app.py

"""A Gradio app for de-identifying text data using FHE."""

import base64
import os
import re
import subprocess
import time
import uuid
from typing import Dict, List

import gradio as gr
import numpy
import pandas as pd
import requests
from fhe_anonymizer import FHEAnonymizer
#from openai import OpenAI
from utils_demo import *

from concrete.ml.deployment import FHEModelClient


# Ensure the directory is clean before starting processes or reading files
clean_directory()  

anonymizer = FHEAnonymizer()
#client = OpenAI(api_key=os.environ.get("openaikey"))

# Start the Uvicorn server hosting the FastAPI app
subprocess.Popen(["uvicorn", "server:app"], cwd=CURRENT_DIR)
time.sleep(3)

# Load data from files required for the application
UUID_MAP = read_json(MAPPING_UUID_PATH)
ANONYMIZED_DOCUMENT = read_txt(ANONYMIZED_FILE_PATH)
MAPPING_ANONYMIZED_SENTENCES = read_pickle(MAPPING_ANONYMIZED_SENTENCES_PATH)
MAPPING_ENCRYPTED_SENTENCES = read_pickle(MAPPING_ENCRYPTED_SENTENCES_PATH)
ORIGINAL_DOCUMENT = read_txt(ORIGINAL_FILE_PATH).split("\n\n")
MAPPING_DOC_EMBEDDING = read_pickle(MAPPING_DOC_EMBEDDING_PATH)

print(f"{ORIGINAL_DOCUMENT=}\n")
print(f"{MAPPING_DOC_EMBEDDING.keys()=}")

# 4. Data Processing and Operations (No specific operations shown here, assuming it's part of anonymizer or client usage)

# 5. Utilizing External Services or APIs
# (Assuming client initialization and anonymizer setup are parts of using external services or application-specific logic)

# Generate a random user ID for this session
USER_ID = numpy.random.randint(0, 2**32)


def select_static_anonymized_sentences_fn(selected_sentences: List):

    selected_sentences = [MAPPING_ANONYMIZED_SENTENCES[sentence] for sentence in selected_sentences]

    anonymized_selected_sentence = sorted(selected_sentences, key=lambda x: x[0])

    anonymized_selected_sentence = [sentence for _, sentence in anonymized_selected_sentence]

    return "\n\n".join(anonymized_selected_sentence)


def key_gen_fn() -> Dict:
    """Generate keys for a given user."""

    print("------------ Step 1: Key Generation:")

    print(f"Your user ID is: {USER_ID}....")


    client = FHEModelClient(path_dir=DEPLOYMENT_DIR, key_dir=KEYS_DIR / f"{USER_ID}")
    client.load()

    # Creates the private and evaluation keys on the client side
    client.generate_private_and_evaluation_keys()

    # Get the serialized evaluation keys
    serialized_evaluation_keys = client.get_serialized_evaluation_keys()
    assert isinstance(serialized_evaluation_keys, bytes)

    # Save the evaluation key
    evaluation_key_path = KEYS_DIR / f"{USER_ID}/evaluation_key"

    write_bytes(evaluation_key_path, serialized_evaluation_keys)

    # anonymizer.generate_key()

    if not evaluation_key_path.is_file():
        error_message = (
            f"Error Encountered While generating the evaluation {evaluation_key_path.is_file()=}"
        )
        print(error_message)
        return {gen_key_btn: gr.update(value=error_message)}
    else:
        print("Keys have been generated ✅")
        return {gen_key_btn: gr.update(value="Keys have been generated ✅")}


def encrypt_doc_fn(doc):

    print(f"\n------------ Step 2.1: Doc encryption: {doc=}")

    if not (KEYS_DIR / f"{USER_ID}/evaluation_key").is_file():
        return {encrypted_doc_box: gr.update(value="Error ❌: Please generate the key first!", lines=10)}

    # Retrieve the client API
    client = FHEModelClient(path_dir=DEPLOYMENT_DIR, key_dir=KEYS_DIR / f"{USER_ID}")
    client.load()

    encrypted_tokens = []
    tokens = re.findall(r"(\b[\w\.\/\-@]+\b|[\s,.!?;:'\"-]+|\$\d+(?:\.\d+)?|\€\d+(?:\.\d+)?)", ' '.join(doc))
    
    for token in tokens:
        if token.strip() and re.match(r"\w+", token):
            emb_x = MAPPING_DOC_EMBEDDING[token]
            assert emb_x.shape == (1, 1024)
            encrypted_x = client.quantize_encrypt_serialize(emb_x)
            assert isinstance(encrypted_x, bytes)
            encrypted_tokens.append(encrypted_x)

    print("Doc encrypted ✅ on Client Side")

    # No need to save it
    # write_bytes(KEYS_DIR / f"{USER_ID}/encrypted_doc", b"".join(encrypted_tokens))

    encrypted_quant_tokens_hex = [token.hex()[500:510] for token in encrypted_tokens]

    return {
        encrypted_doc_box: gr.update(value=" ".join(encrypted_quant_tokens_hex), lines=10),
        anonymized_doc_output: gr.update(visible=True, value=None),
    }
    

def encrypt_query_fn(query):

    print(f"\n------------ Step 2: Query encryption: {query=}")

    if not (KEYS_DIR / f"{USER_ID}/evaluation_key").is_file():
        return {output_encrypted_box: gr.update(value="Error ❌: Please generate the key first!", lines=8)}

    if is_user_query_valid(query):
        return {
            query_box: gr.update(
                value=(
                    "Unable to process ❌: The request exceeds the length limit or falls "
                    "outside the scope of this document. Please refine your query."
                )
            )
        }

    # Retrieve the client API
    client = FHEModelClient(path_dir=DEPLOYMENT_DIR, key_dir=KEYS_DIR / f"{USER_ID}")
    client.load()

    encrypted_tokens = []

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

    for token in tokens:

        # 1- Ignore non-words tokens
        if bool(re.match(r"^\s+$", token)):
            continue

        # 2- Directly append non-word tokens or whitespace to processed_tokens

        # Prediction for each word
        emb_x = get_batch_text_representation([token], EMBEDDINGS_MODEL, TOKENIZER)
        encrypted_x = client.quantize_encrypt_serialize(emb_x)
        assert isinstance(encrypted_x, bytes)

        encrypted_tokens.append(encrypted_x)

    print("Data encrypted ✅ on Client Side")

    assert len({len(token) for token in encrypted_tokens}) == 1

    write_bytes(KEYS_DIR / f"{USER_ID}/encrypted_input", b"".join(encrypted_tokens))
    write_bytes(
        KEYS_DIR / f"{USER_ID}/encrypted_input_len", len(encrypted_tokens[0]).to_bytes(10, "big")
    )

    encrypted_quant_tokens_hex = [token.hex()[500:580] for token in encrypted_tokens]

    return {
        output_encrypted_box: gr.update(value=" ".join(encrypted_quant_tokens_hex), lines=8),
        anonymized_query_output: gr.update(visible=True, value=None),
        identified_words_output_df: gr.update(visible=False, value=None),
    }


def send_input_fn(query) -> Dict:
    """Send the encrypted data and the evaluation key to the server."""

    print("------------ Step 3.1: Send encrypted_data to the Server")

    evaluation_key_path = KEYS_DIR / f"{USER_ID}/evaluation_key"
    encrypted_input_path = KEYS_DIR / f"{USER_ID}/encrypted_input"
    encrypted_input_len_path = KEYS_DIR / f"{USER_ID}/encrypted_input_len"

    if not evaluation_key_path.is_file():
        error_message = (
            "Error Encountered While Sending Data to the Server: "
            f"The key has been generated correctly - {evaluation_key_path.is_file()=}"
        )
        return {anonymized_query_output: gr.update(value=error_message)}

    if not encrypted_input_path.is_file():
        error_message = (
            "Error Encountered While Sending Data to the Server: The data has not been encrypted "
            f"correctly on the client side - {encrypted_input_path.is_file()=}"
        )
        return {anonymized_query_output: gr.update(value=error_message)}

    # Define the data and files to post
    data = {"user_id": USER_ID, "input": query}

    files = [
        ("files", open(evaluation_key_path, "rb")),
        ("files", open(encrypted_input_path, "rb")),
        ("files", open(encrypted_input_len_path, "rb")),
    ]

    # Send the encrypted input and evaluation key to the server
    url = SERVER_URL + "send_input"

    with requests.post(
        url=url,
        data=data,
        files=files,
    ) as resp:
        print("Data sent to the server ✅" if resp.ok else "Error ❌ in sending data to the server")


def run_fhe_in_server_fn() -> Dict:
    """Run in FHE the anonymization of the query"""

    print("------------ Step 3.2: Run in FHE on the Server Side")

    evaluation_key_path = KEYS_DIR / f"{USER_ID}/evaluation_key"
    encrypted_input_path = KEYS_DIR / f"{USER_ID}/encrypted_input"

    if not evaluation_key_path.is_file():
        error_message = (
            "Error Encountered While Sending Data to the Server: "
            f"The key has been generated correctly - {evaluation_key_path.is_file()=}"
        )
        return {anonymized_query_output: gr.update(value=error_message)}

    if not encrypted_input_path.is_file():
        error_message = (
            "Error Encountered While Sending Data to the Server: The data has not been encrypted "
            f"correctly on the client side - {encrypted_input_path.is_file()=}"
        )
        return {anonymized_query_output: gr.update(value=error_message)}

    data = {
        "user_id": USER_ID,
    }

    url = SERVER_URL + "run_fhe"

    with requests.post(
        url=url,
        data=data,
    ) as response:
        if not response.ok:
            return {
                anonymized_query_output: gr.update(
                    value=(
                        "⚠️ An error occurred on the Server Side. "
                        "Please check connectivity and data transmission."
                    ),
                ),
            }
        else:
            time.sleep(1)
            print(f"The query anonymization was computed in {response.json():.2f} s per token.")


def get_output_fn() -> Dict:

    print("------------ Step 3.3: Get the output from the Server Side")

    if not (KEYS_DIR / f"{USER_ID}/evaluation_key").is_file():
        error_message = (
            "Error Encountered While Sending Data to the Server: "
            "The key has not been generated correctly"
        )
        return {anonymized_query_output: gr.update(value=error_message)}

    if not (KEYS_DIR / f"{USER_ID}/encrypted_input").is_file():
        error_message = (
            "Error Encountered While Sending Data to the Server: "
            "The data has not been encrypted correctly on the client side"
        )
        return {anonymized_query_output: gr.update(value=error_message)}

    data = {
        "user_id": USER_ID,
    }

    # Retrieve the encrypted output
    url = SERVER_URL + "get_output"
    with requests.post(
        url=url,
        data=data,
    ) as response:
        if response.ok:
            print("Data received ✅ from the remote Server")
            response_data = response.json()
            encrypted_output_base64 = response_data["encrypted_output"]
            length_encrypted_output_base64 = response_data["length"]

            # Decode the base64 encoded data
            encrypted_output = base64.b64decode(encrypted_output_base64)
            length_encrypted_output = base64.b64decode(length_encrypted_output_base64)

            # Save the encrypted output to bytes in a file as it is too large to pass through
            # regular Gradio buttons (see https://github.com/gradio-app/gradio/issues/1877)

            write_bytes(CLIENT_DIR / f"{USER_ID}_encrypted_output", encrypted_output)
            write_bytes(CLIENT_DIR / f"{USER_ID}_encrypted_output_len", length_encrypted_output)

        else:
            print("Error ❌ in getting data to the server")


def decrypt_fn(text) -> Dict:
    """Dencrypt the data on the `Client Side`."""

    print("------------ Step 4: Dencrypt the data on the `Client Side`")

    # Get the encrypted output path
    encrypted_output_path = CLIENT_DIR / f"{USER_ID}_encrypted_output"

    if not encrypted_output_path.is_file():
        error_message = """⚠️ Please ensure that: \n
                - the connectivity \n
                - the query has been submitted \n
                - the evaluation key has been generated \n
                - the server processed the encrypted data \n
                - the Client received the data from the Server before decrypting the prediction
                """
        print(error_message)

        return error_message, None

    # Retrieve the client API
    client = FHEModelClient(path_dir=DEPLOYMENT_DIR, key_dir=KEYS_DIR / f"{USER_ID}")
    client.load()

    # Load the encrypted output as bytes
    encrypted_output = read_bytes(CLIENT_DIR / f"{USER_ID}_encrypted_output")
    length = int.from_bytes(read_bytes(CLIENT_DIR / f"{USER_ID}_encrypted_output_len"), "big")

    tokens = re.findall(r"(\b[\w\.\/\-@]+\b|[\s,.!?;:'\"-]+)", text)

    decrypted_output, identified_words_with_prob = [], []

    i = 0
    for token in tokens:

        # Directly append non-word tokens or whitespace to processed_tokens
        if bool(re.match(r"^\s+$", token)):
            continue
        else:
            encrypted_token = encrypted_output[i : i + length]
            prediction_proba = client.deserialize_decrypt_dequantize(encrypted_token)
            probability = prediction_proba[0][1]
            i += length

            if probability >= 0.77:
                identified_words_with_prob.append((token, probability))

                # Use the existing UUID if available, otherwise generate a new one
                tmp_uuid = UUID_MAP.get(token, str(uuid.uuid4())[:8])
                decrypted_output.append(tmp_uuid)
                UUID_MAP[token] = tmp_uuid
            else:
                decrypted_output.append(token)

        # Update the UUID map with query.
        write_json(MAPPING_UUID_PATH, UUID_MAP)

    # Removing Spaces Before Punctuation:
    anonymized_text = re.sub(r"\s([,.!?;:])", r"\1", " ".join(decrypted_output))

    # 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"])

    print("Decryption done ✅ on Client Side")

    return anonymized_text, identified_df


def anonymization_with_fn(selected_sentences, query):

    encrypt_query_fn(query)

    send_input_fn(query)

    run_fhe_in_server_fn()

    get_output_fn()

    anonymized_text, identified_df = decrypt_fn(query)

    return {
        anonymized_doc_output: gr.update(value=select_static_anonymized_sentences_fn(selected_sentences)),
        anonymized_query_output: gr.update(value=anonymized_text),
        identified_words_output_df: gr.update(value=identified_df, visible=False),
    }


def query_chatgpt_fn(anonymized_query, anonymized_document):

    print("------------ Step 5: ChatGPT communication")

    if not (KEYS_DIR / f"{USER_ID}/evaluation_key").is_file():
        error_message = "Error ❌: Please generate the key first!"
        return {chatgpt_response_anonymized: gr.update(value=error_message)}

    if not (CLIENT_DIR / f"{USER_ID}_encrypted_output").is_file():
        error_message = "Error ❌: Please encrypt your query first!"
        return {chatgpt_response_anonymized: gr.update(value=error_message)}

    context_prompt = read_txt(PROMPT_PATH)

    # Prepare prompt
    query = (
        "Document content:\n```\n"
        + anonymized_document
        + "\n\n```"
        + "Query:\n```\n"
        + anonymized_query
        + "\n```"
    )
    print(f'Prompt of CHATGPT:\n{query}')

    completion = client.chat.completions.create(
        model="gpt-4-1106-preview",  # Replace with "gpt-4" if available
        messages=[
            {"role": "system", "content": context_prompt},
            {"role": "user", "content": query},
        ],
    )
    anonymized_response = completion.choices[0].message.content
    uuid_map = read_json(MAPPING_UUID_PATH)

    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.)
    tokens = re.findall(r"(\b[\w\.\/\-@]+\b|[\s,.!?;:'\"-]+)", anonymized_response)
    processed_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

        if token in inverse_uuid_map:
            processed_tokens.append(inverse_uuid_map[token])
        else:
            processed_tokens.append(token)
    deanonymized_response = "".join(processed_tokens)

    return {chatgpt_response_anonymized: gr.update(value=anonymized_response), 
            chatgpt_response_deanonymized: gr.update(value=deanonymized_response)}


demo = gr.Blocks(css=".markdown-body { font-size: 18px; }")

with demo:

    gr.Markdown(
        """
        <p align="center">
            <img width=200 src="https://user-images.githubusercontent.com/5758427/197816413-d9cddad3-ba38-4793-847d-120975e1da11.png">
        </p>
        """)
        
    gr.Markdown(
        """
        <h1 style="text-align: center;">Encrypted de-identification 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://community.zama.ai/c/concrete-ml/8"> <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>
        """
    )

    gr.Markdown(
    """
    <p align="center" style="font-size: 16px;">
        Anonymization makes it impossible to identify a person from a data set and thus allows their privacy to be respected (CNIL) </p> Concealing some of PII information is a helpful technique for de-identification. However, this can't be considered as anonymization. 

    <p align="center" style="font-size: 16px;">
        De-identification uses Fully Homomorphic Encryption (FHE) to conceal personally 
        identifiable information (PII) within encrypted documents, enabling computations to be 
        performed on the encrypted data.</p>
        
    """
    )    
    
    ########################## Key Gen Part ##########################

    gr.Markdown(
        "## Step 1: Generate the keys\n\n"
        """In Fully Homomorphic Encryption (FHE) methods, two types of keys are created. The first 
        type, called secret keys, are used to encrypt and decrypt the user's data. The second type, 
        called evaluation keys, enables a server to work on the encrypted data without seeing the 
        actual data.
        """
    )

    gen_key_btn = gr.Button("Generate the secret and evaluation keys")

    gen_key_btn.click(
        key_gen_fn,
        inputs=[],
        outputs=[gen_key_btn],
    )

    ########################## Main document Part ##########################

    gr.Markdown("<hr />")
    gr.Markdown("## Step 2.1: Select the document you want to encrypt\n\n"
        """To make it simple, we pre-compiled the following document, but you are free to choose 
        on which part you want to run this example.
        """
    )

    with gr.Row():
        with gr.Column(scale=5):
            original_sentences_box = gr.CheckboxGroup(
                ORIGINAL_DOCUMENT,
                value=ORIGINAL_DOCUMENT,
                label="Contract:",
                show_label=True,
            )

        with gr.Column(scale=1, min_width=6):
            gr.HTML("<div style='height: 77px;'></div>")
            encrypt_doc_btn = gr.Button("Encrypt the document")

        with gr.Column(scale=5):
            encrypted_doc_box = gr.Textbox(
                label="Encrypted document:", show_label=True, interactive=False, lines=10
            )


    ########################## User Query Part ##########################

    gr.Markdown("<hr />")
    gr.Markdown("## Step 2.2: Select the prompt you want to encrypt\n\n"
        """Please choose from the predefined options in 
        <span style='color:grey'>“Prompt examples”</span> or craft a custom question in 
        the <span style='color:grey'>“Customized prompt”</span> text box.        
        Remain concise and relevant to the context. Any off-topic query will not be processed.""")

    with gr.Row():
        with gr.Column(scale=5):

            with gr.Column(scale=5):
                default_query_box = gr.Dropdown(
                    list(DEFAULT_QUERIES.values()), label="PROMPT EXAMPLES:"
                )

            gr.Markdown("Or")

            query_box = gr.Textbox(
                value="What is Kate international bank account number?", label="CUSTOMIZED PROMPT:", interactive=True
            )

            default_query_box.change(
                fn=lambda default_query_box: default_query_box,
                inputs=[default_query_box],
                outputs=[query_box],
            )

        with gr.Column(scale=1, min_width=6):
            gr.HTML("<div style='height: 77px;'></div>")
            encrypt_query_btn = gr.Button("Encrypt the prompt")
            # gr.HTML("<div style='height: 50px;'></div>")

        with gr.Column(scale=5):
            output_encrypted_box = gr.Textbox(
                label="Encrypted de-identified query that will be sent to the de-identification server:",
                lines=8,
            )

    ########################## FHE processing Part ##########################

    gr.Markdown("<hr />")
    gr.Markdown("## Step 3: De-identify the document and the prompt using FHE")
    gr.Markdown(
        """Once the client encrypts the document and the prompt locally, it will be sent to a remote 
        server to perform the de-identification on encrypted data. When the computation is done, the 
        server will return the result to the client for decryption.
        """
    )

    run_fhe_btn = gr.Button("De-identify using FHE")

    with gr.Row():
        with gr.Column(scale=5):

            anonymized_doc_output = gr.Textbox(
                label="Decrypted and de-idenntified document", lines=10, interactive=True
            )

        with gr.Column(scale=5):

            anonymized_query_output = gr.Textbox(
                label="Decrypted and de-identified prompt", lines=10, interactive=True
            )


    identified_words_output_df = gr.Dataframe(label="Identified words:", visible=False)

    encrypt_doc_btn.click(
        fn=encrypt_doc_fn,
        inputs=[original_sentences_box],
        outputs=[encrypted_doc_box, anonymized_doc_output],
    )

    encrypt_query_btn.click(
        fn=encrypt_query_fn,
        inputs=[query_box],
        outputs=[
            query_box,
            output_encrypted_box,
            anonymized_query_output,
            identified_words_output_df,
        ],
    )

    run_fhe_btn.click(
        anonymization_with_fn,
        inputs=[original_sentences_box, query_box],
        outputs=[anonymized_doc_output, anonymized_query_output, identified_words_output_df],
    )

    ########################## ChatGpt Part ##########################

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