"""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( """

Credit Card Approval Prediction Using Fully Homomorphic Encryption

""" ) 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 ⭐." ) demo.launch(share=False)