Delete app.py

app.py

@@ -1,149 +0,0 @@
-"""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))