Upload app.py

app.py

@@ -0,0 +1,149 @@
+"""Streamlit app for Presidio."""
+
+import json
+from json import JSONEncoder
+
+import pandas as pd
+import streamlit as st
+from presidio_analyzer import AnalyzerEngine, RecognizerRegistry
+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)
+    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
+
+
+st.set_page_config(page_title="Presidio demo (English)", layout="wide")
+
+# Side bar
+st.sidebar.markdown(
+    """
+Anonymize PII entities with [presidio](https://aka.ms/presidio), spaCy and a [PHI detection Roberta model](https://huggingface.co/obi/deid_roberta_i2b2).
+"""
+)
+
+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 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))