Best Bets - Sports Betting Analysis App

A Streamlit-based web application that analyzes sports betting data using RSS feeds and Google Gemini AI to provide intelligent betting insights.

Features

• RSS Feed Processing: Automatically scrapes and parses sports odds from RSS feeds
• AI-Powered Analysis: Uses Google Gemini AI to analyze sports data and generate betting insights
• Interactive Dashboard: Real-time data visualization with charts and statistics
• Data Management: Export data to CSV, backup/restore functionality, and data cleanup tools
• Responsive UI: Modern, mobile-friendly interface built with Streamlit

Prerequisites

• Python 3.8 or higher
• Google Gemini API key
• RSS feed URLs for sports data

Quick Start

Option 1: Automated Setup (Windows)

1. Double-click launch.bat - This will automatically:
   • Check Python installation
   • Install required dependencies
   • Create config template if needed
   • Launch the application

Option 2: Manual Setup

1. Install Python dependencies:

   pip install -r requirements.txt

2. Configure your API key:

   • Copy config.env.example to config.env
   • Add your Gemini API key: GEMINI_API_KEY=your_api_key_here

3. Launch the application:

   streamlit run app.py

Configuration

Environment Variables

Create a config.env file in the project root:

# Required: Your Google Gemini API key
GEMINI_API_KEY=your_api_key_here

# Optional: Debug mode
DEBUG=false

# Optional: Log level
LOG_LEVEL=INFO

RSS Feed Configuration

Add your RSS feed URLs in the Settings page of the application or modify config.py:

DEFAULT_RSS_FEEDS = [
    "https://example.com/sports-feed-1",
    "https://example.com/sports-feed-2"
]

Project Structure

STREAMLIT_MIGRATION/
├── app.py                 # Main Streamlit application
├── config.py             # Configuration management
├── requirements.txt      # Python dependencies
├── config.env           # Environment variables (create this)
├── launch.bat           # Windows launcher script
├── launch.ps1           # PowerShell launcher script
├── services/            # Core services
│   ├── rss_service.py   # RSS feed processing
│   ├── ai_service.py    # Google Gemini AI integration
│   └── data_service.py  # Data storage and management
├── utils/               # Utility functions
│   └── helpers.py       # Helper functions and charts
└── data/               # Data storage directory

Usage

Dashboard

• View real-time sports data and statistics
• Monitor RSS feed status and data freshness
• Access quick actions for data management

Data Viewer

• Browse all collected RSS data
• Filter and search through entries
• Export data to CSV format

AI Analysis

• Generate AI-powered betting insights
• Analyze specific sports events
• View analysis history and recommendations

Settings

• Configure RSS feed URLs
• Manage API keys and settings
• Data backup and restore options

Troubleshooting

Common Issues

1. "No module named 'streamlit'"

   • Run: pip install -r requirements.txt

2. API key not found

   • Ensure config.env exists with GEMINI_API_KEY=your_key

3. RSS feeds not loading

   • Check feed URLs in Settings
   • Verify internet connection
   • Check feed format compatibility

4. Data not persisting

   • Ensure data/ directory exists
   • Check file permissions

Testing

Run the test script to verify setup:

python test_app.py

Development

Adding New Features

1. New Services: Add to services/ directory
2. New Utils: Add to utils/ directory
3. UI Changes: Modify app.py and add to appropriate page

Data Format

The application expects RSS feeds with sports odds data. Each entry should include:

• Title/description with team names
• Odds information (if available)
• Timestamp
• Source information

Deployment

Local Development

streamlit run app.py --server.port 8501

Production

• Use Streamlit Cloud for easy deployment
• Set environment variables in deployment platform
• Ensure proper API key security

Contributing

1. Fork the repository
2. Create a feature branch
3. Make your changes
4. Test thoroughly
5. Submit a pull request

License

This project is for educational and personal use. Please ensure compliance with local gambling laws and regulations.

Support

For issues or questions:

1. Check the troubleshooting section
2. Review the configuration
3. Test with the provided test script
4. Check Streamlit documentation for framework-specific issues