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"""Streamlit app for Presidio."""
from json import JSONEncoder
from typing import List
import pandas as pd
import spacy
import streamlit as st
from annotated_text import annotated_text
from presidio_analyzer import AnalyzerEngine, RecognizerResult, RecognizerRegistry
from presidio_analyzer.nlp_engine import NlpEngineProvider
from presidio_anonymizer import AnonymizerEngine
from presidio_anonymizer.entities import OperatorConfig
from transformers_rec import (
STANFORD_COFIGURATION,
TransformersRecognizer,
BERT_DEID_CONFIGURATION,
)
# Helper methods
@st.cache
def analyzer_engine(model_path: str):
"""Return AnalyzerEngine.
:param model_path: Which model to use for NER:
"StanfordAIMI/stanford-deidentifier-base",
"obi/deid_roberta_i2b2",
"en_core_web_lg"
"""
registry = RecognizerRegistry()
registry.load_predefined_recognizers()
# Set up NLP Engine according to the model of choice
if model_path == "en_core_web_lg":
if not spacy.util.is_package("en_core_web_lg"):
spacy.cli.download("en_core_web_lg")
nlp_configuration = {
"nlp_engine_name": "spacy",
"models": [{"lang_code": "en", "model_name": "en_core_web_lg"}],
}
else:
if not spacy.util.is_package("en_core_web_sm"):
spacy.cli.download("en_core_web_sm")
# Using a small spaCy model + a HF NER model
transformers_recognizer = TransformersRecognizer(model_path=model_path)
if model_path == "StanfordAIMI/stanford-deidentifier-base":
transformers_recognizer.load_transformer(**STANFORD_COFIGURATION)
elif model_path == "obi/deid_roberta_i2b2":
transformers_recognizer.load_transformer(**BERT_DEID_CONFIGURATION)
# Use small spaCy model, no need for both spacy and HF models
# The transformers model is used here as a recognizer, not as an NlpEngine
nlp_configuration = {
"nlp_engine_name": "spacy",
"models": [{"lang_code": "en", "model_name": "en_core_web_sm"}],
}
registry.add_recognizer(transformers_recognizer)
nlp_engine = NlpEngineProvider(nlp_configuration=nlp_configuration).create_engine()
analyzer = AnalyzerEngine(nlp_engine=nlp_engine, registry=registry)
return analyzer
@st.cache
def anonymizer_engine():
"""Return AnonymizerEngine."""
return AnonymizerEngine()
@st.cache
def get_supported_entities():
"""Return supported entities from the Analyzer Engine."""
return analyzer_engine(st_model).get_supported_entities()
@st.cache
def analyze(**kwargs):
"""Analyze input using Analyzer engine and input arguments (kwargs)."""
if "entities" not in kwargs or "All" in kwargs["entities"]:
kwargs["entities"] = None
return analyzer_engine(st_model).analyze(**kwargs)
def anonymize(text: str, analyze_results: List[RecognizerResult]):
"""Anonymize identified input using Presidio Anonymizer.
:param text: Full text
:param analyze_results: list of results from presidio analyzer engine
"""
if st_operator == "mask":
operator_config = {
"type": "mask",
"masking_char": st_mask_char,
"chars_to_mask": st_number_of_chars,
"from_end": False,
}
elif st_operator == "encrypt":
operator_config = {"key": st_encrypt_key}
elif st_operator == "highlight":
operator_config = {"lambda": lambda x: x}
else:
operator_config = None
if st_operator == "highlight":
operator = "custom"
else:
operator = st_operator
res = anonymizer_engine().anonymize(
text,
analyze_results,
operators={"DEFAULT": OperatorConfig(operator, operator_config)},
)
return res
def annotate(text: str, analyze_results: List[RecognizerResult]):
"""
Highlights every identified entity on top of the text.
:param text: full text
:param analyze_results: list of analyzer results.
"""
tokens = []
# Use the anonymizer to resolve overlaps
results = anonymize(text, analyze_results)
# sort by start index
results = sorted(results.items, key=lambda x: x.start)
for i, res in enumerate(results):
if i == 0:
tokens.append(text[: res.start])
# append entity text and entity type
tokens.append((text[res.start: res.end], res.entity_type))
# if another entity coming i.e. we're not at the last results element, add text up to next entity
if i != len(results) - 1:
tokens.append(text[res.end: results[i + 1].start])
# if no more entities coming, add all remaining text
else:
tokens.append(text[res.end:])
return tokens
st.set_page_config(page_title="Presidio demo", layout="wide")
# Sidebar
st.sidebar.header(
"""
PII De-Identification with Microsoft Presidio
"""
)
st.sidebar.info(
"Presidio is an open source customizable framework for PII detection and de-identification\n"
"[Code](https://aka.ms/presidio) | "
"[Tutorial](https://microsoft.github.io/presidio/tutorial/) | "
"[Installation](https://microsoft.github.io/presidio/installation/) | "
"[FAQ](https://microsoft.github.io/presidio/faq/)",
icon="ℹ️",
)
st.sidebar.markdown(
"[![Pypi Downloads](https://img.shields.io/pypi/dm/presidio-analyzer.svg)](https://img.shields.io/pypi/dm/presidio-analyzer.svg)"
"[![MIT license](https://img.shields.io/badge/license-MIT-brightgreen.svg)](http://opensource.org/licenses/MIT)"
"![GitHub Repo stars](https://img.shields.io/github/stars/microsoft/presidio?style=social)"
)
st_model = st.sidebar.selectbox(
"NER model",
[
"StanfordAIMI/stanford-deidentifier-base",
"obi/deid_roberta_i2b2",
"en_core_web_lg",
],
index=1,
)
st.sidebar.markdown("> Note: Models might take some time to download. ")
st_operator = st.sidebar.selectbox(
"De-identification approach",
["redact", "replace", "mask", "hash", "encrypt", "highlight"],
index=1,
)
if st_operator == "mask":
st_number_of_chars = st.sidebar.number_input(
"number of chars", value=15, min_value=0, max_value=100
)
st_mask_char = st.sidebar.text_input("Mask character", value="*", max_chars=1)
elif st_operator == "encrypt":
st_encrypt_key = st.sidebar.text_input("AES key", value="WmZq4t7w!z%C&F)J")
st_threshold = st.sidebar.slider(
label="Acceptance threshold", min_value=0.0, max_value=1.0, value=0.35
)
st_return_decision_process = st.sidebar.checkbox(
"Add analysis explanations to findings", value=False
)
st_entities = st.sidebar.multiselect(
label="Which entities to look for?",
options=get_supported_entities(),
default=list(get_supported_entities()),
)
# Main panel
analyzer_load_state = st.info("Starting Presidio analyzer...")
engine = analyzer_engine(model_path=st_model)
analyzer_load_state.empty()
# Read default text
with open("demo_text.txt") as f:
demo_text = f.readlines()
# Create two columns for before and after
col1, col2 = st.columns(2)
# Before:
col1.subheader("Input string:")
st_text = col1.text_area(
label="Enter text",
value="".join(demo_text),
height=400,
)
st_analyze_results = analyze(
text=st_text,
entities=st_entities,
language="en",
score_threshold=st_threshold,
return_decision_process=st_return_decision_process,
)
# After
if st_operator != "highlight":
with col2:
st.subheader(f"Output")
st_anonymize_results = anonymize(st_text, st_analyze_results)
st.text_area(label="De-identified", value=st_anonymize_results.text, height=400)
else:
st.subheader("Highlighted")
annotated_tokens = annotate(st_text, st_analyze_results)
# annotated_tokens
annotated_text(*annotated_tokens)
# json result
class ToDictEncoder(JSONEncoder):
"""Encode dict to json."""
def default(self, o):
"""Encode to JSON using to_dict."""
return o.to_dict()
# table result
st.subheader(
"Findings" if not st_return_decision_process else "Findings with decision factors"
)
if st_analyze_results:
df = pd.DataFrame.from_records([r.to_dict() for r in st_analyze_results])
df["text"] = [st_text[res.start: res.end] for res in st_analyze_results]
df_subset = df[["entity_type", "text", "start", "end", "score"]].rename(
{
"entity_type": "Entity type",
"text": "Text",
"start": "Start",
"end": "End",
"score": "Confidence",
},
axis=1,
)
df_subset["Text"] = [st_text[res.start: res.end] for res in st_analyze_results]
if st_return_decision_process:
analysis_explanation_df = pd.DataFrame.from_records(
[r.analysis_explanation.to_dict() for r in st_analyze_results]
)
df_subset = pd.concat([df_subset, analysis_explanation_df], axis=1)
st.dataframe(df_subset.reset_index(drop=True), use_container_width=True)
else:
st.text("No findings")
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