GoogleCloudPlatform / genai-product-catalog

Intro

Conditional complexity (also called cyclomatic complexity) is a term used to measure the complexity of software. The term refers to the number of possible paths through a program function. A higher value ofter means higher maintenance and testing costs (infosecinstitute.com).
Conditional complexity is calculated by counting all conditions in the program that can affect the execution path (e.g. if statement, loops, switches, and/or operators, try and catch blocks...).
Conditional complexity is measured at the unit level (methods, functions...).
Units are classified in four categories based on the measured McCabe index: 1-5 (simple units), 6-10 (medium complex units), 11-25 (complex units), 26+ (very complex units).

Learn more...

Conditional Complexity Overall

There are 60 units with 581 lines of code in units (5.3% of code).

0 very complex units (0 lines of code)
0 complex units (0 lines of code)
1 medium complex units (36 lines of code)
4 simple units (94 lines of code)
55 very simple units (451 lines of code)

Legend:

51+

26-50

11-25

6-10

1-5

Alternative Visuals

3D view of all units

Conditional Complexity per Extension

51+

26-50

11-25

6-10

1-5

Conditional Complexity per Logical Component

primary logical decomposition

51+

26-50

11-25

6-10

1-5

Most Complex Units

Top 50 most complex units

Unit	# lines	McCabe index	# params
def get_embedding() in experiments/legacy/backend/embeddings.py	36	12	4
def _rank() in experiments/google/cloud/ml/applied/categories/category.py	27	8	2
def join_categories() in experiments/legacy/backend/category.py	21	6	3
def _rank() in experiments/legacy/backend/category.py	24	6	2
def join_categories() in experiments/google/cloud/ml/applied/categories/category.py	22	6	1
def retrieve() in experiments/legacy/backend/category.py	16	5	5
def get_nn() in experiments/legacy/backend/nearest_neighbors.py	16	5	3
def retrieve() in experiments/legacy/backend/attributes.py	20	5	6
def retrieve_and_rank() in experiments/legacy/backend/category.py	10	4	5
def __init__() in experiments/legacy/backend/embeddings.py	7	4	4
def __init__() in experiments/google/cloud/ml/applied/embeddings/embeddings.py	10	4	1
def image_to_attributes() in experiments/google/cloud/ml/applied/images/image_to_text.py	20	4	1
def rank() in experiments/legacy/backend/category.py	7	3	2
def generate_prompt() in experiments/legacy/backend/attributes.py	7	3	2
def rank() in experiments/google/cloud/ml/applied/categories/category.py	7	3	2
def join_attributes_desc() in experiments/google/cloud/ml/applied/attributes/attributes.py	24	3	1
def generate_prompt() in experiments/google/cloud/ml/applied/attributes/attributes.py	11	3	2
def generate_attributes() in experiments/google/cloud/ml/applied/attributes/attributes.py	18	3	2
def insert_dp() in experiments/google/cloud/ml/applied/embeddings/search.py	28	3	3
def upsert_dp() in experiments/google/cloud/ml/applied/embeddings/search.py	10	3	4
def delete_dp() in experiments/google/cloud/ml/applied/embeddings/search.py	9	3	1
def image_to_product_description() in experiments/google/cloud/ml/applied/images/image_to_text.py	12	3	1
def join_attributes_desc() in experiments/legacy/backend/attributes.py	11	2	1
def parse_answer() in experiments/legacy/backend/attributes.py	6	2	1
def parse_project_attributes_from_dict() in experiments/google/cloud/ml/applied/model/domain_model.py	7	2	1
def parse_list_to_dict() in experiments/google/cloud/ml/applied/model/domain_model.py	7	2	1
def attribute_values_to_dict() in experiments/google/cloud/ml/applied/model/domain_model.py	5	2	1
def dict_to_attribute_values() in experiments/google/cloud/ml/applied/model/domain_model.py	5	2	2
def parse_answer() in experiments/google/cloud/ml/applied/attributes/attributes.py	6	2	1
def remove_dp() in experiments/google/cloud/ml/applied/embeddings/search.py	18	2	1
def get_url_from_gcs() in experiments/google/cloud/ml/applied/images/image_to_text.py	5	2	1
def content_generation() in experiments/google/cloud/ml/applied/images/image_to_text.py	15	2	2
def generate_marketing_copy() in experiments/legacy/backend/marketing.py	11	1	3
def get_client() in experiments/legacy/backend/embeddings.py	2	1	1
def embed() in experiments/legacy/backend/embeddings.py	10	1	4
def get_bq_client() in experiments/legacy/backend/utils.py	2	1	1
def get_llm() in experiments/legacy/backend/utils.py	3	1	2
def suggest_categories() in experiments/legacy/backend/api.py	6	1	1
def generate_marketing_copy() in experiments/legacy/backend/api.py	3	1	3
def suggest_attributes() in experiments/legacy/backend/api.py	7	1	1
def generate_attributes() in experiments/legacy/backend/attributes.py	3	1	0
def retrieve_and_generate_attributes() in experiments/legacy/backend/attributes.py	7	1	0
def generate_marketing_copy() in experiments/google/cloud/ml/applied/marketing/marketing.py	8	1	1
def get_bq_client() in experiments/google/cloud/ml/applied/utils/utils.py	2	1	2
def get_llm() in experiments/google/cloud/ml/applied/utils/utils.py	8	1	0
def get_gemini_pro_vision() in experiments/google/cloud/ml/applied/utils/utils.py	3	1	0
def get_vector_search_index_client() in experiments/google/cloud/ml/applied/utils/utils.py	5	1	0
def retrieve() in experiments/google/cloud/ml/applied/categories/category.py	5	1	0
def retrieve_and_rank() in experiments/google/cloud/ml/applied/categories/category.py	5	1	0
def get_nn() in experiments/google/cloud/ml/applied/knn/nearest_neighbors.py	4	1	0