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import streamlit as st
import pandas as pd
# Title and Description
st.title('Operational Cash Flow Analysis')
st.write("""
This application allows you to analyze and visualize your company's operational cash flow.
""")
# Data Input Section
st.header('Input Financial Data')
# Input fields for financial data
net_income = st.number_input('Net Income', value=0)
depreciation = st.number_input('Depreciation and Amortization', value=0)
change_ar = st.number_input('Change in Accounts Receivable', value=0)
change_inventory = st.number_input('Change in Inventory', value=0)
change_ap = st.number_input('Change in Accounts Payable', value=0)
# Calculating Operational Cash Flow
ocf = net_income + depreciation - change_ar - change_inventory + change_ap
# Displaying the result
st.subheader('Calculated Operational Cash Flow')
st.write(f'Operational Cash Flow: ${ocf}')
# DataFrame for historical data visualization (example data)
data = {
'Year': ['2020', '2021', '2022'],
'Net Income': [100000, 120000, 130000],
'Depreciation and Amortization': [20000, 25000, 27000],
'Change in AR': [-5000, -6000, -5500],
'Change in Inventory': [-8000, -7500, -9000],
'Change in AP': [7000, 8500, 9000],
'Operational Cash Flow': [114000, 137500, 149500]
}
df = pd.DataFrame(data)
# Display the historical data table
st.subheader('Historical Data')
st.dataframe(df)
# Visualize the historical operational cash flow
st.subheader('Operational Cash Flow Over Years')
st.line_chart(df[['Year', 'Operational Cash Flow']].set_index('Year'))
# Scenario Analysis Section
st.header('Scenario Analysis')
# Interactive widgets for scenario analysis
new_net_income = st.slider('New Net Income', min_value=0, max_value=200000, value=net_income)
new_depreciation = st.slider('New Depreciation and Amortization', min_value=0, max_value=50000, value=depreciation)
new_change_ar = st.slider('New Change in Accounts Receivable', min_value=-10000, max_value=10000, value=change_ar)
new_change_inventory = st.slider('New Change in Inventory', min_value=-15000, max_value=15000, value=change_inventory)
new_change_ap = st.slider('New Change in Accounts Payable', min_value=-10000, max_value=10000, value=change_ap)
# Recalculate OCF based on new inputs
new_ocf = new_net_income + new_depreciation - new_change_ar - new_change_inventory + new_change_ap
# Display the new result
st.subheader('Scenario Analysis Result')
st.write(f'New Operational Cash Flow: ${new_ocf}')
# Button to download data as CSV
st.download_button(
label="Download Data as CSV",
data=df.to_csv().encode('utf-8'),
file_name='operational_cash_flow.csv',
mime='text/csv',
)
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