# Vigimétéo - Connected Objects Management

Vigimétéo is an innovative web platform dedicated to real-time visualization of meteorological data from connected sensors. This project allows any user to consult updated weather information while providing a complete administration area to manage sensors, users, and categories.

## Table of Contents

1. [Description](#description)
2. [Features](#features)
3. [Usage (Docker - Cloud Deployment)](#usage-docker---cloud-deployment)
4. [AI Usage](#ai-usage)

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## Description

Vigimétéo is a tool that is both useful and accessible in the face of current climate challenges. Using an architecture combining React for the front-end and Vert.x for the back-end, the system relies on a PostgreSQL database centralizing measurements collected by weather stations.
The interface allows for both intuitive consultation for the general public and complete management for administrators via a secured dashboard.

## Features

- Consult real-time meteorological data (fake data for now, not yet connected with an API)
- IoT Device Management with the ability to set alerts for extreme data values
- User Management: add, delete, and modify users
- Category Management: manage device types
- Responsive Interface (Mobile & Desktop)
- Secure Authentication with JWT
- **Multi-language Support (i18n)**: Interface available in French and English

## Usage (Docker - Cloud Deployment)

This application has been containerized to meet the requirements of the Cloud Computing course (Dev Web Ing1).

### Prerequisites
- **Required OS:** Linux (Ubuntu/Debian) with `docker` and `docker-compose` configured.
- **Required Resources:** Internet connection to download base Docker images (`nginx`, `node`, `maven`, `eclipse-temurin`, `postgres`).

### Services / Containers
The architecture is split into 3 distinct services via `docker-compose.yaml`:
1. **vigimeteo_frontend**: Image built via a multi-stage `Dockerfile` (Node.js to compile the Vite application, then Alpine Nginx to serve static files). This service listens on public port `5000`.
2. **vigimeteo_backend**: Image built via a multi-stage `Dockerfile` (Maven to compile the JAR, then JRE 17 for execution). Runs the Vert.x API and communicates on public port `8888`.
3. **vigimeteo_db**: Official `postgres:15-alpine` image.

### Virtual Networks and Named Volumes
- **Network:** `vigimeteo_net` (Bridge) allows frontend, backend, and DB containers to communicate securely in isolation from the rest of the system.
- **Volume:** The persistent volume `vigimeteo_data` is mounted on the `/var/lib/postgresql/data` directory of the DB container. It ensures data persistence, guaranteeing that weather data and user accounts are not lost when restarting or rebuilding the containers.

### Container Configuration
- **Environment Variables:** The Vert.x backend dynamically reads PostgreSQL credentials (e.g., `DB_HOST`, `DB_PASSWORD`) provided via environment variables in the `docker-compose.yaml` file.
- **DB Initialization:** The PostgreSQL container automatically synchronizes the `sql/init_db.sql` file in its `/docker-entrypoint-initdb.d/init.sql` directory for automatic population upon first initialization.
- **Restart Policy:** All containers include the `restart: always` policy to restart automatically in case of failure.

### How to Run the Application
1. **Prepare the application**: Run `./prepare-app.sh`. This script initiates `docker-compose build` to create/download the images.
2. **Launch the application**: Run `./start-app.sh`. Access the web application via your browser at `http://localhost:5000`. You can log into the administration interface using the email `admin.a@gmail.com` and password `azertyuiop`. You can also use the `complexe@gmail.com` and password `azertyuiop` to try different role.
3. **Pause the application**: Run `./stop-app.sh`. This script stops the containers gracefully without resetting their state or destroying the virtual network.
4. **Remove the application**: Run `./remove-app.sh`. This removes all containers, networks, local images, and **named volumes**, erasing all traces of the deployed application.

## AI Usage (Cloud Computing Course)

- **AI Agent used:** Google Gemini
- **AI's Role:** 
  - Verification and optimization of the DockerCompose file
  - Implementation of the English/French internationalization (i18n) system.
  - Upgrade of the README for technical and overall the project structure