🦅 Eagle Learning Management System (Eagle LMS)

Eagle LMS is a secure, cloud-native learning management platform designed for high-performance enterprise training. Featuring a dual-role (Trainer and Trainee) dashboard web app, a mobile learning client, secure watermarking for intellectual property protection, and an automated DevOps delivery pipeline.

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🔗 Cloud Infrastructure & CI/CD Pipeline Proof

Tip

DevOps Portfolio Proof: For the complete local cloud simulation documentation, including step-by-step resolution of Docker port bindings, S3 path-style routing, database seeding, and the local Jenkins pipeline configuration, see the DevOps & Cloud Simulation Log (DEVOPS_PROOF.md).

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🖼️ Application Interfaces (Website Showcase)

Here are the primary interface views from the Eagle LMS platform:

Trainer Dashboard Overview	Trainer Course Content & Module Detail
Trainer Module Reports & Scores	Trainee Profile Dashboard
Trainee Training Calendar

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🏗️ Core Architecture & Tech Stack

Eagle LMS is organized as a monorepo containing three core subsystems:

1. Backend API Service (/backend)

• Framework: FastAPI (Python 3.11/3.13) for high-performance HTTP request handling.
• ORM / Database: SQLAlchemy with PyMySQL connecting to a MySQL 8.0 instance.
• Security: Role-based JWT access tokens (python-jose) and password hashing (passlib with bcrypt).
• File Storage: AWS S3 (simulated locally via LocalStack) with lazy-downloading caching routines.
• Watermarking Engine: PDF rendering (reportlab) and image compositing (Pillow) for personalizing learning resources with trainees' emails.

2. TrainerPro Web Frontend (/frontend)

• Build Tool & Framework: Vite + React 18.
• Styling: Custom dark-themed responsive layout optimized for analytical dashboards.
• Routing & APIs: React Router DOM and Axios.

3. Mobile Companion Client (/mobile)

• Framework: React Native + Expo.
• Purpose: Immersive, on-the-go training for field security personnel.

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🚀 Quick Start (Local Run)

📋 Prerequisites

Ensure you have the following installed on your machine:

• Docker Desktop (start the daemon before proceeding).
• Terraform CLI.

1. Launch the Stack

Run the containers in detached mode:

docker compose up -d

This spins up:

• MySQL Database: Exposed on host port 3307 (to prevent conflicts with any local MySQL running on port 3306).
• FastAPI Backend: Exposed on host port 8000.
• Vite Frontend: Exposed on host port 5173.
• LocalStack AWS Emulator: Exposed on host port 4566.

2. Provision S3 Bucket

Initialize and apply the Terraform configuration to create the S3 bucket in LocalStack:

cd infra
terraform init
terraform apply -auto-approve

3. Log In

Open your browser and navigate to:

• Web Frontend: http://localhost:5173
• Demo Trainer Credentials: trainer@eagle.com / trainer123
• Demo Trainee Credentials: trainee@eagle.com / trainee123
• FastAPI Swagger Documentation: http://localhost:8000/docs