app.py

"""
This module demonstrates a Streamlit application for masking Personally Identifiable
Information (PII) in Hebrew text using the GolemPII-v1 model.
"""

import time
from typing import List, Dict, Tuple

import streamlit as st
import torch
from transformers import AutoTokenizer, AutoModelForTokenClassification


# Constants for model name and entity colors
MODEL_NAME = "CordwainerSmith/GolemPII-v1"

ENTITY_COLORS = {
    "PHONE_NUM": "#FF9999",
    "ID_NUM": "#99FF99",
    "CC_NUM": "#9999FF",
    "BANK_ACCOUNT_NUM": "#FFFF99",
    "FIRST_NAME": "#FF99FF",
    "LAST_NAME": "#99FFFF",
    "CITY": "#FFB366",
    "STREET": "#B366FF",
    "POSTAL_CODE": "#66FFB3",
    "EMAIL": "#66B3FF",
    "DATE": "#FFB3B3",
    "CC_PROVIDER": "#B3FFB3",
}

# Example sentences for demonstration
EXAMPLE_SENTENCES = [
    "שם מלא: תלמה אריאלי מספר תעודת זהות: 61453324-8 תאריך לידה: 15/09/1983 כתובת:  ארלוזורוב 22  פתח תקווה מיקוד 2731711 אימייל: [email protected] טלפון: 054-8884771  בפגישה זו נדונו פתרונות טכנולוגיים חדשניים לשיפור תהליכי עבודה. המשתתף יתבקש להציג מצגת בנושא בפגישה הבאה אשר שילם ב 5326-1003-5299-5478 מסטרקארד עם הוראת קבע ל 11-77-352300",
]

# Model details for display in the sidebar
MODEL_DETAILS = {
    "name": "GolemPII-v1: Hebrew PII Detection Model",
    "description": """
        The <a href="https://huggingface.co/CordwainerSmith/GolemPII-v1" target="_blank">GolemPII model</a>
        was specifically designed to identify and categorize various types of personally
        identifiable information (PII) present in Hebrew text. Its core intended usage
        revolves around enhancing privacy protection and facilitating the process of data
        anonymization. This makes it a good candidate for applications and systems that
        handle sensitive data, such as legal documents, medical records, or any text data
        containing PII, where the automatic redaction or removal of such information is
        essential for ensuring compliance with data privacy regulations and safeguarding
        individuals' personal information. The model can be deployed on-premise with a
        relatively small hardware footprint, making it suitable for organizations with
        limited computing resources or those prioritizing local data processing.
        The model was trained on the <a href="https://huggingface.co/datasets/CordwainerSmith/GolemGuard"
        target="_blank">GolemGuard</a> dataset, a Hebrew language dataset comprising over
        115,000 examples of PII entities and containing both real and synthetically
        generated text examples. This data represents various document types and
        communication formats commonly found in Israeli professional and administrative
        contexts. GolemGuard covers a wide range of document types and encompasses a
        diverse array of PII entities, making it ideal for training and evaluating PII
        detection models.
    """,
    "base_model": "xlm-roberta-base",
    "training_data": "Custom Hebrew PII dataset",
    "detected_pii_entities": [
        "FIRST_NAME",
        "LAST_NAME",
        "STREET",
        "CITY",
        "PHONE_NUM",
        "EMAIL",
        "ID_NUM",
        "BANK_ACCOUNT_NUM",
        "CC_NUM",
        "CC_PROVIDER",
        "DATE",
        "POSTAL_CODE",
    ],
}


class PIIMaskingModel:
    """
    A class for masking PII in Hebrew text using the GolemPII-v1 model.
    """

    def __init__(self, model_name: str):
        """
        Initializes the PIIMaskingModel with the specified model name.

        Args:
            model_name: The name of the pre-trained model to use.
        """
        self.model_name = model_name
        hf_token = st.secrets["hf_token"]
        self.tokenizer = AutoTokenizer.from_pretrained(model_name, token=hf_token)
        self.model = AutoModelForTokenClassification.from_pretrained(
            model_name, token=hf_token
        )
        self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
        self.model.to(self.device)
        self.model.eval()

    def process_text(
        self, text: str
    ) -> Tuple[str, float, str, List[str], List[str], List[Dict]]:
        """
        Processes the input text and returns the masked text, processing time,
        colored text, tokens, predicted labels, and privacy masks.

        Args:
            text: The input text to process.

        Returns:
            A tuple containing:
                - masked_text: The text with PII masked.
                - processing_time: The time taken to process the text.
                - colored_text: The text with PII highlighted with colors.
                - tokens: The tokens of the input text.
                - predicted_labels: The predicted labels for each token.
                - privacy_masks: A list of dictionaries containing information about
                  the masked PII entities.
        """
        start_time = time.time()

        tokenized_inputs = self.tokenizer(
            text,
            truncation=True,
            padding=False,
            return_tensors="pt",
            return_offsets_mapping=True,
            add_special_tokens=True,
        )

        input_ids = tokenized_inputs.input_ids.to(self.device)
        attention_mask = tokenized_inputs.attention_mask.to(self.device)
        offset_mapping = tokenized_inputs["offset_mapping"][0].tolist()

        # Handle special tokens
        offset_mapping[0] = None  # <s> token
        offset_mapping[-1] = None  # </s> token

        with torch.no_grad():
            outputs = self.model(input_ids=input_ids, attention_mask=attention_mask)

        predictions = outputs.logits.argmax(dim=-1).cpu().numpy()
        predicted_labels = [
            self.model.config.id2label[label_id] for label_id in predictions[0]
        ]
        tokens = self.tokenizer.convert_ids_to_tokens(input_ids[0])

        masked_text, colored_text, privacy_masks = self.mask_pii_in_sentence(
            tokens, predicted_labels, text, offset_mapping
        )
        processing_time = time.time() - start_time

        return (
            masked_text,
            processing_time,
            colored_text,
            tokens,
            predicted_labels,
            privacy_masks,
        )

    def _find_entity_span(
        self,
        i: int,
        labels: List[str],
        tokens: List[str],
        offset_mapping: List[Tuple[int, int]],
    ) -> Tuple[int, str, int]:
        """
        Finds the span of an entity starting at the given index.

        Args:
            i: The starting index of the entity.
            labels: The list of labels for each token.
            tokens: The list of tokens.
            offset_mapping: The offset mapping for each token.

        Returns:
            A tuple containing:
                - The index of the next token after the entity.
                - The type of the entity.
                - The end character offset of the entity.
        """
        current_entity = labels[i][2:] if labels[i].startswith("B-") else labels[i][2:]
        j = i + 1
        last_valid_end = offset_mapping[i][1] if offset_mapping[i] else None

        while j < len(tokens):
            if offset_mapping[j] is None:
                j += 1
                continue

            next_label = labels[j]

            if next_label.startswith("B-") and tokens[j].startswith(" "):
                break

            if next_label.startswith("I-") and next_label[2:] != current_entity:
                break

            if next_label.startswith("I-") and next_label[2:] == current_entity:
                last_valid_end = offset_mapping[j][1]
                j += 1
            elif next_label.startswith("B-") and not tokens[j].startswith(" "):
                last_valid_end = offset_mapping[j][1]
                j += 1
            else:
                break

        return j, current_entity, last_valid_end

    def mask_pii_in_sentence(
        self,
        tokens: List[str],
        labels: List[str],
        original_text: str,
        offset_mapping: List[Tuple[int, int]],
    ) -> Tuple[str, str, List[Dict]]:
        """
        Masks the PII entities in a sentence.

        Args:
            tokens: The list of tokens in the sentence.
            labels: The list of labels for each token.
            original_text: The original text of the sentence.
            offset_mapping: The offset mapping for each token.

        Returns:
            A tuple containing:
                - The masked text.
                - The colored text.
                - A list of dictionaries containing information about the masked
                  PII entities.
        """
        privacy_masks = []
        current_pos = 0
        masked_text_parts = []
        colored_text_parts = []

        i = 0
        while i < len(tokens):
            if offset_mapping[i] is None:
                i += 1
                continue

            current_label = labels[i]

            if current_label.startswith(("B-", "I-")):
                start_char = offset_mapping[i][0]
                next_pos, entity_type, last_valid_end = self._find_entity_span(
                    i, labels, tokens, offset_mapping
                )

                if current_pos < start_char:
                    text_before = original_text[current_pos:start_char]
                    masked_text_parts.append(text_before)
                    colored_text_parts.append(text_before)

                entity_value = original_text[start_char:last_valid_end]
                mask = self._get_mask_for_entity(entity_type)

                privacy_masks.append(
                    {
                        "label": entity_type,
                        "start": start_char,
                        "end": last_valid_end,
                        "value": entity_value,
                        "label_index": len(privacy_masks) + 1,
                    }
                )

                masked_text_parts.append(mask)
                color = ENTITY_COLORS.get(entity_type, "#CCCCCC")
                colored_text_parts.append(
                    f'<span style="background-color: {color}; color: black; padding: 2px; border-radius: 3px;">{mask}</span>'
                )

                current_pos = last_valid_end
                i = next_pos
            else:
                if offset_mapping[i] is not None:
                    start_char = offset_mapping[i][0]
                    end_char = offset_mapping[i][1]

                    if current_pos < end_char:
                        text_chunk = original_text[current_pos:end_char]
                        masked_text_parts.append(text_chunk)
                        colored_text_parts.append(text_chunk)
                        current_pos = end_char
                i += 1

        if current_pos < len(original_text):
            remaining_text = original_text[current_pos:]
            masked_text_parts.append(remaining_text)
            colored_text_parts.append(remaining_text)

        return ("".join(masked_text_parts), "".join(colored_text_parts), privacy_masks)

    def _get_mask_for_entity(self, entity_type: str) -> str:
        """
        Returns the mask for a given entity type.

        Args:
            entity_type: The type of the entity.

        Returns:
            The mask for the entity type.
        """
        return {
            "PHONE_NUM": "[טלפון]",
            "ID_NUM": "[ת.ז]",
            "CC_NUM": "[כרטיס אשראי]",
            "BANK_ACCOUNT_NUM": "[חשבון בנק]",
            "FIRST_NAME": "[שם פרטי]",
            "LAST_NAME": "[שם משפחה]",
            "CITY": "[עיר]",
            "STREET": "[רחוב]",
            "POSTAL_CODE": "[מיקוד]",
            "EMAIL": "[אימייל]",
            "DATE": "[תאריך]",
            "CC_PROVIDER": "[ספק כרטיס אשראי]",
            "BANK": "[בנק]",
        }.get(entity_type, f"[{entity_type}]")


def main():
    """
    The main function for the Streamlit application.
    """
    st.set_page_config(layout="wide")
    st.title("🗿 GolemPII: Hebrew PII Masking Application 🗿")

    st.markdown(
        """
    <style>
        .rtl { direction: rtl; text-align: right; }
        .entity-legend { padding: 5px; margin: 2px; border-radius: 3px; display: inline-block; }
        .masked-text {
            direction: rtl;
            text-align: right;
            line-height: 2;
            padding: 10px;
            background-color: #f6f8fa;
            border-radius: 5px;
            color: black;
            white-space: pre-wrap;
        }
        .main h3 {
            margin-bottom: 10px;
        }
        textarea {
            direction: rtl !important;
            text-align: right !important;
        }
        .stTextArea label {
            direction: ltr !important;
            text-align: left !important;
        }
    </style>
    """,
        unsafe_allow_html=True,
    )

    # Sidebar with model details
    st.sidebar.markdown(
        f"""
        <div>
            <h2>{MODEL_DETAILS['name']}</h2>
            <p>{MODEL_DETAILS['description']}</p>
            <h3>Supported PII Entities</h3>
            <ul>
                {" ".join([f'<li><span style="background-color: {ENTITY_COLORS.get(entity, "#CCCCCC")}; color: black; padding: 3px 5px; border-radius: 3px; margin-right: 5px;">{entity}</span></li>' for entity in MODEL_DETAILS['detected_pii_entities']])}
            </ul>
        </div>
    """,
        unsafe_allow_html=True,
    )

    text_input = st.text_area(
        "Enter text to mask (separate multiple texts with commas):",
        value="\n".join(EXAMPLE_SENTENCES),
        height=200,
    )

    show_json = st.checkbox("Show JSON Output", value=True)

    if st.button("Process Text"):
        texts = [text.strip() for text in text_input.split(",") if text.strip()]
        model = PIIMaskingModel(MODEL_NAME)

        for text in texts:
            st.markdown(
                '<h3 style="text-align: center;">Original Text</h3>',
                unsafe_allow_html=True,
            )
            st.markdown(f'<div class="rtl">{text}</div>', unsafe_allow_html=True)

            (
                masked_text,
                processing_time,
                colored_text,
                tokens,
                predicted_labels,
                privacy_masks,
            ) = model.process_text(text)

            st.markdown(
                '<h3 style="text-align: center;">Masked Text</h3>',
                unsafe_allow_html=True,
            )
            st.markdown(
                f'<div class="masked-text">{colored_text}</div>', unsafe_allow_html=True
            )

            st.markdown(f"Processing Time: {processing_time:.3f} seconds")

            if show_json:
                st.json(
                    {
                        "original": text,
                        "masked": masked_text,
                        "processing_time": processing_time,
                        "tokens": tokens,
                        "token_classes": predicted_labels,
                        "privacy_mask": privacy_masks,
                        "span_labels": [
                            [m["start"], m["end"], m["label"]] for m in privacy_masks
                        ],
                    }
                )

            st.markdown("---")


if __name__ == "__main__":
    main()