Installation Guide

This guide will walk you through installing and setting up the Brain Tumor Monitoring System on your local machine or server.

Prerequisites

System Requirements

Python 3.8 or higher

Node.js 16 or higher (for frontend)

PostgreSQL 12 or higher

Docker and Docker Compose (optional, for containerized setup)

Operating Systems

Linux (Ubuntu 20.04+, CentOS 8+)

macOS (10.15+)

Windows 10/11 (with WSL2 recommended)

Hardware Requirements

CPU: 4+ cores recommended

RAM: 8GB minimum, 16GB recommended

Storage: 20GB free space

GPU: Optional, for ML model inference acceleration

Installation Methods

Choose the installation method that best fits your needs:

Local Installation - Direct installation on your system
Docker Installation - Containerized installation with Docker
cloud-installation - Cloud deployment options

Local Installation

Step 1: Clone the Repository

git clone https://github.com/your-org/brain-tumor-monitoring.git
cd brain-tumor-monitoring

Step 2: Set Up Python Environment

# Create virtual environment
python -m venv env
source env/bin/activate  # On Windows: env\Scripts\activate

# Install Python dependencies
pip install -r requirements.txt
pip install -r requirements_dev.txt  # For development

Step 3: Install Node.js Dependencies

cd frontend
npm install
cd ..

Step 4: Set Up PostgreSQL Database

Install PostgreSQL on your system:

Ubuntu/Debian:

sudo apt update
sudo apt install postgresql postgresql-contrib
sudo systemctl start postgresql
sudo systemctl enable postgresql

macOS:

brew install postgresql
brew services start postgresql

Windows: Download and install from https://www.postgresql.org/download/windows/

Create Database and User:

sudo -u postgres psql

CREATE DATABASE monitoring;
CREATE USER monitoring_user WITH PASSWORD 'your_password';
GRANT ALL PRIVILEGES ON DATABASE monitoring TO monitoring_user;
\q

Step 5: Run Database Migrations

# Set environment variable
export DATABASE_URL="postgresql://monitoring_user:your_password@localhost:5432/monitoring"

# Run migrations
python backend/migrations/create_monitoring_tables.sql

Step 6: Configure Environment Variables

Create a .env file in the project root:

# Database configuration
DATABASE_URL=postgresql://monitoring_user:your_password@localhost:5432/monitoring

# API configuration
API_HOST=0.0.0.0
API_PORT=8000

# Monitoring configuration
DRIFT_THRESHOLD=1.0
REPORTS_DIR=reports/monitoring

# Development settings
DEBUG=True
LOG_LEVEL=INFO

Step 7: Verify Installation

# Test backend
uvicorn backend.src.api:app --reload --host 0.0.0.0 --port 8000

# In another terminal, test frontend
cd frontend
npm start

Docker Installation

Step 1: Install Docker and Docker Compose

Ubuntu/Debian:

sudo apt update
sudo apt install docker.io docker-compose
sudo usermod -aG docker $USER
newgrp docker

macOS: Download Docker Desktop from https://www.docker.com/products/docker-desktop

Windows: Download Docker Desktop from https://www.docker.com/products/docker-desktop

Step 2: Clone and Configure

git clone https://github.com/minhNgnn/MedView-LMU-MLOps-SoSe25-GroupB.git
cd MedView-LMU-MLOps-SoSe25-GroupB

# Copy environment file
cp .env.example .env

# Edit .env file with your configuration
nano .env

Step 3: Start Services

# Start all services
docker-compose up -d

# View logs
docker-compose logs -f

Step 4: Verify Installation

# Check service status
docker-compose ps

# Test API
curl http://localhost:8000/health

# Access frontend
open http://localhost:3000

Configuration

Database Configuration

The system supports PostgreSQL with the following configuration options:

# Example database configuration
DATABASE_URL = "postgresql://user:password@host:5432/database"

# Connection pool settings
DB_POOL_SIZE = 10
DB_MAX_OVERFLOW = 20
DB_POOL_TIMEOUT = 30

Monitoring Configuration

# Drift detection settings
DRIFT_THRESHOLD = 1.0  # Sensitivity for drift detection
REFERENCE_DAYS = 30    # Days of data for reference set
CURRENT_DAYS = 7       # Days of data for current set

# Feature extraction settings
IMAGE_MAX_SIZE = 10 * 1024 * 1024  # 10MB
FEATURE_EXTRACTION_TIMEOUT = 30     # seconds

API Configuration

# Server settings
API_HOST = "0.0.0.0"
API_PORT = 8000
API_WORKERS = 4

# CORS settings
CORS_ORIGINS = ["http://localhost:3000", "https://your-domain.com"]

# Rate limiting
RATE_LIMIT_PER_MINUTE = 100

Frontend Configuration

// API endpoint configuration
const API_BASE_URL = process.env.REACT_APP_API_URL || 'http://localhost:8000';

// Dashboard refresh interval (seconds)
const REFRESH_INTERVAL = 30;

// Chart configuration
const CHART_COLORS = {
  primary: '#2980B9',
  secondary: '#E74C3C',
  success: '#27AE60'
};

Verification

Backend Verification

# Test health endpoint
curl http://localhost:8000/health

# Test monitoring endpoints
curl http://localhost:8000/monitoring/dashboard
curl http://localhost:8000/monitoring/feature-analysis

# Check API documentation
open http://localhost:8000/docs

Frontend Verification

# Start frontend development server
cd frontend
npm start

# Open browser and verify dashboard loads
open http://localhost:3000

Database Verification

# Connect to database
psql -h localhost -U monitoring_user -d monitoring

# Check tables exist
\dt

# Verify monitoring tables
SELECT COUNT(*) FROM predictions_log;

ML Pipeline Verification

# Test image prediction
python ml/predict.py --image path/to/test/image.jpg

# Test model training
python ml/train.py --config ml/configs/model/config.yaml

Troubleshooting

Common Issues

Database Connection Failed

Verify PostgreSQL is running: sudo systemctl status postgresql
Check connection string format
Ensure firewall allows connections
Verify user permissions

API Won’t Start

Check port availability: netstat -tulpn | grep 8000
Verify environment variables are set
Check Python dependencies: pip list
Review logs: tail -f logs/api.log

Frontend Build Fails

Clear node_modules: rm -rf node_modules && npm install
Check Node.js version: node –version
Verify npm cache: npm cache clean –force

Docker Issues

Check Docker service: sudo systemctl status docker
Verify Docker Compose version: docker-compose –version
Clear Docker cache: docker system prune -a

Performance Issues

Monitor resource usage: htop or top
Check database connection pool
Verify memory allocation for containers
Review API response times

Getting Help

Documentation: Check this documentation for detailed guides
Issues: Report bugs on GitHub Issues
Discussions: Join community discussions
Support: Contact the development team

Next Steps

After successful installation:

Quick Start: Follow the Quick Start Guide guide
API Reference: Explore the api/index documentation
Monitoring Setup: Configure monitoring in monitoring/index