Antigravity × Electron デスクトップAIアプリケーション開発

// main/ipc/llm.ts
ipcMain.handle('llm:generate', async (event, prompt: string) => {
  try {
    const response = await llmService.generate(prompt)
    return { success: true, data: response }
  } catch (error) {
    return { success: false, error: error.message }
  }
})
 
// renderer/hooks/useLLM.ts
async function generateText(prompt: string) {
  const result = await ipcRenderer.invoke('llm:generate', prompt)
  if (result.success) {
    setResponse(result.data)
  } else {
    setError(result.error)
  }
}

// main/ipc/llm.ts - ストリーミング推論
ipcMain.on('llm:stream', async (event, prompt: string) => {
  const stream = llmService.generateStream(prompt)
 
  for await (const chunk of stream) {
    event.sender.send('llm:stream:chunk', chunk)
  }
  event.sender.send('llm:stream:done')
})
 
// renderer/components/ChatBox.tsx
useEffect(() => {
  ipcRenderer.on('llm:stream:chunk', (chunk) => {
    setMessages(prev => [...prev, chunk])
  })
 
  return () => ipcRenderer.removeAllListeners('llm:stream:chunk')
}, [])

// main/services/inference.ts
const INFERENCE_TIMEOUT = 5 * 60 * 1000 // 5分
 
async function invokeWithTimeout<T>(
  name: string,
  args: unknown[],
  timeout: number = INFERENCE_TIMEOUT
): Promise<T> {
  return Promise.race([
    ipcRenderer.invoke(name, ...args),
    new Promise((_, reject) =>
      setTimeout(
        () => reject(new Error(`${name} timed out after ${timeout}ms`)),
        timeout
      )
    ),
  ])
}
 
// renderer での使用
try {
  const result = await invokeWithTimeout('llm:generate', [prompt], 30000)
} catch (error) {
  if (error.message.includes('timed out')) {
    showNotification('推論がタイムアウトしました。プロンプトを短くしてください。')
  }
}

// main/services/model-manager.ts
class ModelManager extends EventEmitter {
  private models: Map<string, AIModel> = new Map()
 
  async loadModel(modelId: string) {
    this.emit('model:loading', modelId)
 
    try {
      const model = await downloadAndCacheModel(modelId)
      this.models.set(modelId, model)
      this.emit('model:loaded', modelId)
    } catch (error) {
      this.emit('model:error', { modelId, error: error.message })
    }
  }
}
 
// main/ipc/model.ts
modelManager.on('model:loading', (modelId) => {
  mainWindow.webContents.send('model:status', { modelId, status: 'loading' })
})
 
// renderer/hooks/useModelStatus.ts
export function useModelStatus() {
  const [models, setModels] = useState({})
 
  useEffect(() => {
    ipcRenderer.on('model:status', (status) => {
      setModels(prev => ({
        ...prev,
        [status.modelId]: status.status
      }))
    })
  }, [])
 
  return models
}

// main/services/model-manager.ts
const MODEL_CACHE_DIR = path.join(app.getPath('userData'), 'models')
 
async function ensureModelExists(modelId: string): Promise<string> {
  const modelPath = path.join(MODEL_CACHE_DIR, modelId, 'model.gguf')
 
  // キャッシュに存在する場合、スキップ
  if (fs.existsSync(modelPath)) {
    return modelPath
  }
 
  // ダウンロード開始
  mainWindow.webContents.send('model:download:start', { modelId })
 
  try {
    await downloadModelWithProgress(modelId, modelPath, (progress) => {
      mainWindow.webContents.send('model:download:progress', {
        modelId,
        progress, // 0-100
      })
    })
 
    mainWindow.webContents.send('model:download:complete', { modelId })
    return modelPath
  } catch (error) {
    mainWindow.webContents.send('model:download:error', {
      modelId,
      error: error.message,
    })
    throw error
  }
}

// main/services/inference.ts
import { spawn } from 'child_process'
import { EventEmitter } from 'events'
 
class LLMInference extends EventEmitter {
  private pythonProcess: ChildProcess | null = null
 
  async initialize(modelPath: string) {
    this.pythonProcess = spawn('python', ['python/inference.py', modelPath], {
      stdio: ['pipe', 'pipe', 'pipe'],
      env: {
        ...process.env,
        CUDA_VISIBLE_DEVICES: '0', // GPU インデックス
        PYTHONUNBUFFERED: '1',
      },
    })
 
    this.pythonProcess.stdout!.on('data', (data) => {
      const message = JSON.parse(data.toString())
      if (message.type === 'ready') {
        this.emit('ready')
      }
    })
  }
 
  async generate(prompt: string, options: GenerateOptions) {
    return new Promise((resolve, reject) => {
      const requestId = crypto.randomUUID()
 
      this.pythonProcess!.stdin!.write(
        JSON.stringify({ requestId, prompt, options }) + '\n'
      )
 
      const handleMessage = (data) => {
        const message = JSON.parse(data.toString())
        if (message.requestId === requestId) {
          if (message.type === 'complete') {
            this.pythonProcess!.stdout!.removeListener('data', handleMessage)
            resolve(message.output)
          } else if (message.type === 'error') {
            reject(new Error(message.error))
          }
          // 'chunk' イベントは UI に ストリーミング送信
          this.emit('chunk', message)
        }
      }
 
      this.pythonProcess!.stdout!.on('data', handleMessage)
    })
  }
}

import json
import sys
from llama_cpp import Llama
 
model_path = sys.argv[1]
llm = Llama(model_path, n_gpu_layers=-1, verbose=False)
 
# 起動完了通知
sys.stdout.write(json.dumps({"type": "ready"}) + "\n")
sys.stdout.flush()
 
# リクエスト処理ループ
for line in sys.stdin:
    request = json.loads(line)
    request_id = request['requestId']
    prompt = request['prompt']
    options = request.get('options', {})
 
    try:
        output = ""
        # ストリーミング生成
        for chunk in llm(
            prompt,
            max_tokens=options.get('max_tokens', 256),
            temperature=options.get('temperature', 0.7),
            stream=True,
        ):
            token = chunk['choices'][0]['text']
            output += token
 
            # チャンク送信
            sys.stdout.write(json.stringify({
                'requestId': request_id,
                'type': 'chunk',
                'token': token,
            }) + "\n")
            sys.stdout.flush()
 
        # 完了通知
        sys.stdout.write(json.stringify({
            'requestId': request_id,
            'type': 'complete',
            'output': output,
        }) + "\n")
        sys.stdout.flush()
 
    except Exception as e:
        sys.stdout.write(json.stringify({
            'requestId': request_id,
            'type': 'error',
            'error': str(e),
        }) + "\n")
        sys.stdout.flush()

// native/src/gpu_buffer.cc
#include <napi.h>
#include <cuda_runtime.h>
 
Napi::Value AllocateGPUBuffer(const Napi::CallbackInfo& info) {
  Napi::Env env = info.Env();
 
  if (info.Length() < 1) {
    Napi::TypeError::New(env, "Expected 1 argument")
      .ThrowAsJavaScriptException();
    return env.Null();
  }
 
  size_t size = info[0].As<Napi::Number>().Uint32Value();
 
  float* device_ptr;
  cudaMalloc(&device_ptr, size * sizeof(float));
 
  // ポインタをバッファハンドルとして返す
  return Napi::Buffer<float>::New(env, device_ptr, size);
}
 
Napi::Object Init(Napi::Env env, Napi::Object exports) {
  exports.Set(
    Napi::String::New(env, "allocateGPUBuffer"),
    Napi::Function::New(env, AllocateGPUBuffer)
  );
  return exports;
}
 
NODE_API_MODULE(gpu_buffer, Init)

{
  "targets": [
    {
      "target_name": "gpu_buffer",
      "sources": ["src/gpu_buffer.cc"],
      "include_dirs": ["<!@(node -p \"require('node-addon-api').include\")"],
      "dependencies": ["<!(node -p \"require('node-addon-api').gyp\")"],
      "cflags_cc": ["-fexceptions"],
      "xcode_settings": {
        "GCC_ENABLE_CPP_EXCEPTIONS": "YES"
      }
    }
  ]
}

const gpu = require('./build/Release/gpu_buffer.node')
 
const buffer = gpu.allocateGPUBuffer(1024 * 1024 * 4) // 4MB GPU メモリ

import Library from 'ffi-napi'
import ref from 'ref-napi'
 
const libCuda = new Library('libcuda.so.1', {
  cuInit: ['int', ['int']],
  cuDeviceGetCount: ['int', [ref.refType(ref.types.int)]],
})
 
const count = ref.alloc(ref.types.int)
libCuda.cuDeviceGetCount(count)
console.log(`Detected ${count.deref()} CUDA devices`)

// main.ts
const win = new BrowserWindow({
  webPreferences: {
    nodeIntegration: false,
    contextIsolation: true,
    enableRemoteModule: false,
    preload: path.join(__dirname, 'preload.ts'),
    sandbox: true,
  },
})

// main/preload.ts
import { contextBridge, ipcRenderer } from 'electron'
 
const allowedChannels = {
  // LLM チャネル
  'llm:generate': true,
  'llm:stream': true,
  'model:status': true,
 
  // ファイル操作
  'file:open': true,
  'file:save': true,
 
  // システム情報
  'system:info': true,
}
 
contextBridge.exposeInMainWorld('electronAPI', {
  ipcInvoke: (channel: string, args: any) => {
    if (allowedChannels[channel]) {
      return ipcRenderer.invoke(channel, args)
    }
    throw new Error(`IPC channel "${channel}" is not allowed`)
  },
 
  ipcOn: (channel: string, callback: (data: any) => void) => {
    if (allowedChannels[channel]) {
      ipcRenderer.on(channel, (_, data) => callback(data))
    }
  },
})
 
// TypeScript 型定義
declare global {
  interface Window {
    electronAPI: {
      ipcInvoke: (channel: string, args: any) => Promise<any>
      ipcOn: (channel: string, callback: (data: any) => void) => void
    }
  }
}

// renderer/hooks/useIPC.ts
export function useIPC() {
  return {
    async invoke(channel: string, args: any) {
      return window.electronAPI.ipcInvoke(channel, args)
    },
 
    on(channel: string, callback: (data: any) => void) {
      window.electronAPI.ipcOn(channel, callback)
    },
  }
}

// main/ipc/file.ts
ipcMain.handle('file:open-dialog', async (event) => {
  const { filePaths } = await dialog.showOpenDialog(mainWindow, {
    properties: ['openFile'],
    filters: [
      { name: 'Text Files', extensions: ['txt', 'md'] },
      { name: 'All Files', extensions: ['*'] },
    ],
  })
 
  return filePaths
})
 
ipcMain.handle('file:read', async (event, filePath: string) => {
  // パス走査攻撃を防止
  const normalizedPath = path.normalize(path.resolve(filePath))
  const userDir = app.getPath('userData')
 
  if (!normalizedPath.startsWith(userDir)) {
    throw new Error('Access denied: file is outside user data directory')
  }
 
  return fs.promises.readFile(normalizedPath, 'utf-8')
})
 
ipcMain.handle('file:save', async (event, filePath: string, content: string) => {
  const normalizedPath = path.normalize(path.resolve(filePath))
  const userDir = app.getPath('userData')
 
  if (!normalizedPath.startsWith(userDir)) {
    throw new Error('Access denied: cannot write outside user data directory')
  }
 
  await fs.promises.writeFile(normalizedPath, content, 'utf-8')
})

// main.ts
const win = new BrowserWindow({
  webPreferences: {
    contentSecurityPolicy: "default-src 'self'; script-src 'self'",
  },
})

// main/services/auto-updater.ts
import { autoUpdater } from 'electron-updater'
 
export class AutoUpdater {
  constructor(private mainWindow: BrowserWindow) {
    this.setupAutoUpdater()
  }
 
  private setupAutoUpdater() {
    // GitHub Releases からの自動ダウンロード
    autoUpdater.checkForUpdatesAndNotify()
 
    autoUpdater.on('update-available', (info) => {
      console.log(`New version available: ${info.version}`)
 
      this.mainWindow.webContents.send('update:available', {
        version: info.version,
        releaseDate: info.releaseDate,
      })
    })
 
    autoUpdater.on('update-not-available', () => {
      console.log('App is up to date')
    })
 
    autoUpdater.on('download-progress', (progress) => {
      this.mainWindow.webContents.send('update:progress', {
        percent: progress.percent,
        bytesPerSecond: progress.bytesPerSecond,
      })
    })
 
    autoUpdater.on('update-downloaded', () => {
      this.mainWindow.webContents.send('update:downloaded')
    })
 
    autoUpdater.on('error', (error) => {
      console.error('Auto-updater error:', error)
      this.mainWindow.webContents.send('update:error', {
        error: error.message,
      })
    })
  }
 
  async checkForUpdates() {
    return autoUpdater.checkForUpdates()
  }
 
  async installUpdates() {
    autoUpdater.quitAndInstall()
  }
}

// main/ipc/updater.ts
import { autoUpdater } from 'electron-updater'
 
ipcMain.handle('updater:check', async () => {
  return autoUpdater.checkForUpdates()
})
 
ipcMain.handle('updater:install', async () => {
  autoUpdater.quitAndInstall()
})

// renderer/components/UpdatePrompt.tsx
import { useEffect, useState } from 'react'
 
export function UpdatePrompt() {
  const [updateInfo, setUpdateInfo] = useState(null)
  const [downloadProgress, setDownloadProgress] = useState(0)
 
  useEffect(() => {
    // アップデート利用可能通知
    window.electronAPI.ipcOn('update:available', (info) => {
      setUpdateInfo(info)
    })
 
    // ダウンロード進捗
    window.electronAPI.ipcOn('update:progress', (progress) => {
      setDownloadProgress(progress.percent)
    })
 
    // ダウンロード完了
    window.electronAPI.ipcOn('update:downloaded', () => {
      setDownloadProgress(100)
    })
  }, [])
 
  if (!updateInfo) return null
 
  return (
    <div className="update-banner">
      <h3>新しいバージョンが利用可能です ({updateInfo.version})</h3>
      <div className="progress-bar">
        <div style={{ width: `${downloadProgress}%` }} />
      </div>
      <p>{downloadProgress.toFixed(0)}% ダウンロード中...</p>
 
      {downloadProgress === 100 && (
        <button onClick={() => window.electronAPI.ipcInvoke('updater:install')}>
          再起動して更新
        </button>
      )}
    </div>
  )
}

{
  "build": {
    "publish": {
      "provider": "github",
      "owner": "your-org",
      "repo": "your-app"
    }
  }
}

# macOS (DMG + App Store)
npm run build -- --mac dmg mas
 
# Windows (NSIS + Portable)
npm run build -- --win nsis portable
 
# Linux (AppImage + deb)
npm run build -- --linux appimage deb

{
  "appId": "com.example.electron-ai-app",
  "productName": "AI Assistant",
  "directories": {
    "buildResources": "resources",
    "output": "dist"
  },
  "mac": {
    "target": ["dmg", "zip"],
    "hardenedRuntime": true,
    "entitlements": "resources/entitlements.mac.plist",
    "gatekeeper-assess": false
  },
  "win": {
    "target": [
      {
        "target": "nsis",
        "arch": ["x64", "arm64"]
      },
      {
        "target": "portable",
        "arch": ["x64"]
      }
    ],
    "signingHashAlgorithms": ["sha256"],
    "certificateFile": "resources/cert.pfx",
    "certificatePassword": "YOUR_PASSWORD"
  },
  "linux": {
    "target": ["AppImage", "deb"],
    "category": "Utility"
  },
  "nsis": {
    "oneClick": false,
    "allowToChangeInstallationDirectory": true,
    "createDesktopShortcut": true,
    "createStartMenuShortcut": true
  }
}

name: Build and Release
 
on:
  push:
    tags:
      - 'v*'
 
jobs:
  build:
    runs-on: ${{ matrix.os }}
    strategy:
      matrix:
        os: [ubuntu-latest, windows-latest, macos-latest]
 
    steps:
      - uses: actions/checkout@v3
 
      - name: Setup Node.js
        uses: actions/setup-node@v3
        with:
          node-version: '18'
 
      - name: Setup Python
        uses: actions/setup-python@v4
        with:
          python-version: '3.10'
 
      - name: Install dependencies
        run: |
          npm ci
          pip install -r python/requirements.txt
 
      - name: Build
        env:
          GH_TOKEN: ${{ secrets.GITHUB_TOKEN }}
          APPLE_ID: ${{ secrets.APPLE_ID }}
          APPLE_PASSWORD: ${{ secrets.APPLE_PASSWORD }}
        run: npm run build
 
      - name: Upload artifacts
        uses: softprops/action-gh-release@v1
        with:
          files: dist/*

// ❌ 悪い例：リスナーが登録されたままになる
function setupListener() {
  ipcRenderer.on('model:status', (status) => {
    updateUI(status)
  })
  // リスナー削除なし → メモリリーク
}
 
// ✅ 良い例：コンポーネント アンマウント時にリスナー削除
useEffect(() => {
  function handleModelStatus(status) {
    updateUI(status)
  }
 
  ipcRenderer.on('model:status', handleModelStatus)
 
  return () => {
    ipcRenderer.removeListener('model:status', handleModelStatus)
  }
}, [])

// renderer/hooks/useMemoryMonitor.ts
export function useMemoryMonitor(interval: number = 5000) {
  useEffect(() => {
    const timer = setInterval(() => {
      const usage = process.getProcessMemoryInfo()
      console.log(`Memory: ${(usage.heapUsed / 1024 / 1024).toFixed(2)} MB`)
 
      if (usage.heapUsed > 500 * 1024 * 1024) {
        console.warn('High memory usage detected!')
      }
    }, interval)
 
    return () => clearInterval(timer)
  }, [interval])
}

// main/services/inference-queue.ts
class InferenceQueue {
  private queue: QueueItem[] = []
  private processing = false
  private concurrency = 1
 
  async enqueue(prompt: string, options: GenerateOptions) {
    return new Promise((resolve, reject) => {
      this.queue.push({ prompt, options, resolve, reject })
      this.processQueue()
    })
  }
 
  private async processQueue() {
    if (this.processing || this.queue.length === 0) {
      return
    }
 
    this.processing = true
 
    while (this.queue.length > 0) {
      const item = this.queue.shift()!
 
      try {
        const result = await this.llmService.generate(
          item.prompt,
          item.options
        )
        item.resolve(result)
      } catch (error) {
        item.reject(error)
      }
    }
 
    this.processing = false
  }
}

// electron.vms.config.js
module.exports = {
  snapshotCompiler: true,
  snapshotImage: 'snapshot.bin',
 
  // preload スクリプトもスナップショットに含める
  v8Code: 'src/main/preload.ts',
}