Finance tracker ui - v0 by Vercel

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

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467d ago

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:

Save the main dashboard code as 📈_Main_Dashboard.py in your project directory.
Create a pages folder in the same directory.
Save the individual account pages (like 1_🏦_Salary_Account.py and 3_💼_Investment_Account.py) in the pages folder.
Create similar pages for the Savings and Spending accounts.
Run the app using the command: streamlit run 📈_Main_Dashboard.py

This 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:

Implement a login system (simulated for this example).
Add a "Security Alerts" section on the main dashboard to display any suspicious activities.
Include a "Security Settings" page where users can manage their security preferences.

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.

467d ago

show me the preview

467d ago

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):
- Title: "Finance Tracker Dashboard"
- Sidebar: Welcomes the user by name
- Total Balance displayed prominently
- Two charts side by side:
  - Bar chart showing balances for each account
  - Pie chart showing balance distribution across accounts
- Recent Transactions table
- Financial Health score with a progress bar
Salary Account Page (1_🏦_Salary_Account.py):
- Title: "CitiBank Salary Account"
- Current Balance displayed
- Transaction History table
- Line chart showing balance trend over time
- Pie chart displaying monthly spending breakdown by category
Investment Account Page (3_💼_Investment_Account.py):
- Title: "Vanguard Investment Account"
- Current Balance displayed
- Line chart showing investment performance over time
- Pie chart displaying asset allocation
- Table showing top holdings

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:

Save all the Python files as described in the previous response.
Ensure you have Streamlit and other required libraries (pandas, plotly) installed.
Run the Streamlit application using the command streamlit run 📈_Main_Dashboard.py in your terminal.
Open the provided local URL (usually http://localhost:8501) in your web browser.

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.

467d ago

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:

Save the main dashboard code as 📈_Main_Dashboard.py in your project directory.
Create a pages folder in the same directory.
Save the individual account pages (like 1_🏦_Salary_Account.py and 3_💼_Investment_Account.py) in the pages folder.
Create similar pages for the Savings and Spending accounts.
Run the app using the command: streamlit run 📈_Main_Dashboard.py

To enhance security and add fraud detection features, you could:

Implement a login system (simulated for this example).
Add a "Security Alerts" section on the main dashboard to display any suspicious activities.
Include a "Security Settings" page where users can manage their security preferences.