Cloud-Native AI Agent

An intelligent AI agent designed to help developers and speakers in the cloud-native ecosystem by:

• 🔍 Discovering Events: Finding cloud-native events from Linux Foundation and CNCF
• 📝 Generating Talk Proposals: Creating compelling proposals using historical KubeCon data
• 🎓 Scholarship Applications: Assisting with scholarship application processes
• ✈️ Travel Funding: Helping with travel funding applications

Features

Event Discovery

• Scrapes Linux Foundation and CNCF websites for upcoming events
• Filters events by location, date, and relevance
• Provides event details, deadlines, and application requirements
• Caches results for improved performance

Talk Proposal Generator

• Analyzes historical KubeCon talk data for trending topics
• Generates unique and compelling talk proposals
• Suggests optimal timing and track placement
• Provides speaker tips and best practices
• Includes learning objectives and talk outlines

Scholarship Application Assistant

• Guides through scholarship application processes
• Helps craft compelling personal statements
• Tracks application deadlines and requirements
• Provides templates and examples
• Checks eligibility for different programs

Travel Funding Assistant

• Identifies available travel funding opportunities
• Assists with budget planning and justification
• Helps with travel grant applications
• Tracks funding deadlines and requirements
• Provides cost estimation and optimization tips

Quick Start

Prerequisites

• Python 3.8 or higher
• OpenAI API key (for AI-powered features)
• Internet connection for web scraping

Installation

1. Clone the repository

   git clone <repository-url>
   cd ai-agent

2. Run the setup script

   python setup.py

   This will:

   • Install all required dependencies
   • Create necessary directories
   • Generate a .env file template
   • Run basic tests

3. Configure your environment

   # Edit the .env file and add your OpenAI API key
   nano .env

4. Start the application

   streamlit run app.py

5. Open your browser Navigate to http://localhost:8501

Configuration

Environment Variables

Create a .env file with the following variables:

# Required
OPENAI_API_KEY=your_openai_api_key_here

# Optional - for enhanced scraping
LINUX_FOUNDATION_USERNAME=your_username
LINUX_FOUNDATION_PASSWORD=your_password
CNCF_USERNAME=your_username
CNCF_PASSWORD=your_password

# Application settings
DEBUG=False
LOG_LEVEL=INFO
REQUEST_TIMEOUT=30
MAX_RETRIES=3
CACHE_EXPIRY_HOURS=6

Getting an OpenAI API Key

1. Visit OpenAI's website
2. Create an account or sign in
3. Navigate to the API section
4. Generate a new API key
5. Add the key to your .env file

Usage

Web Interface

The main application provides a user-friendly web interface with the following sections:

Dashboard

• Overview of discovered events, generated proposals, and tracked applications
• Quick action buttons for common tasks
• Recent activity feed

Event Discovery

• Search and filter cloud-native events
• View event details, deadlines, and requirements
• Get recommendations based on your interests

Talk Proposals

• Generate compelling talk proposals
• Customize based on your expertise and target audience
• Get improvement suggestions and export options

Scholarships

• Browse available scholarship programs
• Check your eligibility
• Generate application materials

Travel Funding

• Find travel funding opportunities
• Estimate travel costs
• Generate funding applications

Command Line Testing

Run the test script to verify functionality:

python test_agent.py

Architecture

The agent is built with a modular architecture:

ai-agent/
├── agents/                 # Core AI agent modules
│   ├── base_agent.py      # Base agent class
│   ├── event_discovery.py # Event discovery agent
│   ├── proposal_generator.py # Proposal generation agent
│   ├── scholarship_assistant.py # Scholarship assistance agent
│   └── travel_funding_assistant.py # Travel funding agent
├── utils/                  # Utility functions
│   ├── web_scraper.py     # Web scraping utilities
│   ├── config.py          # Configuration management
│   └── data_processor.py  # Data processing utilities
├── data/                   # Sample data and templates
├── web/                    # Web interface components
├── app.py                  # Main Streamlit application
├── test_agent.py          # Test script
└── setup.py               # Setup script

API Reference

Event Discovery Agent

from agents.event_discovery import EventDiscoveryAgent

agent = EventDiscoveryAgent()

# Discover events
result = await agent.discover_events({'type': 'discover'})

# Filter events
result = await agent.filter_events({
    'type': 'filter',
    'filters': {'location': 'San Francisco', 'min_relevance': 7.0}
})

Proposal Generator Agent

from agents.proposal_generator import ProposalGeneratorAgent

agent = ProposalGeneratorAgent()

# Generate proposal
result = await agent.generate_proposal({
    'type': 'generate',
    'topic': 'Kubernetes Operators in Production',
    'speaker_expertise': ['Kubernetes', 'DevOps'],
    'target_audience': 'intermediate',
    'talk_type': 'session'
})

Scholarship Assistant Agent

from agents.scholarship_assistant import ScholarshipAssistantAgent

agent = ScholarshipAssistantAgent()

# Check eligibility
result = await agent.check_eligibility({
    'type': 'check_eligibility',
    'applicant_info': {
        'is_student': True,
        'financial_need': True,
        'community_involvement': True
    }
})

Travel Funding Assistant Agent

from agents.travel_funding_assistant import TravelFundingAssistantAgent

agent = TravelFundingAssistantAgent()

# Estimate costs
result = await agent.estimate_costs({
    'type': 'estimate_costs',
    'event_details': {
        'location': 'San Francisco, CA',
        'duration_days': 3
    },
    'travel_preferences': {
        'departure_location': 'New York, NY',
        'accommodation': 'standard'
    }
})

Development

Setting up a development environment

1. Clone the repository

   git clone <repository-url>
   cd ai-agent

2. Create 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. Install development dependencies

   pip install -r requirements-dev.txt

5. Run tests

   python -m pytest tests/

Contributing

1. Fork the repository
2. Create a feature branch: git checkout -b feature-name
3. Make your changes
4. Add tests for new functionality
5. Run the test suite: python -m pytest
6. Commit your changes: git commit -am 'Add feature'
7. Push to the branch: git push origin feature-name
8. Submit a pull request

Code Style

This project follows PEP 8 style guidelines. Use a linter like flake8 or black to ensure code quality:

pip install flake8 black
flake8 .
black .

Troubleshooting

Common Issues

OpenAI API Key Error

Error: OPENAI_API_KEY environment variable is required

Solution: Add your OpenAI API key to the .env file

Import Errors

ModuleNotFoundError: No module named 'agents'

Solution: Make sure you're running from the project root directory

Web Scraping Issues

Error scraping website: Connection timeout

Solution: Check your internet connection and try again later

Streamlit Issues

Streamlit is not recognized as a command

Solution: Install Streamlit: pip install streamlit

Getting Help

1. Check the Issues page
2. Search existing discussions
3. Create a new issue with:
   • Description of the problem
   • Steps to reproduce
   • Error messages
   • System information

License

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

Acknowledgments

• OpenAI for providing the GPT models
• Streamlit for the web framework
• Linux Foundation and CNCF for the cloud-native ecosystem
• The open-source community for inspiration and contributions

Roadmap

• Database integration for persistent storage
• Advanced web scraping with authentication
• Integration with calendar systems
• Email notifications for deadlines
• Mobile app version
• Multi-language support
• Advanced analytics and reporting
• Integration with conference submission systems

Support

If you find this project helpful, please consider:

• ⭐ Starring the repository
• 🐛 Reporting bugs
• 💡 Suggesting new features
• 🤝 Contributing code
• 📢 Sharing with the community

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Happy cloud-native development! 🚀