#!/bin/bash
# OpenMAIC Education Setup Script
# Purpose: Deploy OpenMAIC (Open Multi-Agent AI Collaboration) educational environment
# Version: 1.0.0

set -e

echo "=== OpenMAIC Educational Environment Setup ==="

# Configuration
OPENMAIC_DIR="${HOME}/projects/openmaic"
EDUCATION_DIR="${OPENMAIC_DIR}/education"

# Create directory structure
echo "Setting up OpenMAIC directory structure..."
mkdir -p "$OPENMAIC_DIR"
mkdir -p "$EDUCATION_DIR/curriculum"
mkdir -p "$EDUCATION_DIR/demos"
mkdir -p "$EDUCATION_DIR/tutorials"

# Install OpenMAIC core
echo "Installing OpenMAIC framework..."
pip install openmaic-core openmaic-editor openmaic-visualizer

# Create sample agent definitions for education
echo "Creating educational agent examples..."
cat > "$EDUCATION_DIR/curriculum/agent1_basic_researcher.json" << 'EOF'
{
  "name": "researcher",
  "role": "Research Assistant",
  "capabilities": ["web_search", "data_analysis", "summarization"],
  "context_window": 8192,
  "model": "qwen3.5:35b"
}
EOF

cat > "$EDUCATION_DIR/curriculum/agent2_data_analyst.json" << 'EOF'
{
  "name": "data_analyst",
  "role": "Data Analysis Specialist",
  "capabilities": ["pandas", "numpy", "visualization", "statistical_testing"],
  "context_window": 8192,
  "model": "qwen3.5:35b"
}
EOF

cat > "$EDUCATION_DIR/curriculum/agent3_content_writer.json" << 'EOF'
{
  "name": "content_writer",
  "role": "Content Generator",
  "capabilities": ["writing", "editing", "style_transfer"],
  "context_window": 8192,
  "model": "qwen3.5:35b"
}
EOF

# Create educational demos
echo "Creating educational demo workflows..."
cat > "$EDUCATION_DIR/demos/agent_collaboration_example.py" << 'EOF'
#!/usr/bin/env python3
"""
OpenMAIC Educational Demo: Multi-Agent Collaboration
This demonstrates how agents coordinate to solve complex problems
"""

from openmaic.core import Agent, Orchestrator
from openmaic.visualizer import CollaborationVisualizer

def demonstrate_agent_collaboration():
    """Show how agents collaborate to complete a task."""
    
    # Initialize orchestrator
    orchestrator = Orchestrator(
        max_agents=3,
        collaboration_mode="sequential",
        output_format="markdown"
    )
    
    # Create agents from definitions
    researcher = Agent.load("$EDUCATION_DIR/curriculum/agent1_basic_researcher.json")
    analyst = Agent.load("$EDUCATION_DIR/curriculum/agent2_data_analyst.json")
    writer = Agent.load("$EDUCATION_DIR/curriculum/agent3_content_writer.json")
    
    # Define collaboration workflow
    workflow = [
        {"agent": researcher, "task": "Research AI trends in SMB market"},
        {"agent": analyst, "task": "Analyze market data and identify opportunities"},
        {"agent": writer, "task": "Write comprehensive market analysis report"}
    ]
    
    # Execute and visualize
    orchestrator.run_workflow(workflow)
    
    # Display results
    visualizer = CollaborationVisualizer(orchestrator)
    print(visualizer.render_collaboration_flow())
    print(orchestrator.get_final_output())
    
    return orchestrator.get_results()

if __name__ == "__main__":
    demonstrate_agent_collaboration()
EOF

chmod +x "$EDUCATION_DIR/demos/agent_collaboration_example.py"

# Create curriculum documentation
echo "Creating curriculum documentation..."
cat > "$EDUCATION_DIR/curriculum/README.md" << 'EOF'
# OpenMAIC Educational Curriculum

This curriculum introduces multi-agent AI systems through hands-on projects.

## Module 1: Fundamentals
- Understanding agent roles and capabilities
- Basic Orchestrator configuration
- Simple agent collaboration patterns

## Module 2: Advanced Collaboration
- Multi-agent workflow design
- Context sharing between agents
- Error handling and recovery

## Module 3: Production Patterns
- Scaling to 10+ agents
- Performance optimization
- Monitoring and diagnostics

## Module 4: Real-World Applications
- Market analysis automation
- Customer support orchestration
- Content pipeline automation

## Getting Started

1. Install OpenMAIC: `pip install openmaic-core openmaic-visualizer`
2. Review curriculum guides in `/curriculum/`
3. Run demo workflows in `/demos/`
4. Participate in hands-on exercises

## Prerequisites
- Python 3.10+
- Ollama or similar LLM service
- Basic understanding of AI/ML concepts
EOF

# Set up virtual environment
echo "Creating virtual environment..."
python3 -m venv "$OPENMAIC_DIR/venv"
source "$OPENMAIC_DIR/venv/bin/activate"
pip install openmaic-core openmaic-editor openmaic-visualizer pandas numpy matplotlib

# Create environment variables
cat > "$OPENMAIC_DIR/.env" << 'EOF'
# OpenMAIC Configuration
OPENMAIC_LOG_LEVEL=INFO
OPENMAIC_MAX_CONCURRENT_AGENTS=5
OPENMAIC_COLLABORATION_MODE=sequential

# Model Setup
OLLAMA_BASE_URL=http://localhost:11434
QWEN35_ENDPOINT=192.168.68.8:8080

# Collaboration Settings
COLLABORATION_CONTEXT_WINDOW=8192
COLLABORATION_TIMEOUT=3600
EOF

# Display summary
echo ""
echo "=== OpenMAIC Education Environment Ready ==="
echo "Directory: $OPENMAIC_DIR"
echo "Curriculum: $EDUCATION_DIR/curriculum/"
echo "Demos: $EDUCATION_DIR/demos/"
echo ""
echo "To start learning:"
echo "1. cd $OPENMAIC_DIR"
echo "2. source venv/bin/activate"
echo "3. Run demos: python $EDUCATION_DIR/demos/agent_collaboration_example.py"
echo "4. Study curriculum guides in $EDUCATION_DIR/curriculum/"
echo ""
echo "Perfect for Jerome and Theodore's strategy sessions on AI agent frameworks!"