fix: make logging call uniform

robotoff/app/api.py

@@ -472,7 +472,7 @@ def on_post(self, req: falcon.Request, resp: falcon.Response):
         server_domain = req.get_param("server_domain", required=True)
 
         if server_domain != settings.OFF_SERVER_DOMAIN:
-            logger.info(f"Rejecting image import from {server_domain}")
+            logger.info("Rejecting image import from %s", server_domain)
             resp.media = {
                 "status": "rejected",
             }

robotoff/app/events.py

@@ -57,7 +57,7 @@ def send_event(
         "device_id": device_id,
         "barcode": barcode,
     }
-    logger.debug(f"Event: {event}")
+    logger.debug("Event: %s", event)
     response = requests.post(api_url, json=event)
-    logger.debug(f"Event API response: {response}")
+    logger.debug("Event API response: %s", response)
     return response

robotoff/cli/main.py

@@ -72,7 +72,7 @@ def regenerate_ocr_insights(
     with db:
         imported = importer.import_insights(predictions, settings.OFF_SERVER_DOMAIN)
 
-    logger.info(f"Import finished, {imported} insights imported")
+    logger.info("Import finished, %s insights imported", imported)
 
 
 @app.command()

robotoff/elasticsearch/export.py

@@ -26,7 +26,7 @@ def _delete_existing_data(self, index: str) -> None:
             doc_type=settings.ELASTICSEARCH_TYPE,
         )
 
-        logger.info(f"Deleted %d documents from {index}", resp["deleted"])
+        logger.info("Deleted %d documents from %s", resp["deleted"], index)
 
     def _get_data(self, index: str) -> Iterable[tuple[str, dict]]:
         if index == settings.ElasticsearchIndex.PRODUCT:
@@ -37,7 +37,7 @@ def _get_data(self, index: str) -> Iterable[tuple[str, dict]]:
     def load_index(self, index: str, index_filepath: Path) -> None:
         """Creates the given index if it doesn't already exist."""
         if not self.es_client.indices.exists(index=index):
-            logger.info(f"Creating index: {index}")
+            logger.info("Creating index: %s", index)
             with open(index_filepath, "rb") as f:
                 conf = orjson.loads(f.read())
             self.es_client.indices.create(index=index, body=conf)
@@ -52,14 +52,14 @@ def export_index_data(self, index: str) -> int:
            but only async ones for categories.
 
         Returns the number of rows inserted into the index."""
-        logger.info(f"Deleting existing {index} data...")
+        logger.info("Deleting existing %s data...", index)
         self._delete_existing_data(index)
 
         index_data = self._get_data(index)
 
-        logger.info(f"Starting {index} export to Elasticsearch...")
+        logger.info("Starting %s export to Elasticsearch...", index)
 
         rows_inserted = perform_export(self.es_client, index_data, index)
 
-        logger.info(f"Inserted %d rows for index {index}", rows_inserted)
+        logger.info("Inserted %d rows for index %s", rows_inserted, index)
         return rows_inserted

robotoff/insights/extraction.py

@@ -117,7 +117,7 @@ def get_predictions_from_product_name(
 def extract_ocr_predictions(
     barcode: str, ocr_url: str, prediction_types: Iterable[PredictionType]
 ) -> list[Prediction]:
-    logger.info(f"Generating OCR predictions from OCR {ocr_url}")
+    logger.info("Generating OCR predictions from OCR %s", ocr_url)
 
     predictions_all: list[Prediction] = []
     source_image = get_source_from_url(ocr_url)

robotoff/insights/importer.py

@@ -85,7 +85,7 @@ def is_recent_image(
 
     for upload_datetime in remaining_datetimes:
         if upload_datetime - image_datetime > max_timedelta:
-            logger.debug(f"More recent image: {upload_datetime} > {image_datetime}")
+            logger.debug("More recent image: %s > %s", upload_datetime, image_datetime)
             return False
 
     return True
@@ -306,7 +306,7 @@ def import_insights(
         )
         if to_delete:
             to_delete_ids = [insight.id for insight in to_delete]
-            logger.info(f"Deleting {len(to_delete_ids)} insights")
+            logger.info("Deleting %s insights", len(to_delete_ids))
             ProductInsight.delete().where(
                 ProductInsight.id.in_(to_delete_ids)
             ).execute()
@@ -862,7 +862,7 @@ def is_in_barcode_range(barcode: str, tag: str) -> bool:
         brand_prefix = get_brand_prefix()
 
         if not in_barcode_range(brand_prefix, tag, barcode):
-            logger.info(f"Barcode {barcode} of brand {tag} not in barcode range")
+            logger.info("Barcode %s of brand %s not in barcode range", barcode, tag)
             return False
 
         return True
@@ -938,7 +938,7 @@ def is_valid_product_prediction(
     """
     if not product:
         # the product does not exist (deleted)
-        logger.info(f"Prediction of deleted product {prediction.barcode}")
+        logger.info("Prediction of deleted product %s", prediction.barcode)
         return False
 
     if prediction.source_image and not is_valid_image(
@@ -1138,7 +1138,7 @@ def import_predictions(
         updated_prediction_types_by_barcode[barcode] = set(
             prediction.type for prediction in product_predictions_group
         )
-    logger.info(f"{predictions_imported} predictions imported")
+    logger.info("%s predictions imported", predictions_imported)
     return updated_prediction_types_by_barcode
 
 

robotoff/logos.py

@@ -344,7 +344,7 @@ def generate_insights_from_annotated_logos(
     imported = import_insights(predictions, server_domain)
 
     if imported:
-        logger.info(f"{imported} logo insights imported after annotation")
+        logger.info("%s logo insights imported after annotation", imported)
     return imported
 
 

robotoff/off.py

@@ -426,7 +426,7 @@ def update_product(
     status = json.get("status_verbose")
 
     if status != "fields saved":
-        logger.warning(f"Unexpected status during product update: {status}")
+        logger.warning("Unexpected status during product update: %s", status)
 
 
 def move_to(barcode: str, to: ServerType, timeout: Optional[int] = 10) -> bool:

robotoff/prediction/category/matcher.py

@@ -103,7 +103,7 @@ def load_resources():
     get_intersect_categories_ingredients()
 
     for lang in SUPPORTED_LANG:
-        logger.info(f"Loading NLP for {lang}...")
+        logger.info("Loading NLP for %s...", lang)
         get_lemmatizing_nlp(lang)
 
 

robotoff/prediction/object_detection/core.py

@@ -213,10 +213,10 @@ def load_all(cls):
             model_name = model.value
             file_path = settings.MODELS_DIR / model_name
             if file_path.is_dir():
-                logger.info(f"Model '{model_name}' found")
+                logger.info("Model %s found", model_name)
                 cls.models[model_name] = cls.load(model_name, file_path)
             else:
-                logger.info(f"Missing model: '{model_name}'")
+                logger.info("Missing model: %s", model_name)
         cls._loaded = True
 
     @classmethod

robotoff/scheduler/__init__.py

@@ -216,7 +216,7 @@ def generate_insights():
         imported = import_insights(
             product_predictions_iter, server_domain=settings.OFF_SERVER_DOMAIN
         )
-    logger.info(f"{imported} category insights imported")
+    logger.info("%s category insights imported", imported)
 
 
 def transform_insight_iter(insights_iter: Iterable[dict]):

robotoff/taxonomy.py

@@ -286,7 +286,7 @@ def fetch_taxonomy(
         r = http_session.get(url, timeout=120)  # might take some time
         if r.status_code >= 300:
             raise requests.HTTPError(
-                "Taxonomy download at %s returned status code {r.status_code}", url
+                "Taxonomy download at %s returned status code %s", url, r.status_code
             )
         data = r.json()
     except Exception as e:

robotoff/workers/tasks/__init__.py

@@ -24,7 +24,7 @@ def delete_product_insights_job(barcode: str, server_domain: str):
     we must delete all of the associated predictions and insights that have
     not been annotated.
     """
-    logger.info(f"Product {barcode} deleted, deleting associated insights...")
+    logger.info("Product %s deleted, deleting associated insights...", barcode)
     deleted_predictions = (
         Prediction.delete()
         .where(

robotoff/workers/tasks/import_image.py

@@ -54,7 +54,7 @@ def run_import_image_job(
     image = get_image_from_url(image_url, error_raise=False, session=http_session)
 
     if image is None:
-        logger.info(f"Error while downloading image {image_url}")
+        logger.info("Error while downloading image %s", image_url)
         return
 
     source_image = get_source_from_url(image_url)
@@ -106,7 +106,7 @@ def import_insights_from_image(
     image = get_image_from_url(image_url, error_raise=False, session=http_session)
 
     if image is None:
-        logger.info(f"Error while downloading image {image_url}")
+        logger.info("Error while downloading image %s", image_url)
         return
 
     source_image = get_source_from_url(image_url)
@@ -133,7 +133,7 @@ def import_insights_from_image(
 
     with db:
         imported = import_insights(predictions, server_domain)
-        logger.info(f"Import finished, {imported} insights imported")
+        logger.info("Import finished, %s insights imported", imported)
 
 
 def save_image_job(batch: list[tuple[str, str]], server_domain: str):
@@ -221,7 +221,7 @@ def run_nutrition_table_object_detection(
     image = get_image_from_url(image_url, error_raise=False, session=http_session)
 
     if image is None:
-        logger.info(f"Error while downloading image {image_url}")
+        logger.info("Error while downloading image %s", image_url)
         return
 
     source_image = get_source_from_url(image_url)
@@ -250,7 +250,7 @@ def run_nutriscore_object_detection(barcode: str, image_url: str, server_domain:
     image = get_image_from_url(image_url, error_raise=False, session=http_session)
 
     if image is None:
-        logger.info(f"Error while downloading image {image_url}")
+        logger.info("Error while downloading image %s", image_url)
         return
 
     source_image = get_source_from_url(image_url)
@@ -312,7 +312,7 @@ def run_logo_object_detection(
     image = get_image_from_url(image_url, error_raise=False, session=http_session)
 
     if image is None:
-        logger.info(f"Error while downloading image {image_url}")
+        logger.info("Error while downloading image %s", image_url)
         return
 
     source_image = get_source_from_url(image_url)
@@ -342,7 +342,7 @@ def run_logo_object_detection(
                 ).id
             )
 
-    logger.info(f"{len(logo_ids)} logos found for image {source_image}")
+    logger.info("%s logos found for image %s", len(logo_ids), source_image)
     if logo_ids and process_logos:
         enqueue_job(
             process_created_logos,
@@ -369,15 +369,19 @@ def process_created_logos(image_prediction_id: int, server_domain: str):
             add_logos_to_ann(image_instance, logos)
         except (HTTPError, Timeout) as e:
             logger.info(
-                f"Request error during logo addition to ANN: {type(e).__name__}, {e}"
+                "Request error during logo addition to ANN: %s, %s",
+                type(e).__name__,
+                e,
             )
             return
 
         try:
             save_nearest_neighbors(logos)
         except (HTTPError, Timeout) as e:
             logger.info(
-                f"Request error during ANN batch query: {type(e).__name__}: {e}",
+                "Request error during ANN batch query: %s, %s",
+                type(e).__name__,
+                e,
             )
             return
 

robotoff/workers/tasks/product_updated.py

@@ -25,7 +25,7 @@ def update_insights_job(barcode: str, server_domain: str):
     1. Generate new predictions related to the product's category and name.
     2. Regenerate all insights from the product associated predictions.
     """
-    logger.info(f"Running `update_insights` for product {barcode} ({server_domain})")
+    logger.info("Running `update_insights` for product %s (%s)", barcode, server_domain)
 
     try:
         with Lock(
@@ -45,7 +45,7 @@ def update_insights_job(barcode: str, server_domain: str):
             updated_product_predict_insights(barcode, product_dict, server_domain)
             logger.info("Refreshing insights...")
             imported = refresh_insights(barcode, server_domain)
-            logger.info(f"{imported} insights created after refresh")
+            logger.info("%s insights created after refresh", imported)
     except LockedResourceException:
         logger.info(
             f"Couldn't acquire product_update lock, skipping product_update for product {barcode}"
@@ -85,7 +85,7 @@ def add_category_insight(barcode: str, product: JSONType, server_domain: str) ->
         prediction.barcode = barcode
 
     imported = import_insights(product_predictions, server_domain)
-    logger.info(f"{imported} category insight imported for product {barcode}")
+    logger.info("%s category insight imported for product %s", imported, barcode)
 
     return bool(imported)
 
@@ -102,7 +102,7 @@ def updated_product_predict_insights(
     logger.info("Generating predictions from product name...")
     predictions_all = get_predictions_from_product_name(barcode, product_name)
     imported = import_insights(predictions_all, server_domain)
-    logger.info(f"{imported} insights imported for product {barcode}")
+    logger.info("%s insights imported for product %s", imported, barcode)
 
     if imported:
         updated = True