╔══════════════════════════════════════════════╗

║ J.A.R.V.I.S — Just A Rather Very Intelligent System ║

║ Voice-Activated Personal AI Assistant ║

╚══════════════════════════════════════════════╝

J.A.R.V.I.S is a futuristic, voice-controlled personal AI assistant for Windows, powered by the Google Gemini API. It features a cinematic, web-based 3D HUD interface, offline console keyboard fallback, automated room/microphone calibration, and native system integration to control apps, folders, volume, brightness, screenshots, and power states.

---

🚀 Key Features

• 🎤 Voice & Clap Activation: Wake-word-free voice triggering (always listening for commands), double-clap detection to activate, or classic voice activation ("Hey Jarvis").
• 🧠 Gemini AI Brain: Dynamic conversation powered by Google Gemini (gemini-2.5-flash with automatic fallback models). Remembers recent context for seamless follow-up interactions.
• 🌐 Cinematic 3D HUD: Web-based interface built with React, Vite, Framer Motion, and Three.js / React Three Fiber on http://localhost:5050. Displays real-time Jarvis states (idle, listening, thinking, speaking), command logging, and hardware usage metrics.
• 💻 Windows System Operations:
  • App Launcher & Terminate: Start or close processes (Chrome, VS Code, Notepad, Calculator, Task Manager, etc.).
  • Folder Navigator: Quick access to default folders (Desktop, Downloads, Documents, Projects) or custom file paths.
  • Volume & Brightness Controls: Precise master volume adjustment (via pycaw) and screen brightness tuning (via screen-brightness-control).
  • Screenshots: Captures your screen and saves it straight to the Desktop.
  • Live System Diagnostics: Audio reports of current CPU, RAM, GPU utilization, and Battery status.
  • PC Power Management: Locked screen, sleep mode, and confirmation-guarded system shutdowns or restarts (which can be cancelled mid-countdown).
• 🖐️ AI Hand Gesture Control System: webcam-based input module using OpenCV and MediaPipe for virtual mouse control, scrolling, muting, and Spotify controls.
• 🎵 Spotify Controller: Launch Spotify, search for songs/artists, or trigger themed playlists (chill, focus, workout, sleep).
• ⌨️ Console Fallback: No mic? No problem. Simply press ENTER in the terminal and type commands directly.

---

🖥️ Screenshots

Below is an overview of the cinematic HUD dashboard:

+-------------------------------------------------------------+
| J.A.R.V.I.S               TIME: 19:45  DATE: FRI, 12 JUN    |
+-------------------------+-----------------------------------+
|  [Vitals Gauge]         |                                   |
|   CPU: 23%  [=======]   |            [Reactor 3D Core]      |
|   RAM: 56%  [====   ]   |                                   |
|   GPU: 41%  [=====  ]   |                 / \               |
|                         |                | * |              |
|  [Theme Selector]       |                 \ /               |
|   Cyan Stark Stealth    |                                   |
|                         |                                   |
|  [Console Output Logs]  |  [Terminal Commands Input]        |
|   CMD: open notepad     |   > Ask me anything...            |
|   RSP: Opening Notepad. |                                   |
+-------------------------+-----------------------------------+
| J.A.R.V.I.S v3.5 — POWERED BY REACT & THREE.JS              |
+-------------------------------------------------------------+

(Place your screenshots and UI demonstration recordings here)

---

🛠️ System Architecture

The project is structured around a decoupled local communication model. The React client polls the Python API server for state updates and triggers OS commands dynamically.

graph TD
    subgraph Frontend ["Frontend (React / WebGL)"]
        HUD["React UI / HTML HUD"] --> Context["HudContext - State Store"]
        Context --> APIClient["api.js - Service Client"]
    end

    subgraph Backend ["Backend (Python Package)"]
        Wrapper["jarvis.py - Startup Wrapper"] --> Main["backend/main.py - Orchestrator"]
        Main --> AudioEngine["backend/voice/audio_engine.py - VAD/TTS"]
        Main --> UIServer["backend/api/ui_server.py - HTTPServer"]
        
        AudioEngine --> Commands["backend/assistant/commands.py - Handler"]
        UIServer --> Commands
        
        Commands --> SystemOps["backend/system/system_ops.py - OS Control"]
        Commands --> Brain["backend/assistant/brain.py - Gemini Client"]
        Main --> Gesture["backend/system/gesture_engine.py - MediaPipe"]
    end

    %% Communication Connections
    APIClient -- "Polling /state & /stats" --> UIServer
    APIClient -- "Submit /command?text=" --> UIServer
    Brain --> Gemini["Google Gemini API"]
    SystemOps --> OS["Windows Desktop / Shell"]
    Gesture --> OS

Loading

For a detailed breakdown of modules, threading logic, and network flows, see architecture.md.

---

📁 Project Folder Structure

The project code is organized as follows:

jarvis-ai/
├── jarvis.py                  # Root entry point wrapper (runs backend/main.py)
├── jarvis_hud.html            # Standalone static HUD fallback
├── jarvis_ui.html             # Legacy HUD fallback
├── .env                       # API Configuration variables
│
├── backend/                   # Python Backend Package
│   ├── __init__.py            # Main backend export definition
│   ├── main.py                # Main orchestrator loop
│   ├── core/                  # Core constants & configuration
│   │   ├── __init__.py
│   │   └── config.py          # App paths, playlist URLs, parameters
│   ├── assistant/             # AI Brain & Command Routers
│   │   ├── __init__.py
│   │   ├── brain.py           # Gemini API interface and history
│   │   └── commands.py        # System trigger matching
│   ├── voice/                 # Sound and speech components
│   │   ├── __init__.py
│   │   └── audio_engine.py    # VAD listener and TTS voice engines
│   ├── system/                # OS APIs & CV triggers
│   │   ├── __init__.py
│   │   ├── system_ops.py      # Volume, brightness, battery metrics
│   │   └── gesture_engine.py  # OpenCV & MediaPipe camera tracking
│   ├── api/                   # HTTP Local Web Bridge
│   │   ├── __init__.py
│   │   └── ui_server.py       # API endpoints and static assets hosting
│   └── utils/                 # Utility scripts
│       ├── __init__.py
│       └── dotenv.py          # Environment key loader helper
│
├── frontend/                  # React Frontend Project
│   ├── index.html             # HTML shell
│   ├── vite.config.js         # Bundler settings
│   ├── package.json           # npm manifest
│   └── src/
│       ├── main.jsx           # Mount entry point
│       ├── App.jsx            # Application provider shell
│       ├── pages/             # Layout views (Dashboard.jsx)
│       ├── components/        # Presentational blocks (Reactor3D, GlowCard)
│       ├── widgets/           # Dashboard control cards (Console, Vitals)
│       ├── services/          # Fetch API clients (api.js)
│       ├── hooks/             # Shared hooks (useHud.js)
│       ├── store/             # Global state stores (HudContext.jsx)
│       ├── animations/        # framer-motion parameter configs
│       ├── shaders/           # WebGL GLSL shader configurations
│       └── styles/            # CSS theme variables (index.css)
│
└── docs/                      # Technical Documentation
    ├── architecture.md        # Deep architectural specifications
    ├── roadmap.md             # Development goals and features
    └── changelog.md           # Version histories and changes

---

📋 Prerequisites & Installation Guide

1. Python Environment Setup

Install the necessary system dependencies. For full functionality, the following packages are required:

pip install numpy PyAudio SpeechRecognition pyttsx3 requests psutil GPUtil pyautogui screen-brightness-control pycaw comtypes opencv-python mediapipe

Note

If you experience errors installing PyAudio on Windows, download the appropriate precompiled wheel for your Python version from official sources or run:

pip install pipwin
pipwin install pyaudio

2. Configure Gemini API Key

Jarvis needs a Gemini API key to power its conversational responses.

1. Get a free API Key from Google AI Studio.
2. Create a file named .env in the root folder of this project:

   GEMINI_API_KEY=your_actual_api_key_here

3. Build the Frontend HUD (Optional but Recommended)

The project comes with a gorgeous, high-fidelity Three.js HUD dashboard in the frontend folder. To compile it:

1. Make sure you have Node.js installed.
2. Open a terminal in the frontend folder and run:

   npm install
   npm run build

If not compiled, the server will automatically serve a fallback static HUD page (jarvis_hud.html) so the system remains operational.

---

🚀 Running Jarvis

1. Open a terminal in the root directory of the project and execute:

   python jarvis.py

2. The UI server will boot on http://localhost:5050 and automatically launch the HUD in your default web browser.
3. Jarvis will calibrate your room's ambient noise levels for 1.5 seconds. Once finished, you will hear a confirmation message: "Jarvis online. Good to see you, Boss."

Ways to Interact

• Speak Directly: If ALWAYS_LISTEN is active, say commands like "open YouTube" or "what is the time".
• Double-Clap: Clap twice within 1.2 seconds to manually wake Jarvis up.
• Press Enter: Press ENTER in the command window to pause recording and type commands directly.

---

⚙️ Customization (backend/core/config.py)

You can edit backend/core/config.py to adapt Jarvis to your personal preference:

• YOUR_NAME: Changes how Jarvis addresses you (default: "Boss").
• YOUR_CITY: City location used for weather queries (default: "Hyderabad").
• APPS: Dictionary mapping spoken app names to executable commands.
• FOLDERS: Paths pointing to your localized system directories.
• SPOTIFY_PLAYLISTS: Connect your favorite Spotify playlist links for direct access.

---

🗺️ Project Roadmap

Our short-term and medium-term plans include:

• Integrating dynamic React subrouting to support detail panels.
• Incorporating local offline LLMs (via Ollama) as fallbacks when disconnected.
• Transitioning to offline wake-word engines (like Porcupine) to reduce background CPU cycles.
• Enhancing integration with Spotify APIs for in-client track queuing.

Check out the full roadmap.md and changelog.md for more details.