app.py

"""A gradio app for credit card approval prediction using FHE."""

import subprocess
import time
import gradio as gr

from settings import (
    REPO_DIR,
    ACCOUNT_MIN_MAX,
    CHILDREN_MIN_MAX,
    INCOME_MIN_MAX,
    AGE_MIN_MAX,
    EMPLOYED_MIN_MAX,
    FAMILY_MIN_MAX,
    INCOME_TYPES,
    OCCUPATION_TYPES,
    HOUSING_TYPES,
    EDUCATION_TYPES,
    FAMILY_STATUS,
)
from backend import (
    pre_process_keygen_encrypt_send_user,
    pre_process_keygen_encrypt_send_bank,
    pre_process_keygen_encrypt_send_third_party,
    run_fhe,
    get_output,
    decrypt_output,
)


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


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("### User")
            gender = gr.Radio(["Female", "Male"], label="Gender")
            bool_inputs = gr.CheckboxGroup(["Car", "Property", "Work phone", "Phone", "Email"], label="What do you own ?")
            num_children = gr.Slider(**CHILDREN_MIN_MAX, step=1, label="Number of children", info="How many children do you have (0 to 19) ?")
            num_family = gr.Slider(**FAMILY_MIN_MAX, step=1, label="Family", info="How many members does your family have? (1 to 20) ?")
            total_income = gr.Slider(**INCOME_MIN_MAX, label="Income", info="What's you total yearly income (in euros, 3780 to 220500) ?")
            age = gr.Slider(**AGE_MIN_MAX, step=1, label="Age", info="How old are you (20 to 68) ?")
            income_type = gr.Dropdown(choices=INCOME_TYPES, label="Income type", info="What is your main type of income ?")
            education_type = gr.Dropdown(choices=EDUCATION_TYPES, label="Education", info="What is your education background ?")
            family_status = gr.Dropdown(choices=FAMILY_STATUS, label="Family", info="What is your family status ?")
            occupation_type = gr.Dropdown(choices=OCCUPATION_TYPES, label="Occupation", info="What is your main occupation ?")
            housing_type = gr.Dropdown(choices=HOUSING_TYPES, label="Housing", info="In what type of housing do you live ?")

        with gr.Column():
            gr.Markdown("### Bank ")
            account_length = gr.Slider(**ACCOUNT_MIN_MAX, step=1, label="Account length", info="How long have this person had this account (in months, 0 to 60) ?")

        with gr.Column():
            gr.Markdown("### Third party ")
            employed = gr.Radio(["Yes", "No"], label="Is the person employed ?")
            years_employed = gr.Slider(**EMPLOYED_MIN_MAX, step=1, label="Years of employment", info="How long have this person been employed (in years, 0 to 43) ?")


    gr.Markdown("### Step 2: Keygen, encrypt  using FHE and send the inputs to the server.")
    with gr.Row():
        with gr.Column():
            gr.Markdown("### User")
            encrypt_button_user = gr.Button("Encrypt the inputs and send to server.")
            
            user_id = gr.Textbox(label="", max_lines=2, interactive=False, visible=False)
            encrypted_input_user = gr.Textbox(
                label="Encrypted input representation:", max_lines=2, interactive=False
            )
            # keys_user = gr.Textbox(
            #     label="Keys representation:", max_lines=2, interactive=False
            # )



        with gr.Column():
            gr.Markdown("### Bank ")
            encrypt_button_bank = gr.Button("Encrypt the inputs and send to server.")

            bank_id = gr.Textbox(label="", max_lines=2, interactive=False, visible=False)
            encrypted_input_bank = gr.Textbox(
                label="Encrypted input representation:", max_lines=2, interactive=False
            )
            # keys_bank = gr.Textbox(
            #     label="Keys representation:", max_lines=2, interactive=False
            # )


        with gr.Column():
            gr.Markdown("### Third Party ")
            encrypt_button_third_party = gr.Button("Encrypt the inputs and send to server.")

            third_party_id = gr.Textbox(label="", max_lines=2, interactive=False, visible=False)
            encrypted_input_third_party = gr.Textbox(
                label="Encrypted input representation:", max_lines=2, interactive=False
            )
            # keys_3 = gr.Textbox(
            #     label="Keys representation:", max_lines=2, interactive=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="Encrypted output representation: ", 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 pre-process, generate the key, encrypt and send the user inputs from the client 
    # side to the server
    encrypt_button_user.click(
        pre_process_keygen_encrypt_send_user,
        inputs=[gender, bool_inputs, num_children, num_family, total_income, age, income_type, \
                education_type, family_status, occupation_type, housing_type],
        outputs=[user_id, encrypted_input_user],
    )

    # Button to pre-process, generate the key, encrypt and send the bank inputs from the client 
    # side to the server
    encrypt_button_bank.click(
        pre_process_keygen_encrypt_send_bank,
        inputs=[account_length],
        outputs=[bank_id, encrypted_input_bank],
    )

    # Button to pre-process, generate the key, encrypt and send the third party inputs from the 
    # client side to the server    
    encrypt_button_third_party.click(
        pre_process_keygen_encrypt_send_third_party,
        inputs=[employed, years_employed],
        outputs=[third_party_id, encrypted_input_third_party],
    )

    # TODO : ID should be unique
    # Button to send the encodings to the server using post method
    execute_fhe_button.click(run_fhe, inputs=[user_id, bank_id, third_party_id], outputs=[fhe_execution_time])

    # TODO : ID should be unique
    # Button to send the encodings to the server using post method
    get_output_button.click(
        get_output, 
        inputs=[user_id, bank_id, third_party_id], 
        # outputs=[encrypted_output_representation]
    )

    # TODO : ID should be unique
    # Button to decrypt the output as the user
    decrypt_button.click(
        decrypt_output,
        inputs=[user_id, bank_id, third_party_id],
        outputs=[prediction_output],
    )

    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)