fix: fix unit and integration tests

robotoff/cli/main.py

@@ -153,7 +153,7 @@ def categorize(barcode: str, deepest_only: bool = False) -> None:
 
     if predictions:
         for prediction in predictions:
-            print(f"{prediction.value_tag}: {prediction.data['confidence']}")
+            print(f"{prediction.value_tag}: {prediction.confidence}")
     else:
         print(f"Nothing predicted for product {barcode}")
 

robotoff/insights/importer.py

@@ -933,17 +933,12 @@ def create_prediction_model(
     server_domain: str,
     timestamp: datetime.datetime,
 ):
+    prediction_dict = prediction.to_dict()
+    prediction_dict.pop("id")
     return {
-        "barcode": prediction.barcode,
-        "type": prediction.type.name,
-        "data": prediction.data,
+        **prediction_dict,
         "timestamp": timestamp,
-        "value_tag": prediction.value_tag,
-        "value": prediction.value,
-        "source_image": prediction.source_image,
-        "automatic_processing": prediction.automatic_processing,
         "server_domain": server_domain,
-        "predictor": prediction.predictor,
     }
 
 

robotoff/logos.py

@@ -339,11 +339,7 @@ def import_logo_insights(
         )
         & (~(PredictionModel.data["is_annotation"].cast("bool") == True))  # noqa: E712
         # Add a filter on barcode to speed-up filtering
-        & (
-            PredictionModel.barcode.in_(
-                [logo.image_prediction.image.barcode for logo in logos]
-            )
-        )
+        & (PredictionModel.barcode.in_([logo.barcode for logo in logos]))
     ).execute()
     predictions = predict_logo_predictions(selected_logos, logo_probs)
     import_result = import_insights(predictions, server_domain)

robotoff/models.py

@@ -277,9 +277,7 @@ class Meta:
         constraints = [peewee.SQL("UNIQUE(image_prediction_id, index)")]
 
     def get_crop_image_url(self) -> str:
-        return crop_image_url(
-            self.image_prediction.image.source_image, self.bounding_box
-        )
+        return crop_image_url(self.source_image, self.bounding_box)
 
 
 class LogoEmbedding(BaseModel):

robotoff/slack.py

@@ -271,7 +271,7 @@ def send_logo_notification(
                 )
             )
         )
-        barcode = logo.image_prediction.image.barcode
+        barcode = logo.barcode
         base_off_url = settings.BaseURLProvider().get()
         text = (
             f"Prediction for <{crop_url}|image> "

robotoff/types.py

@@ -191,7 +191,7 @@ def __repr__(self) -> str:
         return (
             f"<InsightImportResult insights: created={self.created_insights_count()}, "
             f"updated={self.updated_insights_count()}, "
-            f"deleted={self.deleted_insights_count()}\n"
-            f"types: {list(set(result.type.value for result in self.product_insight_import_results))}"
-            f" predictions: created: {self.created_predictions_count()}>"
+            f"deleted={self.deleted_insights_count()}, "
+            f"types: {list(set(result.type.value for result in self.product_insight_import_results))}, "
+            f"predictions: created={self.created_predictions_count()}>"
         )

tests/integration/models_utils.py

@@ -5,7 +5,7 @@
 """
 import uuid
 from datetime import datetime
-from typing import Any
+from typing import Any, Optional
 
 import factory
 import numpy as np
@@ -67,7 +67,7 @@ class Meta:
     server_domain = factory.LazyFunction(lambda: settings.OFF_SERVER_DOMAIN)
     automatic_processing = None
     predictor = None
-    confidence = None
+    confidence: Optional[float] = None
 
 
 class AnnotationVoteFactory(UuidSequencer, PeeweeModelFactory):
@@ -125,6 +125,8 @@ class Meta:
     taxonomy_value = "fr:ab-agriculture-biologique"
     annotation_type = "label"
     nearest_neighbors = {"logo_ids": [111111, 222222], "distances": [11.1, 12.4]}
+    barcode = factory.Sequence(lambda n: f"{n:013}")
+    source_image = factory.Sequence(lambda n: f"/images/{n:02}.jpg")
 
 
 class LogoConfidenceThresholdFactory(PeeweeModelFactory):

tests/integration/test_annotate_image.py

@@ -155,12 +155,14 @@ def test_logo_annotation_incorrect_value_label_type(client, peewee_db):
 
 
 def test_logo_annotation_brand(client, peewee_db, monkeypatch, mocker, fake_taxonomy):
+    barcode = "0000000000001"
+    source_image = "/000/000/000/0001/2.jpg"
     with peewee_db:
         ann = LogoAnnotationFactory(
-            image_prediction__image__source_image="/images/2.jpg",
+            barcode=barcode,
+            source_image=source_image,
             annotation_type=None,
         )
-    barcode = ann.image_prediction.image.barcode
     _fake_store(monkeypatch, barcode)
     mocker.patch(
         "robotoff.brands.get_brand_prefix", return_value={("Etorki", "0000000xxxxxx")}
@@ -190,7 +192,7 @@ def test_logo_annotation_brand(client, peewee_db, monkeypatch, mocker, fake_taxo
     # we generate a prediction
 
     with peewee_db:
-        predictions = list(Prediction.select().filter(barcode=barcode).execute())
+        predictions = list(Prediction.select().where(Prediction.barcode == barcode))
     assert len(predictions) == 1
     (prediction,) = predictions
     assert prediction.type == "brand"
@@ -209,7 +211,9 @@ def test_logo_annotation_brand(client, peewee_db, monkeypatch, mocker, fake_taxo
     # We check that this prediction in turn generates an insight
 
     with peewee_db:
-        insights = list(ProductInsight.select().filter(barcode=barcode).execute())
+        insights = list(
+            ProductInsight.select().where(ProductInsight.barcode == barcode)
+        )
     assert len(insights) == 1
     (insight,) = insights
     assert insight.type == "brand"
@@ -233,11 +237,12 @@ def test_logo_annotation_label(client, peewee_db, monkeypatch, fake_taxonomy, mo
     """This test will check that, given an image with a logo above the confidence threshold,
     that is then fed into the ANN logos and labels model, we annotate properly a product.
     """
+    barcode = "0000000000001"
+    source_image = "/000/000/000/0001/2.jpg"
     with peewee_db:
         ann = LogoAnnotationFactory(
-            image_prediction__image__source_image="/images/2.jpg", annotation_type=None
+            barcode=barcode, source_image=source_image, annotation_type=None
         )
-    barcode = ann.image_prediction.image.barcode
     _fake_store(monkeypatch, barcode)
     mocker.patch("robotoff.logos.annotate", return_value=UPDATED_ANNOTATION_RESULT)
     start = datetime.utcnow()

tests/integration/test_api.py

@@ -821,11 +821,13 @@ def test_image_prediction_collection_empty(client):
 def test_image_prediction_collection(client, peewee_db):
     with peewee_db:
         logo_annotation_category_123 = LogoAnnotationFactory(
+            barcode="123",
             image_prediction__image__barcode="123",
             image_prediction__type="category",
         )
         prediction_category_123 = logo_annotation_category_123.image_prediction
         logo_annotation_label_789 = LogoAnnotationFactory(
+            barcode="789",
             image_prediction__image__barcode="789",
             image_prediction__type="label",
         )
@@ -904,31 +906,37 @@ def test_logo_annotation_collection_api(client, peewee_db):
     with peewee_db:
         LogoAnnotation.delete().execute()  # remove default sample
         annotation_123_1 = LogoAnnotationFactory(
+            barcode="123",
             image_prediction__image__barcode="123",
             annotation_value_tag="etorki",
             annotation_type="brand",
         )
         annotation_123_2 = LogoAnnotationFactory(
+            barcode="123",
             image_prediction__image__barcode="123",
             annotation_value_tag="etorki",
             annotation_type="brand",
         )
         annotation_295 = LogoAnnotationFactory(
+            barcode="295",
             image_prediction__image__barcode="295",
             annotation_value_tag="cheese",
             annotation_type="dairies",
         )
         annotation_789 = LogoAnnotationFactory(
+            barcode="789",
             image_prediction__image__barcode="789",
             annotation_value_tag="creme",
             annotation_type="dairies",
         )
         annotation_306 = LogoAnnotationFactory(
+            barcode="306",
             image_prediction__image__barcode="306",
             annotation_value_tag="yoghurt",
             annotation_type="dairies",
         )
         annotation_604 = LogoAnnotationFactory(
+            barcode="604",
             image_prediction__image__barcode="604",
             annotation_value_tag="meat",
             annotation_type="category",

tests/integration/test_core_integration.py

@@ -203,29 +203,34 @@ def test_get_unanswered_questions_list():
 
 def test_get_logo_annotation():
     annotation_123 = LogoAnnotationFactory(
+        barcode="123",
         image_prediction__image__barcode="123",
         annotation_value_tag="etorki",
         annotation_type="brand",
     )
 
     annotation_789 = LogoAnnotationFactory(
+        barcode="789",
         image_prediction__image__barcode="789",
         annotation_value_tag="creme",
         annotation_type="dairies",
     )
 
     annotation_295 = LogoAnnotationFactory(
+        barcode="295",
         image_prediction__image__barcode="295",
         annotation_value_tag="cheese",
         annotation_type="dairies",
     )
 
     annotation_396 = LogoAnnotationFactory(
+        barcode="396",
         image_prediction__image__barcode="396",
         annotation_type="label",
     )
 
     LogoAnnotationFactory(
+        barcode="306",
         image_prediction__image__barcode="306",
         annotation_value_tag="yoghurt",
         annotation_type="dairies",

tests/unit/test_logos.py

@@ -21,8 +21,8 @@ def test_compute_iou(box_1, box_2, expected_iou):
 @pytest.mark.parametrize(
     "logo_type,logo_value,data,automatic_processing,confidence,prediction",
     [
-        ("category", "en:breads", {}, False, None),
-        ("label", None, {}, False, None),
+        ("category", "en:breads", {}, 0.1, False, None),
+        ("label", None, {}, 0.1, False, None),
         (
             "label",
             "en:eu-organic",
@@ -36,7 +36,7 @@ def test_compute_iou(box_1, box_2, expected_iou):
                 value=None,
                 automatic_processing=False,
                 predictor="universal-logo-detector",
-                confidence=0.5,
+                confidence=0.8,
             ),
         ),
         (

tests/unit/test_models.py

@@ -11,16 +11,18 @@ def test_crop_image_url(monkeypatch):
             model_version="1.0",
             image=ImageModel(
                 barcode="123",
-                image_id="image_id",
-                source_image="/image",
+                image_id="1",
+                source_image="/123/1.jpg",
                 width=20,
                 height=20,
             ),
         ),
         bounding_box=(1, 1, 2, 2),
+        barcode="123",
+        source_image="/123/1.jpg",
     )
 
     assert logo_annotation.get_crop_image_url() == (
         f"https://robotoff.{settings._robotoff_domain}/api/v1/images/crop"
-        + f"?image_url={settings.OFF_IMAGE_BASE_URL}/image&y_min=1&x_min=1&y_max=2&x_max=2"
+        + f"?image_url={settings.OFF_IMAGE_BASE_URL}/123/1.jpg&y_min=1&x_min=1&y_max=2&x_max=2"
     )

tests/unit/test_slack.py

@@ -211,12 +211,13 @@ def test_noop_slack_notifier_logging(caplog):
     notifier.send_logo_notification(
         LogoAnnotation(
             image_prediction=ImagePrediction(
-                barcode="123",
                 image=ImageModel(
-                    source_image="/path/to/image.jpg", width=10, height=10
+                    barcode="123", source_image="/123/1.jpg", width=10, height=10
                 ),
             ),
             bounding_box=(1, 1, 2, 2),
+            barcode="123",
+            source_image="/123/1.jpg",
         ),
         {},
     )