# queen_orchestrator.py
import time
import datetime
import subprocess
import json
import os
import logging
import random

# Mocking external dependencies for demonstration.  In a real system, these would be actual integrations.
class MockOllama:
    def __init__(self):
        self.is_connected = False
        self.response_time = 0.1 #seconds

    def connect(self):
        #Simulate connection
        time.sleep(0.5) #Simulate connection time
        self.is_connected = True
        return self.is_connected

    def query(self, prompt):
        #Simulate query and response
        time.sleep(random.uniform(0.05, 0.2)) #Simulate variable response time
        if "revenue" in prompt.lower():
            return "Generate a marketing campaign focused on tradespeople using AI automation, emphasizing a free trial."
        elif "memory" in prompt.lower():
            return "Memory retrieval accuracy is at 97%."
        elif "evolution" in prompt.lower():
            return "The self-improvement loop has proposed a new algorithm for knowledge consolidation."
        else:
            return "I am processing information."

class MockRedis:
    def __init__(self):
        self.is_connected = False
        self.latency = 0.01 #seconds

    def connect(self):
        #Simulate connection
        time.sleep(0.2) #Simulate connection time
        self.is_connected = True
        return self.is_connected

    def publish(self, channel, message):
        #Simulate publishing
        time.sleep(0.005) #Simulate publishing time
        print(f"Redis: Published to {channel}: {message}")
        return True

    def subscribe(self, channel):
        #Simulate subscribing
        time.sleep(0.005) #Simulate subscribing time
        print(f"Redis: Subscribed to {channel}")
        return True

class MockPostgreSQL:
    def __init__(self):
        self.is_connected = False
        self.entity_count = 100

    def connect(self):
        #Simulate connection
        time.sleep(0.3) #Simulate connection time
        self.is_connected = True
        return self.is_connected

    def execute_query(self, query):
        #Simulate executing query
        time.sleep(0.01) #Simulate query time
        if "count" in query.lower():
            return self.entity_count
        else:
            return True

class MockQdrant:
    def __init__(self):
        self.is_healthy = False

    def connect(self):
        #Simulate connection
        time.sleep(0.4) #Simulate connection time
        self.is_healthy = True
        return self.is_healthy

    def search(self, query):
        #Simulate search
        time.sleep(0.02) #Simulate search time
        return ["result1", "result2"]

class TokenBudgetMonitor:
    BUDGET_LIMIT = 10.00  # USD
    EMERGENCY_STOP = 9.50  # USD

    def __init__(self):
        self.current_spend = 0.0
        self.start_time = time.time()
        self.token_count = 0

    def consume_tokens(self, input_tokens, output_tokens, input_price_per_million, output_price_per_million):
      input_cost = (input_tokens / 1000000) * input_price_per_million
      output_cost = (output_tokens / 1000000) * output_price_per_million
      self.current_spend += input_cost + output_cost
      self.token_count += input_tokens + output_tokens
      self.check_budget()

    def check_budget(self):
        if self.current_spend >= self.EMERGENCY_STOP:
            self.trigger_graceful_shutdown()

    def trigger_graceful_shutdown(self):
        print("EMERGENCY: Token budget exceeded. Initiating graceful shutdown.")
        # Implement shutdown procedure here
        os._exit(1) #Force exit

    def get_status(self):
        elapsed_time = time.time() - self.start_time
        hourly_rate = self.current_spend / (elapsed_time / 3600) if elapsed_time > 0 else 0
        return {
            "total_budget": self.BUDGET_LIMIT,
            "spent": self.current_spend,
            "remaining": self.BUDGET_LIMIT - self.current_spend,
            "rate": hourly_rate,
            "tokens_input": 0,
            "tokens_output": 0,
            "tokens_total": self.token_count,
            "percent_used": (self.current_spend / self.BUDGET_LIMIT) * 100
        }

class Agent:
    def __init__(self, agent_id, role, task):
        self.agent_id = agent_id
        self.role = role
        self.task = task
        self.status = "pending"
        self.result = None
        self.error = None
        self.start_time = None
        self.end_time = None
        self.input_tokens = 0
        self.output_tokens = 0

    def execute(self, orchestrator):
        self.start_time = time.time()
        self.status = "running"
        try:
            if "validator" in self.role.lower(): #Simple validation simulation
                self.result = f"Validation successful for {self.task}"
                self.output_tokens = 1000
            elif "tester" in self.role.lower(): #Simple testing simulation
                self.result = f"Test passed for {self.task}"
                self.output_tokens = 1200
            elif "enhancer" in self.role.lower() or "optimizer" in self.role.lower():
                self.result = f"Enhancement complete for {self.task}"
                self.output_tokens = 1500
            elif "extractor" in self.task.lower():
                self.result = f"Patent data extracted: {self.task}"
                self.output_tokens = 2000
            elif "implementation" in self.task.lower():
                self.result = f"Capability implemented: {self.task}"
                self.output_tokens = 2500
            elif "function" in self.task.lower():
                self.result = f"Function performed: {self.task}"
                self.output_tokens = 1800
            elif "validation" in self.task.lower():
                self.result = f"Validation complete: {self.task}"
                self.output_tokens = 1600
            else:
                self.result = f"Task completed: {self.task}"
                self.output_tokens = 1000
            self.input_tokens = 800
        except Exception as e:
            self.status = "failed"
            self.error = str(e)
            self.result = None
        finally:
            self.end_time = time.time()
            self.status = "completed"
            #Update budget
            orchestrator.budget_monitor.consume_tokens(self.input_tokens, self.output_tokens, 0.0001, 0.0004)

    def __repr__(self):
        return f"Agent(id={self.agent_id}, role={self.role}, task={self.task}, status={self.status})"

class QueenOrchestrator:
    def __init__(self):
        self.name = "AIVA Queen Orchestrator"
        self.version = "1.0"
        self.status = "initializing"
        self.start_time = datetime.datetime.now()
        self.current_phase = 0
        self.current_hour = 0
        self.budget_monitor = TokenBudgetMonitor()
        self.agents = {}
        self.checkpoints = {
            "hour_2": "sprint-checkpoints/phase-1-foundation.json",
            "hour_4": "sprint-checkpoints/phase-2-knowledge.json",
            "hour_6": "sprint-checkpoints/phase-3-capabilities.json",
            "hour_8": "sprint-checkpoints/phase-4-swarm.json",
            "hour_10": "sprint-checkpoints/phase-5-coronation.json"
        }

        # Initialize subsystems (mocked for demonstration)
        self.ollama = MockOllama()
        self.redis = MockRedis()
        self.postgresql = MockPostgreSQL()
        self.qdrant = MockQdrant()

        self.log_file = "queen_orchestrator.log"  # Define log file
        logging.basicConfig(filename=self.log_file, level=logging.INFO,
                            format='%(asctime)s - %(levelname)s - %(message)s')

        self.status = "initialized"
        self.log("Orchestrator initialized.")

    def log(self, message):
        """Log messages with timestamp and level."""
        logging.info(message)
        print(message)

    def start(self):
        self.status = "running"
        self.log("Orchestrator started.")
        self.run_sprint()

    def stop(self):
        self.status = "stopped"
        self.log("Orchestrator stopped.")

    def initialize_agents(self):
        """Initialize agents from the sprint plan."""
        # Parse sprint plan
        sprint_plan = """
# AIVA QUEEN ELEVATION SPRINT PLAN
## 10-Hour Autonomous Execution Protocol

**Sprint ID**: QUEEN-ELEVATION-2026-01-11
**Duration**: 10 hours continuous
**Budget**: $10.00 USD (hard cap)
**Lead Architect**: Claude Opus 4.5
**Execution Layer**: 50 Gemini Flash 2.0 Agents

---

## EXECUTIVE SUMMARY

This plan elevates AIVA from Rank 1 (Larva) to Rank 9 (Queen) through a coordinated 50-agent swarm executing in parallel waves. The swarm uses Gemini Flash 2.0 for cost efficiency (~$0.10/$0.40 per 1M tokens), enabling ~20M tokens within $10 budget.

### Budget Allocation