File size: 8,504 Bytes
28a039d
 
 
 
7172378
28a039d
 
 
 
 
 
7172378
28a039d
 
 
 
 
 
 
7172378
e188d4a
28a039d
 
 
 
 
41e004f
 
28a039d
 
7172378
 
28a039d
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
41e004f
28a039d
41e004f
 
 
28a039d
41e004f
28a039d
41e004f
 
28a039d
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
7172378
 
 
 
 
28a039d
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
7172378
28a039d
 
7172378
28a039d
 
 
7172378
e188d4a
28a039d
 
 
 
7172378
 
 
 
 
 
28a039d
 
 
 
 
 
 
 
 
7172378
 
 
28a039d
7172378
 
 
 
 
 
 
 
 
 
 
 
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
"""
Helper methods for the Presidio Streamlit app
"""
from typing import List, Optional, Tuple
import logging
import streamlit as st
from presidio_analyzer import (
    AnalyzerEngine,
    RecognizerResult,
    RecognizerRegistry,
    PatternRecognizer,
    Pattern,
)
from presidio_analyzer.nlp_engine import NlpEngine
from presidio_anonymizer import AnonymizerEngine
from presidio_anonymizer.entities import OperatorConfig

from openai_fake_data_generator import (
    call_completion_model,
    OpenAIParams,
    create_prompt,
)
from presidio_nlp_engine_config import (
    create_nlp_engine_with_spacy,
    create_nlp_engine_with_flair,
    create_nlp_engine_with_transformers,
    create_nlp_engine_with_azure_ai_language,
    create_nlp_engine_with_stanza,
)

logger = logging.getLogger("presidio-streamlit")


@st.cache_resource
def nlp_engine_and_registry(
    model_family: str,
    model_path: str,
    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_lg"
    :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)
    if "stanza" in model_family.lower():
        return create_nlp_engine_with_stanza(model_path)
    elif "flair" in model_family.lower():
        return create_nlp_engine_with_flair(model_path)
    elif "huggingface" in model_family.lower():
        return create_nlp_engine_with_transformers(model_path)
    elif "azure ai language" in model_family.lower():
        return create_nlp_engine_with_azure_ai_language(ta_key, ta_endpoint)
    else:
        raise ValueError(f"Model family {model_family} not supported")


@st.cache_resource
def analyzer_engine(
    model_family: str,
    model_path: str,
    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_lg"
    :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


@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