app.py

"""A local gradio app that filters images using FHE."""

import os
import shutil
import subprocess
import time
import gradio as gr
import numpy
import requests
from itertools import chain

from settings import (
    REPO_DIR,
    SERVER_URL,
    FHE_KEYS,
    CLIENT_FILES,
    SERVER_FILES,
)

# from concrete.ml.deployment.fhe_client_server import FHEModelClient


subprocess.Popen(["uvicorn", "server:app"], cwd=REPO_DIR)
time.sleep(3)


def shorten_bytes_object(bytes_object, limit=500):
    """Shorten the input bytes object to a given length.

    Encrypted data is too large for displaying it in the browser using Gradio. This function
    provides a shorten representation of it.

    Args:
        bytes_object (bytes): The input to shorten
        limit (int): The length to consider. Default to 500.

    Returns:
        str: Hexadecimal string shorten representation of the input byte object. 

    """
    # Define a shift for better display
    shift = 100
    return bytes_object[shift : limit + shift].hex()


def get_client(user_id, filter_name):
    """Get the client API.

    Args:
        user_id (int): The current user's ID.
        filter_name (str): The filter chosen by the user

    Returns:
        FHEModelClient: The client API.
    """
    # TODO
    # return FHEModelClient(
    #     FILTERS_PATH / f"{filter_name}/deployment", 
    #     filter_name,
    #     key_dir=FHE_KEYS / f"{filter_name}_{user_id}", 
    # )

    return None


def get_client_file_path(name, user_id, filter_name):
    """Get the correct temporary file path for the client.

    Args:
        name (str): The desired file name.
        user_id (int): The current user's ID.
        filter_name (str): The filter chosen by the user

    Returns:
        pathlib.Path: The file path.
    """
    return CLIENT_FILES / f"{name}_{filter_name}_{user_id}"


def clean_temporary_files(n_keys=20):
    """Clean keys and encrypted images.

    A maximum of n_keys keys and associated temporary files are allowed to be stored. Once this 
    limit is reached, the oldest files are deleted.

    Args:
        n_keys (int): The maximum number of keys and associated files to be stored. Default to 20.

    """
    # Get the oldest key files in the key directory
    key_dirs = sorted(FHE_KEYS.iterdir(), key=os.path.getmtime)

    # If more than n_keys keys are found, remove the oldest
    user_ids = []
    if len(key_dirs) > n_keys:
        n_keys_to_delete = len(key_dirs) - n_keys
        for key_dir in key_dirs[:n_keys_to_delete]:
            user_ids.append(key_dir.name)
            shutil.rmtree(key_dir)

    # Get all the encrypted objects in the temporary folder
    client_files = CLIENT_FILES.iterdir()
    server_files = SERVER_FILES.iterdir()

    # Delete all files related to the ids whose keys were deleted
    for file in chain(client_files, server_files):
        for user_id in user_ids:
            if user_id in file.name:
                file.unlink()


def keygen(filter_name):
    """Generate the private key associated to a filter.

    Args:
        filter_name (str): The current filter to consider.

    Returns:
        (user_id, True) (Tuple[int, bool]): The current user's ID and a boolean used for visual display.

    """
    # Clean temporary files
    clean_temporary_files()

    # Create an ID for the current user
    user_id = numpy.random.randint(0, 2**32)

    # Retrieve the client API
    client = get_client(user_id, filter_name)

    # Generate a private key
    client.generate_private_and_evaluation_keys(force=True)

    # Retrieve the serialized evaluation key. In this case, as circuits are fully leveled, this
    # evaluation key is empty. However, for software reasons, it is still needed for proper FHE
    # execution
    evaluation_key = client.get_serialized_evaluation_keys()

    # Save evaluation_key as bytes in a file as it is too large to pass through regular Gradio
    # buttons (see https://github.com/gradio-app/gradio/issues/1877)
    evaluation_key_path = get_client_file_path("evaluation_key", user_id, filter_name)

    with evaluation_key_path.open("wb") as evaluation_key_file:
        evaluation_key_file.write(evaluation_key)

    return (user_id, True)


def encrypt(user_id, input_image, filter_name):
    """Encrypt the given image for a specific user and filter.

    Args:
        user_id (int): The current user's ID.
        input_image (numpy.ndarray): The image to encrypt.
        filter_name (str): The current filter to consider.

    Returns:
        (input_image, encrypted_image_short) (Tuple[bytes]): The encrypted image and one of its
        representation.

    """
    user_id = keygen

    if user_id == "":
        raise gr.Error("Please generate the private key first.")

    if input_image is None:
        raise gr.Error("Please choose an image first.")

    # Retrieve the client API
    client = get_client(user_id, filter_name)

    # Pre-process, encrypt and serialize the image
    encrypted_image = client.encrypt_serialize(input_image)

    # Save encrypted_image to bytes in a file, since too large to pass through regular Gradio
    # buttons, https://github.com/gradio-app/gradio/issues/1877
    encrypted_image_path = get_client_file_path("encrypted_image", user_id, filter_name)

    with encrypted_image_path.open("wb") as encrypted_image_file:
        encrypted_image_file.write(encrypted_image)

    # Create a truncated version of the encrypted image for display
    encrypted_image_short = shorten_bytes_object(encrypted_image)

    send_input()

    return (input_image, encrypted_image_short)


def send_input(user_id, filter_name):
    """Send the encrypted input image as well as the evaluation key to the server.

    Args:
        user_id (int): The current user's ID.
        filter_name (str): The current filter to consider.
    """
    # Get the evaluation key path
    evaluation_key_path = get_client_file_path("evaluation_key", user_id, filter_name)

    if user_id == "" or not evaluation_key_path.is_file():
        raise gr.Error("Please generate the private key first.")

    encrypted_input_path = get_client_file_path("encrypted_image", user_id, filter_name)

    if not encrypted_input_path.is_file():
        raise gr.Error("Please generate the private key and then encrypt an image first.")

    # Define the data and files to post
    data = {
        "user_id": user_id,
        "filter": filter_name,
    }

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

    # Send the encrypted input image and evaluation key to the server
    url = SERVER_URL + "send_input"
    with requests.post(
        url=url,
        data=data,
        files=files,
    ) as response:
        return response.ok


def run_fhe(user_id, filter_name):
    """Apply the filter on the encrypted image previously sent using FHE.

    Args:
        user_id (int): The current user's ID.
        filter_name (str): The current filter to consider.
    """
    data = {
        "user_id": user_id,
        "filter": filter_name,
    }

    # Trigger the FHE execution on the encrypted image previously sent
    url = SERVER_URL + "run_fhe"
    with requests.post(
        url=url,
        data=data,
    ) as response:
        if response.ok:
            return response.json()
        else:
            raise gr.Error("Please wait for the input image to be sent to the server.")


def get_output(user_id, filter_name):
    """Retrieve the encrypted output image.

    Args:
        user_id (int): The current user's ID.
        filter_name (str): The current filter to consider.

    Returns:
        encrypted_output_image_short (bytes): A representation of the encrypted result.

    """
    data = {
        "user_id": user_id,
        "filter": filter_name,
    }

    # Retrieve the encrypted output image
    url = SERVER_URL + "get_output"
    with requests.post(
        url=url,
        data=data,
    ) as response:
        if response.ok:
            encrypted_output = response.content

            # 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)
            encrypted_output_path = get_client_file_path("encrypted_output", user_id, filter_name)

            with encrypted_output_path.open("wb") as encrypted_output_file:
                encrypted_output_file.write(encrypted_output)
            
            # TODO 
            # Decrypt the image using a different (wrong) key for display
            # output_image_representation = decrypt_output_with_wrong_key(encrypted_output, filter_name)

            # return output_image_representation

            return None
        else:
            raise gr.Error("Please wait for the FHE execution to be completed.")


def decrypt_output(user_id, filter_name):
    """Decrypt the result.

    Args:
        user_id (int): The current user's ID.
        filter_name (str): The current filter to consider.

    Returns:
        (output_image, False, False) ((Tuple[numpy.ndarray, bool, bool]): The decrypted output, as
            well as two booleans used for resetting Gradio checkboxes

    """
    if user_id == "":
        raise gr.Error("Please generate the private key first.")

    # Get the encrypted output path
    encrypted_output_path = get_client_file_path("encrypted_output", user_id, filter_name)

    if not encrypted_output_path.is_file():
        raise gr.Error("Please run the FHE execution first.")

    # Load the encrypted output as bytes
    with encrypted_output_path.open("rb") as encrypted_output_file:
        encrypted_output_image = encrypted_output_file.read()

    # Retrieve the client API
    client = get_client(user_id, filter_name)

    # Deserialize, decrypt and post-process the encrypted output
    output_image = client.deserialize_decrypt_post_process(encrypted_output_image)

    return output_image, False, False


demo = gr.Blocks()


print("Starting the demo...")
with demo:
    gr.Markdown(
        """
        <h1 align="center">Credit Card Approval Prediction Using Fully Homomorphic Encryption</h1>
        """
    )

    gr.Markdown("## Client side")

    gr.Markdown("### Step 1: Infos. ")
    with gr.Row():
        with gr.Column():
            gr.Markdown("### Client ")
            # TODO : change infos
            choice_1 = gr.Dropdown(choices=["Yes, No"], label="Choose", interactive=True)
            slide_1 = gr.Slider(2, 20, value=4, label="Count", info="Choose between 2 and 20")

        with gr.Column():
            gr.Markdown("### Bank ")
            # TODO : change infos
            checkbox_1 = gr.CheckboxGroup(["USA", "Japan", "Pakistan"], label="Countries", info="Where are they from?")

        with gr.Column():
            gr.Markdown("### Third Party ")
            # TODO : change infos
            radio_1 = gr.Radio(["park", "zoo", "road"], label="Location", info="Where did they go?")
        

    gr.Markdown("### Step 2: Keygen, encrypt  using FHE and send the inputs to the server.")
    with gr.Row():
        with gr.Column():
            gr.Markdown("### Client ")
            encrypt_button_1 = gr.Button("Encrypt the inputs and send to server.")
            encrypted_input_1 = gr.Textbox(
                label="Encrypted input representation:", max_lines=2, interactive=False
            )

            client_id = gr.Textbox(label="", max_lines=2, interactive=False, visible=False)


        with gr.Column():
            gr.Markdown("### Bank ")
            encrypt_button_2 = gr.Button("Encrypt the inputs and send to server.")
            encrypted_input_2 = gr.Textbox(
                label="Encrypted input representation:", max_lines=2, interactive=False
            )

            bank_id = gr.Textbox(label="", max_lines=2, interactive=False, visible=False)


        with gr.Column():
            gr.Markdown("### Third Party ")
            encrypt_button_3 = gr.Button("Encrypt the inputs and send to server.")
            encrypted_input_3 = gr.Textbox(
                label="Encrypted input representation:", max_lines=2, interactive=False
            )

            party_id = gr.Textbox(label="", max_lines=2, interactive=False, visible=False)

    gr.Markdown("## Server side")
    gr.Markdown(
        "The encrypted values are received by the server. The server can then compute the prediction "
        "directly over them. Once the computation is finished, the server returns "
        "the encrypted result to the client."
    )

    gr.Markdown("### Step 6: Run FHE execution.")
    execute_fhe_button = gr.Button("Run FHE execution.")
    fhe_execution_time = gr.Textbox(
        label="Total FHE execution time (in seconds):", max_lines=1, interactive=False
    )

    gr.Markdown("## Client side")  
    gr.Markdown(
        "The encrypted output is sent back to the client, who can finally decrypt it with the "
        "private key."
    )

    gr.Markdown("### Step 7: Receive the encrypted output from the server.")
    gr.Markdown(
        "The output displayed here is the encrypted result sent by the server, which has been "
        "decrypted using a different private key. This is only used to visually represent an "
        "encrypted output."
    )
    get_output_button = gr.Button("Receive the encrypted output from the server.")

    encrypted_output_representation = gr.Textbox(
        label="Credit card approval decision: ", max_lines=1, interactive=False
    )

    gr.Markdown("### Step 8: Decrypt the output.")
    decrypt_button = gr.Button("Decrypt the output")

    prediction_output = gr.Textbox(
        label="Credit card approval decision: ", max_lines=1, interactive=False
    )

    # # Button to generate the private key
    # keygen_button.click(
    #     keygen,
    #     inputs=[filter_name],
    #     outputs=[user_id, keygen_checkbox],
    # )

    # # Button to encrypt inputs on the client side
    # encrypt_button.click(
    #     encrypt,
    #     inputs=[user_id, input_image, filter_name],
    #     outputs=[original_image, encrypted_input],
    # )

    # # Button to send the encodings to the server using post method
    # send_input_button.click(
    #     send_input, inputs=[user_id, filter_name], outputs=[send_input_checkbox]
    # )

    # # Button to send the encodings to the server using post method
    # execute_fhe_button.click(run_fhe, inputs=[user_id, filter_name], outputs=[fhe_execution_time])

    # # Button to send the encodings to the server using post method
    # get_output_button.click(
    #     get_output, 
    #     inputs=[user_id, filter_name], 
    #     outputs=[encrypted_output_representation]
    # )

    # # Button to decrypt the output on the client side
    # decrypt_button.click(
    #     decrypt_output,
    #     inputs=[user_id, filter_name],
    #     outputs=[output_image, keygen_checkbox, send_input_checkbox],
    # )

    gr.Markdown(
        "The app was built with [Concrete-ML](https://github.com/zama-ai/concrete-ml), a "
        "Privacy-Preserving Machine Learning (PPML) open-source set of tools by [Zama](https://zama.ai/). "
        "Try it yourself and don't forget to star on Github &#11088;."
    )

demo.launch(share=False)