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File size: 1,699 Bytes
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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", None)
return scanners
# convert a list of scanners to yaml file
def write_scanners(scanners):
with open(YAML_PATH, "r") as f:
config = yaml.load(f, Loader=yaml.FullLoader)
config["detectors"] = scanners
with open(YAML_PATH, "w") as f:
# save scanners to detectors in yaml
yaml.dump(config, f, Dumper=Dumper)
# read model_type from yaml file
def read_model_type(path):
model_type = ""
with open(path, "r") as f:
config = yaml.load(f, Loader=yaml.FullLoader)
model_type = config.get("model_type", None)
return model_type
# write model_type to yaml file
def write_model_type(use_inference):
with open(YAML_PATH, "r") as f:
config = yaml.load(f, Loader=yaml.FullLoader)
if use_inference:
config["model_type"] = ['hf_inference_api']
else:
config["model_type"] = ['hf_pipeline']
with open(YAML_PATH, "w") as f:
# save model_type to model_type 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 |