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fix: Do not apply nutriscore prediction if it does not match the current nutriscore of the product #772

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fix: Do not apply nutriscore prediction if it does not match the current nutriscore of the product #772

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6680b9d
Testcase written.
Jagrutiti May 19, 2022
201911f
Merge https://github.com/openfoodfacts/robotoff into robtoff-#115
Jagrutiti May 24, 2022
c570894
Modifying test cases
Jagrutiti May 24, 2022
a37d23f
Modifying test cases
Jagrutiti May 24, 2022
a0e2805
Modified test cases. They are still incomplete
Jagrutiti May 31, 2022
92e91ed
Updated test cases
Jagrutiti Jun 19, 2022
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5 changes: 5 additions & 0 deletions tests/data.py
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from robotoff.utils import get_image_from_url

image = get_image_from_url('https://world.openfoodfacts.org/images/products/330/274/003/0949/front_fr.110.400.jpg', error_raise=False)
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You misinterpret my previous command about putting the image in test/data, it was to put the image file in a data/ folder :-) (that is the jpg file).

But I think you don't need that.


# expected_predictions_all = [Prediction(type=<PredictionType.nutrient: 'nutrient'>, data={'nutrients': {'salt': [{'raw': 'sel: 1,7 g', 'nutrient': 'salt', 'value': '1.7', 'unit': 'g'}]}, 'version': '2'}, value_tag=None, value=None, automatic_processing=None, predictor=None, barcode='3302740030949', timestamp=None, source_image='/802/509/314/0251/4.jpg', server_domain=None, id=None), Prediction(type=<PredictionType.nutrient_mention: 'nutrient_mention'>, data={'mentions': {'saturated_fat': [{'raw': 'acides gras saturés', 'span': [101, 120], 'languages': ['fr']}], 'fat': [{'raw': 'lipides', 'span': [87, 94], 'languages': ['fr']}], 'sugar': [{'raw': 'sucres', 'span': [151, 157], 'languages': ['fr']}], 'protein': [{'raw': 'proteines', 'span': [158, 167], 'languages': ['fr']}], 'salt': [{'raw': 'sel', 'span': [168, 171], 'languages': ['fr']}], 'nutrition_values': [{'raw': 'valeur nutritionnelle', 'span': [0, 21], 'languages': ['fr']}]}, 'version': '2'}, value_tag=None, value=None, automatic_processing=None, predictor=None, barcode='3302740030949', timestamp=None, source_image='/802/509/314/0251/4.jpg', server_domain=None, id=None), Prediction(type=<PredictionType.image_lang: 'image_lang'>, data={'count': {'fr': 25, 'null': 5, 'words': 35, 'en': 2, 'it': 3}, 'percent': {'fr': 71.42857142857143, 'null': 14.285714285714286, 'en': 5.714285714285714, 'it': 8.571428571428571}}, value_tag=None, value=None, automatic_processing=None, predictor=None, barcode='3302740030949', timestamp=None, source_image='/802/509/314/0251/4.jpg', server_domain=None, id=None), Prediction(type=<PredictionType.image_orientation: 'image_orientation'>, data={'count': {'up': 35}, 'orientation': 'up', 'rotation': 0}, value_tag=None, value=None, automatic_processing=None, predictor=None, barcode='3302740030949', timestamp=None, source_image='/802/509/314/0251/4.jpg', server_domain=None, id=None)]
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I think that's what you get when you run the prediction locally, but as you don't have models for nutriscore locally (they are big models), this give you no prediction with value_tag="en:nutriscore-c". You only get predictions computed by other kind of predictors.

But as told below, this is not a problem, we are not interested in testing get_image_from_url but the import process.

16 changes: 16 additions & 0 deletions tests/unit/workers/tasks/test_import_image.py
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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


def test_import_insights_from_image(mock):
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 = get_predictions_from_image(barcode, data.image, source_image, ocr_url)
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We don't want to really call get_predictions_from_image because this would need to have the model, and we don't want to have it in dev / test environment.

That's why you will mock the function below.

Instead you should have something like:

expected_predictions_all = [
   PredictionFactory(type=label,  barcode=barcode, data={"model": "nutriscore", "confidence": 0.871872]}, value_tag="en:nutriscore-c", value="", automatic_processing="f")
   PredictionFactory(type=label,  barcode=barcode, data={"....

Where parameter of your instances of PredictionFactory are taken from #115 (comment) but I don't take unnecessary values (some values are automatically generated by PredictionFactory, you can look at its code).


get_predictions_from_image = mock.Mock(return_value=data.expected_predictions_all)