| import pandas as pd |
| import streamlit as st |
| from utils import add_navigation, add_instruction_text, add_red_text |
|
|
| ALL_FEATURES = [ |
| ("Liquid Assets", ["Liquid Assets"], |
| "Great choice! With access to bank accounts, a measure of all liquid assets can really help make a decision about loan approval."), |
| ("Illiquid Assets", ["Property appraisals", "Insurance valuations"], |
| "Oops, it is not possible to precisely calculate the value of illiquid assets! We will use some approximate alternatives instead."), |
| ("Debt Obligations", ["Credit Card Debts", "Bank Loans", "Self-reported Debts"], |
| "Oops, it is not possible to precisely measure all debt obligations! We will use credit card debts, bank loans, and other self-reported debts as approximate alternatives instead."), |
| ("Income Stability", ["Past Income Stability"], |
| "Sorry, it is not possible to know precisely how stable someone's income will be in future. Until someone invents a time machine (we're hopeful!), we can instead approximate with past income stability."), |
| ("Health Trajectory", ["Current Health Indicators"], |
| "Sorry, it is not possible to know precisely how will someone's health change in the future. Until someone invents a time machine (we're hopeful!), we can instead approximate with current health indicators."), |
| ("Financial Discipline", ["Credit Score"], |
| "Oops, financial discipline is not a directly measurable quality. We will use credit score as an approximation instead.") |
| ] |
|
|
| def render(): |
| add_navigation("txt_information_loss", "txt_rashomon_effect") |
|
|
| add_instruction_text( |
| """ |
| Consider the automation of loan approval using AI models. <br> |
| Select the features below that you would like to collect to train your AI model. <br> |
| Notice that, in trying to capture data from the real world, you loose information. |
| """ |
| ) |
| |
| |
| sec_bg = st.get_option("theme.secondaryBackgroundColor") |
| text = st.get_option("theme.textColor") |
| |
| css = f""" |
| <style> |
| button[kind="tertiary"] {{ |
| background-color: {sec_bg} !important; |
| color: {text} !important; |
| border: 1px solid {sec_bg} !important; |
| opacity: 0.2 !important; /* faded look */ |
| cursor: not-allowed !important; /* shows it's inactive */ |
| pointer-events: none !important; /* disable clicking */ |
| }} |
| </style> |
| """ |
| st.markdown(css, unsafe_allow_html=True) |
|
|
| |
| |
| if "selected_features" not in st.session_state: |
| st.session_state.selected_features = [] |
|
|
| cols_list = st.columns([1, 1, 1, 1]) |
|
|
| with cols_list[0]: |
| st.image( |
| "loan.png", |
| width=100, |
| ) |
|
|
| for ite, feature in enumerate(ALL_FEATURES.copy()): |
| with cols_list[ite%3 + 1]: |
| if feature in st.session_state.selected_features: |
| _ = st.button(feature[0], type="tertiary") |
| else: |
| if st.button(feature[0]): |
| st.session_state.selected_features.append(feature) |
| st.session_state.show_message = feature[2] |
| st.rerun() |
|
|
| if "show_message" in st.session_state: |
| add_red_text(st.session_state.show_message) |
| del st.session_state.show_message |
|
|
| |
| display_features_list = [] |
| for ele in st.session_state.selected_features: |
| display_features_list.extend(ele[1]) |
| |
| st.markdown("---") |
| st.markdown("**Set of Selected Features:**") |
| cols_list = st.columns(3) |
| for i, item in enumerate(display_features_list): |
| cols_list[i%3].markdown(f"- {item}") |
