Spaces:
Sleeping
Sleeping
File size: 6,716 Bytes
80bb933 | 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 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 | import streamlit as st
import pandas as pd
import numpy as np
from inference import predict_emi # Ensure inference.py is in the same folder
# -------------------------------
# PAGE CONFIG
# -------------------------------
st.set_page_config(
page_title="EMI Eligibility Pro",
page_icon="💰",
layout="wide"
)
# Custom CSS for better styling
st.markdown("""
<style>
.main {
background-color: #f5f7f9;
}
.stMetric {
background-color: #ffffff;
padding: 15px;
border-radius: 10px;
box-shadow: 0 2px 4px rgba(0,0,0,0.05);
}
</style>
""", unsafe_allow_html=True)
st.title("🚀 EMI Eligibility & Risk Prediction")
st.write("Fill in the details below to check your loan eligibility and maximum safe EMI.")
# -------------------------------
# INPUT FORM
# -------------------------------
with st.container():
# SECTION 1: Personal & Demographic
st.subheader("👤 Personal Information")
col1, col2, col3 = st.columns(3)
with col1:
age = st.number_input("Age", 18, 70, 30)
gender = st.selectbox("Gender", ["Male", "Female"])
marital_status = st.selectbox("Marital Status", ["Single", "Married"])
with col2:
education = st.selectbox("Education", ["High School", "Graduate", "Post Graduate", "Professional"])
family_size = st.number_input("Family Size", 1, 10, 3)
dependents = st.number_input("Dependents", 0, 10, 1)
with col3:
house_type = st.selectbox("House Type", ["Rented", "Own", "Family"])
company_type = st.selectbox("Company Type", ["Startup", "SME", "MNC", "Government"])
st.divider()
# SECTION 2: Employment & Income
st.subheader("💼 Employment & Financials")
col4, col5, col6 = st.columns(3)
with col4:
employment_type = st.selectbox("Employment Type", ["Private", "Government", "Self-employed"])
years_of_employment = st.number_input("Years of Employment", 0, 40, 5)
with col5:
monthly_salary = st.number_input("Monthly Salary (INR)", 10000, 500000, 50000, step=5000)
credit_score = st.number_input("Credit Score", 300, 900, 700)
with col6:
bank_balance = st.number_input("Bank Balance (INR)", 0, 10000000, 200000)
existing_loans = st.selectbox("Existing Loans", ["No", "Yes"])
st.divider()
# SECTION 3: Expenses & Current Debt
st.subheader("📉 Monthly Outgoings")
col7, col8, col9 = st.columns(3)
with col7:
monthly_rent = st.number_input("Monthly Rent (INR)", 0, 100000, 10000)
current_emi_amount = st.number_input("Current EMI Totals", 0, 100000, 0)
with col8:
groceries_utilities = st.number_input("Groceries & Utilities", 0, 50000, 8000)
travel_expenses = st.number_input("Travel Expenses", 0, 50000, 3000)
with col9:
school_college_fees = st.number_input("Education Fees (Total)", 0, 150000, 0)
other_monthly_expenses = st.number_input("Other Expenses", 0, 50000, 5000)
st.divider()
# SECTION 4: Loan Details
st.subheader("📝 Loan Application Details")
col10, col11, col12 = st.columns(3)
with col10:
emi_scenario = st.selectbox("EMI Type", ["Personal Loan EMI", "Vehicle EMI", "Home Appliances EMI", "Education EMI", "E-commerce Shopping EMI"])
with col11:
requested_amount = st.number_input("Requested Loan Amount (INR)", 10000, 20000000, 300000)
with col12:
requested_tenure = st.number_input("Requested Tenure (Months)", 3, 84, 24)
# -------------------------------
# PREDICTION ENGINE
# -------------------------------
st.markdown("<br>", unsafe_allow_html=True)
if st.button("Analyze Eligibility", use_container_width=True, type="primary"):
# Bundle input for Inference
raw_input = {
"age": age,
"gender": gender,
"marital_status": marital_status,
"education": education,
"monthly_salary": monthly_salary,
"employment_type": employment_type,
"years_of_employment": years_of_employment,
"company_type": company_type,
"house_type": house_type,
"monthly_rent": monthly_rent,
"family_size": family_size,
"dependents": dependents,
"school_fees": school_college_fees * 0.4, # Heuristic split if your model expects separate
"college_fees": school_college_fees * 0.6,
"travel_expenses": travel_expenses,
"groceries_utilities": groceries_utilities,
"other_monthly_expenses": other_monthly_expenses,
"existing_loans": existing_loans,
"current_emi_amount": current_emi_amount,
"credit_score": credit_score,
"bank_balance": bank_balance,
"emergency_fund": bank_balance * 0.2, # Assumption if not provided
"emi_scenario": emi_scenario,
"requested_amount": requested_amount,
"requested_tenure": requested_tenure
}
with st.spinner("Consulting the AI Risk Model..."):
eligibility, max_emi = predict_emi(raw_input)
st.markdown("---")
# DISPLAY RESULTS
res_col1, res_col2 = st.columns([1, 2])
with res_col1:
if eligibility == "Eligible":
st.success(f"### Result: {eligibility} ✅")
st.metric("Safe EMI Limit", f"₹ {max_emi:,.2f}")
elif eligibility == "High Risk":
st.warning(f"### Result: {eligibility} ⚠️")
st.metric("Risk-Adjusted EMI", f"₹ {max_emi:,.2f}")
else:
st.error(f"### Result: {eligibility} ❌")
st.metric("Approved EMI", "₹ 0.00")
with res_col2:
st.write("#### AI Analysis Summary")
if eligibility == "Eligible":
st.write(f"Based on your credit score of **{credit_score}** and disposable income, you are highly likely to be approved. Your monthly surplus supports an EMI of up to **₹{max_emi:,.2f}**.")
elif eligibility == "High Risk":
st.write("You are borderline eligible. We recommend either increasing your loan tenure (to lower the monthly burden) or closing small existing debts to improve your score.")
else:
st.write("Unfortunately, based on current debt-to-income ratios and credit history, we cannot approve this loan. Try again after improving your credit score or increasing your monthly bank balance.")
# Show "What-If" Analysis
if eligibility != "Eligible":
st.info("💡 **Pro-Tip:** Lowering your 'Requested Amount' or increasing your 'Tenure' usually improves eligibility results.") |