Spaces:
Sleeping
Sleeping
Create app.py
Browse files
app.py
ADDED
@@ -0,0 +1,70 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
import streamlit as st
|
2 |
+
import pandas as pd
|
3 |
+
|
4 |
+
# Title and Description
|
5 |
+
st.title('Operational Cash Flow Analysis')
|
6 |
+
st.write("""
|
7 |
+
This application allows you to analyze and visualize your company's operational cash flow.
|
8 |
+
""")
|
9 |
+
|
10 |
+
# Data Input Section
|
11 |
+
st.header('Input Financial Data')
|
12 |
+
|
13 |
+
# Input fields for financial data
|
14 |
+
net_income = st.number_input('Net Income', value=0)
|
15 |
+
depreciation = st.number_input('Depreciation and Amortization', value=0)
|
16 |
+
change_ar = st.number_input('Change in Accounts Receivable', value=0)
|
17 |
+
change_inventory = st.number_input('Change in Inventory', value=0)
|
18 |
+
change_ap = st.number_input('Change in Accounts Payable', value=0)
|
19 |
+
|
20 |
+
# Calculating Operational Cash Flow
|
21 |
+
ocf = net_income + depreciation - change_ar - change_inventory + change_ap
|
22 |
+
|
23 |
+
# Displaying the result
|
24 |
+
st.subheader('Calculated Operational Cash Flow')
|
25 |
+
st.write(f'Operational Cash Flow: ${ocf}')
|
26 |
+
|
27 |
+
# DataFrame for historical data visualization (example data)
|
28 |
+
data = {
|
29 |
+
'Year': ['2020', '2021', '2022'],
|
30 |
+
'Net Income': [100000, 120000, 130000],
|
31 |
+
'Depreciation and Amortization': [20000, 25000, 27000],
|
32 |
+
'Change in AR': [-5000, -6000, -5500],
|
33 |
+
'Change in Inventory': [-8000, -7500, -9000],
|
34 |
+
'Change in AP': [7000, 8500, 9000],
|
35 |
+
'Operational Cash Flow': [114000, 137500, 149500]
|
36 |
+
}
|
37 |
+
df = pd.DataFrame(data)
|
38 |
+
|
39 |
+
# Display the historical data table
|
40 |
+
st.subheader('Historical Data')
|
41 |
+
st.dataframe(df)
|
42 |
+
|
43 |
+
# Visualize the historical operational cash flow
|
44 |
+
st.subheader('Operational Cash Flow Over Years')
|
45 |
+
st.line_chart(df[['Year', 'Operational Cash Flow']].set_index('Year'))
|
46 |
+
|
47 |
+
# Scenario Analysis Section
|
48 |
+
st.header('Scenario Analysis')
|
49 |
+
|
50 |
+
# Interactive widgets for scenario analysis
|
51 |
+
new_net_income = st.slider('New Net Income', min_value=0, max_value=200000, value=net_income)
|
52 |
+
new_depreciation = st.slider('New Depreciation and Amortization', min_value=0, max_value=50000, value=depreciation)
|
53 |
+
new_change_ar = st.slider('New Change in Accounts Receivable', min_value=-10000, max_value=10000, value=change_ar)
|
54 |
+
new_change_inventory = st.slider('New Change in Inventory', min_value=-15000, max_value=15000, value=change_inventory)
|
55 |
+
new_change_ap = st.slider('New Change in Accounts Payable', min_value=-10000, max_value=10000, value=change_ap)
|
56 |
+
|
57 |
+
# Recalculate OCF based on new inputs
|
58 |
+
new_ocf = new_net_income + new_depreciation - new_change_ar - new_change_inventory + new_change_ap
|
59 |
+
|
60 |
+
# Display the new result
|
61 |
+
st.subheader('Scenario Analysis Result')
|
62 |
+
st.write(f'New Operational Cash Flow: ${new_ocf}')
|
63 |
+
|
64 |
+
# Button to download data as CSV
|
65 |
+
st.download_button(
|
66 |
+
label="Download Data as CSV",
|
67 |
+
data=df.to_csv().encode('utf-8'),
|
68 |
+
file_name='operational_cash_flow.csv',
|
69 |
+
mime='text/csv',
|
70 |
+
)
|