🤖 SuperMini - AI on Mini Mac Assistant

A powerful desktop AI assistant that combines Claude API and local Ollama models for task automation, autonomous exploration, and intelligent enhancement capabilities.

✨ Features

🔧 Task Automation

• Code Generation: Write, debug, and optimize code in multiple programming languages
• Script Creation: Generate automation scripts for system tasks and workflows
• File Processing: Batch process files with intelligent operations

🎨 Multimedia Processing

• Image Analysis: Describe, analyze, and extract information from images
• Audio Processing: Transcribe and analyze audio files (planned)
• Video Enhancement: Process and enhance video content (planned)

📊 Data Analytics

• CSV Analysis: Analyze data files and generate insights
• Visualization: Create charts and graphs from data
• Statistical Analysis: Perform advanced data analysis

📄 Document Intelligence

• RAG Processing: Summarize and analyze documents with retrieval-augmented generation
• Q&A Systems: Answer questions based on document content
• Content Extraction: Extract key information from various file formats

🧠 Memory System

• Context Awareness: Remember previous tasks and conversations
• Learning: Improve responses based on interaction history
• Personalization: Adapt to user preferences and patterns

🚀 Quick Start

Prerequisites

• macOS 10.15+ (optimized for macOS, but works on other platforms)
• Python 3.9 or higher
• 8GB+ RAM recommended
• Internet connection for Claude API (optional)

Installation

Option 1: Automated Installation

# Clone the repository
git clone https://github.com/rohnspringfield/supermini.git
cd supermini

# Run automated setup
chmod +x dependencies/install.sh
./dependencies/install.sh

Option 2: Manual Installation

# Install Python dependencies
pip3 install PyQt6 anthropic requests pandas numpy psutil chromadb

# Install Ollama
curl -fsSL https://ollama.ai/install.sh | sh

# Start Ollama service
ollama serve &

# Pull recommended models
ollama pull qwen2.5-coder:7b
ollama pull llama3.2:3b

# Create directories
mkdir -p ~/SuperMini_Output/data ~/SuperMini_Output/logs

# Run the application
python3 supermini.py

First Run Setup

1. Launch SuperMini: Run python3 supermini.py
2. Configure API Key (optional but recommended):
   • Click "⚙️ Settings"
   • Enter your Claude API key from Anthropic Console
   • Save settings
3. Verify Ollama: Ensure local models are downloaded and running
4. Start Creating: Enter your first task and click "🚀 Process Task"

💡 Usage Examples

Code Generation

Task: "Create a Python script to sort a CSV file by date column"
Files: data.csv
Result: Complete Python script with error handling

Image Analysis

Task: "Analyze this screenshot and describe the UI elements"
Files: screenshot.png
Result: Detailed description of interface components

Data Analysis

Task: "Analyze sales data and create visualizations"
Files: sales_data.csv
Result: Statistical analysis + Python visualization code

Document Summarization

Task: "Summarize the key points from these research papers"
Files: paper1.pdf, paper2.pdf
Result: Comprehensive summary with key insights

Automation Scripts

Task: "Create a backup script for my Documents folder"
Result: Bash script with scheduling options

🔧 Configuration

AI Models

• Claude API: Primary model for complex reasoning (requires API key)
• Ollama Local: Fallback models that run locally
  • qwen2.5-coder:7b - Best for coding tasks
  • llama3.2:3b - General purpose, faster responses

Settings Options

• Max Tokens: Control response length (512-8192)
• Temperature: Creativity level (0.0-1.0)
• Memory: Enable/disable context awareness
• Task Types: Auto-detection or manual selection

📁 File Structure

~/SuperMini_Output/
├── data/
│   ├── memory/          # ChromaDB memory database
│   ├── collaboration/   # Task sharing data
│   └── generated_*      # Your generated files
└── logs/
    └── supermini.log        # Application logs

🎯 Task Types

🔹 Code

• Description: Programming, scripting, debugging
• Input: Code requirements, existing files
• Output: Complete scripts, functions, or applications
• Examples: "Fix this Python script", "Create a web scraper"

🔹 Multimedia

• Description: Image, audio, and video processing
• Input: Media files, processing requirements
• Output: Analysis, enhanced files, descriptions
• Examples: "Describe this image", "Enhance video quality"

🔹 RAG (Retrieval Augmented Generation)

• Description: Document analysis and question answering
• Input: Documents, questions
• Output: Summaries, answers, insights
• Examples: "Summarize these PDFs", "Answer questions about this document"

🔹 Automation

• Description: System tasks and workflow automation
• Input: Task requirements, file operations
• Output: Scripts, shortcuts, automation workflows
• Examples: "Backup my files", "Organize downloads folder"

🔹 Analytics

• Description: Data analysis and visualization
• Input: Data files (CSV, JSON), analysis requirements
• Output: Statistical analysis, charts, insights
• Examples: "Analyze sales trends", "Create data dashboard"

🛠️ Advanced Features

Memory System

SuperMini remembers your previous tasks and uses them for context:

• Automatic Learning: Improves responses based on your preferences
• Context Awareness: References previous tasks when relevant
• Pattern Recognition: Learns your common workflows

🤖 Autonomous Enhancement Mode

SuperMini can improve itself autonomously and contribute back to the open-source project:

• 🔍 Self-Analysis: Automatically analyzes code quality and performance
• ✨ Code Enhancement: Suggests and implements improvements with safety validation
• 🔄 GitHub Integration: Automatically creates pull requests back to the repository
• 🛡️ Safety Framework: Multi-layer validation and rollback capabilities
• 🤝 Community Contributions: Autonomous agents help with open-source development
• 📊 Impact Tracking: Measures and reports enhancement effectiveness
• 🎯 Smart Targeting: Only modifies safe files with appropriate permissions

System Monitoring

Track resource usage in real-time:

• CPU Usage: Monitor processing load
• Memory Usage: Track RAM consumption
• Performance Metrics: Optimize for your system
• Activity Logging: Comprehensive logging of all operations

🔄 Autonomous GitHub Integration

SuperMini v2.1.0 introduces groundbreaking autonomous contribution capabilities:

• 🤖 Automatic Pull Requests: Creates PRs automatically when improvements are found
• 🛡️ Safety Controls: Strict validation ensures only safe, beneficial changes
• 📋 Professional Templates: Uses proper PR templates with detailed change descriptions
• 🎯 Smart File Filtering: Only modifies approved file types (*.py, *.md, etc.)
• 🔍 Human Oversight: All autonomous PRs require human review before merging
• 📊 Impact Measurement: Tracks and reports enhancement effectiveness

Setting Up Autonomous Contributions

# 1. Set your GitHub token (optional, for autonomous PRs)
export GITHUB_TOKEN="your-github-token"

# 2. Enable autonomous mode in SuperMini
# - Check "Autonomous Mode" in the interface
# - Configure GitHub integration in Settings
# - Review autonomous suggestions before approval

# 3. Monitor autonomous contributions
# - Check GitHub for automatically created PRs
# - Review and merge beneficial changes
# - Provide feedback to improve AI suggestions

Collaboration Features

• Local Storage: All data stored securely on your machine
• Task History: Complete record of all interactions
• Export Options: Share results and generated files
• Open Source Workflow: Seamless integration with Git and GitHub
• Autonomous Contributions: AI can contribute improvements back to the project

🔍 Troubleshooting

Common Issues

"No AI models available"

# Check Ollama status
ollama list

# Restart Ollama service
pkill ollama
ollama serve &

# Re-download models
ollama pull qwen2.5-coder:7b

"Claude API errors"

• Verify API key in Settings
• Check internet connection
• Ensure sufficient API credits

"Memory errors"

# Clear memory database
rm -rf ~/SuperMini_Output/data/memory/*
# Restart application

"Installation issues"

# Update pip and dependencies
pip3 install --upgrade pip
pip3 install --upgrade -r requirements.txt

Performance Optimization

For Better Speed:

• Use smaller Ollama models: llama3.2:1b
• Reduce max tokens in settings
• Disable memory for simple tasks

For Better Quality:

• Use Claude API for complex tasks
• Enable memory for context
• Use larger models: qwen2.5-coder:14b

🔐 Privacy & Security

• Local First: All processing can be done locally with Ollama
• No Data Collection: Your tasks and files stay on your machine
• Optional Cloud: Claude API only used when configured
• Secure Storage: All data encrypted and stored locally

🤝 Contributing

We welcome contributions! Here's how to get started:

1. Fork the Repository
2. Create Feature Branch: git checkout -b feature/amazing-feature
3. Make Changes: Implement your improvements
4. Test Thoroughly: Ensure everything works
5. Submit Pull Request: Describe your changes

Development Setup

# Clone repository
git clone https://github.com/rohnspringfield/supermini.git
cd supermini

# Install development dependencies
pip3 install -r requirements.txt
pip3 install -r requirements-test.txt

# Run in development mode
python3 supermini.py

📋 Roadmap

Version 2.1 (Next Release)

• Voice input/output support
• Plugin system for custom tools
• Web interface option
• Enhanced video processing

Version 2.2 (Future)

• Multi-language support
• Advanced automation workflows
• Team collaboration features
• Cloud synchronization option

⚖️ License

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

🙏 Acknowledgments

• Anthropic for the Claude API
• Ollama for local AI model hosting
• Qt/PyQt for the user interface framework
• ChromaDB for vector memory storage

📞 Support

• Documentation: Wiki
• Issues: GitHub Issues
• Discussions: GitHub Discussions
• Project: Open-source AI desktop assistant

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Made with ❤️ for the AI community

SuperMini - Where AI meets productivity