Code Editor for app.py

import streamlit as st
import pandas as pd
import plotly.graph_objects as go
from plotly.subplots import make_subplots

# Set page config for a cleaner look
st.set_page_config(layout="wide", page_title="DRE Dashboard")

# Custom CSS for styling
st.markdown("""
    <style>
        .main {
            background-color: #f8f9fa;
        }
        .stMetric {
            background-color: white;
            padding: 15px;
            border-radius: 5px;
            box-shadow: 0 2px 4px rgba(0,0,0,0.05);
        }
        .section-header {
            color: #2c3e50;
            font-weight: 600;
            font-size: 1.2rem;
            margin-bottom: 1rem;
        }
    </style>
""", unsafe_allow_html=True)

# Sample data
def generate_sample_data():
    data = {
        'Revenue': {
            'Product Sales': 850000,
            'Service Revenue': 450000,
            'Other Income': 75000
        },
        'Costs': {
            'Cost of Goods Sold': 425000,
            'Direct Labor': 275000,
            'Materials': 150000
        },
        'Operating_Expenses': {
            'Salaries': 225000,
            'Marketing': 95000,
            'Rent': 85000,
            'Utilities': 45000,
            'Insurance': 35000
        }
    }
    return data

# Initialize session state for editable values
if 'data' not in st.session_state:
    st.session_state.data = generate_sample_data()

# Title
st.title('Dynamic Revenue & Expense Dashboard')
st.markdown('---')

# Main content
col1, col2 = st.columns([2, 1])

with col1:
    # Revenue Section
    st.markdown('<p class="section-header">📈 REVENUE</p>', unsafe_allow_html=True)
    revenue_data = st.session_state.data['Revenue']
    revenue_df = pd.DataFrame(revenue_data.items(), columns=['Item', 'Amount'])
    total_revenue = sum(revenue_data.values())
    
    # Create an editable revenue table
    for index, (item, amount) in enumerate(revenue_data.items()):
        new_value = st.number_input(
            f"{item}",
            value=float(amount),
            key=f"revenue_{index}",
            format="%,.0f"
        )
        st.session_state.data['Revenue'][item] = new_value
    
    st.markdown('---')
    
    # Costs Section
    st.markdown('<p class="section-header">💰 COSTS</p>', unsafe_allow_html=True)
    costs_data = st.session_state.data['Costs']
    for index, (item, amount) in enumerate(costs_data.items()):
        new_value = st.number_input(
            f"{item}",
            value=float(amount),
            key=f"costs_{index}",
            format="%,.0f"
        )
        st.session_state.data['Costs'][item] = new_value
    
    st.markdown('---')
    
    # Operating Expenses Section
    st.markdown('<p class="section-header">📊 OPERATING EXPENSES</p>', unsafe_allow_html=True)
    opex_data = st.session_state.data['Operating_Expenses']
    for index, (item, amount) in enumerate(opex_data.items()):
        new_value = st.number_input(
            f"{item}",
            value=float(amount),
            key=f"opex_{index}",
            format="%,.0f"
        )
        st.session_state.data['Operating_Expenses'][item] = new_value

with col2:
    # Calculate totals
    total_revenue = sum(st.session_state.data['Revenue'].values())
    total_costs = sum(st.session_state.data['Costs'].values())
    total_opex = sum(st.session_state.data['Operating_Expenses'].values())
    net_income = total_revenue - total_costs - total_opex
    
    # Display metrics
    st.metric("Total Revenue", f"${total_revenue:,.0f}")
    st.metric("Total Costs", f"${total_costs:,.0f}")
    st.metric("Total Operating Expenses", f"${total_opex:,.0f}")
    st.metric("Net Income", f"${net_income:,.0f}", 
              delta=f"{(net_income/total_revenue)*100:.1f}% of Revenue")
    
    # Create waterfall chart
    fig = go.Figure(go.Waterfall(
        name="DRE", orientation="v",
        measure=["relative", "relative", "relative", "total"],
        x=["Revenue", "Costs", "Operating Expenses", "Net Income"],
        text=[f"${val:,.0f}" for val in [total_revenue, -total_costs, -total_opex, net_income]],
        y=[total_revenue, -total_costs, -total_opex, 0],
        connector={"line": {"color": "rgb(63, 63, 63)"}},
        decreasing={"marker": {"color": "#EF553B"}},
        increasing={"marker": {"color": "#00B5F7"}},
        totals={"marker": {"color": "#2c3e50"}}
    ))
    
    fig.update_layout(
        title="Financial Waterfall",
        showlegend=False,
        plot_bgcolor='white',
        height=400,
        margin=dict(t=50, l=0, r=0, b=0)
    )
    
    st.plotly_chart(fig, use_container_width=True)

# Create pie charts for breakdown
fig = make_subplots(rows=1, cols=3, specs=[[{'type':'domain'}, {'type':'domain'}, {'type':'domain'}]])

fig.add_trace(go.Pie(labels=list(st.session_state.data['Revenue'].keys()), 
                     values=list(st.session_state.data['Revenue'].values()),
                     name="Revenue"), 1, 1)
fig.add_trace(go.Pie(labels=list(st.session_state.data['Costs'].keys()), 
                     values=list(st.session_state.data['Costs'].values()),
                     name="Costs"), 1, 2)
fig.add_trace(go.Pie(labels=list(st.session_state.data['Operating_Expenses'].keys()), 
                     values=list(st.session_state.data['Operating_Expenses'].values()),
                     name="Operating Expenses"), 1, 3)

fig.update_layout(
    height=300,
    title_text="Breakdown Analysis",
    annotations=[
        dict(text='Revenue', x=0.11, y=0.5, font_size=12, showarrow=False),
        dict(text='Costs', x=0.50, y=0.5, font_size=12, showarrow=False),
        dict(text='Operating Expenses', x=0.89, y=0.5, font_size=12, showarrow=False)
    ]
)

st.plotly_chart(fig, use_container_width=True)