ByteWatch 🖥️

ByteWatch is a premium, high-fidelity hardware telemetry dashboard. It monitors active resource utilization on your system, logs metrics in real-time, and displays them via a modern visual interface that requires zero heavy JavaScript libraries or trackers.

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✨ Features

• 🕒 Real-Time Polling: Drives live dashboard updates every 2 seconds using HTMX linear swaps with zero custom heavy JS listeners.
• 🌓 Dual-Theme Design: Sleek, glassmorphic Cyber-Neon Dark Theme (default) and high-contrast, premium soft slate Light Theme, with selections persisted in localStorage.
• 📊 Resource Analytics: Captures precise live CPU core statistics, system load averages, clock speeds, and memory consumption.
• 🗃️ Self-Pruning SQLite Logs: Stores telemetry values to a local database (bytewatch.db) with an automated rolling cleanup thread that prunes records older than 30 minutes to maintain an ultra-lightweight footprint.
• ⚡ Process Inspector: Fetches and sorts the Top 15 resource-intensive system threads by CPU load, highlighting high-utilization processes dynamically.
• 🚀 Pre-rendered Load State: Renders telemetry values instantly on the very first page request to prevent blank screen delays before the initial 2-second poll triggers.

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🛠️ Technology Stack

• Backend: FastAPI (Asynchronous Web Framework)
• Telemetry: psutil (System Metrics Interface)
• Database: SQLite (Single-file SQL engine)
• Frontend: HTMX (High-speed HTML swaps)
• Styling: Vanilla CSS (Responsive variables and modern glassmorphism layouts)

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📂 Project Structure

bytewatch/
├── static/
│   └── style.css         # Modern styling rules (variable sheets & responsive layouts)
├── templates/
│   ├── index.html        # Main dashboard base template (head tags, toggle, inline scripts)
│   └── vitals.html       # Metric panels and process list tables (HTMX swap fragments)
├── database.py           # SQLite initialization, logging, and metrics auto-pruning
├── monitor.py            # Multithreaded psutil daemon telemetry engine
├── main.py               # FastAPI application, templates router, and server startup
├── .gitignore            # Excludes local databases, caches, and IDE profiles
└── README.md             # Project documentation (this file!)

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🚀 Getting Started

📋 Prerequisites

Ensure you have Python 3.8+ and Git installed on your system.

1. Clone the Project

git clone https://github.com/YOUR_USERNAME/ByteWatch.git
cd ByteWatch

2. Install Dependencies

Install all required libraries using pip:

pip install fastapi uvicorn psutil jinja2

3. Run the Server

Launch the server using Python:

python main.py

4. Open the Dashboard

Navigate to your web browser and access the live application: 👉 http://localhost:8000

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⚙️ Architecture

graph TD
    subgraph "Python Backend"
        Thread[Rolling psutil Thread] -->|Periodically inserts metrics| DB[(SQLite Database)]
        App[FastAPI Web App] -->|Reads history| DB
        App -->|Fetches current processes & stats| Thread
    end
    subgraph "Frontend (Web Browser)"
        UI[HTMX Dashboard] -->|hx-get every 2s| App
        App -->|Returns clean HTML tiles| UI
    end

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☁️ Vercel Deployment

ByteWatch is pre-configured to run out of the box in Serverless environments (like Vercel).

How it handles Serverless:

1. Dynamic Pathing: Automatically detects serverless environments and writes SQLite logs to /tmp/bytewatch.db (ephemeral writeable storage) instead of the read-only file system.
2. On-Demand Polling: Switches from rolling background threads (which are frozen in serverless contexts between requests) to high-speed synchronous querying of psutil data on demand per incoming page load or HTMX swap.

Deploying to Vercel:

1. Make sure your project is committed to GitHub.
2. Log into your Vercel Dashboard.
3. Click Add New -> Project.
4. Import your ByteWatch repository.
5. Vercel will automatically detect vercel.json and build the Python serverless functions.
6. Click Deploy!

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📄 License

This project is open-source and available under the MIT License.