fix: Do not apply nutriscore prediction if it does not match the current nutriscore of the product

tests/integration/insights/test_category_import.py

@@ -66,6 +66,7 @@ class TestCategoryImporter:
 
     We only test the scenarios that are of actual use
     """
+
     def fake_product_store(self):
         return {barcode1: Product({"categories_tags": ["en:Fish"]})}
 
@@ -131,7 +132,6 @@ def test_import_one(self, predictions):
         assert not inserted.automatic_processing
 
     def test_import_auto(self):
-        import pdb; pdb.set_trace()
         imported = self._run_import(
             [neural_prediction("en:smoked-salmons", confidence=0.91, auto=True)]
         )

tests/unit/insights/test_importer.py

@@ -686,8 +686,6 @@ def test_generate_candidates(self):
 
 
 class TestLabelInsightImporter:
-    import pdb;pdb.set_trace();
-
     def test_get_type(self):
         assert LabelInsightImporter.get_type() == InsightType.label
 

tests/unit/workers/tasks/test_import_image.py

@@ -1,27 +1,82 @@
+import requests
 from PIL import Image
-from tests import data
-from robotoff.insights.extraction import get_predictions_from_image
-from robotoff.insights.importer import import_insights
-from unittest.mock import Mock
 
+from robotoff import settings
+from tests.integration.models_utils import PredictionFactory
 
-def test_import_insights_from_image(mock):
+
+def test_import_insights_from_image(mocker):
     barcode = "3302740030949"
-    source_image = '/330/274/003/0949/6.jpg '
-    ocr_url = 'https://world.openfoodfacts.org/images/products/802/509/314/0251/4.json'
-    server_domain = 'api.openfoodfacts.org'
-
-    expected_predictions_all =  [
-        PredictionFactory(type='label',  barcode=3302740030949, data={"model": "nutriscore", "confidence": 0.871872, "bounding_box": [1441.3848, 1036.8024, 1685.6928, 1430.2488]}, value_tag="en:nutriscore-c", value="", automatic_processing=False),
-        PredictionFactory(type='label',  barcode=3302740030949, data={"model": "nutriscore", "confidence": 0.816114128,  "bounding_box": [0.591395915, 0.477426708, 0.688566923, 0.624632716]}, value_tag="en:nutriscore-a", value="", automatic_processing=False),
-        PredictionFactory(type='label',  barcode=3302740030949, data={"model": "nutriscore", "confidence": 0.913557172, "bounding_box": [0.596912444, 0.429353863, 0.70557, 0.592592061]}, value_tag="en:nutriscore-c", value="", automatic_processing=False),
-        PredictionFactory(type='label',  barcode=3302740030949, data={"model": "nutriscore", "confidence": 0.99035, "bounding_box": [0.575399518, 0.472130537, 0.691237211, 0.625134587]}, value_tag="en:nutriscore-e", value="", automatic_processing=False)
-    ]
+    source_image = "https://world.openfoodfacts.org/images/products/330/274/003/0949/front_fr.110.400.jpg"
+    ocr_url = "https://world.openfoodfacts.org/images/products/802/509/314/0251/4.json"
+    server_domain = settings.OFF_SERVER_DOMAIN
 
-    get_predictions_from_image = mock.Mock(return_value=expected_predictions_all)
+    image = Image.open(requests.get(source_image, stream=True).raw)
 
-    get_product = {"code":"3302740030949","product":{"labels":"Sans colorants, Nutriscore, Nutriscore C, Fumé au bois de hêtre","product_name":"Pommes de terre gratinées aux lardons et champignons"},"status":1,"status_verbose":"product found"}
+    mock_import_insights = mocker.patch(
+        "robotoff.insights.importer.import_insights", return_value=4
+    )
+    mock_get_predictions_from_image = mocker.patch(
+        "robotoff.insights.extraction.get_predictions_from_image"
+    )
+
+    predictions_list = [
+        PredictionFactory(
+            type="label",
+            barcode=3302740030949,
+            data={
+                "model": "nutriscore",
+                "confidence": 0.871872,
+                "bounding_box": [1441.3848, 1036.8024, 1685.6928, 1430.2488],
+            },
+            value_tag="en:nutriscore-c",
+            value="",
+            automatic_processing=False,
+        ),
+        PredictionFactory(
+            type="label",
+            barcode=3302740030949,
+            data={
+                "model": "nutriscore",
+                "confidence": 0.816114128,
+                "bounding_box": [0.591395915, 0.477426708, 0.688566923, 0.624632716],
+            },
+            value_tag="en:nutriscore-a",
+            value="",
+            automatic_processing=False,
+        ),
+        PredictionFactory(
+            type="label",
+            barcode=3302740030949,
+            data={
+                "model": "nutriscore",
+                "confidence": 0.913557172,
+                "bounding_box": [0.596912444, 0.429353863, 0.70557, 0.592592061],
+            },
+            value_tag="en:nutriscore-c",
+            value="",
+            automatic_processing=False,
+        ),
+        PredictionFactory(
+            type="label",
+            barcode=3302740030949,
+            data={
+                "model": "nutriscore",
+                "confidence": 0.99035,
+                "bounding_box": [0.575399518, 0.472130537, 0.691237211, 0.625134587],
+            },
+            value_tag="en:nutriscore-e",
+            value="",
+            automatic_processing=False,
+        ),
+    ]
 
-    # import_insights = mock.Mock(return_value=)
-
+    mock_import_insights.assert_called_once_with(
+        predictions=predictions_list,
+        server_domain=server_domain,
+        automatic=True,
+    )
 
+    mock_get_predictions_from_image.assert_called_once_with(
+        barcode=barcode, image=image, source_image=source_image, ocr_url=ocr_url
+    )