app.py

"""Streamlit app for Presidio."""

import json
from json import JSONEncoder

import pandas as pd
import streamlit as st
from presidio_analyzer import AnalyzerEngine
from presidio_anonymizer import AnonymizerEngine

from transformers_recognizer import TransformersRecognizer


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


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

    transformers_recognizer = TransformersRecognizer()
    
    registry = RecognizerRegistry()
    registry.add_recognizer(transformers_recognizer)
    
    analyzer = AnalyzerEngine(registry=registry)
    return analyzer

    
    @return AnalyzerEngine()


@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


st.set_page_config(page_title="Presidio demo", layout="wide")

# Side bar
st.sidebar.markdown(
    """
Anonymize PII entities with [presidio](https://aka.ms/presidio).
"""
)

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. "
    "For more info visit [aka.ms/presidio](https://aka.ms/presidio)"
)


# Main panel
analyzer_load_state = st.info("Starting Presidio analyzer...")
engine = analyzer_engine()
analyzer_load_state.empty()


# 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="Type in some text, "
    "like a phone number (212-141-4544) "
    "or a name (Lebron James).",
    height=400,
)

# After
col2.subheader("Output:")

st_analyze_results = analyze(
    text=st_text,
    entities=st_entities,
    language="en",
    score_threshold=st_threhsold,
    return_decision_process=st_return_decision_process,
)
st_anonymize_results = anonymize(st_text, st_analyze_results)
col2.text_area(label="", value=st_anonymize_results, height=400)


# 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 ToDictEncoder(JSONEncoder):
    """Encode dict to json."""

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


st.json(json.dumps(st_analyze_results, cls=ToDictEncoder))




import gradio as gr

from presidio_analyzer import AnalyzerEngine, RecognizerRegistry