feat(2.0): Phase 2 — System/OS + Connect
System: services/system.py (psutil CPU/RAM/Disk, sysfs GPU/Temp guarded), routers/system.py GET status + restart (Whitelist) + self-update (systemd- USER, sudo-frei). Connect: services/connect.py (Cline/OpenCode/Zed/Continue/ Claude Code/Memory-MCP → Gateway model:auto, LAN-IP-Override), routers/ connect.py. Frontend: SystemView (Metrik-Bars) + ConnectView (Tool-Tabs, Copy, IP-Override). Lokal verifiziert: Backend-Smoke + Frontend-Build + Browser (System-Bars, Connect-Snippets). Docs aktualisiert (README/STATUS/Plan). Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
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Hitonabi
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"""
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Connect: erzeugt saubere, getestete Konfig-Snippets für IDEs/Agenten auf dem
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LOKALEN PC (separate Maschine im LAN). Alle zeigen auf den **Gateway** der Box
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(`model: auto`, LiteLLM :4000) + den **Shared-Memory-MCP** (MC :9000).
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Wichtig: Host ist die LAN-IP der Box (NICHT eine Proxy-Domain) — das war in v1
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die häufigste Fehlerquelle. Der Aufrufer übergibt den Host explizit.
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"""
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import json
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DEFAULT_HOST = "192.168.178.151"
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GATEWAY_PORT = 4000
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MC_PORT = 9000
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# Modelle, die der Gateway anbietet (model:auto = Standard).
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GATEWAY_MODELS = ["auto", "fast", "heavy", "coder", "vision"]
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def _gw(host: str) -> str:
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return f"http://{host}:{GATEWAY_PORT}/v1"
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def build_snippets(host: str = DEFAULT_HOST,
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mcp_script_path: str = r"C:\\Users\\TobisPC\\mission-control-v2\\mcp\\mcp_memory.py",
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mcp_python: str = "python") -> dict:
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gw = _gw(host)
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mc_url = f"http://{host}:{MC_PORT}"
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cline = json.dumps({
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"apiProvider": "openai",
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"openAiBaseUrl": gw,
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"openAiApiKey": "local",
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"openAiModelId": "auto",
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}, indent=2)
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opencode = json.dumps({
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"providers": {
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"litellm": {
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"npm": "@ai-sdk/openai-compatible",
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"name": "Bosgame Gateway",
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"options": {"baseURL": gw, "apiKey": "local"},
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"models": {m: {"name": m} for m in GATEWAY_MODELS},
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}
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}
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}, indent=2)
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zed = json.dumps({
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"language_models": {
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"openai_compatible": {
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"bosgame": {
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"api_url": gw,
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"available_models": [
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{"name": m, "display_name": m, "max_tokens": 131072,
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"capabilities": {"tools": True}} for m in GATEWAY_MODELS
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],
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}
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}
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}
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}, indent=2)
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cont = json.dumps({
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"models": [
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{"title": f"Bosgame / {m}", "provider": "openai", "model": m,
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"apiBase": gw, "apiKey": "local"} for m in ("auto", "coder", "heavy")
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]
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}, indent=2)
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# Claude Code: Anthropic-Format → LiteLLM kann /v1/messages anbieten.
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claude_code = (
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f'# Claude Code gegen den lokalen Gateway (Anthropic-kompatibel via LiteLLM):\n'
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f'export ANTHROPIC_BASE_URL="http://{host}:{GATEWAY_PORT}"\n'
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f'export ANTHROPIC_API_KEY="local"\n'
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f'export ANTHROPIC_MODEL="auto"\n'
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f'# (LiteLLM muss den Anthropic-/v1/messages-Endpunkt aktiviert haben — auf der Box prüfen.)'
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)
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memory_mcp = json.dumps({
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"mcpServers": {
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"mission-control-memory": {
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"command": mcp_python,
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"args": [mcp_script_path],
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"env": {"MC_URL": mc_url},
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}
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}
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}, indent=2)
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return {
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"host": host,
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"gateway_url": gw,
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"tools": {
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"cline": {"label": "Cline (VS Code)", "lang": "json", "snippet": cline,
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"note": "OpenAI-Provider → Gateway. Modell 'auto' (schnell, eskaliert bei Bedarf)."},
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"opencode": {"label": "OpenCode", "lang": "jsonc", "snippet": opencode,
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"note": "Datei opencode.jsonc, Key 'providers'."},
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"zed": {"label": "Zed", "lang": "json", "snippet": zed,
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"note": "settings.json → language_models.openai_compatible."},
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"continue": {"label": "Continue", "lang": "json", "snippet": cont,
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"note": "~/.continue/config.json."},
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"claude_code": {"label": "Claude Code", "lang": "bash", "snippet": claude_code,
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"note": "Anthropic-Format über LiteLLM /v1/messages."},
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"memory_mcp": {"label": "Shared Memory (MCP)", "lang": "json", "snippet": memory_mcp,
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"note": "Für jedes MCP-fähige Tool. mcp_memory.py muss lokal liegen."},
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},
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}
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"""
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System/OS-Metriken für die Box (Bosgame / Strix Halo).
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CPU/RAM/Disk via psutil (plattformübergreifend). GPU-Auslastung/VRAM/Temperatur
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via sysfs (amdgpu) — nur Linux; auf anderen Plattformen None (amd-smi fehlt auf
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der Box, daher sysfs). Verschachtelte Struktur wie v1 (cpu.percent, ram.used Bytes).
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"""
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import glob
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import os
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import psutil
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from config import MODELS_DIR
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def _read_int(path: str) -> int | None:
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try:
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with open(path) as f:
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return int(f.read().strip())
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except Exception:
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return None
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def _gpu_sysfs() -> dict | None:
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"""AMD-GPU-Auslastung + VRAM via sysfs (Linux). None, wenn nicht vorhanden."""
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base = "/sys/class/drm/card0/device"
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if not os.path.isdir(base):
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return None
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busy = _read_int(f"{base}/gpu_busy_percent")
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vram_used = _read_int(f"{base}/mem_info_vram_used")
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vram_total = _read_int(f"{base}/mem_info_vram_total")
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if busy is None and vram_used is None:
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return None
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return {"busy_percent": busy, "vram_used": vram_used, "vram_total": vram_total}
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def _temps() -> dict | None:
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"""CPU/GPU-Temperatur via hwmon (Linux). None bei Fehlen."""
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out: dict = {}
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for hw in glob.glob("/sys/class/hwmon/hwmon*"):
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name = ""
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try:
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with open(f"{hw}/name") as f:
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name = f.read().strip()
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except Exception:
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continue
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t = _read_int(f"{hw}/temp1_input")
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if t is None:
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continue
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c = round(t / 1000.0, 1)
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if name in ("k10temp", "zenpower", "coretemp"):
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out["cpu"] = c
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elif name in ("amdgpu", "edge"):
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out["gpu"] = c
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return out or None
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def system_status() -> dict:
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vm = psutil.virtual_memory()
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try:
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du = psutil.disk_usage(str(MODELS_DIR) if MODELS_DIR.exists() else os.getcwd())
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disk = {"total": du.total, "used": du.used, "percent": du.percent}
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except Exception:
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disk = None
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return {
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"cpu": {"percent": psutil.cpu_percent(interval=0.1), "cores": psutil.cpu_count()},
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"ram": {"total": vm.total, "used": vm.used, "percent": vm.percent},
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"gpu": _gpu_sysfs(),
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"temp": _temps(),
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"disk": disk,
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}
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