app.py

"""Streamlit app for Student Name Detection models."""

import spacy
from spacy_recognizer import CustomSpacyRecognizer
from presidio_analyzer.nlp_engine import NlpEngineProvider
from presidio_anonymizer import AnonymizerEngine
from presidio_analyzer import AnalyzerEngine, RecognizerRegistry
from presidio_anonymizer.entities import OperatorConfig
import pandas as pd
from annotated_text import annotated_text
from json import JSONEncoder
import json
import warnings
import streamlit as st
import os
os.environ["TOKENIZERS_PARALLELISM"] = "false"
warnings.filterwarnings('ignore')

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

    spacy_recognizer = CustomSpacyRecognizer()

    configuration = {
        "nlp_engine_name": "spacy",
        "models": [
            {"lang_code": "en", "model_name": "en_student_name_detector"}],
    }

    # Create NLP engine based on configuration
    provider = NlpEngineProvider(nlp_configuration=configuration)
    nlp_engine = provider.create_engine()

    registry = RecognizerRegistry()
    # add rule-based recognizers
    registry.load_predefined_recognizers(nlp_engine=nlp_engine)
    registry.add_recognizer(spacy_recognizer)
    # remove the nlp engine we passed, to use custom label mappings
    registry.remove_recognizer("SpacyRecognizer")

    analyzer = AnalyzerEngine(nlp_engine=nlp_engine,
                              registry=registry, supported_languages=["en"])

    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 generate_surrogate(name):
    """Return appropriate surrogate name from text string"""
    if "John" in name:
        return "Jill"
    else:
        return "SURROGATE_NAME"

def anonymize(text, analyze_results):
    """Anonymize identified input using Presidio Anonymizer."""
    if not text:
        return
    res = anonymizer_engine().anonymize(
        text,
        analyze_results,
        operators={"STUDENT": OperatorConfig("custom", {"lambda": generate_surrogate})}

    )
    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="Student Name Detector (English)", layout="wide")

# Side bar
st.sidebar.markdown(
    """Detect and anonymize PII in text using an [NLP model](https://huggingface.co./langdonholmes/en_student_name_detector) [trained](https://github.com/aialoe/deidentification-pipeline) on student-generated text collected by Coursera.
"""
)

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

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 in json")

st.sidebar.info(
    "This is part of a deidentification project for student-generated text."
)


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


st_text = st.text_area(
    label="Type in some text",
    value="Learning Reflection\n\nJohn Williams and Samantha Morales\n\nIn this course I learned many things. As Liedtke (2004) said, \"Students grow when they learn\" (Erickson et al. 1998).\n\nBy John H. Williams -- (714) 328-9989 -- [email protected]",
    height=200,
)

button = st.button("Detect PII")

if 'first_load' not in st.session_state:
    st.session_state['first_load'] = True

# After
st.subheader("Analyzed")
with st.spinner("Analyzing..."):
    if button or st.session_state.first_load:
        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)
# vertical space
st.text("")

st.subheader("Anonymized")

with st.spinner("Anonymizing..."):
    if button or st.session_state.first_load:
        st_anonymize_results = anonymize(st_text, st_analyze_results)
        st_anonymize_results


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

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

st.session_state['first_load'] = True

# 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))