app.py

import streamlit as st
import pandas as pd
import numpy as np
import plotly.graph_objects as go
from financial_model import calculate_mall_economics
from report_generator import create_pdf_report

st.set_page_config(page_title="WestProp Financial Twin (Pro)", layout="wide", page_icon="🏦")

st.markdown("""
<style>
    .metric-box {padding:10px; background-color:#f0f2f6; border-radius:5px;}
</style>
""", unsafe_allow_html=True)

# --- SIDEBAR: INITIALIZATION ---
if 'cost_key' not in st.session_state: st.session_state.cost_key = 180
if 'rent_key' not in st.session_state: st.session_state.rent_key = 28.0
if 'vac_key' not in st.session_state: st.session_state.vac_key = 8
if 'ltv_key' not in st.session_state: st.session_state.ltv_key = 50
if 'int_key' not in st.session_state: st.session_state.int_key = 11.0

def reset_simulation():
    st.session_state.cost_key = 180 
    st.session_state.rent_key = 28.0
    st.session_state.vac_key = 8
    st.session_state.ltv_key = 50
    st.session_state.int_key = 11.0

if st.sidebar.button("🔄 Reset Defaults", on_click=reset_simulation):
    pass

st.sidebar.header("🏗️ Project Inputs")
cost_input = st.sidebar.slider("Total Capex ($M)", 100, 300, step=5, key="cost_key") * 1_000_000
rent_input = st.sidebar.slider("Start Rent ($/sqm)", 15.0, 50.0, step=0.5, key="rent_key")
vacancy_input = st.sidebar.slider("Vacancy (%)", 0, 30, step=1, key="vac_key") / 100

st.sidebar.divider()
st.sidebar.header("🏦 Bank Structure (Debt)")
ltv_input = st.sidebar.slider("Loan-to-Value (%)", 0, 80, step=5, key="ltv_key") / 100
interest_input = st.sidebar.slider("Interest Rate (%)", 5.0, 18.0, step=0.5, key="int_key") / 100
escalation_input = 0.03 # Hardcoded 3% annual inflation for simplicity

st.sidebar.divider()

# --- CALCULATE ENGINE ---
data = calculate_mall_economics(
    cost_input, rent_input, vacancy_input, 0.20, 
    ltv_input, interest_input, escalation_input
)

cash_flows = data["Cash_Flows"]
equity_irr = data["Equity_IRR"]

# --- PDF GENERATOR BUTTON ---
if st.sidebar.button("Generate Investment Memo"):
    cumulative_cash = np.cumsum(cash_flows)
    break_even_index = next((i for i, x in enumerate(cumulative_cash) if x >= 0), None)
    real_break_year = f"Year {break_even_index}" if break_even_index else "Never"

    pdf_bytes = create_pdf_report(
        cost_input, 
        rent_input, 
        vacancy_input, 
        0.20,             # OpEx (hardcoded or slider)
        equity_irr, 
        sum(cash_flows),  # Total Profit
        real_break_year, 
        cash_flows,
        ltv_input,        # <--- NEW: Send LTV
        interest_input    # <--- NEW: Send Interest
    )
    st.sidebar.download_button("📥 Download Report", pdf_bytes, "Mall_Strategy_Report.pdf", "application/pdf")

# --- MAIN DASHBOARD ---
st.title("🏦 Mall of Zimbabwe: Leveraged Investment Model")
st.markdown(f"**Debt Structure:** {ltv_input*100:.0f}% Loan @ {interest_input*100:.1f}% Interest | **Inflation:** 3% p.a.")

# 1. CALCULATE BREAK-EVEN (Logic moved up so we can use it in metrics)
cumulative_cash = np.cumsum(cash_flows)
break_even_index = next((i for i, x in enumerate(cumulative_cash) if x >= 0), None)
dashboard_break_year = f"Year {break_even_index}" if break_even_index else "> 12 Years"

# 2. METRICS ROW (Now with 5 Columns)
col1, col2, col3, col4, col5 = st.columns(5)

# Color Logic for IRR
irr_color = "normal"
if equity_irr >= 20: irr_color = "normal"
elif equity_irr < 10: irr_color = "off"

col1.metric("Equity IRR", f"{equity_irr:.2f}%", f"{equity_irr - data['Project_IRR']:.1f}% vs Project", delta_color=irr_color)
col2.metric("Equity Required", f"${data['Equity_Required']/1_000_000:.1f} M")
col3.metric("Bank Loan", f"${data['Loan_Amount']/1_000_000:.1f} M")
col4.metric("Equity Multiple", f"{data['Equity_Multiple']:.2f}x")
col5.metric("Payback Period", dashboard_break_year) # <--- IT IS BACK!

st.divider()

# --- TABS ---
tab1, tab2 = st.tabs(["🔥 Risk Heatmap", "📊 Cash Flow Waterfall"])

with tab1:
    st.subheader("Sensitivity: How does Rent impact Investor Returns?")
    costs = np.linspace(100_000_000, 300_000_000, 10)
    rents = np.linspace(15, 50, 10)
    z_values = []
    
    for r in rents:
        row = []
        for c in costs:
            # Recalculate full model for every pixel
            res = calculate_mall_economics(c, r, vacancy_input, 0.20, ltv_input, interest_input, 0.03)
            row.append(res["Equity_IRR"])
        z_values.append(row)
        
    fig = go.Figure(data=go.Heatmap(
        z=z_values,
        x=[f"${c/1e6:.0f}M" for c in costs],
        y=[f"${r:.0f}" for r in rents],
        colorscale='RdYlGn', zmin=0, zmax=30,
        colorbar=dict(title='Equity IRR %')
    ))
    fig.update_layout(xaxis_title="Total Project Cost", yaxis_title="Start Rent ($/sqm)")
    st.plotly_chart(fig, use_container_width=True)

with tab2:
    st.subheader("Net Cash Flow to Investors (After Debt Service)")
    years = [f"Y{i+1}" for i in range(len(cash_flows))]
    colors = ['crimson' if x < 0 else 'forestgreen' for x in cash_flows]
    
    fig_cf = go.Figure(data=[go.Bar(
        x=years, y=cash_flows, marker_color=colors,
        hovertemplate='$%{y:,.0f}<extra></extra>'
    )])
    fig_cf.update_layout(yaxis_title="Net Equity Cash Flow ($)")
    st.plotly_chart(fig_cf, use_container_width=True)

    # Cumulative Line
    # (We already calculated 'cumulative_cash' at the top, so we just plot it)
    fig_cum = go.Figure()
    fig_cum.add_trace(go.Scatter(x=years, y=cumulative_cash, mode='lines+markers', line=dict(color='blue', width=3)))
    fig_cum.add_hline(y=0, line_dash="dash", line_color="red")
    fig_cum.update_layout(title="Time to Break-Even on Equity", yaxis_title="Cumulative Wealth ($)")
    st.plotly_chart(fig_cum, use_container_width=True)