#!/usr/bin/env python3
"""
Hive Cluster Coordination for n8n Workflow Development
Coordinates distributed development of intelligent task allocation workflows
"""

import asyncio
import aiohttp
import json
import time
from typing import Dict, List
from datetime import datetime

# Cluster configuration
AGENTS = {
    "walnut": {
        "endpoint": "http://192.168.1.27:11434",
        "model": "starcoder2:15b",
        "specialty": "Senior Full-Stack Development & Architecture",
        "capabilities": ["workflow_design", "frontend_architecture", "complex_coordination"]
    },
    "ironwood": {
        "endpoint": "http://192.168.1.113:11434", 
        "model": "deepseek-coder-v2",
        "specialty": "Backend Development & Code Analysis",
        "capabilities": ["api_design", "database_schema", "backend_logic"]
    },
    "acacia": {
        "endpoint": "http://192.168.1.72:11434",
        "model": "deepseek-r1:7b", 
        "specialty": "Infrastructure, DevOps & System Architecture",
        "capabilities": ["deployment", "n8n_integration", "system_architecture"]
    },
    "rosewood": {
        "endpoint": "http://192.168.1.132:11434",
        "model": "deepseek-r1:8b",
        "specialty": "Quality Assurance, Testing & Code Review", 
        "capabilities": ["testing_workflows", "quality_validation", "performance_testing"]
    },
    "oak": {
        "endpoint": "http://192.168.1.135:11434",
        "model": "mistral:7b-instruct",
        "specialty": "iOS/macOS Development & Apple Ecosystem",
        "capabilities": ["mobile_integration", "apple_ecosystem", "native_apps"]
    }
}

class HiveN8NCoordinator:
    def __init__(self):
        self.session = None
        self.results = {}
        
    async def __aenter__(self):
        self.session = aiohttp.ClientSession(timeout=aiohttp.ClientTimeout(total=300))
        return self
        
    async def __aexit__(self, exc_type, exc_val, exc_tb):
        if self.session:
            await self.session.close()
    
    async def execute_agent_task(self, agent_id: str, task: Dict) -> Dict:
        """Execute a task on a specific agent"""
        agent = AGENTS[agent_id]
        
        print(f"🎯 Assigning to {agent_id.upper()}: {task['title']}")
        
        prompt = f"""You are a {agent['specialty']} specialist working as part of a distributed AI development cluster. 

TASK: {task['title']}

CONTEXT: We are building intelligent n8n workflows for automatic task allocation based on a 25-person software company model. The existing framework includes role-based AI agent workflows with:

- Executive Leadership Roles (CEO, CTO, Product Manager)
- Engineering Roles (Frontend, Backend, DevOps, Security, QA)  
- Support & Business Roles (Technical Writer, Developer Advocate, Marketing, Customer Success)
- Coordination & Management Roles (Agent Coordinator, Knowledge Manager, Scrum Master)

Your specific assignment: {task['description']}

REQUIREMENTS:
{chr(10).join(f"- {req}" for req in task['requirements'])}

DELIVERABLES:
{chr(10).join(f"- {deliverable}" for deliverable in task['deliverables'])}

Please provide a comprehensive solution that integrates with the existing framework and enhances the automatic task allocation capabilities. Focus on your area of expertise while considering the broader system architecture.

Respond with detailed technical solutions, code examples, and implementation guidance."""

        try:
            async with self.session.post(
                f"{agent['endpoint']}/api/generate",
                json={
                    "model": agent['model'],
                    "prompt": prompt,
                    "stream": False,
                    "options": {
                        "num_predict": 4000,
                        "temperature": 0.7
                    }
                }
            ) as response:
                if response.status == 200:
                    result = await response.json()
                    
                    return {
                        "agent": agent_id,
                        "task": task['title'],
                        "status": "completed",
                        "response": result.get('response', ''),
                        "model": agent['model'],
                        "tokens_generated": result.get('eval_count', 0),
                        "generation_time": result.get('eval_duration', 0) / 1000000000,  # Convert to seconds
                        "timestamp": datetime.now().isoformat()
                    }
                else:
                    return {
                        "agent": agent_id,
                        "task": task['title'], 
                        "status": "failed",
                        "error": f"HTTP {response.status}",
                        "timestamp": datetime.now().isoformat()
                    }
                    
        except Exception as e:
            return {
                "agent": agent_id,
                "task": task['title'],
                "status": "failed", 
                "error": str(e),
                "timestamp": datetime.now().isoformat()
            }
    
    async def coordinate_development(self):
        """Coordinate the distributed development of n8n workflows"""
        
        print("🐝 HIVE CLUSTER N8N WORKFLOW DEVELOPMENT COORDINATION")
        print("=" * 70)
        print(f"🚀 Coordinating {len(AGENTS)} specialized AI agents")
        print(f"🎯 Target: Intelligent task allocation workflows for 25-person software company")
        print()
        
        # Define tasks for each agent
        tasks = {
            "walnut": {
                "title": "Intelligent Task Allocation Algorithm Design",
                "description": "Design the core intelligent task allocation algorithms and workflow architecture for n8n",
                "requirements": [
                    "Analyze agent capabilities and performance characteristics",
                    "Design dynamic task routing based on complexity and specialty",
                    "Create load balancing algorithms for optimal resource utilization", 
                    "Design failure handling and fallback mechanisms",
                    "Plan integration with existing role-based workflow system"
                ],
                "deliverables": [
                    "Task allocation algorithm specifications",
                    "Workflow architecture diagrams and documentation",
                    "Agent capability mapping and scoring system",
                    "Dynamic routing logic and decision trees",
                    "Integration plan with existing n8n workflows"
                ]
            },
            
            "ironwood": {
                "title": "Backend APIs and Database Schema for Task Routing",
                "description": "Implement robust backend APIs and database schema for intelligent task routing and monitoring",
                "requirements": [
                    "Design REST APIs for task submission and agent management",
                    "Create database schema for task tracking and agent performance",
                    "Implement real-time task queue management system",
                    "Build agent health monitoring and performance metrics",
                    "Design webhook endpoints for n8n integration"
                ],
                "deliverables": [
                    "Complete REST API specification and implementation",
                    "Database schema with indexes and performance optimization",
                    "Task queue management system with priority handling",
                    "Real-time monitoring APIs with metrics collection",
                    "Webhook endpoints for seamless n8n integration"
                ]
            },
            
            "acacia": {
                "title": "n8n Workflow Deployment and Cluster Integration",
                "description": "Set up production-ready n8n workflow deployment with full cluster integration",
                "requirements": [
                    "Deploy enhanced n8n workflows to production environment",
                    "Configure cluster integration with all 6 agents",
                    "Set up monitoring and alerting for workflow performance",
                    "Implement backup and recovery procedures",
                    "Configure security and access controls"
                ],
                "deliverables": [
                    "Production deployment scripts and configurations",
                    "Complete cluster integration with agent discovery",
                    "Monitoring dashboard and alerting system",
                    "Backup and recovery documentation and scripts", 
                    "Security configuration and access control setup"
                ]
            },
            
            "rosewood": {
                "title": "Comprehensive Testing and Quality Assurance Workflows",
                "description": "Develop comprehensive testing strategies and quality assurance workflows for the task allocation system",
                "requirements": [
                    "Create automated testing suites for all workflow components",
                    "Design performance testing and load testing strategies",
                    "Implement quality metrics and success criteria",
                    "Create integration testing for agent coordination",
                    "Design monitoring and alerting for system health"
                ],
                "deliverables": [
                    "Automated test suites for n8n workflows and APIs",
                    "Performance testing framework and benchmarks",
                    "Quality metrics dashboard and reporting",
                    "Integration testing scenarios and validation",
                    "System health monitoring and alerting configuration"
                ]
            },
            
            "oak": {
                "title": "iOS/macOS Integration and Mobile Task Management",
                "description": "Create iOS/macOS integration components for mobile task management and monitoring",
                "requirements": [
                    "Design native iOS/macOS app for task monitoring",
                    "Create API integration for real-time cluster status",
                    "Implement push notifications for task completion",
                    "Design mobile-friendly task submission interface",
                    "Plan Apple ecosystem integration features"
                ],
                "deliverables": [
                    "iOS/macOS app design and architecture",
                    "API integration specifications and implementation",
                    "Push notification system design",
                    "Mobile task submission interface mockups",
                    "Apple ecosystem integration roadmap"
                ]
            }
        }
        
        # Execute all tasks in parallel
        print("🔄 Executing tasks across the cluster...")
        print()
        
        task_coroutines = []
        for agent_id, task in tasks.items():
            task_coroutines.append(self.execute_agent_task(agent_id, task))
        
        # Wait for all tasks to complete
        results = await asyncio.gather(*task_coroutines, return_exceptions=True)
        
        # Process results
        successful_tasks = 0
        failed_tasks = 0
        
        print("\n" + "=" * 70)
        print("📊 DEVELOPMENT COORDINATION RESULTS")
        print("=" * 70)
        
        for result in results:
            if isinstance(result, Exception):
                print(f"❌ Task failed with exception: {result}")
                failed_tasks += 1
                continue
                
            if result['status'] == 'completed':
                print(f"✅ {result['agent'].upper()}: {result['task']}")
                print(f"   📝 Response: {len(result['response'])} characters")
                print(f"   🎯 Tokens: {result['tokens_generated']}")
                print(f"   ⏱️  Time: {result['generation_time']:.1f}s")
                successful_tasks += 1
            else:
                print(f"❌ {result['agent'].upper()}: {result['task']} - {result.get('error', 'Unknown error')}")
                failed_tasks += 1
            print()
        
        # Save detailed results
        timestamp = int(time.time())
        results_file = f"/home/tony/AI/projects/hive/results/n8n_coordination_{timestamp}.json"
        
        with open(results_file, 'w') as f:
            json.dump({
                "coordination_summary": {
                    "total_agents": len(AGENTS),
                    "successful_tasks": successful_tasks, 
                    "failed_tasks": failed_tasks,
                    "coordination_time": datetime.now().isoformat(),
                    "target": "n8n intelligent task allocation workflows"
                },
                "task_results": [r for r in results if not isinstance(r, Exception)],
                "agent_configuration": AGENTS
            }, f, indent=2)
        
        print("🎉 COORDINATION SUMMARY")
        print(f"   📊 Total Agents: {len(AGENTS)}")
        print(f"   ✅ Successful: {successful_tasks}")
        print(f"   ❌ Failed: {failed_tasks}")
        print(f"   📁 Results: {results_file}")
        print()
        
        if successful_tasks > 0:
            print("🚀 Next Steps:")
            print("   1. Review detailed agent responses for implementation details")
            print("   2. Integrate solutions from each agent into cohesive system")
            print("   3. Deploy enhanced workflows to n8n production environment")
            print("   4. Test intelligent task allocation with real workloads")
            print("   5. Monitor performance and optimize based on metrics")
        
        return results

async def main():
    """Main coordination function"""
    
    # Ensure results directory exists
    import os
    os.makedirs("/home/tony/AI/projects/hive/results", exist_ok=True)
    
    async with HiveN8NCoordinator() as coordinator:
        await coordinator.coordinate_development()

if __name__ == "__main__":
    asyncio.run(main())