EnergyGuardPlatform

Overview

EnergyGuard is a Django-based platform for AI trustworthiness assessment, project and dataset management, robustness testing, and digital twin exploration — built for European energy systems research.

Architecture

Docker Services

Service	Description	Port
web	Main Django application	${PORT:-8080} → 8000
db	PostgreSQL 16	internal
qcluster	Django-Q2 background task worker	internal
pgadmin	Database administration UI	5051

Django Apps

App	Purpose
core	Shared base models, home page, dashboard, documentation
accounts	User auth, profiles, teams, invitations, notifications, Keycloak SSO
datasets	Dataset management with MinIO/S3 storage
projects	Project and experiment tracking with MLflow integration
billing	Billing records and payment methods
code_analysis	Static code trustworthiness scanning via Semgrep (GitHub, Jupyter, file upload)
robustness	AI robustness testing via external adversarial attack API
digitaltwins	Digital twin facility map and detail views
questionnaire	AI trustworthiness survey questionnaire (integrated app)

Repository Structure

EnergyGuardPlatform/
├── Dockerfile                 # Main app image
├── docker-compose.yml         # Orchestrates web, db, qcluster, pgadmin
├── requirements.txt           # Main app Python dependencies
├── manage.py
├── main/                      # Django project settings and root URLs
├── core/                      # Shared models, home, dashboard, docs
├── accounts/                  # Auth, profiles, teams, notifications
├── datasets/                  # Dataset management and storage
├── projects/                  # Projects and experiments
├── billing/                   # Billing and payments
├── code_analysis/             # Semgrep-based trustworthiness scanning
├── robustness/                # Adversarial robustness testing
├── digitaltwins/              # Digital twin facilities
├── questionnaire/             # Trustworthiness survey (also runnable standalone)
│   ├── manage.py
│   ├── requirements.txt
│   ├── questions.json         # Question bank for import
│   └── config/               # Standalone settings and URLs
├── static/                    # Frontend assets (CSS, JS, DataTables, TinyMCE)
└── media/                     # User-uploaded files

Local Development

Prerequisites

• Docker Desktop running

Setup

1. Copy .env and fill in required values (see Environment Variables):

2. Build and start all services:

docker compose up --build

3. Default URLs:
   • Main app: http://localhost:8080
   • PgAdmin: http://localhost:5051

Questionnaire Standalone Mode

The questionnaire app is integrated into the main Django project. It can also be run as a standalone service:

cd questionnaire
pip install -r requirements.txt
python manage.py migrate
python manage.py import_questions questions.json
python manage.py runserver 8001

Environment Variables

Key variables required in .env:

Variable	Description
PORT	Host port for the web service (default: 8080)
POSTGRES_DB / POSTGRES_USER / POSTGRES_PASSWORD	PostgreSQL credentials
POSTGRES_HOST / POSTGRES_PORT	PostgreSQL connection
PGADMIN_DEFAULT_EMAIL / PGADMIN_DEFAULT_PASSWORD	PgAdmin login
OIDC_RP_CLIENT_ID / OIDC_RP_CLIENT_SECRET	Keycloak OIDC credentials
KEYCLOAK_USER_SYNC_ID / KEYCLOAK_USER_SYNC_SECRET	Keycloak user sync
EMAIL_HOST / EMAIL_PORT / EMAIL_HOST_USER / EMAIL_HOST_PASSWORD	SMTP email
OBJECT_STORAGE_ENDPOINT / ACCESS_KEY / SECRET_KEY	MinIO/S3 storage
MEDIA_BUCKET / USE_S3_FOR_MEDIA	Media storage configuration
SCAN_API_URL	Semgrep code analysis API
ROBUSTNESS_API_URL	Adversarial robustness testing API
DATA_MANAGEMENT_SERVER_URL	External data management service
JUPYTERHUB_URL	JupyterHub integration
MLFLOW_TRACKING_USERNAME / MLFLOW_TRACKING_PASSWORD	MLflow experiment tracking

Tech Stack

• Backend: Django 6.0, Python 3.12
• Database: PostgreSQL 16 (main app), SQLite (questionnaire standalone)
• Auth: django-allauth + Keycloak OpenID Connect
• Storage: MinIO (S3-compatible) via boto3 + django-storages
• Background tasks: Django-Q2 (qcluster worker)
• Frontend: DataTables, TinyMCE, jQuery
• Static files: WhiteNoise
• Production server: Gunicorn

External Integrations

• Keycloak — SSO and user federation
• Semgrep — Static analysis for code trustworthiness scans
• Robustness API — Adversarial attack testing for AI models
• MLflow — Experiment tracking within projects
• JupyterHub — Notebook-based code analysis source

Team Workflow

• Main platform changes stay in the root Django project.
• Questionnaire-specific changes stay under questionnaire/.
• Cross-service integration (if questionnaire runs standalone) should happen via URLs/API contracts, not by importing code between services.