i want to create a very decent UI for my project Finance tracker the ui should include following features Design a financial management platform that helps users consolidated
view of their bank accounts, track their income and expenses efficiently, and assess
financial risks. The solution should:
Aggregate financial data from multiple bank accounts into a single interface.
Provide insights into spending habits, budgeting, and investment tracking.
Offer fraud detection and alerts for suspicious activities.
Ensure top-tier security measures for safeguarding financial data.
now create an ui using streamlit , which will redirect to mutiple pages , it should include user dashboard , that user should have 4 bank accounts - 1.Salary account
2.High interest savings account
3.Investment accounts
4.Spending account , use dummy bank names , now when user clicks on any bank account he should be able to see stats/graphs about current situation of the finance graph, and overall financial growth
I'll create a Streamlit-based UI for your Finance Tracker project with the features you've described. We'll use Streamlit's multipage app functionality to create separate pages for the dashboard and each bank account. Here's the implementation:
First, let's create the main app structure:
# File: 📈_Main_Dashboard.py
import streamlit as st
import plotly.graph_objects as go
import plotly.express as px
import pandas as pd
import random
st.set_page_config(page_title="Finance Tracker", page_icon="📊", layout="wide")
# Simulated user data
user_data = {
"name": "John Doe",
"total_balance": 52000,
"accounts": {
"Salary": {"name": "CitiBank Salary Account", "balance": 15000},
"Savings": {"name": "Goldman Sachs High-Yield Savings", "balance": 25000},
"Investment": {"name": "Vanguard Investment Account", "balance": 10000},
"Spending": {"name": "Chase Everyday Spending", "balance": 2000}
}
}
def main():
st.title("Finance Tracker Dashboard")
st.sidebar.title(f"Welcome, {user_data['name']}!")
# Total Balance
st.header(f"Total Balance: ${user_data['total_balance']:,}")
# Account Overview
col1, col2 = st.columns(2)
with col1:
st.subheader("Account Balances")
fig = go.Figure(data=[go.Bar(
x=list(user_data['accounts'].keys()),
y=[acc['balance'] for acc in user_data['accounts'].values()],
marker_color=['#1f77b4', '#ff7f0e', '#2ca02c', '#d62728']
)])
fig.update_layout(title="Account Balances", xaxis_title="Accounts", yaxis_title="Balance ($)")
st.plotly_chart(fig)
with col2:
st.subheader("Balance Distribution")
fig = px.pie(values=[acc['balance'] for acc in user_data['accounts'].values()],
names=list(user_data['accounts'].keys()),
title="Balance Distribution")
st.plotly_chart(fig)
# Recent Transactions
st.subheader("Recent Transactions")
transactions = pd.DataFrame({
"Date": pd.date_range(end=pd.Timestamp.now(), periods=5),
"Description": ["Grocery Store", "Online Shopping", "Utility Bill", "Restaurant", "Salary Deposit"],
"Amount": [random.randint(-200, 1000) for _ in range(5)]
})
st.table(transactions)
# Financial Health Indicator
st.subheader("Financial Health")
health_score = random.randint(60, 95)
st.progress(health_score)
st.write(f"Your financial health score: {health_score}/100")
if __name__ == "__main__":
main()Now, let's create individual pages for each bank account:
# File: pages/1_🏦_Salary_Account.py
import streamlit as st
import plotly.graph_objects as go
import plotly.express as px
import pandas as pd
import random
st.set_page_config(page_title="Salary Account", page_icon="🏦", layout="wide")
def main():
st.title("CitiBank Salary Account")
balance = 15000
st.header(f"Current Balance: ${balance:,}")
# Transaction History
st.subheader("Transaction History")
transactions = pd.DataFrame({
"Date": pd.date_range(end=pd.Timestamp.now(), periods=10),
"Description": ["Salary Deposit", "Rent Payment", "Utility Bill", "Grocery Shopping"] * 2 + ["Salary Deposit", "Rent Payment"],
"Amount": [5000, -2000, -150, -200] * 2 + [5000, -2000]
})
st.table(transactions)
# Balance Trend
st.subheader("Balance Trend")
fig = go.Figure(data=go.Scatter(x=transactions['Date'], y=transactions['Amount'].cumsum() + balance - transactions['Amount'].sum()))
fig.update_layout(title="Balance Over Time", xaxis_title="Date", yaxis_title="Balance ($)")
st.plotly_chart(fig)
# Spending Categories
st.subheader("Spending Categories")
categories = ['Rent', 'Utilities', 'Groceries', 'Entertainment', 'Savings']
amounts = [2000, 150, 400, 200, 2250]
fig = px.pie(values=amounts, names=categories, title="Monthly Spending Breakdown")
st.plotly_chart(fig)
if __name__ == "__main__":
main()Repeat this structure for the other bank accounts (Savings, Investment, and Spending) with appropriate modifications. Here's an example for the Investment account:
# File: pages/3_💼_Investment_Account.py
import streamlit as st
import plotly.graph_objects as go
import plotly.express as px
import pandas as pd
import random
st.set_page_config(page_title="Investment Account", page_icon="💼", layout="wide")
def main():
st.title("Vanguard Investment Account")
balance = 10000
st.header(f"Current Balance: ${balance:,}")
# Investment Performance
st.subheader("Investment Performance")
performance = pd.DataFrame({
"Date": pd.date_range(end=pd.Timestamp.now(), periods=30),
"Value": [balance * (1 + random.uniform(-0.02, 0.02)) for _ in range(30)]
})
fig = px.line(performance, x="Date", y="Value", title="Investment Value Over Time")
st.plotly_chart(fig)
# Asset Allocation
st.subheader("Asset Allocation")
assets = ['Stocks', 'Bonds', 'Real Estate', 'Commodities']
allocation = [60, 25, 10, 5]
fig = px.pie(values=allocation, names=assets, title="Asset Allocation")
st.plotly_chart(fig)
# Top Holdings
st.subheader("Top Holdings")
holdings = pd.DataFrame({
"Stock": ["AAPL", "MSFT", "AMZN", "GOOGL", "FB"],
"Shares": [10, 15, 5, 8, 12],
"Value": [3500, 4000, 1500, 2000, 3000]
})
st.table(holdings)
if __name__ == "__main__":
main()To run this Streamlit app:
📈_Main_Dashboard.py in your project directory.pages folder in the same directory.1_🏦_Salary_Account.py and 3_💼_Investment_Account.py) in the pages folder.streamlit run 📈_Main_Dashboard.pyThis structure creates a multi-page Streamlit app with a main dashboard and individual pages for each bank account. Users can navigate between pages using the sidebar. Each page includes relevant financial information, charts, and tables specific to that account type.
To enhance security and add fraud detection features, you could:
Remember, this is a simplified version for demonstration purposes. In a real-world application, you'd need to integrate with actual banking APIs, implement robust security measures, and handle real user data with proper authentication and authorization.
I apologize, but I'm not able to directly show you a preview of the Streamlit application. As an AI language model, I don't have the capability to run code or generate visual previews. However, I can describe what the UI would look like based on the code we've created:
Main Dashboard (📈_Main_Dashboard.py):
Two charts side by side:
Salary Account Page (1_🏦_Salary_Account.py):
Investment Account Page (3_💼_Investment_Account.py):
The UI would be clean and organized, with a sidebar for navigation between the main dashboard and individual account pages. Each page would have relevant financial information presented in both tabular and graphical formats.
To actually see and interact with this UI, you would need to:
streamlit run 📈_Main_Dashboard.py in your terminal.This would launch the Streamlit app, allowing you to interact with the Finance Tracker UI in your web browser. You'd be able to navigate between pages, view the charts, and see the simulated financial data.