def test_product_insights_merge():
insights_1 = [
RawInsight(type=InsightType.label, data={}, value_tag="en:organic")
]
product_insights_1 = ProductInsights(
insights=insights_1,
barcode="123",
type=InsightType.label,
source_image="/123/1.jpg",
)
insights_2 = [
RawInsight(type=InsightType.label, data={}, value_tag="en:pgi")
]
product_insights_2 = ProductInsights(
insights=insights_2,
barcode="123",
type=InsightType.label,
source_image="/123/1.jpg",
)
merged_product_insights = ProductInsights.merge(
[product_insights_1, product_insights_2])
assert merged_product_insights.type == InsightType.label
assert merged_product_insights.barcode == "123"
assert merged_product_insights.source_image == "/123/1.jpg"
assert merged_product_insights.insights == insights_1 + insights_2
def add_category_insight(barcode: str, product: JSONType, server_domain: str) -> bool:
if get_server_type(server_domain) != ServerType.off:
return False
product_insights = []
product_insight = predict_category_from_product_es(product)
if product_insight is not None:
product_insights.append(product_insight)
product_insight = predict_category_from_product_ml(product, filter_blacklisted=True)
if product_insight is not None:
product_insights.append(product_insight)
if not product_insights:
return False
merged_product_insight = ProductInsights.merge(product_insights)
product_store = get_product_store()
importer = InsightImporterFactory.create(InsightType.category, product_store)
imported = importer.import_insights(
[merged_product_insight],
server_domain=server_domain,
automatic=False,
)
if imported:
logger.info("Category insight imported for product {}".format(barcode))
return bool(imported)
def get_insights_from_image(
barcode: str, image_url: str,
ocr_url: str) -> Dict[InsightType, ProductInsights]:
try:
ocr_insights = extract_ocr_insights(ocr_url,
IMAGE_IMPORT_INSIGHT_TYPES)
except requests.exceptions.RequestException as e:
logger.info("error during OCR JSON download", exc_info=e)
return {}
except OCRParsingException as e:
logger.error("OCR JSON Parsing error", exc_info=e)
return {}
extract_nutriscore = has_nutriscore_insight(
ocr_insights.get(InsightType.label, None))
image_ml_insights = extract_image_ml_insights(
image_url, extract_nutriscore=extract_nutriscore)
insight_types = set(ocr_insights.keys()).union(image_ml_insights.keys())
results: Dict[InsightType, ProductInsights] = {}
for insight_type in insight_types:
product_insights: List[ProductInsights] = []
if insight_type in ocr_insights:
product_insights.append(ocr_insights[insight_type])
if insight_type in image_ml_insights:
product_insights.append(image_ml_insights[insight_type])
results[insight_type] = ProductInsights.merge(product_insights)
return results
def test_product_insights_failed_merge():
with pytest.raises(ValueError):
ProductInsights.merge([])
with pytest.raises(ValueError):
ProductInsights.merge([
ProductInsights(
insights=[],
barcode="123",
type=InsightType.label,
source_image="/123/1.jpg",
),
ProductInsights(
insights=[],
barcode="234",
type=InsightType.label,
source_image="/123/1.jpg",
),
])
with pytest.raises(ValueError):
ProductInsights.merge([
ProductInsights(
insights=[],
barcode="123",
type=InsightType.label,
source_image="/123/1.jpg",
),
ProductInsights(
insights=[],
barcode="123",
type=InsightType.category,
source_image="/123/1.jpg",
),
])
with pytest.raises(ValueError):
ProductInsights.merge([
ProductInsights(
insights=[],
barcode="123",
type=InsightType.label,
source_image="/123/1.jpg",
),
ProductInsights(
insights=[],
barcode="123",
type=InsightType.label,
source_image="/123/2.jpg",
),
])
with pytest.raises(ValueError):
ProductInsights.merge([
ProductInsights(
insights=[],
barcode="123",
type=InsightType.label,
source_image="/123/1.jpg",
),
ProductInsights(
insights=[],
barcode="123",
type=InsightType.category,
source_image="/123/2.jpg",
),
])