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