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import re
import json
from unidecode import unidecode
import pandas as pd
from presidio_anonymizer.entities import OperatorConfig
from presidio_analyzer import AnalyzerEngine, PatternRecognizer
from presidio_analyzer.nlp_engine import NlpEngineProvider
from presidio_anonymizer import AnonymizerEngine
import streamlit as st
from .web_utilities import st_cache_data_if, st_cache_resource_if, supported_cache
@st_cache_data_if(supported_cache, max_entries=5, ttl=3600)
def anonymize_analyzer(MarianText_letter, _analyzer, proper_noun, Last_name, First_name):
MarianText_anonymize_letter = MarianText_letter
# st.write(MarianText_anonymize_letter)
analyzer_results_keep = []
analyzer_results_return = []
analyzer_results_saved = []
analyzer_results = _analyzer.analyze(
text=MarianText_letter,
language="en",
entities=["DATE_TIME", "PERSON", "FRENCH_CITY"],
allow_list=[
"evening",
"day",
"the day",
"the age of",
"age",
"years",
"week",
"years old",
"months",
"hours",
"night",
"noon",
"nights",
"tomorrow",
"today",
"yesterday",
],
)
len_to_add = 0
analyser_results_to_sort = {}
i = 0
detect_duplicated = []
for element in analyzer_results:
if element.start not in detect_duplicated:
analyser_results_to_sort[i] = element.start
detect_duplicated.append(element.start)
else:
pass
i = i + 1
sorted_tuples = sorted(analyser_results_to_sort.items(), key=lambda x: x[1])
sorted_dict = {k: v for k, v in sorted_tuples}
print(sorted_dict)
exception_list_presidio = ["age", "year", "month", "day", "hour", "week"]
for element_raw in sorted_dict:
element = analyzer_results[element_raw]
word = MarianText_letter[element.start : element.end]
exception_detected = [e for e in exception_list_presidio if e in word.lower()]
if word.count("/") == 1 or word.count("/") > 2:
exception_detected.append("/ or ///")
if len(exception_detected) == 0:
if word.lower().strip() in proper_noun:
word_to_replace = (
"**:green[" + word + "]** `[" + element.entity_type + "]`"
)
MarianText_anonymize_letter = (
MarianText_anonymize_letter[: element.start + len_to_add]
+ word_to_replace
+ MarianText_anonymize_letter[element.end + len_to_add :]
)
analyzer_results_saved.append(
{
"name": Last_name,
"surname": First_name,
"type": "deidentification",
"value": word,
"correction": element.entity_type,
"lf_detected": False,
"manual_validation": False,
}
)
# analyzer_results_saved.append(str(element) + ", word:" + word)
else:
word_to_replace = (
"**:red[" + word + "]** `[" + element.entity_type + "]`"
)
MarianText_anonymize_letter = (
MarianText_anonymize_letter[: element.start + len_to_add]
+ word_to_replace
+ MarianText_anonymize_letter[element.end + len_to_add :]
)
analyzer_results_keep.append(
{
"name": Last_name,
"surname": First_name,
"type": "deidentification",
"value": word,
"correction": element.entity_type,
"lf_detected": True,
"manual_validation": True,
}
)
# analyzer_results_keep.append(str(element) + ", word:" + word)
analyzer_results_return.append(element)
len_to_add = len_to_add + len(word_to_replace) - len(word)
else:
analyzer_results_saved.append(
{
"name": Last_name,
"surname": First_name,
"type": "deidentification",
"value": word,
"correction": element.entity_type,
"lf_detected": False,
"manual_validation": False,
}
)
# analyzer_results_saved.append(str(element) + ", word:" + word)
del analyzer_results
del len_to_add
del exception_list_presidio
del analyser_results_to_sort
del sorted_tuples
del sorted_dict
return (
MarianText_anonymize_letter,
analyzer_results_return,
analyzer_results_keep,
analyzer_results_saved,
)
@st_cache_data_if(supported_cache, max_entries=5, ttl=3600)
def anonymize_engine(MarianText_letter, _analyzer_results_return, _engine, _nlp):
result = _engine.anonymize(
text=MarianText_letter,
analyzer_results=_analyzer_results_return,
operators={
"PERSON": OperatorConfig("replace", {"new_value": ""}),
"LOCATION": OperatorConfig("replace", {"new_value": ""}),
"FRENCH_CITY": OperatorConfig("replace", {"new_value": ""}),
},
)
return reformat_to_report(result.text, _nlp)
@st_cache_data_if(supported_cache, max_entries=10, ttl=3600)
def add_space_to_comma(texte, _nlp):
text_list = []
regex = "(?<!\d)(\,)(?!\d)(?!.*\1)"
for sentence in _nlp.process(texte).sentences:
text_space = re.sub(regex, " , ", sentence.text.replace("\n", " "))
text_space_no_db = text_space.replace(" ", " ")
text_list.append(text_space_no_db)
# print(text_space_no_db)
return " ".join(text_list)
@st_cache_data_if(supported_cache, max_entries=10, ttl=3600)
def add_space_to_endpoint(texte, _nlp):
text_list = []
regex = "(?<!\d)(\.)(?!\d)(?!.*\1)"
for sentence in _nlp.process(texte).sentences:
text_space = re.sub(regex, " . ", sentence.text.replace("\n", " "))
text_space_no_db = text_space.replace(" ", " ")
text_list.append(text_space_no_db)
# print(text_space_no_db)
return " ".join(text_list)
@st_cache_data_if(supported_cache, max_entries=10, ttl=3600)
def add_space_to_leftp(texte, _nlp):
text_list = []
regex = "(?<!\d)(\()(?!\d)(?!.*\1)"
for sentence in _nlp.process(texte).sentences:
text_space = re.sub(regex, " ( ", sentence.text.replace("\n", " "))
text_space_no_db = text_space.replace(" ", " ")
text_list.append(text_space_no_db)
# print(text_space_no_db)
return " ".join(text_list)
@st_cache_data_if(supported_cache, max_entries=10, ttl=3600)
def add_space_to_rightp(texte, _nlp):
text_list = []
regex = "(?<!\d)(\))(?!\d)(?!.*\1)"
for sentence in _nlp.process(texte).sentences:
text_space = re.sub(regex, " ) ", sentence.text.replace("\n", " "))
text_space_no_db = text_space.replace(" ", " ")
text_list.append(text_space_no_db)
# print(text_space_no_db)
return " ".join(text_list)
@st_cache_data_if(supported_cache, max_entries=10, ttl=3600)
def add_space_to_stroph(texte, _nlp):
text_list = []
regex = "(?<!\d)(')(?!\d)(?!.*\1)"
for sentence in _nlp.process(texte).sentences:
text_space = re.sub(regex, " ' ", sentence.text.replace("\n", " "))
text_space_no_db = text_space.replace(" ", " ")
text_list.append(text_space_no_db)
# print(text_space_no_db)
return " ".join(text_list)
@st_cache_data_if(supported_cache, max_entries=10, ttl=3600)
def add_space_to_comma_endpoint(texte, _nlp):
text_fr_comma = add_space_to_comma(texte, _nlp)
text_fr_comma_endpoint = add_space_to_endpoint(text_fr_comma, _nlp)
text_fr_comma_endpoint_leftpc = add_space_to_leftp(text_fr_comma_endpoint, _nlp)
text_fr_comma_endpoint_leftpc_right_pc = add_space_to_rightp(
text_fr_comma_endpoint_leftpc, _nlp
)
del text_fr_comma
del text_fr_comma_endpoint
del text_fr_comma_endpoint_leftpc
return text_fr_comma_endpoint_leftpc_right_pc
@st_cache_resource_if(supported_cache, max_entries=5, ttl=3600)
def get_abbreviation_dict_correction():
# dict_correction = {}
with open("data/fr_abbreviations.json", "r") as outfile:
hpo_abbreviations = json.load(outfile)
return hpo_abbreviations # dict_correction
@st_cache_data_if(supported_cache, max_entries=10, ttl=3600)
def reformat_to_report(text, _nlp):
cutsentence = []
for sentence in _nlp.process(text).sentences:
cutsentence.append(
sentence.text.replace(" ,", ",")
.replace(" .", ".")
.replace(" )", ")")
.replace(" (", "(")
.replace(" '", "'")
)
return " \n".join(cutsentence)
@st_cache_resource_if(supported_cache, max_entries=5, ttl=3600)
def get_cities_list():
cities = pd.read_csv("data/proper_noun_location_sort.csv")
cities.columns = ["ville"]
whole_cities_patterns = []
list_cities = cities["ville"].to_list()
for element in list_cities:
whole_cities_patterns.append(element)
whole_cities_patterns.append(element.lower().capitalize())
whole_cities_patterns.append(element.upper())
whole_cities_patterns.append(unidecode(element))
whole_cities_patterns.append(unidecode(element).lower().capitalize())
whole_cities_patterns.append(unidecode(element).upper())
del cities
del list_cities
return whole_cities_patterns
@st_cache_resource_if(supported_cache, max_entries=5, ttl=3600)
def get_list_not_deidentify():
proper_noun_data = pd.read_csv(
"data/exception_list_anonymization.tsv", sep="\t", header=None
).astype(str)
drug_data = pd.read_csv("data/drug_name.tsv", sep="\t", header=None).astype(str)
gene_data = pd.read_csv("data/gene_name.tsv", sep="\t", header=None).astype(str)
proper_noun_list = (
proper_noun_data[0].to_list()
+ drug_data[0].to_list()
+ gene_data[0].to_list()
+ [
"PN",
"TN",
"SD",
"PCN",
"cher",
"chere",
"CAS",
"INDEX",
"APGAR",
"M",
"Ms",
"Mr",
"Behçet",
"hypoacousia",
]
)
proper_noun = [x.lower() for x in proper_noun_list]
del proper_noun_data
del drug_data
del gene_data
del proper_noun_list
return proper_noun
@st_cache_resource_if(supported_cache, max_entries=5, ttl=3600)
def change_name_patient_abbreviations(Report, Last_name, First_name, abbreviations_dict):
Report_name = Report
dict_correction_name_abbreviations = {
"M.": "M",
"Mme.": "Mme",
"Mlle.": "Mlle",
"Dr.": "Docteur",
"Dr": "Docteur",
"Pr.": "Professeur",
"Pr": "Professeur",
}
for firstname in First_name.split():
dict_correction_name_abbreviations[firstname] = "CAS"
for lastname in Last_name.split():
dict_correction_name_abbreviations[lastname] = "INDEX"
for key, value in abbreviations_dict.items():
dict_correction_name_abbreviations[key] = value # + " [" + key + "]"
list_replaced = []
splitted_Report = Report_name.replace("\n", " ").split(" ")
replaced_Report = []
for i in splitted_Report:
append_word = i
replace_word = None
for key, value in dict_correction_name_abbreviations.items():
i_check = i.lower().strip().replace(",", "").replace(".", "")
if i_check == key.lower().strip():
to_replace = i.strip().replace(",", "").replace(".", "")
replace_word = value
if i_check == Last_name or i_check == First_name:
list_replaced.append(
{
"name": Last_name,
"surname": First_name,
"type": "index_case",
"value": i.strip().replace(",", "").replace(".", ""),
"correction": value,
"lf_detected": True,
"manual_validation": True,
}
)
else:
list_replaced.append(
{
"name": Last_name,
"surname": First_name,
"type": "abbreviations",
"value": i.strip().replace(",", "").replace(".", ""),
"correction": value,
"lf_detected": True,
"manual_validation": True,
}
)
if replace_word:
append_word = append_word.replace(to_replace, replace_word)
replaced_Report.append(append_word)
del dict_correction_name_abbreviations
del splitted_Report
return " ".join(replaced_Report), list_replaced
@st_cache_resource_if(supported_cache, max_entries=5, ttl=3600)
def config_deidentify(cities_list):
configuration = {
"nlp_engine_name": "spacy",
"models": [{"lang_code": "en", "model_name": "en_core_web_lg"}],
}
# Create NLP engine based on configuration
provider = NlpEngineProvider(nlp_configuration=configuration)
nlp_engine = provider.create_engine()
frcity_recognizer = PatternRecognizer(
supported_entity="FRENCH_CITY", deny_list=cities_list
)
analyzer = AnalyzerEngine(nlp_engine=nlp_engine, supported_languages=["en"])
analyzer.registry.add_recognizer(frcity_recognizer)
engine = AnonymizerEngine()
del configuration
del provider
del nlp_engine
del frcity_recognizer
return analyzer, engine |