app.py

"""A Gradio app for anonymizing text data using FHE."""

import gradio as gr
from fhe_anonymizer import FHEAnonymizer
import pandas as pd
from openai import OpenAI
import os
import json
import re

anonymizer = FHEAnonymizer()

client = OpenAI(
    api_key=os.environ.get("openaikey"),
)


def deidentify_text(input_text):
    anonymized_text, identified_words_with_prob = anonymizer(input_text)

    # Convert the list of identified words and probabilities into a DataFrame
    if identified_words_with_prob:
        identified_df = pd.DataFrame(
            identified_words_with_prob, columns=["Identified Words", "Probability"]
        )
    else:
        identified_df = pd.DataFrame(columns=["Identified Words", "Probability"])
    return anonymized_text, identified_df


def query_chatgpt(anonymized_query):

    with open("files/anonymized_document.txt", "r") as file:
        anonymized_document = file.read()
    with open("files/chatgpt_prompt.txt", "r") as file:
        prompt = file.read()

    # Prepare prompt
    full_prompt = (
        prompt + "\n" 
    )
    query = "Document content:\n```\n" + anonymized_document + "\n\n```" + "Query:\n```\n" + anonymized_query + "\n```"
    print(full_prompt)

    completion = client.chat.completions.create(
        model="gpt-4-1106-preview",  # Replace with "gpt-4" if available
        messages=[
            {"role": "system", "content": prompt},
            {"role": "user", "content": query},
        ],
    )
    anonymized_response = completion.choices[0].message.content
    with open("original_document_uuid_mapping.json", "r") as file:
        uuid_map = json.load(file)
    inverse_uuid_map = {v: k for k, v in uuid_map.items()}  # TODO load the inverse mapping from disk for efficiency

    # Pattern to identify words and non-words (including punctuation, spaces, etc.)
    token_pattern = r"(\b[\w\.\/\-@]+\b|[\s,.!?;:'\"-]+)"
    tokens = re.findall(token_pattern, anonymized_response)
    processed_tokens = []

    print(tokens)
    for token in tokens:
        # Directly append non-word tokens or whitespace to processed_tokens
        if not token.strip() or not re.match(r"\w+", token):
            processed_tokens.append(token)
            continue
        print(token)
        if token in inverse_uuid_map:
            processed_tokens.append(inverse_uuid_map[token])
        else:
            processed_tokens.append(token)
    deanonymized_response = "".join(processed_tokens)
    return anonymized_response, deanonymized_response


# Default demo text from the file
with open("demo_text.txt", "r") as file:
    default_demo_text = file.read()

with open("files/original_document.txt", "r") as file:
    original_document = file.read()

with open("files/anonymized_document.txt", "r") as file:
    anonymized_document = file.read()

demo = gr.Blocks()

with demo:
    gr.Markdown(
        """
        <p align="center">
            <img width=200 src="file/images/logos/zama.jpg">
        </p>
        <h1 style="text-align: center;">Encrypted Anonymization Using Fully Homomorphic Encryption</h1>
        <p align="center">
            <a href="https://github.com/zama-ai/concrete-ml"> <img style="vertical-align: middle; display:inline-block; margin-right: 3px;" width=15 src="file/images/logos/github.png">Concrete-ML</a>
            —
            <a href="https://docs.zama.ai/concrete-ml"> <img style="vertical-align: middle; display:inline-block; margin-right: 3px;" width=15 src="file/images/logos/documentation.png">Documentation</a>
            —
            <a href="https://zama.ai/community"> <img style="vertical-align: middle; display:inline-block; margin-right: 3px;" width=15 src="file/images/logos/community.png">Community</a>
            —
            <a href="https://twitter.com/zama_fhe"> <img style="vertical-align: middle; display:inline-block; margin-right: 3px;" width=15 src="file/images/logos/x.png">@zama_fhe</a>
        </p>
        """
    )

    with gr.Accordion("What is Encrypted Anonymization?", open=False):
        gr.Markdown(
            """
            Encrypted Anonymization leverages Fully Homomorphic Encryption (FHE) to protect sensitive information during data processing. This approach allows for the anonymization of text data, such as personal identifiers, while ensuring that the data remains encrypted throughout the entire process.
            """
        )

    with gr.Accordion("Original Document", open=False):
        gr.Markdown(original_document)

    with gr.Accordion("Anonymized Document", open=False):
        gr.Markdown(anonymized_document)

    # gr.Markdown(
    #     """
    #     <p align="center">
    #         <img src="file/images/banner.png">
    #     </p>
    #     """
    # )

    with gr.Row():
        input_text = gr.Textbox(
            value=default_demo_text,
            lines=13,
            placeholder="Input text here...",
            label="Input",
        )

        anonymized_text_output = gr.Textbox(label="Anonymized Text", lines=13)

    identified_words_output = gr.Dataframe(label="Identified Words", visible=False)

    submit_button = gr.Button("Anonymize")

    submit_button.click(
        deidentify_text,
        inputs=[input_text],
        outputs=[anonymized_text_output, identified_words_output],
    )

    with gr.Row():
        chatgpt_response_anonymized = gr.Textbox(label="ChatGPT Anonymized Response", lines=13)
        chatgpt_response_deanonymized = gr.Textbox(label="ChatGPT Deanonymized Response", lines=13)

    chatgpt_button = gr.Button("Query ChatGPT")
    chatgpt_button.click(
        query_chatgpt,
        inputs=[anonymized_text_output],
        outputs=[chatgpt_response_anonymized, chatgpt_response_deanonymized],
    )

# Launch the app
demo.launch(share=False)