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Connecting Streamlit

In this article, we will walk through the process of building a dashboard using Streamlit. To make it more illustrative, we will create a dashboard that connects to the analytical database and monitors the real-time status of vehicles.

This guide is part of the Private Telematics Lakehouse documentation suite and specifically covers connecting Streamlit to your data warehouse. If you're still deciding which BI tool to use, refer to the Selecting BI tools overview.

Dashboard features

  • Display total number of objects
  • Visualize movement statuses (moving/stopped/parked)
  • Visualize connection statuses (active/idle/offline)
  • Detailed table with current status of all vehicles
  • Filtering by vehicle type, group, movement status, and connection status
  • Automatic data refresh every 5 minutes
  • Toggle between light and dark themes

Technical requirements

  • Python 3.8+
  • Internet access for database connection
  • Minimum 2 GB RAM

Installation and setup

1. Clone the repository

git clone https://github.com/SquareGPS/bi-intergrations.git

2. Create a virtual environment

# Windows
python -m venv venv
venv\Scripts\activate

# Linux/macOS
python -m venv venv
source venv/bin/activate

Make sure you have Python 3.8 or higher installed. You can check the version with the command python --version.

3. Install dependencies

After activating the virtual environment, install all necessary libraries:

pip install -r requirements.txt

Database connection

1. Create a configuration file

Create a .env file in the project's root directory:

DB_HOST=your_db_host
DB_NAME=your_db_name
DB_USER=your_db_user
DB_PASS=your_db_password
DB_PORT=5432
DB_SCHEMA=raw_business_data

Connection parameter reference

Lakehouse Parameter Streamlit Setting Location Notes
Host DB_HOST in .env file The database server address provided in your welcome email
Port DB_PORT in .env file Default is 5432 for PostgreSQL
Database name DB_NAME in .env file Your assigned database name
Username DB_USER in .env file Your database username
Password DB_PASS in .env file Your secure database password
SSL mode Connection string in Python code Set to require in the connection string
Schema DB_SCHEMA in .env file Specify schema (raw_business_data or raw_telematics_data)

2. Obtaining credentials

Request credentials for connecting to the demonstration database by contacting the administrator.

The .env file should not be included in version control (GitHub) to ensure credentials security. The .gitignore file is already configured to exclude this file.

Running the dashboard

After setting up the database connection, start the dashboard with the command:

streamlit run moving_status_dashboard.py

After launching, you'll see a message similar to:

  You can now view your Streamlit app in your browser.

  Local URL: http://localhost:8501
  Network URL: http://192.168.1.5:8501

Open the specified URL in your browser. The dashboard will be available at http://localhost:8501 (or at the network URL if you want to open it from another device on the network).

Project structure

├── moving_status_dashboard.py  # Main application file
├── style.css                  # Styles for improved appearance
├── requirements.txt           # Project dependencies
├── .env                       # Database connection settings (created by the user)
└── README.md                  # This guide

Developing custom components

If you want to modify the dashboard or create new components:

1. Modifying the existing dashboard

Streamlit automatically reloads the application when you change the source code. Simply edit the moving_status_dashboard.py file and save your changes.

2. Adding new visualizations

To add new charts and diagrams, use libraries:

  • Plotly: import plotly.express as px or import plotly.graph_objects as go
  • Built-in Streamlit visualizations: st.bar_chart(), st.line_chart(), etc.

Example of adding a new chart:

import plotly.express as px

# Get data from the database
df = ... # your database query

# Create chart
fig = px.pie(df, values='count', names='status', title='Vehicle Statuses')
st.plotly_chart(fig, use_container_width=True)

3. Debugging

For debugging, use

# Output to Streamlit interface
st.write(f"Debug: {your_variable}")

# Output to console
print(f"Console debug: {your_variable}")

# Extended data output
st.json(data_dict)
st.dataframe(pandas_dataframe)

Troubleshooting

Database connection issues

  • Connection error: Check the correctness of credentials in the .env file and database availability
  • SSL error: Make sure your IP is on the allowlist for database access
  • Timeout errors: Check your internet connection stability and firewall settings

Dependency issues

Error installing psycopg2-binary:

  • Windows: pip install pipwin && pipwin install psycopg2-binary
  • Linux: sudo apt install python3-dev libpq-dev
  • macOS: brew install postgresql

Dependency conflicts:

  • Create a new virtual environment
  • Install dependencies one by one, starting with streamlit

Other issues

Here are some tricks that can help you fix common issues:

  1. Update dependencies: pip install -r requirements.txt --upgrade
  2. Check Python compatibility: python --version (should be 3.8+)
  3. When changing code, include debug messages: 
    st.write(f"Debug: {your_variable}")
  4. Streamlit cache errors: stop the application and run with the --clear_cache flag: 
    streamlit run moving_status_dashboard.py --clear_cache

Next steps

After successfully connecting Power BI to your Private Telematics Lakehouse instance, we recommend you to:

  • Explore the available data schemas by reviewing the Schema overview section to better understand the data structure and relationships.
  • Start with simple queries focused on specific business entities before building complex dashboards - check our Example queries for reference.

Support

For technical questions or requests for access to the demonstration database, please contact: support@squaregps.com