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| """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 ( | |
| keygen_send, | |
| pre_process_encrypt_send_user, | |
| pre_process_encrypt_send_bank, | |
| pre_process_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">Encrypted Credit Card Approval Prediction Using Fully Homomorphic Encryption</h1> | |
| """ | |
| ) | |
| gr.Markdown("# Client side") | |
| gr.Markdown("## Step 1: Generate the keys.") | |
| gr.Markdown( | |
| """ | |
| - The private key is used to encrypt and decrypt the data and shall never be shared. | |
| - The evaluation key is a public key that the server needs to process encrypted data. It is | |
| therefore transmitted to the server for further processing as well. | |
| """ | |
| ) | |
| keygen_button = gr.Button("Generate the keys and send evaluation key to the server.") | |
| evaluation_key = gr.Textbox( | |
| label="Evaluation key representation:", max_lines=2, interactive=False | |
| ) | |
| client_id = gr.Textbox(label="", max_lines=2, interactive=False, visible=False) | |
| gr.Markdown("## Step 2: Fill in some information.") | |
| gr.Markdown( | |
| """ | |
| Select the information that corresponds to the profile you want to evaluate. Three sources | |
| of information are represented in this model: | |
| - a user's personal information in order to evaluate his/her credit card eligibility; | |
| - the user’s bank account history, which provides any type of information on the user's | |
| banking information relevant to the decision (here, we consider duration of account); | |
| - and third party information, which represents any other information (here, employment | |
| history) that could provide additional insight relevant to the decision. | |
| """ | |
| ) | |
| with gr.Row(): | |
| with gr.Column(): | |
| gr.Markdown("### User") | |
| bool_inputs = gr.CheckboxGroup(["Car", "Property", "Mobile phone"], label="Which of the following do you actively hold or own?") | |
| num_children = gr.Slider(**CHILDREN_MIN_MAX, step=1, label="Number of children", info="How many children do you have ?") | |
| household_size = gr.Slider(**FAMILY_MIN_MAX, step=1, label="Household size", info="How many members does your household have? ?") | |
| total_income = gr.Slider(**INCOME_MIN_MAX, label="Income", info="What's you total yearly income (in euros) ?") | |
| age = gr.Slider(**AGE_MIN_MAX, step=1, label="Age", info="How old are you ?") | |
| income_type = gr.Dropdown(choices=INCOME_TYPES, value=INCOME_TYPES[0], label="Income type", info="What is your main type of income ?") | |
| education_type = gr.Dropdown(choices=EDUCATION_TYPES, value=EDUCATION_TYPES[0], label="Education", info="What is your education background ?") | |
| family_status = gr.Dropdown(choices=FAMILY_STATUS, value=FAMILY_STATUS[0], label="Family", info="What is your family status ?") | |
| occupation_type = gr.Dropdown(choices=OCCUPATION_TYPES, value=OCCUPATION_TYPES[0], label="Occupation", info="What is your main occupation ?") | |
| housing_type = gr.Dropdown(choices=HOUSING_TYPES, value=HOUSING_TYPES[0], label="Housing", info="In what type of housing do you live ?") | |
| with gr.Column(): | |
| gr.Markdown("### Bank ") | |
| account_age = gr.Slider(**ACCOUNT_MIN_MAX, step=1, label="Account age (months)", info="How long have this person had this bank account (in months) ?") | |
| with gr.Column(): | |
| gr.Markdown("### Third party ") | |
| employed = gr.Radio(["Yes", "No"], label="Is the person employed ?", value="Yes") | |
| years_employed = gr.Slider(**EMPLOYED_MIN_MAX, step=1, label="Years of employment", info="How long have this person been employed (in years) ?") | |
| gr.Markdown("## Step 3: Encrypt the inputs using FHE and send them to the server.") | |
| with gr.Row(): | |
| with gr.Column(): | |
| gr.Markdown("### User") | |
| encrypt_button_user = gr.Button("Encrypt the inputs and send to server.") | |
| encrypted_input_user = gr.Textbox( | |
| label="Encrypted input representation:", max_lines=2, interactive=False | |
| ) | |
| with gr.Column(): | |
| gr.Markdown("### Bank ") | |
| encrypt_button_bank = gr.Button("Encrypt the inputs and send to server.") | |
| encrypted_input_bank = gr.Textbox( | |
| label="Encrypted input 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.") | |
| encrypted_input_third_party = gr.Textbox( | |
| label="Encrypted input representation:", max_lines=2, interactive=False | |
| ) | |
| gr.Markdown("# Server side") | |
| gr.Markdown( | |
| """ | |
| Once the server receives the encrypted inputs, it can compute the prediction without ever | |
| needing to decrypt any value. | |
| This server employs an [XGBoost](https://github.com/dmlc/xgboost) classifier model that has | |
| been trained on a synthetic data-set. | |
| """ | |
| ) | |
| gr.Markdown("## Step 4: 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( | |
| """ | |
| Once the server completed the inference, the encrypted output is returned to the user. | |
| """ | |
| ) | |
| gr.Markdown("## Step 5: Receive the encrypted output from the server.") | |
| gr.Markdown( | |
| """ | |
| The value displayed below is a shortened byte representation of the actual 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=2, interactive=False | |
| ) | |
| gr.Markdown("## Step 6: Decrypt the output.") | |
| gr.Markdown( | |
| """ | |
| The user is able to decrypt the prediction using its private key. | |
| """ | |
| ) | |
| decrypt_button = gr.Button("Decrypt the output") | |
| prediction_output = gr.Textbox( | |
| label="Prediction", max_lines=1, interactive=False | |
| ) | |
| # Button generate the keys | |
| keygen_button.click( | |
| keygen_send, | |
| outputs=[client_id, evaluation_key, keygen_button], | |
| ) | |
| # 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_encrypt_send_user, | |
| inputs=[client_id, bool_inputs, num_children, household_size, total_income, age, \ | |
| income_type, education_type, family_status, occupation_type, housing_type], | |
| outputs=[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_encrypt_send_bank, | |
| inputs=[client_id, account_age], | |
| outputs=[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_encrypt_send_third_party, | |
| inputs=[client_id, employed, years_employed], | |
| outputs=[encrypted_input_third_party], | |
| ) | |
| # Button to send the encodings to the server using post method | |
| execute_fhe_button.click(run_fhe, inputs=[client_id], outputs=[fhe_execution_time]) | |
| # Button to send the encodings to the server using post method | |
| get_output_button.click( | |
| get_output, | |
| inputs=[client_id], | |
| outputs=[encrypted_output_representation], | |
| ) | |
| # Button to decrypt the output | |
| decrypt_button.click( | |
| decrypt_output, | |
| inputs=[client_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) | |