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import yaml
YAML_PATH = "./config.yaml"
class Dumper(yaml.Dumper):
def increase_indent(self, flow=False, *args, **kwargs):
return super().increase_indent(flow=flow, indentless=False)
# read scanners from yaml file
# return a list of scanners
def read_scanners(path):
scanners = []
with open(path, "r") as f:
config = yaml.load(f, Loader=yaml.FullLoader)
scanners = config.get("detectors", [])
return scanners
# convert a list of scanners to yaml file
def write_scanners(scanners):
print(scanners)
with open(YAML_PATH, "r+") as f:
config = yaml.load(f, Loader=yaml.FullLoader)
if config:
config["detectors"] = scanners
# save scanners to detectors in yaml
yaml.dump(config, f, Dumper=Dumper)
# read model_type from yaml file
def read_inference_type(path):
inference_type = ""
with open(path, "r") as f:
config = yaml.load(f, Loader=yaml.FullLoader)
inference_type = config.get("inference_type", "")
return inference_type
# write model_type to yaml file
def write_inference_type(use_inference):
with open(YAML_PATH, "r+") as f:
config = yaml.load(f, Loader=yaml.FullLoader)
if use_inference:
config["inference_type"] = 'hf_inference_api'
else:
config["inference_type"] = 'hf_pipeline'
# save inference_type to inference_type in yaml
yaml.dump(config, f, Dumper=Dumper)
# read column mapping from yaml file
def read_column_mapping(path):
column_mapping = {}
with open(path, "r") as f:
config = yaml.load(f, Loader=yaml.FullLoader)
column_mapping = config.get("column_mapping", dict())
return column_mapping
# write column mapping to yaml file
def write_column_mapping(mapping):
with open(YAML_PATH, "r") as f:
config = yaml.load(f, Loader=yaml.FullLoader)
if mapping is None:
del config["column_mapping"]
else:
config["column_mapping"] = mapping
with open(YAML_PATH, "w") as f:
# save column_mapping to column_mapping in yaml
yaml.dump(config, f, Dumper=Dumper)
# convert column mapping dataframe to json
def convert_column_mapping_to_json(df, label=""):
column_mapping = {}
column_mapping[label] = []
for _, row in df.iterrows():
column_mapping[label].append(row.tolist())
return column_mapping
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