📊 Invoice Management Dashboard

Interactive Arabic invoice management and analytics dashboard built with Python, Streamlit, Pandas, and Plotly.

🚀 Live Demo

📌 Overview

This project is an interactive invoice management and analytics dashboard designed to help businesses monitor invoice payments, outstanding balances, overdue invoices, and payment activity.

Users can upload invoice data in Excel, CSV, or TXT format, apply interactive filters, and analyze the data through KPI cards and interactive visualizations.

The dashboard supports both Arabic and English column names, making it suitable for organizations working with Arabic-language financial data.

🚀 Features

📁 Flexible Data Upload

Upload invoice data in:

.xlsx
.xls
.csv
.txt

The application automatically detects commonly used invoice-related columns in both Arabic and English.

Supported fields include:

Invoice amount
Paid amount
Payment status
Delay days
Invoice date
Due date
Payment date
Client
Payment method
Invoice number

🔄 Automated Data Preprocessing

The application automatically:

Detects relevant columns using predefined column-name alternatives.
Converts currency values such as SAR 35,830 into numerical values.
Converts date fields into datetime objects.
Calculates outstanding invoice amounts.
Handles missing columns without breaking the dashboard.
Caches processed data using Streamlit's caching functionality.

Outstanding amount is calculated as:

Outstanding Amount = Invoice Amount - Paid Amount

🎯 Interactive Filters

Users can filter the dashboard by:

👤 Client
💳 Payment status
💰 Payment method
📅 Invoice date range
📅 Due date range
⏱️ Number of delay days

A Reset Filters button is also available.

📈 Key Performance Indicators

The dashboard provides up to six main KPIs:

KPI	Description
💰 Total Invoice Amount	Total value of invoices
💵 Total Paid Amount	Total amount collected
🔴 Total Outstanding	Remaining unpaid amount
✅ Paid Invoices	Number of fully paid invoices
⚠️ Late Invoices	Number of overdue invoices
⏱️ Average Delay	Average number of overdue days

The KPIs automatically adapt depending on which columns are available in the uploaded dataset.

📊 Visualizations

The dashboard contains 9 interactive visualizations:

1. Invoice Amount by Client

Compares the total invoice amount across clients.

2. Payment Status Distribution

Shows the distribution of invoices according to their payment status.

3. Invoice Amount Over Time

Tracks total invoice amounts on a monthly basis.

4. Outstanding Amount by Client

Identifies clients with the highest outstanding balances.

5. Payment Method Distribution

Shows how invoices are distributed across different payment methods.

6. Days Overdue Distribution

Analyzes the distribution of overdue days for late and partially paid invoices.

7. Invoice Count per Month

Tracks the number of invoices issued each month.

8. Monthly Paid Amount Trend

Shows how collected payments change over time.

9. Outstanding Amount by Payment Method

Compares remaining outstanding balances across payment methods.

🛠️ Tech Stack

Python
Streamlit — Interactive web application
Pandas — Data manipulation and preprocessing
Plotly Express — Interactive data visualization
HTML/CSS — Dashboard customization

📂 Project Structure

invoice-dashboard/
│
├── app.py
├── requirements.txt
├── README.md
└── data/
    └── sample_invoice_data.xlsx

The sample dataset is optional and can be replaced with your own invoice data.

⚙️ Installation

1. Clone the repository

git clone https://github.com/YOUR_USERNAME/invoice-dashboard.git
cd invoice-dashboard

2. Create a virtual environment

python -m venv venv

Activate it:

Windows:

venv\Scripts\activate

macOS/Linux:

source venv/bin/activate

3. Install dependencies

pip install -r requirements.txt

4. Run the application

streamlit run app.py

The dashboard will open in your browser.

📦 Requirements

Create a requirements.txt file containing:

streamlit
pandas
plotly
openpyxl
xlrd

📄 Expected Data

The application is designed to work with invoice datasets containing some or all of the following fields:

Field	Purpose
Invoice Amount	Total invoice value
Paid Amount	Amount already paid
Payment Status	Current payment status
Delay Days	Number of days overdue
Issue Date	Invoice creation date
Due Date	Payment due date
Payment Date	Date payment was received
Client	Customer/client name
Payment Method	Payment method
Invoice Number	Invoice identifier

The application does not require every column to be present. Available dashboard features automatically adjust according to the data provided.

🌐 Arabic & English Data Support

One of the key features of the application is its ability to recognize both Arabic and English column names.

For example:

Invoice Amount

can also be detected from:

مبلغ الفاتورة (ر.س)

Similarly:

Payment Status

can be detected from:

حالة الدفع

This allows the dashboard to work with datasets using different naming conventions.

🎨 Dashboard Design

The dashboard includes:

Arabic user interface
RTL-oriented layout
Custom CSS styling
Color-coded KPI cards
Responsive multi-column layout
Interactive Plotly charts
Sidebar filtering
Raw-data inspection
Interactive charts and hover information

The design focuses on providing a quick overview of invoice collection and outstanding payment performance.

💼 Business Use Cases

This dashboard can help finance and operations teams:

Monitor total invoiced amounts
Track collected payments
Identify outstanding balances
Identify overdue invoices
Analyze payment behavior
Monitor invoice volume over time
Compare clients by outstanding amounts
Analyze payment methods
Monitor payment trends
Identify potential collection issues

🔍 Example Business Questions

The dashboard can help answer questions such as:

How much has been invoiced?
How much has been collected?
How much money is still outstanding?
Which clients have the highest outstanding balances?
How many invoices are overdue?
How long are invoices typically overdue?
Which payment methods are most commonly used?
How are invoice amounts changing month over month?
How much money is being collected each month?

📌 Future Improvements

Potential improvements include:

👨‍💻 Author

Abdullah

Computer Engineering graduate focused on Data Analytics, Business Intelligence, SQL, Python, Power BI, and data-driven decision making.

⭐ If you find this project useful, consider giving the repository a star.

Name		Name	Last commit message	Last commit date
.devcontainer		.devcontainer
Amiri-Regular.ttf		Amiri-Regular.ttf
Amiri.ttf		Amiri.ttf
Mojaz.py		Mojaz.py
README.md		README.md
_⁨نموذج_داشبورد_حسابات_العملاء.xlsx		_⁨نموذج_داشبورد_حسابات_العملاء.xlsx
app.py		app.py
mojazfatoora.py		mojazfatoora.py
mojazgpt.py		mojazgpt.py
mojazlegal.py		mojazlegal.py
plotly.py		plotly.py
requirements.txt		requirements.txt

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