Update presidio_helpers.py

presidio_helpers.py

@@ -1,3 +1,6 @@
+"""
+Helper methods for the Presidio Streamlit app
+"""
 from typing import List, Optional, Tuple
 import logging
 import streamlit as st
@@ -24,6 +27,7 @@ from presidio_nlp_engine_config import (
 
 logger = logging.getLogger("presidio-streamlit")
 
+
 @st.cache_resource
 def nlp_engine_and_registry(
     model_family: str,
@@ -31,6 +35,17 @@ def nlp_engine_and_registry(
     ta_key: Optional[str] = None,
     ta_endpoint: Optional[str] = None,
 ) -> Tuple[NlpEngine, RecognizerRegistry]:
+    """Create the NLP Engine instance based on the requested model.
+    :param model_family: Which model package to use for NER.
+    :param model_path: Which model to use for NER. E.g.,
+        "StanfordAIMI/stanford-deidentifier-base",
+        "obi/deid_roberta_i2b2",
+        "en_core_web_sm"
+    :param ta_key: Key to the Text Analytics endpoint (only if model_path = "Azure Text Analytics")
+    :param ta_endpoint: Endpoint of the Text Analytics instance (only if model_path = "Azure Text Analytics")
+    """
+
+    # Set up NLP Engine according to the model of choice
     if "spacy" in model_family.lower():
         return create_nlp_engine_with_spacy(model_path)
     elif "transformers" in model_family.lower() or "iiiorg" in model_family.lower():
@@ -38,6 +53,7 @@ def nlp_engine_and_registry(
     else:
         raise ValueError(f"Model family {model_family} not supported")
 
+
 @st.cache_resource
 def analyzer_engine(
     model_family: str,
@@ -45,10 +61,188 @@ def analyzer_engine(
     ta_key: Optional[str] = None,
     ta_endpoint: Optional[str] = None,
 ) -> AnalyzerEngine:
+    """Create the NLP Engine instance based on the requested model.
+    :param model_family: Which model package to use for NER.
+    :param model_path: Which model to use for NER:
+        "StanfordAIMI/stanford-deidentifier-base",
+        "obi/deid_roberta_i2b2",
+        "en_core_web_sm"
+    :param ta_key: Key to the Text Analytics endpoint (only if model_path = "Azure Text Analytics")
+    :param ta_endpoint: Endpoint of the Text Analytics instance (only if model_path = "Azure Text Analytics")
+    """
     nlp_engine, registry = nlp_engine_and_registry(
         model_family, model_path, ta_key, ta_endpoint
     )
     analyzer = AnalyzerEngine(nlp_engine=nlp_engine, registry=registry)
     return analyzer
 
-# ... (zbytek pomocných funkcí)
+
+@st.cache_resource
+def anonymizer_engine():
+    """Return AnonymizerEngine."""
+    return AnonymizerEngine()
+
+
+@st.cache_data
+def get_supported_entities(
+    model_family: str, model_path: str, ta_key: str, ta_endpoint: str
+):
+    """Return supported entities from the Analyzer Engine."""
+    return analyzer_engine(
+        model_family, model_path, ta_key, ta_endpoint
+    ).get_supported_entities() + ["GENERIC_PII"]
+
+
+@st.cache_data
+def analyze(
+    model_family: str, model_path: str, ta_key: str, ta_endpoint: str, **kwargs
+):
+    """Analyze input using Analyzer engine and input arguments (kwargs)."""
+    if "entities" not in kwargs or "All" in kwargs["entities"]:
+        kwargs["entities"] = None
+
+    if "deny_list" in kwargs and kwargs["deny_list"] is not None:
+        ad_hoc_recognizer = create_ad_hoc_deny_list_recognizer(kwargs["deny_list"])
+        kwargs["ad_hoc_recognizers"] = [ad_hoc_recognizer] if ad_hoc_recognizer else []
+        del kwargs["deny_list"]
+
+    if "regex_params" in kwargs and len(kwargs["regex_params"]) > 0:
+        ad_hoc_recognizer = create_ad_hoc_regex_recognizer(*kwargs["regex_params"])
+        kwargs["ad_hoc_recognizers"] = [ad_hoc_recognizer] if ad_hoc_recognizer else []
+        del kwargs["regex_params"]
+
+    return analyzer_engine(model_family, model_path, ta_key, ta_endpoint).analyze(
+        **kwargs
+    )
+
+
+def anonymize(
+    text: str,
+    operator: str,
+    analyze_results: List[RecognizerResult],
+    mask_char: Optional[str] = None,
+    number_of_chars: Optional[str] = None,
+    encrypt_key: Optional[str] = None,
+):
+    """Anonymize identified input using Presidio Anonymizer.
+
+    :param text: Full text
+    :param operator: Operator name
+    :param mask_char: Mask char (for mask operator)
+    :param number_of_chars: Number of characters to mask (for mask operator)
+    :param encrypt_key: Encryption key (for encrypt operator)
+    :param analyze_results: list of results from presidio analyzer engine
+    """
+
+    if operator == "mask":
+        operator_config = {
+            "type": "mask",
+            "masking_char": mask_char,
+            "chars_to_mask": number_of_chars,
+            "from_end": False,
+        }
+
+    # Define operator config
+    elif operator == "encrypt":
+        operator_config = {"key": encrypt_key}
+    elif operator == "highlight":
+        operator_config = {"lambda": lambda x: x}
+    else:
+        operator_config = None
+
+    # Change operator if needed as intermediate step
+    if operator == "highlight":
+        operator = "custom"
+    elif operator == "synthesize":
+        operator = "replace"
+    else:
+        operator = operator
+
+    res = anonymizer_engine().anonymize(
+        text,
+        analyze_results,
+        operators={"DEFAULT": OperatorConfig(operator, operator_config)},
+    )
+    return res
+
+
+def annotate(text: str, analyze_results: List[RecognizerResult]):
+    """Highlight the identified PII entities on the original text
+
+    :param text: Full text
+    :param analyze_results: list of results from presidio analyzer engine
+    """
+    tokens = []
+
+    # Use the anonymizer to resolve overlaps
+    results = anonymize(
+        text=text,
+        operator="highlight",
+        analyze_results=analyze_results,
+    )
+
+    # sort by start index
+    results = sorted(results.items, 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
+
+
+def create_fake_data(
+    text: str,
+    analyze_results: List[RecognizerResult],
+    openai_params: OpenAIParams,
+):
+    """Creates a synthetic version of the text using OpenAI APIs"""
+    if not openai_params.openai_key:
+        return "Please provide your OpenAI key"
+    results = anonymize(text=text, operator="replace", analyze_results=analyze_results)
+    prompt = create_prompt(results.text)
+    print(f"Prompt: {prompt}")
+    fake = call_completion_model(prompt=prompt, openai_params=openai_params)
+    return fake
+
+
+@st.cache_data
+def call_openai_api(
+    prompt: str, openai_model_name: str, openai_deployment_name: Optional[str] = None
+) -> str:
+    fake_data = call_completion_model(
+        prompt, model=openai_model_name, deployment_id=openai_deployment_name
+    )
+    return fake_data
+
+
+def create_ad_hoc_deny_list_recognizer(
+    deny_list=Optional[List[str]],
+) -> Optional[PatternRecognizer]:
+    if not deny_list:
+        return None
+
+    deny_list_recognizer = PatternRecognizer(
+        supported_entity="GENERIC_PII", deny_list=deny_list
+    )
+    return deny_list_recognizer
+
+
+def create_ad_hoc_regex_recognizer(
+    regex: str, entity_type: str, score: float, context: Optional[List[str]] = None
+) -> Optional[PatternRecognizer]:
+    if not regex:
+        return None
+    pattern = Pattern(name="Regex pattern", regex=regex, score=score)
+    regex_recognizer = PatternRecognizer(
+        supported_entity=entity_type, patterns=[pattern], context=context
+    )
+    return regex_recognizer