src/cloud_agents/orchestrator.py

"""
cloud_agents.py - Cloud Agents Integration for Intelligent Task Orchestration

This module integrates OpenPeer AI's Cloud Agents for AI-driven task distribution,
resource optimization, and intelligent scheduling of molecular docking workloads.

Authors: OpenPeer AI, Riemann Computing Inc., Bleunomics, Andrew Magdy Kamal
Version: 1.0.0
Date: 2025
"""

import os
import json
import asyncio
from typing import Dict, List, Optional, Any
from dataclasses import dataclass, asdict
from datetime import datetime
import logging

try:
    from huggingface_hub import InferenceClient
    from transformers import AutoTokenizer, AutoModel
except ImportError:
    print("Warning: HuggingFace libraries not installed. Install with: pip install transformers huggingface-hub")

# Configure logging
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)


@dataclass
class Task:
    """Represents a molecular docking task"""
    task_id: str
    ligand_file: str
    receptor_file: str
    priority: str = "normal"  # low, normal, high, critical
    estimated_compute_time: float = 0.0
    required_memory: int = 0
    use_gpu: bool = True
    status: str = "pending"
    assigned_node: Optional[str] = None
    created_at: datetime = None

    def __post_init__(self):
        if self.created_at is None:
            self.created_at = datetime.now()


@dataclass
class ComputeNode:
    """Represents a compute node in the distributed system"""
    node_id: str
    cpu_cores: int
    gpu_available: bool
    gpu_type: Optional[str] = None
    memory_gb: int = 16
    current_load: float = 0.0
    tasks_completed: int = 0
    average_task_time: float = 0.0
    is_active: bool = True
    location: Optional[str] = None


class CloudAgentsOrchestrator:
    """
    AI-powered orchestration using Cloud Agents for intelligent
    task distribution and resource optimization
    """

    def __init__(self, config: Optional[Dict] = None):
        """
        Initialize Cloud Agents orchestrator
        
        Args:
            config: Configuration dictionary
        """
        self.config = config or {}
        self.hf_token = self.config.get('hf_token', os.getenv('HF_TOKEN'))
        self.model_name = self.config.get('model_name', 'OpenPeer AI/Cloud-Agents')
        
        self.tasks: Dict[str, Task] = {}
        self.nodes: Dict[str, ComputeNode] = {}
        self.task_queue: List[str] = []
        
        self.client: Optional[InferenceClient] = None
        self.is_initialized = False
        
        logger.info("CloudAgentsOrchestrator initialized")

    async def initialize(self) -> bool:
        """
        Initialize the Cloud Agents system
        
        Returns:
            True if initialization successful
        """
        try:
            logger.info("Initializing Cloud Agents...")
            
            # Initialize HuggingFace Inference Client
            if self.hf_token:
                self.client = InferenceClient(
                    model=self.model_name,
                    token=self.hf_token
                )
                logger.info(f"Connected to Cloud Agents model: {self.model_name}")
            else:
                logger.warning("HuggingFace token not provided. Using local mode.")
            
            self.is_initialized = True
            logger.info("Cloud Agents initialized successfully")
            
            return True
            
        except Exception as e:
            logger.error(f"Failed to initialize Cloud Agents: {e}")
            return False

    def register_node(self, node: ComputeNode) -> bool:
        """
        Register a compute node with the orchestrator
        
        Args:
            node: ComputeNode instance
            
        Returns:
            True if registration successful
        """
        try:
            self.nodes[node.node_id] = node
            logger.info(f"Node registered: {node.node_id} (GPU: {node.gpu_available})")
            return True
        except Exception as e:
            logger.error(f"Failed to register node: {e}")
            return False

    def submit_task(self, task: Task) -> str:
        """
        Submit a new docking task
        
        Args:
            task: Task instance
            
        Returns:
            Task ID
        """
        self.tasks[task.task_id] = task
        self.task_queue.append(task.task_id)
        
        logger.info(f"Task submitted: {task.task_id} (Priority: {task.priority})")
        
        return task.task_id

    async def optimize_task_distribution(self) -> Dict[str, Any]:
        """
        Use AI to optimize task distribution across nodes
        
        Returns:
            Optimization recommendations
        """
        try:
            # Prepare context for AI agent
            context = {
                "total_tasks": len(self.tasks),
                "pending_tasks": len(self.task_queue),
                "active_nodes": len([n for n in self.nodes.values() if n.is_active]),
                "gpu_nodes": len([n for n in self.nodes.values() if n.gpu_available]),
                "avg_node_load": sum(n.current_load for n in self.nodes.values()) / max(len(self.nodes), 1)
            }
            
            # Use Cloud Agents for intelligent decision making
            prompt = self._create_optimization_prompt(context)
            
            if self.client:
                # Query Cloud Agents model
                response = await self._query_cloud_agents(prompt)
                recommendations = self._parse_ai_response(response)
            else:
                # Fallback to rule-based optimization
                recommendations = self._rule_based_optimization()
            
            logger.info(f"Optimization complete: {recommendations}")
            
            return recommendations
            
        except Exception as e:
            logger.error(f"Optimization failed: {e}")
            return self._rule_based_optimization()

    async def schedule_tasks(self) -> List[Dict[str, str]]:
        """
        Schedule pending tasks to available nodes using AI optimization
        
        Returns:
            List of task assignments
        """
        assignments = []
        
        # Get optimization recommendations
        recommendations = await self.optimize_task_distribution()
        
        # Process pending tasks
        for task_id in self.task_queue[:]:
            task = self.tasks.get(task_id)
            if not task or task.status != "pending":
                continue
            
            # Find optimal node for this task
            node = self._select_optimal_node(task, recommendations)
            
            if node:
                # Assign task to node
                task.assigned_node = node.node_id
                task.status = "assigned"
                node.current_load += 0.1  # Increment load
                
                assignments.append({
                    "task_id": task_id,
                    "node_id": node.node_id,
                    "priority": task.priority
                })
                
                self.task_queue.remove(task_id)
                logger.info(f"Task {task_id} assigned to node {node.node_id}")
        
        return assignments

    def _select_optimal_node(self, task: Task, recommendations: Dict) -> Optional[ComputeNode]:
        """
        Select the optimal node for a task based on AI recommendations
        
        Args:
            task: Task to assign
            recommendations: AI recommendations
            
        Returns:
            Selected ComputeNode or None
        """
        # Filter available nodes
        available_nodes = [
            node for node in self.nodes.values()
            if node.is_active and node.current_load < 0.9
        ]
        
        if not available_nodes:
            return None
        
        # Prefer GPU nodes for GPU tasks
        if task.use_gpu:
            gpu_nodes = [n for n in available_nodes if n.gpu_available]
            if gpu_nodes:
                available_nodes = gpu_nodes
        
        # Sort by load and performance
        available_nodes.sort(key=lambda n: (
            n.current_load,
            -n.tasks_completed,
            n.average_task_time
        ))
        
        # Apply priority boost for high-priority tasks
        if task.priority == "critical":
            # Select fastest node regardless of load
            available_nodes.sort(key=lambda n: n.average_task_time)
        
        return available_nodes[0] if available_nodes else None

    async def _query_cloud_agents(self, prompt: str) -> str:
        """
        Query Cloud Agents model for intelligent decision making
        
        Args:
            prompt: Input prompt
            
        Returns:
            AI response
        """
        try:
            # Use HuggingFace Inference API
            response = self.client.text_generation(
                prompt,
                max_new_tokens=500,
                temperature=0.7,
                top_p=0.9
            )
            return response
        except Exception as e:
            logger.error(f"Cloud Agents query failed: {e}")
            return ""

    def _create_optimization_prompt(self, context: Dict) -> str:
        """
        Create optimization prompt for Cloud Agents
        
        Args:
            context: System context
            
        Returns:
            Formatted prompt
        """
        prompt = f"""
You are an AI orchestrator for a distributed molecular docking system.

Current System Status:
- Total Tasks: {context['total_tasks']}
- Pending Tasks: {context['pending_tasks']}
- Active Nodes: {context['active_nodes']}
- GPU-enabled Nodes: {context['gpu_nodes']}
- Average Node Load: {context['avg_node_load']:.2f}

Task: Optimize task distribution to:
1. Maximize throughput
2. Minimize waiting time for high-priority tasks
3. Balance load across nodes
4. Utilize GPU resources efficiently

Provide recommendations for:
- Load balancing strategy
- Priority handling
- GPU allocation
- Estimated completion time

Response format (JSON):
"""
        return prompt

    def _parse_ai_response(self, response: str) -> Dict[str, Any]:
        """
        Parse AI response into actionable recommendations
        
        Args:
            response: Raw AI response
            
        Returns:
            Parsed recommendations
        """
        try:
            # Attempt to parse JSON response
            recommendations = json.loads(response)
            return recommendations
        except:
            # Fallback to default recommendations
            return self._rule_based_optimization()

    def _rule_based_optimization(self) -> Dict[str, Any]:
        """
        Fallback rule-based optimization
        
        Returns:
            Optimization recommendations
        """
        return {
            "strategy": "load_balanced",
            "gpu_priority": True,
            "max_tasks_per_node": 10,
            "rebalance_threshold": 0.8
        }

    def get_task_status(self, task_id: str) -> Optional[Dict[str, Any]]:
        """
        Get status of a specific task
        
        Args:
            task_id: Task ID
            
        Returns:
            Task status dictionary
        """
        task = self.tasks.get(task_id)
        if not task:
            return None
        
        return asdict(task)

    def get_system_statistics(self) -> Dict[str, Any]:
        """
        Get overall system statistics
        
        Returns:
            Statistics dictionary
        """
        total_tasks = len(self.tasks)
        completed_tasks = len([t for t in self.tasks.values() if t.status == "completed"])
        pending_tasks = len(self.task_queue)
        
        active_nodes = [n for n in self.nodes.values() if n.is_active]
        total_compute_power = sum(n.cpu_cores for n in active_nodes)
        
        return {
            "total_tasks": total_tasks,
            "completed_tasks": completed_tasks,
            "pending_tasks": pending_tasks,
            "active_nodes": len(active_nodes),
            "total_compute_power": total_compute_power,
            "gpu_nodes": len([n for n in active_nodes if n.gpu_available]),
            "average_node_load": sum(n.current_load for n in active_nodes) / max(len(active_nodes), 1),
            "throughput": completed_tasks / max((datetime.now() - list(self.tasks.values())[0].created_at).total_seconds() / 3600, 1) if self.tasks else 0
        }

    async def auto_scale(self) -> Dict[str, Any]:
        """
        Automatically scale resources based on workload
        
        Returns:
            Scaling recommendations
        """
        stats = self.get_system_statistics()
        
        recommendations = {
            "action": "none",
            "reason": "",
            "suggested_nodes": 0
        }
        
        # Check if we need more resources
        if stats["pending_tasks"] > stats["active_nodes"] * 5:
            recommendations["action"] = "scale_up"
            recommendations["suggested_nodes"] = stats["pending_tasks"] // 5
            recommendations["reason"] = "High pending task count"
        
        # Check if we have excess capacity
        elif stats["average_node_load"] < 0.3 and stats["pending_tasks"] == 0:
            recommendations["action"] = "scale_down"
            recommendations["suggested_nodes"] = -1
            recommendations["reason"] = "Low resource utilization"
        
        logger.info(f"Auto-scale recommendation: {recommendations}")
        
        return recommendations


async def main():
    """Example usage of Cloud Agents orchestrator"""
    
    # Initialize orchestrator
    orchestrator = CloudAgentsOrchestrator()
    await orchestrator.initialize()
    
    # Register some compute nodes
    node1 = ComputeNode(
        node_id="node_1",
        cpu_cores=16,
        gpu_available=True,
        gpu_type="RTX 3090",
        memory_gb=64
    )
    node2 = ComputeNode(
        node_id="node_2",
        cpu_cores=8,
        gpu_available=False,
        memory_gb=32
    )
    
    orchestrator.register_node(node1)
    orchestrator.register_node(node2)
    
    # Submit tasks
    for i in range(5):
        task = Task(
            task_id=f"task_{i}",
            ligand_file=f"ligand_{i}.pdbqt",
            receptor_file="receptor.pdbqt",
            priority="normal" if i < 3 else "high"
        )
        orchestrator.submit_task(task)
    
    # Schedule tasks
    assignments = await orchestrator.schedule_tasks()
    print(f"Scheduled {len(assignments)} tasks")
    
    # Get statistics
    stats = orchestrator.get_system_statistics()
    print(f"System stats: {json.dumps(stats, indent=2)}")


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