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93a1da5 6e2c065 c6fa63e 0733035 a400f33 ee2c687 a400f33 ee2c687 a400f33 ee2c687 a400f33 0733035 191cbb3 5f3a336 5d51b2c 5f3a336 191cbb3 87dd542 6e2c065 87dd542 0733035 87dd542 0733035 87dd542 0733035 30c29e9 93a1da5 0733035 87dd542 d3b8b95 87dd542 d3b8b95 87dd542 c04f062 87dd542 d3b8b95 6e2c065 0733035 c6fa63e f6dd689 f1a9c5d ee2c687 c04f062 ee2c687 c04f062 6bbeeab d1736b4 c04f062 d1736b4 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 | 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.
"""
)
#### Setup Button Colors and Theme
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)
#### Add the icon and buttons at the top
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
#### Selected features shown at the bottom
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}") |