+from image_prediction.config import CONFIG
+
 
 @contextmanager
 def temporary_pdf_file(pdf: bytes):
     with tempfile.NamedTemporaryFile() as f:
         f.write(pdf)
         yield f.name
+
+
+def make_logger_getter():
+
+    logger = logging.getLogger("imgclf")
+    logger.propagate = False
+
+    handler = logging.StreamHandler()
+    handler.setLevel(CONFIG.service.logging_level)
+
+    log_format = "[%(levelname)s]: %(message)s"
+    formatter = logging.Formatter(log_format)
+
+    handler.setFormatter(formatter)
+    logger.addHandler(handler)
+
+    def get_logger():
+        return logger
+
+    return get_logger
+
+
+get_logger = make_logger_getter()