Introducing Heroku-Streamlit: Seamless Data Visualization

We’re excited to announce the release of Heroku-Streamlit, a template that makes deploying interactive data visualization applications on Heroku simpler than ever before. Streamlit is an open-source app framework built for machine learning and data science projects. This Streamlit App brings together Heroku’s scalable cloud platform and Streamlit’s intuitive Python-based data application framework. Whether you’re a data scientist, educator, or developer, you can now spin up a cloud-based Streamlit environment in minutes.

What is Heroku-Streamlit?

Heroku-Streamlit is a ready-to-deploy template that allows data scientists, analysts, and developers to quickly share their data insights through interactive web applications. With minimal configuration, you can transform your data scripts into engaging web applications that anyone can access.

The repository comes pre-configured with:

• One-click deployment to Heroku
• Streamlit’s powerful visualization capabilities
• Sample Uber NYC pickup data application
• Easy customization options for your own projects

Why Use Heroku-Streamlit?

For Data Scientists

• Focus on Analysis, Not Deployment: Write Python code and let Heroku handle the infrastructure
• Share Your Work Easily: Give stakeholders access to your insights through a web browser
• Interactive Presentations: Create dynamic dashboards instead of static reports

For Developers

• Rapid Prototyping: Build and deploy data applications in minutes, not days
• Simplified Workflow: Streamlined deployment process with pre-configured settings
• Customizable: Easily extend with additional Python packages

Getting Started in Minutes

Deploying your first Streamlit application on Heroku is as simple as:

1. Click the “Deploy to Heroku” button in the repository
2. Wait a few minutes for your app to deploy
3. Access your live, interactive Streamlit application

For those who prefer a more hands-on approach, the repository includes detailed instructions for manual deployment.

Customizing Your Application

While the template comes with a sample Uber pickup visualization, you can easily customize it to showcase your own data:

1. Add your Python dependencies to requirements.txt
2. Update the Procfile to point to your Streamlit script
3. Push your changes to Heroku

Supercharge Streamlit Apps with Heroku AI: MIA

Take your Streamlit applications to the next level by integrating Heroku MIA (Managed Inference and Agents)—now in pilot!

• Zero Infrastructure Management: Deploy complex LLM models without the need for servers, GPUs, or scaling
• Production-Ready Performance: Automatic scaling, high availability, and optimized inference
• Cost-Effective: Flexible pricing – only pay for what you use

Build sophisticated AI agents to:

• Create Conversational Interfaces: Add natural language chat to your Streamlit apps
• Enable Autonomous Workflows: Build agents that can process data, make decisions, and take action

The Future of Data Sharing

Heroku-Streamlit represents a step forward in sharing data insights on Heroku. By removing the barriers between data analysis and web deployment, we’re enabling more teams to make data-driven decisions through interactive applications.

We’re excited to see what you build with this template and look forward to your feedback and contributions!

Ready to get started? Visit the repository and deploy your first Streamlit app on Heroku today!