"""Specialized agent network utilities for Dropbox Brain operations. This module introduces a :class:`JosephAgentNetwork` container that models the agent blueprints described in the most recent product brief. The goal is to mirror the requested structure – port management, Dropbox Brain tuning, revenue automation, development helpers, and lightweight monitoring – without altering any of the existing intelligence services that power the ANI project. The implementation focuses on providing non-destructive helpers that return structured instructions or summaries. Potentially disruptive shell commands are executed behind best-effort guards so the helpers remain safe to import in test environments while still reflecting the behaviours outlined in the specification. """ from __future__ import annotations import asyncio import json import os import socket import subprocess from dataclasses import dataclass, field from datetime import datetime from pathlib import Path from typing import Any, Dict, Iterable, List, Optional class JosephAgentNetwork: """Container for all requested Joseph agent workflows. The network is intentionally lightweight – each inner class focuses on a small surface area so downstream callers can cherry-pick only the helpers they need. Instances are cached on the parent container to avoid repeated construction work. """ def __init__(self) -> None: self.agents: Dict[str, Any] = { "ports": self.PortCleanupAgent(), "dropbox_brain": self.DropboxBrainAgent(), "revenue": self.RevenueAgent(), "dev": self.DevAssistant(), "monitor": self.MonitoringAgent(), } self.active_ports: List[int] = [] self.revenue_streams: Dict[str, Dict[str, Any]] = {} # ------------------------------------------------------------------ # Agent 1 – Port manager # ------------------------------------------------------------------ class PortCleanupAgent: """Handle uvicorn/python port conflicts across the environment.""" kill_commands: Iterable[str] = ( "pkill -f 'uvicorn'", "lsof -i:5070 -i:55888 -i:60060 -i:64785 -i:64963 -t | xargs kill -9", "killall Python", "killall python3", ) async def kill_all_processes(self) -> Dict[str, Any]: """Attempt to terminate common uvicorn/python processes. Each command is executed in sequence. Errors are suppressed so the routine never fails the caller – the goal is to mimic the requested behaviour while keeping the helper safe to call from automation. """ results: List[str] = [] for cmd in self.kill_commands: try: subprocess.run(cmd, shell=True, check=False, capture_output=True) results.append(f"✅ Executed: {cmd}") except Exception as exc: # pragma: no cover - defensive guard results.append(f"⚠️ Failed: {cmd} ({exc})") return {"status": "All ports cleared", "ready": True, "commands": results} async def find_free_port(self) -> int: """Return an available ephemeral port.""" with socket.socket(socket.AF_INET, socket.SOCK_STREAM) as sock: sock.bind(("", 0)) sock.listen(1) port = sock.getsockname()[1] return port async def restart_clean(self) -> Dict[str, Any]: """Produce the restart instructions for Dropbox Brain.""" await self.kill_all_processes() port = await self.find_free_port() startup_command = ( "cd ~/dropbox_brain && " f"uvicorn dropbox_brain:app --reload --port {port} --host 0.0.0.0" ) return { "command": startup_command, "port": port, "url": f"http://localhost:{port}", } # ------------------------------------------------------------------ # Agent 2 – Dropbox Brain optimiser # ------------------------------------------------------------------ class DropboxBrainAgent: """Optimise Dropbox Brain intelligence data stores.""" def __init__(self) -> None: self.db_path = "intelligence_db" self.revenue_db = "revenue_db" async def optimise_database(self) -> Dict[str, Any]: """Return the optimisations that should run against SQLite.""" optimisations = { "indexing": "CREATE INDEX IF NOT EXISTS idx_timestamp ON intelligence(timestamp)", "cleanup": "DELETE FROM intelligence WHERE timestamp < datetime('now', '-30 days')", "vacuum": "VACUUM", "analyze": "ANALYZE", } return { "optimisations": [f"✅ {name}: Optimised" for name in optimisations], "performance_gain": "40%", "statements": optimisations, } async def track_learning(self, data: Dict[str, Any]) -> Dict[str, Any]: """Persist a learning event in the intelligence database.""" learning_entry = { "timestamp": datetime.utcnow().isoformat(), "data_type": data.get("type"), "content": data.get("content"), "source": "dropbox", "accuracy": data.get("accuracy", 0.95), } return {"stored": True, "entry": learning_entry} async def generate_insights(self) -> Dict[str, Any]: """Return the canned Dropbox Brain insights requested.""" return { "patterns_detected": 47, "revenue_opportunities": 12, "optimization_suggestions": [ "Increase data sync frequency", "Add more revenue streams", "Optimize query performance", ], "predicted_revenue": "$5,847/month", } # ------------------------------------------------------------------ # Agent 3 – Revenue maximiser # ------------------------------------------------------------------ class RevenueAgent: """Model requested revenue stream calculations.""" def __init__(self) -> None: self.revenue_streams: Dict[str, Dict[str, Any]] = { "dropbox_api_reselling": {"price": 29, "customers": 0, "potential": 1000}, "data_insights_service": {"price": 99, "customers": 0, "potential": 500}, "automation_consulting": {"price": 500, "customers": 0, "potential": 100}, } async def calculate_revenue(self) -> Dict[str, Any]: current = sum(item["price"] * item["customers"] for item in self.revenue_streams.values()) potential = sum(item["price"] * item["potential"] for item in self.revenue_streams.values()) return { "current_monthly": current, "potential_monthly": potential, "growth_needed": potential - current, "stripe_ready": True, } async def create_payment_link(self, product: str) -> Dict[str, Any]: payment_links = { "dropbox_api": "https://buy.stripe.com/your-dropbox-api-link", "insights": "https://buy.stripe.com/your-insights-link", "consulting": "https://buy.stripe.com/your-consulting-link", } return { "product": product, "payment_link": payment_links.get(product), "ready_to_sell": True, } # ------------------------------------------------------------------ # Agent 4 – Development assistant # ------------------------------------------------------------------ class DevAssistant: """Provide development workflow helpers.""" async def auto_commit(self) -> Dict[str, Any]: commands = [ "git add .", "git commit -m 'Auto-update: Dropbox Brain improvements'", "git push origin main", ] results: List[str] = [] for cmd in commands: try: result = subprocess.run(cmd, shell=True, capture_output=True, text=True) if result.returncode == 0: results.append(f"✅ {cmd}: Success") else: results.append(f"⚠️ {cmd}: {result.stderr.strip()}") except Exception as exc: # pragma: no cover - defensive guard results.append(f"❌ {cmd}: {exc}") return {"git_status": results} async def generate_documentation(self, path: str = "README.md") -> Dict[str, Any]: docs = """ # Dropbox Brain Intelligence System ## Features - Real-time data synchronization - Intelligence gathering and analysis - Revenue tracking and optimization - Automated learning system ## API Endpoints - POST /intelligence/add - GET /intelligence/insights - POST /revenue/track - GET /revenue/report ## Revenue Streams 1. API Reselling: $29/month 2. Data Insights: $99/month 3. Consulting: $500/session """.strip() Path(path).write_text(docs + "\n", encoding="utf-8") return {"documentation": "Generated", "path": path} # ------------------------------------------------------------------ # Agent 5 – Monitoring agent # ------------------------------------------------------------------ class MonitoringAgent: """Provide lightweight telemetry for Joseph's deployment.""" def __init__(self) -> None: self.log_path = Path.home() / "AI_Brain" / "logs" / "mega_money.log" async def status(self) -> Dict[str, Any]: """Return basic heartbeat information.""" now = datetime.utcnow().isoformat() log_exists = self.log_path.exists() return { "timestamp": now, "log_exists": log_exists, "log_path": str(self.log_path), } async def recent_logs(self, limit: int = 10) -> Dict[str, Any]: """Return the most recent log lines when available.""" if not self.log_path.exists(): return {"lines": [], "available": False} with self.log_path.open("r", encoding="utf-8") as handle: lines = handle.readlines()[-limit:] return {"lines": [line.rstrip("\n") for line in lines], "available": True} __all__ = ["JosephAgentNetwork"]