OrionX Podcast Analytics

A Streamlit-based web application for analyzing podcast download statistics. This application processes podcast download data from Excel files, stores it in a SQLite database, and provides interactive visualizations and analysis tools.

Features

• Data Import: Import podcast download statistics from Excel files
• Interactive Dashboard: View download trends, top podcasts, and bandwidth usage
• Admin Interface: Manage data imports and database operations
• Data Export: Export filtered data to CSV format
• Cloud Deployment: Ready for deployment to Google Cloud Run
• Backup System: Automated backup to Google Cloud Storage

Documentation

• Data Ingestion and Database Schema - Details about data import process and database structure
• Deployment Guide - Instructions for deploying to Google Cloud Run
• Backup and Restore - Guide for database backup and restore operations

Setup

1. Clone the repository:

   git clone https://github.com/yourusername/orionxlog.git
   cd orionxlog

2. Create and activate a virtual environment:

   python -m venv venv
   source venv/bin/activate  # On Windows: venv\Scripts\activate

3. Install dependencies:

   pip install -r requirements.txt

4. Set up configuration:

   • Copy config/config.yaml.sample to config/config.yaml
   • Copy .env.sample to .env
   • Update the configuration files with your settings

5. Initialize the database:

   python scripts/init_db.py

Development

Local Development

1. Start the development server:

   streamlit run app.py

2. Access the application at http://localhost:8501

Docker Development

1. Build and start the containers:

   docker-compose up --build

2. Access the application at http://localhost:8501

Project Structure

orionxlog/
├── app.py                 # Main Streamlit application
├── config/               # Configuration files
│   ├── config.yaml      # Main configuration (not tracked)
│   └── config.yaml.sample # Sample configuration
├── data/                # Data directory
│   └── podcasts.db      # SQLite database
├── docs/                # Documentation
│   ├── DATA_INGESTION.md
│   ├── DEPLOY_TO_CLOUD_RUN.md
│   └── BACKUP_AND_RESTORE.md
├── scripts/             # Utility scripts
│   ├── import_data.py   # Data import script
│   └── init_db.py       # Database initialization
├── src/                 # Source code
│   ├── database.py      # Database operations
│   ├── importers.py     # Data importers
│   └── utils.py         # Utility functions
├── .env                 # Environment variables (not tracked)
├── .env.sample          # Sample environment variables
├── .gitignore          # Git ignore file
├── docker-compose.yml   # Docker Compose configuration
├── Dockerfile          # Docker configuration
└── requirements.txt     # Python dependencies

Configuration

The application uses two types of configuration files:

1. Environment Variables (.env):

   • Database connection details
   • Cloud storage credentials
   • Application secrets

2. YAML Configuration (config/config.yaml):

   • Application settings
   • Feature flags
   • UI customization

See the sample files (.env.sample and config/config.yaml.sample) for configuration options.

Contributing

1. Fork the repository
2. Create a feature branch
3. Commit your changes
4. Push to the branch
5. Create a Pull Request

License

This project is licensed under the MIT License - see the LICENSE file for details.