app.py

"""Streamlit app for Presidio."""

import json
from json import JSONEncoder
from annotated_text import annotated_text
import pandas as pd
import streamlit as st
from presidio_analyzer import AnalyzerEngine, RecognizerRegistry
from presidio_anonymizer import AnonymizerEngine

from flair_recognizer import FlairRecognizer


import spacy
spacy.cli.download("en_core_web_lg")


# Helper methods
@st.cache(allow_output_mutation=True)
def analyzer_engine():
    """Return AnalyzerEngine."""

    flair_recognizer = FlairRecognizer()
    
    registry = RecognizerRegistry()
    registry.add_recognizer(flair_recognizer)
    registry.load_predefined_recognizers()
    
    analyzer = AnalyzerEngine(registry=registry)
    return analyzer

    
@st.cache(allow_output_mutation=True)
def anonymizer_engine():
    """Return AnonymizerEngine."""
    return AnonymizerEngine()


def get_supported_entities():
    """Return supported entities from the Analyzer Engine."""
    return analyzer_engine().get_supported_entities()


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().analyze(**kwargs)


def anonymize(text, analyze_results):
    """Anonymize identified input using Presidio Abonymizer."""

    res = anonymizer_engine().anonymize(text, analyze_results)
    return res.text

def annotate(text, st_analyze_results, st_entities):
    tokens = []
    # sort by start index
    results = sorted(st_analyze_results, 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 (English)", layout="wide")

# Side bar
st.sidebar.markdown(
    """
Anonymize PII entities in text with [presidio](https://aka.ms/presidio), spaCy and a [PII detection model](https://huggingface.co/beki/flair-pii-english) trained on protocol trace data generated by [privy](https://github.com/pixie-io/pixie/tree/main/src/datagen/pii/privy).
"""
)

st_entities = st.sidebar.multiselect(
    label="Which entities to look for?",
    options=get_supported_entities(),
    default=list(get_supported_entities()),
)

st_threhsold = 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 in json")

st.sidebar.info(
    "Presidio is an open source framework for PII detection and anonymization. Privy is an open source framework for synthetic data generation in protocol trace formats (json, sql, html etc)"
    "For more info visit [aka.ms/presidio](https://aka.ms/presidio) and [privy](https://github.com/pixie-io/pixie/tree/main/src/datagen/pii/privy)"
)


# Main panel
analyzer_load_state = st.info("Starting Presidio analyzer and loading Privy-trained model...")
engine = analyzer_engine()
analyzer_load_state.empty()


st_text = st.text_area(
    label="Type in some text",
    value=
    "like a phone number (212-141-4544) "
    "or a name (Lebron James).",
    height=200,
    # label_visibility="collapsed",
)

# After
st.subheader("Analyzed")
with st.spinner("Analyzing..."):
    st_analyze_results = analyze(
        text=st_text,
        entities=st_entities,
        language="en",
        score_threshold=st_threshold,
        return_decision_process=st_return_decision_process,
    )
    annotated_tokens = annotate(st_text, st_analyze_results, st_entities)
    # annotated_tokens
    annotated_text(*annotated_tokens)
st.text("")
    
st.subheader("Anonymized")

with st.spinner("Anonymizing..."):
    st_anonymize_results = anonymize(st_text, st_analyze_results)
    st_anonymize_results


# table result
st.subheader("Findings")
if st_analyze_results:
    df = pd.DataFrame.from_records([r.to_dict() for r in st_analyze_results])
    df = df[["entity_type", "start", "end", "score"]].rename(
        {
            "entity_type": "Entity type",
            "start": "Start",
            "end": "End",
            "score": "Confidence",
        },
        axis=1,
    )

    st.dataframe(df, width=1000)
else:
    st.text("No findings")


# json result
class ToDictListEncoder(JSONEncoder):
    """Encode dict to json."""

    def default(self, o):
        """Encode to JSON using to_dict."""
        if o:
            return o.to_dict()
        return []


if st_return_decision_process:
    st.json(json.dumps(st_analyze_results, cls=ToDictListEncoder))