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.")