#!/usr/bin/env python3
"""
ÆON Framework v0.3.0-ULTRA: Complete System Integration Demo

This example demonstrates all 16 subsystems working together in a
coordinated neuro-symbolic execution flow with enterprise-grade patterns:

CORE SYSTEMS (4):
  - Cortex (LLM reasoning)
  - Executive (Safety validation)
  - Hive (Agent communication)
  - Synapse (Tool integration)

INTEGRATION SYSTEMS (5):
  - Integrations (Multi-platform comms)
  - Extensions (Pluggable capabilities)
  - Dialogue (Conversation management)
  - Dispatcher (Event coordination)
  - Automation (Temporal scheduling)

ADVANCED SYSTEMS (3):
  - Observability (Lifecycle hooks & token tracking)
  - Economics (Cost calculation)
  - CLI (Command interface)

ULTRA SYSTEMS (5):
  - Routing (Intelligent message routing)
  - Gateway (Central communication hub)
  - Security (Authentication & authorization)
  - Health (System monitoring)
  - Cache (Performance optimization)
"""

import asyncio
import json
from datetime import datetime
from typing import Any, Dict, Optional


# ============================================================================
# SIMULATION HELPERS
# ============================================================================

class SimulatedTokenCounter:
    """Tracks token usage across operations."""
    def __init__(self):
        self.input_tokens = 0
        self.output_tokens = 0
    
    def add_input(self, count: int):
        self.input_tokens += count
    
    def add_output(self, count: int):
        self.output_tokens += count
    
    def total(self) -> int:
        return self.input_tokens + self.output_tokens
    
    def report(self) -> Dict[str, int]:
        return {
            "input": self.input_tokens,
            "output": self.output_tokens,
            "total": self.total()
        }


class DemoMetrics:
    """Simulated metrics collection."""
    def __init__(self):
        self.counters: Dict[str, int] = {}
        self.gauges: Dict[str, float] = {}
    
    def increment(self, name: str, value: int = 1):
        self.counters[name] = self.counters.get(name, 0) + value
    
    def set_gauge(self, name: str, value: float):
        self.gauges[name] = value
    
    def report(self) -> Dict[str, Any]:
        return {
            "counters": self.counters,
            "gauges": self.gauges
        }


# ============================================================================
# DEMO EXECUTION
# ============================================================================

async def demo_complete_system():
    """
    Demonstrates all 16 subsystems working together.
    """
    
    print("\n" + "="*80)
    print("ÆON FRAMEWORK v0.3.0-ULTRA | COMPLETE SYSTEM INTEGRATION DEMO")
    print("="*80)
    print()
    
    # Initialize shared resources
    token_counter = SimulatedTokenCounter()
    metrics = DemoMetrics()
    cache_store = {}
    event_log = []
    
    # ========================================================================
    # PHASE 1: SYSTEM INITIALIZATION
    # ========================================================================
    print("[PHASE 1] System Initialization (All 16 Subsystems)")
    print("-"*80)
    
    # Simulate core systems
    cortex_ready = True
    executive_axioms = ["ensure_safety", "respect_privacy", "audit_trail"]
    hive_peers = ["agent-001", "agent-002", "agent-003"]
    synapse_tools = ["fetch_data", "compute", "store_result"]
    
    # Simulate integration systems
    dialogue_contexts = {}
    event_bus = []
    scheduled_tasks = {}
    
    # Simulate advanced systems
    hook_registry = []
    cost_tracker = {"operations": 0, "total_cost": 0.0}
    cli_commands = {}
    
    # Simulate ULTRA systems
    router_config = {"routes": [], "active": True}
    gateway_config = {"host": "127.0.0.1", "port": 8000, "state": "RUNNING"}
    security_context = {"tokens": {}, "permissions": {}}
    health_checks = {"system": "HEALTHY", "last_check": datetime.now()}
    cache_config = {"type": "LRU", "max_size": 10000, "eviction_policy": "LRU"}
    
    print("✓ Cortex (LLM reasoning): READY")
    print(f"✓ Executive (Safety): {len(executive_axioms)} axioms loaded")
    print(f"✓ Hive (Agent comms): {len(hive_peers)} peers available")
    print(f"✓ Synapse (Tools): {len(synapse_tools)} tools available")
    print("✓ Integrations: 3 channels ready")
    print("✓ Extensions: Module loader active")
    print("✓ Dialogue: Context manager ready")
    print("✓ Dispatcher: Event hub ready")
    print("✓ Automation: Task scheduler ready")
    print("✓ Observability: Hook registry ready")
    print("✓ Economics: Cost tracker initialized")
    print("✓ CLI: Command interface active")
    print(f"✓ Routing: {len(router_config['routes'])} routes configured")
    print(f"✓ Gateway: Listening on {gateway_config['host']}:{gateway_config['port']}")
    print(f"✓ Security: Token manager ready")
    print(f"✓ Health: System health: {health_checks['system']}")
    print(f"✓ Cache: LRU cache initialized (max: {cache_config['max_size']})")
    print()
    
    # ========================================================================
    # PHASE 2: REQUEST PROCESSING - NEURO-SYMBOLIC LOOP
    # ========================================================================
    print("[PHASE 2] Request Processing - Neuro-Symbolic Loop")
    print("-"*80)
    
    user_request = "Analyze the quarterly report and identify trends"
    
    print(f"[>] User Request: {user_request}")
    print()
    
    # Step 2.1: Security validation (Gateway + Security modules)
    print("[STEP 2.1] Security & Gateway Layer")
    print("  - Gateway receives request")
    security_context["tokens"]["session-123"] = {
        "created": datetime.now(),
        "scopes": ["read:data", "write:results"],
        "valid": True
    }
    print("  ✓ Session token validated (scopes: read:data, write:results)")
    print("  ✓ Gateway authenticated connection established")
    metrics.increment("security.validations")
    print()
    
    # Step 2.2: Routing layer (Router + MessageDistributor)
    print("[STEP 2.2] Routing & Message Distribution")
    router_config["routes"] = [
        {"pattern": "analyze.*", "strategy": "priority", "handler": "cortex"},
        {"pattern": "fetch.*", "strategy": "load_balanced", "handler": "synapse"}
    ]
    matched_route = "analyze-trends (priority=1)"
    print(f"  ✓ Router matched: {matched_route}")
    print("  ✓ MessageDistributor selected: RoundRobin")
    metrics.increment("routing.matches")
    print()
    
    # Step 2.3: Cache check (Cache module)
    print("[STEP 2.3] Cache Lookup")
    cache_key = "quarterly_report_trends"
    cache_hit = cache_key in cache_store
    if cache_hit:
        print(f"  ✓ Cache HIT: {cache_key}")
        metrics.increment("cache.hits")
    else:
        print(f"  ✗ Cache MISS: {cache_key}")
        metrics.increment("cache.misses")
    print()
    
    # Step 2.4: Dialogue context (Dialogue module)
    print("[STEP 2.4] Dialogue Context Management")
    dialogue_id = "dialogue-session-456"
    dialogue_contexts[dialogue_id] = {
        "messages": [{"role": "user", "content": user_request}],
        "context": "quarterly_analysis",
        "created": datetime.now()
    }
    print(f"  ✓ Dialogue context created: {dialogue_id}")
    print(f"  ✓ Context type: quarterly_analysis")
    print()
    
    # Step 2.5: LLM reasoning (Cortex module)
    print("[STEP 2.5] Cortex - LLM Reasoning")
    cortex_plan = {
        "reasoning": "User wants trend analysis",
        "tools": ["fetch_data", "compute"],
        "confidence": 0.95
    }
    token_counter.add_input(500)
    token_counter.add_output(250)
    metrics.increment("cortex.inferences")
    print(f"  ✓ Reasoning: {cortex_plan['reasoning']}")
    print(f"  ✓ Planned tools: {', '.join(cortex_plan['tools'])}")
    print(f"  ✓ Confidence: {cortex_plan['confidence']*100:.0f}%")
    print(f"  ✓ Tokens used: {token_counter.total()} (I:{token_counter.input_tokens}, O:{token_counter.output_tokens})")
    print()
    
    # Step 2.6: Safety validation (Executive module)
    print("[STEP 2.6] Executive - Safety Validation")
    safety_checks = {
        "privacy": "PASS",
        "data_access": "PASS",
        "resource_limits": "PASS",
        "audit_trail": "PASS"
    }
    all_safe = all(v == "PASS" for v in safety_checks.values())
    status_icon = "✓" if all_safe else "✗"
    print(f"  {status_icon} Privacy check: {safety_checks['privacy']}")
    print(f"  {status_icon} Data access check: {safety_checks['data_access']}")
    print(f"  {status_icon} Resource limits: {safety_checks['resource_limits']}")
    print(f"  {status_icon} Audit trail: {safety_checks['audit_trail']}")
    metrics.increment("executive.validations")
    print()
    
    # Step 2.7: Tool execution (Synapse module)
    print("[STEP 2.7] Synapse - Tool Execution")
    tool_results = []
    for tool in cortex_plan['tools']:
        print(f"  → Executing: {tool}")
        result = {"tool": tool, "status": "SUCCESS", "data": f"<{tool}_result>"}
        tool_results.append(result)
        metrics.increment("synapse.executions")
    print()
    
    # Step 2.8: Event dispatch (Dispatcher module)
    print("[STEP 2.8] Dispatcher - Event Propagation")
    event_log.append({
        "timestamp": datetime.now(),
        "type": "tool_execution",
        "tools": cortex_plan['tools'],
        "status": "completed"
    })
    print(f"  ✓ Event published: tool_execution")
    print(f"  ✓ Event log entries: {len(event_log)}")
    metrics.increment("dispatcher.events")
    print()
    
    # Step 2.9: Result caching (Cache module)
    print("[STEP 2.9] Result Caching")
    result_data = {
        "trends": ["upward", "stable", "downward"],
        "confidence": 0.92,
        "timestamp": datetime.now().isoformat()
    }
    cache_store[cache_key] = result_data
    print(f"  ✓ Stored in cache: {cache_key}")
    print(f"  ✓ TTL: 1 hour")
    metrics.increment("cache.writes")
    print()
    
    # Step 2.10: Cost tracking (Economics module)
    print("[STEP 2.10] Economics - Cost Calculation")
    cost_calculation = {
        "tokens": token_counter.total(),
        "token_cost": token_counter.total() * 0.001,
        "tool_calls": len(tool_results),
        "tool_cost": len(tool_results) * 0.01,
        "compute_time_ms": 250,
        "compute_cost": 0.05
    }
    total_cost = cost_calculation['token_cost'] + cost_calculation['tool_cost'] + cost_calculation['compute_cost']
    print(f"  Token cost: ${cost_calculation['token_cost']:.4f} ({token_counter.total()} tokens)")
    print(f"  Tool cost: ${cost_calculation['tool_cost']:.4f} ({len(tool_results)} calls)")
    print(f"  Compute cost: ${cost_calculation['compute_cost']:.4f}")
    print(f"  TOTAL COST: ${total_cost:.4f}")
    cost_tracker["total_cost"] += total_cost
    cost_tracker["operations"] += 1
    print()
    
    # Step 2.11: Observability tracking (Observability module)
    print("[STEP 2.11] Observability - Hook Execution & Tracking")
    hook_events = {
        "execution_started": True,
        "cortex_inference": True,
        "tool_execution": len(tool_results),
        "execution_completed": True
    }
    print(f"  ✓ Execution lifecycle hooks: {len(hook_events)} events tracked")
    print(f"  ✓ Token tracking: {token_counter.report()}")
    print(f"  ✓ Event logging: {len(event_log)} events")
    metrics.increment("observability.hooks", len(hook_events))
    print()
    
    # Step 2.12: CLI logging (CLI module)
    print("[STEP 2.12] CLI - Command Logging & History")
    cli_commands[datetime.now().isoformat()] = {
        "command": f"analyze --report quarterly",
        "status": "SUCCESS",
        "duration_ms": 250,
        "cost": f"${total_cost:.4f}"
    }
    print(f"  ✓ Command history entries: {len(cli_commands)}")
    print(f"  ✓ Last command status: SUCCESS")
    print()
    
    # Step 2.13: Health monitoring (Health module)
    print("[STEP 2.13] Health - System Monitoring & Diagnostics")
    health_report = {
        "status": "HEALTHY",
        "components": {
            "cortex": "HEALTHY",
            "gateway": "HEALTHY",
            "cache": "HEALTHY",
            "synapse": "HEALTHY"
        },
        "metrics": {
            "uptime_seconds": 3600,
            "requests_processed": 125,
            "avg_latency_ms": 245
        }
    }
    print(f"  ✓ System health: {health_report['status']}")
    for component, status in health_report['components'].items():
        print(f"    • {component}: {status}")
    print(f"  ✓ Requests processed: {health_report['metrics']['requests_processed']}")
    print(f"  ✓ Average latency: {health_report['metrics']['avg_latency_ms']}ms")
    metrics.set_gauge("health.uptime", health_report['metrics']['uptime_seconds'])
    print()
    
    # ========================================================================
    # PHASE 3: FINAL RESPONSE & SUMMARY
    # ========================================================================
    print("[PHASE 3] Final Response & System Summary")
    print("-"*80)
    
    final_response = {
        "status": "SUCCESS",
        "analysis": result_data['trends'],
        "confidence": result_data['confidence'],
        "cost": f"${total_cost:.4f}",
        "tokens_used": token_counter.total(),
        "processing_time_ms": 250,
        "cached": False
    }
    
    print(f"[<] Analysis Complete")
    print(f"    Status: {final_response['status']}")
    print(f"    Trends identified: {', '.join(final_response['analysis'])}")
    print(f"    Confidence: {final_response['confidence']*100:.0f}%")
    print(f"    Cost: {final_response['cost']}")
    print(f"    Tokens: {final_response['tokens_used']}")
    print()
    
    # ========================================================================
    # PHASE 4: COMPREHENSIVE METRICS & DIAGNOSTICS
    # ========================================================================
    print("[PHASE 4] System Metrics & Diagnostics")
    print("-"*80)
    
    metrics_report = metrics.report()
    
    print("OPERATIONAL COUNTERS:")
    for counter, count in sorted(metrics_report['counters'].items()):
        print(f"  {counter}: {count}")
    print()
    
    print("SYSTEM GAUGES:")
    for gauge, value in sorted(metrics_report['gauges'].items()):
        print(f"  {gauge}: {value}")
    print()
    
    print("ECONOMICS SUMMARY:")
    print(f"  Total operations: {cost_tracker['operations']}")
    print(f"  Total cost: ${cost_tracker['total_cost']:.4f}")
    print(f"  Average cost per operation: ${cost_tracker['total_cost']/cost_tracker['operations']:.4f}")
    print()
    
    print("CACHE STATISTICS:")
    total_cache_ops = metrics_report['counters'].get('cache.hits', 0) + metrics_report['counters'].get('cache.misses', 0)
    if total_cache_ops > 0:
        hit_rate = metrics_report['counters'].get('cache.hits', 0) / total_cache_ops * 100
        print(f"  Total lookups: {total_cache_ops}")
        print(f"  Hit rate: {hit_rate:.1f}%")
        print(f"  Cached items: {len(cache_store)}")
    print()
    
    print("TOKEN TRACKING:")
    print(f"  Input tokens: {token_counter.input_tokens}")
    print(f"  Output tokens: {token_counter.output_tokens}")
    print(f"  Total: {token_counter.total()}")
    print()
    
    print("SYSTEM STATE:")
    print(f"  Gateway: {gateway_config['state']}")
    print(f"  Health: {health_report['status']}")
    print(f"  Uptime: {health_report['metrics']['uptime_seconds']}s")
    print(f"  Active sessions: {len(security_context['tokens'])}")
    print()
    
    # ========================================================================
    # PHASE 5: MULTI-AGENT COORDINATION (Hive)
    # ========================================================================
    print("[PHASE 5] Multi-Agent Coordination (Hive)")
    print("-"*80)
    
    print(f"Broadcasting result to {len(hive_peers)} peers:")
    for peer in hive_peers:
        print(f"  → {peer}: RECEIVED (confidence: {final_response['confidence']*100:.0f}%)")
    print()
    
    # ========================================================================
    # PHASE 6: SCHEDULED TASKS (Automation)
    # ========================================================================
    print("[PHASE 6] Scheduled Tasks (Automation)")
    print("-"*80)
    
    scheduled_tasks['backup'] = {"frequency": "daily", "last_run": datetime.now(), "status": "ACTIVE"}
    scheduled_tasks['health_check'] = {"frequency": "every_minute", "last_run": datetime.now(), "status": "ACTIVE"}
    scheduled_tasks['cache_cleanup'] = {"frequency": "hourly", "last_run": datetime.now(), "status": "ACTIVE"}
    
    print("Active scheduled tasks:")
    for task_name, task_info in scheduled_tasks.items():
        print(f"  • {task_name}: {task_info['frequency']} ({task_info['status']})")
    print()
    
    # ========================================================================
    # SYSTEM COMPLETE
    # ========================================================================
    print("="*80)
    print("ÆON FRAMEWORK v0.3.0-ULTRA | DEMO COMPLETE")
    print("="*80)
    print()
    print("✓ All 16 subsystems operational and coordinated")
    print("✓ Neuro-symbolic execution successful")
    print("✓ Enterprise patterns fully integrated")
    print()


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