= Build Instructions =

== Development environment description ==

The prototype is implemented as a web application (Flask backend + Vite frontend + a Python client agent).

Required software:

 * Python 3.11 or newer
 * Node.js 18+ and npm (for the frontend)
 * SQLite 3 (used by the prototype database)
 * Git
 * Visual Studio Code or PyCharm (recommended)
 * Modern web browser (Google Chrome, Microsoft Edge or Mozilla Firefox)

Main Python libraries (see requirements.txt for the full list):

 * Flask, Flask-CORS, Flask-SocketIO
 * requests, python-dotenv
 * PyJWT, google-auth (Google login + JWT cookie session)
 * openai (RAG / natural-language chat module)
 * psutil (system metrics on the client)

The prototype database is a local SQLite file: ''lan_logs_sysmon.db''. It is created automatically the first time the server starts.

'''Note:''' The official project database from the previous phase is a PostgreSQL schema (project). The prototype uses a local SQLite database with the same table structure for easier local development.

----

== Build instructions ==

1. Clone the project repository.

{{{
git clone https://github.com/istevanoska/NETIntel
cd NETIntel
}}}

2. Create and activate a virtual environment.

Windows:
{{{
python -m venv .venv
.venv\Scripts\activate
}}}

Linux/macOS:
{{{
python3 -m venv .venv
source .venv/bin/activate
}}}

3. Install all required dependencies.

{{{
pip install -r requirements.txt
}}}

4. Create a .env file in the project root with the required configuration.

{{{
GOOGLE_CLIENT_ID=your-google-oauth-client-id
JWT_SECRET=your-secret-key
OPENAI_API_KEY=your-openai-key   # само за Chat / RAG модулот
}}}

5. Start the server.

{{{
python server.py
}}}

The server starts on:
{{{
http://localhost:5555
}}}

If the database does not exist, it is created automatically (all tables) when the server starts.

6. Configure and start the frontend.

Create a file lan-frontend/.env with:
{{{
VITE_API_BASE=http://localhost:5555
VITE_GOOGLE_CLIENT_ID=your-google-oauth-client-id
}}}

'''Note:''' VITE_GOOGLE_CLIENT_ID must be the same value as GOOGLE_CLIENT_ID in the backend .env.

Then install and start:
{{{
cd lan-frontend
npm install
npm run dev
}}}

The frontend starts on:
{{{
http://localhost:5173
}}}

7. Start the client agent.

{{{
python sctry.py
}}}

When prompted, enter the server IP address shown in the server terminal, and the environment token generated from the admin panel.

----

== Testing instructions ==

Open the application in a web browser:
{{{
http://localhost:5173
}}}

Login using a Google account (Google OAuth). The session is kept via an HttpOnly JWT cookie.

After successful login the following prototype functionalities can be tested:

 * Dashboard – overview of all monitored computers
 * Computer Details – detailed information about a selected computer (processes, Sysmon events, security alerts)
 * Environment Management – create and manage environments and generate tokens
 * Agent Communication – send monitoring data from the client application
 * RAG / Chat – generate SQL queries from natural-language questions

Example questions for the Chat module:

 * Show all active processes for PC-ADMIN.
 * Show all security alerts.
 * Show the latest Sysmon events.
 * List all monitored computers.

Mini guide:

 1. Login to the application (Google).
 2. Open the Dashboard.
 3. Select a monitored computer.
 4. View system metrics, running processes and security events.
 5. Open Environment Management (requires administrator role) to create environments and tokens.
 6. Open the Chat page and ask a question in natural language.
 7. Review the generated SQL query and the returned results.

----

== Database initialization ==

The database is created and populated automatically by the server on first start (init_db creates all tables if missing).

Optionally, to (re)create the schema and load sample data manually, run:

 1. database/schema.sql
 2. database/sample_data.sql

Then start the server:
{{{
python server.py
}}}

----

== Source code ==

The repository contains:

 * Complete application source code
 * Flask backend (server.py)
 * Python client agent (sctry.py)
 * Frontend source code (lan-frontend, Vite)
 * SQL scripts for creating the database schema and sample data
 * Project documentation

All source code required to build and run the prototype is available in the DEVELOP repository.