presidio_WW / presidio_streamlit.py
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"""Streamlit app for Presidio."""
import os
from json import JSONEncoder
import pandas as pd
import streamlit as st
import streamlit.components.v1 as components
from annotated_text import annotated_text
from presidio_helpers import (
get_supported_entities,
analyze,
anonymize,
annotate,
create_fake_data,
analyzer_engine,
)
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)" # noqa
"[![MIT license](https://img.shields.io/badge/license-MIT-brightgreen.svg)](https://opensource.org/licenses/MIT)"
"![GitHub Repo stars](https://img.shields.io/github/stars/microsoft/presidio?style=social)"
)
st_model = st.sidebar.selectbox(
"NER model for PII detection",
[
"StanfordAIMI/stanford-deidentifier-base",
"obi/deid_roberta_i2b2",
"flair/ner-english-large",
"en_core_web_lg",
],
index=1,
help="""
Select which Named Entity Recognition (NER) model to use for PII detection, in parallel to rule-based recognizers.
Presidio supports multiple NER packages off-the-shelf, such as spaCy, Huggingface, Stanza and Flair.
""",
)
st.sidebar.markdown("> Note: Models might take some time to download. ")
st_operator = st.sidebar.selectbox(
"De-identification approach",
["redact", "replace", "synthesize", "highlight", "mask", "hash", "encrypt"],
index=1,
help="""
Select which manipulation to the text is requested after PII has been identified.\n
- Redact: Completely remove the PII text\n
- Replace: Replace the PII text with a constant, e.g. <PERSON>\n
- Synthesize: Replace with fake values (requires an OpenAI key)\n
- Highlight: Shows the original text with PII highlighted in colors\n
- Mask: Replaces a requested number of characters with an asterisk (or other mask character)\n
- Hash: Replaces with the hash of the PII string\n
- Encrypt: Replaces with an AES encryption of the PII string, allowing the process to be reversed
""",
)
st_mask_char = "*"
st_number_of_chars = 15
st_encrypt_key = "WmZq4t7w!z%C&F)J"
st_openai_key = ""
st_openai_model = "text-davinci-003"
if st_operator == "mask":
st_number_of_chars = st.sidebar.number_input(
"number of chars", value=st_number_of_chars, min_value=0, max_value=100
)
st_mask_char = st.sidebar.text_input(
"Mask character", value=st_mask_char, max_chars=1
)
elif st_operator == "encrypt":
st_encrypt_key = st.sidebar.text_input("AES key", value=st_encrypt_key)
elif st_operator == "synthesize":
st_openai_key = st.sidebar.text_input(
"OPENAI_KEY",
value=os.getenv("OPENAI_KEY", default=""),
help="See https://help.openai.com/en/articles/4936850-where-do-i-find-my-secret-api-key for more info.",
type="password",
)
st_openai_model = st.sidebar.text_input(
"OpenAI model for text synthesis",
value=st_openai_model,
help="See more here: https://platform.openai.com/docs/models/",
)
st_threshold = st.sidebar.slider(
label="Acceptance threshold",
min_value=0.0,
max_value=1.0,
value=0.35,
help="Define the threshold for accepting a detection as PII.",
)
st_return_decision_process = st.sidebar.checkbox(
"Add analysis explanations to findings",
value=False,
help="Add the decision process to the output table. "
"More information can be found here: https://microsoft.github.io/presidio/analyzer/decision_process/",
)
st_entities = st.sidebar.multiselect(
label="Which entities to look for?",
options=get_supported_entities(st_model),
default=list(get_supported_entities(st_model)),
help="Limit the list of PII entities detected. "
"This list is dynamic and based on the NER model and registered recognizers. "
"More information can be found here: https://microsoft.github.io/presidio/analyzer/adding_recognizers/",
)
# 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(
st_model=st_model,
text=st_text,
entities=st_entities,
language="en",
score_threshold=st_threshold,
return_decision_process=st_return_decision_process,
)
# After
if st_operator not in ("highlight", "synthesize"):
with col2:
st.subheader(f"Output")
st_anonymize_results = anonymize(
text=st_text,
operator=st_operator,
mask_char=st_mask_char,
number_of_chars=st_number_of_chars,
encrypt_key=st_encrypt_key,
analyze_results=st_analyze_results,
)
st.text_area(label="De-identified", value=st_anonymize_results.text, height=400)
elif st_operator == "synthesize":
with col2:
st.subheader(f"OpenAI Generated output")
fake_data = create_fake_data(
st_text,
st_analyze_results,
openai_key=st_openai_key,
openai_model_name=st_openai_model,
)
st.text_area(label="Synthetic data", value=fake_data, height=400)
else:
st.subheader("Highlighted")
annotated_tokens = annotate(
text=st_text,
analyze_results=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")
components.html(
"""
<script type="text/javascript">
(function(c,l,a,r,i,t,y){
c[a]=c[a]||function(){(c[a].q=c[a].q||[]).push(arguments)};
t=l.createElement(r);t.async=1;t.src="https://www.clarity.ms/tag/"+i;
y=l.getElementsByTagName(r)[0];y.parentNode.insertBefore(t,y);
})(window, document, "clarity", "script", "h7f8bp42n8");
</script>
"""
)