FestSafe AI — Predictive Hospital Readiness Agent

A production-ready SaaS system that predicts hospital surge capacity, recommends resource allocation, provides public health advisories, and enables multi-agent healthcare intelligence for event-driven healthcare management.

🎯 Overview

FestSafe AI ingests real-time and historical data to:

• Predict hospital surge capacity for events (festivals, concerts, marathons)
• Recommend optimal resource allocation (staffing, supplies, beds)
• Provide public health advisories
• Enable multi-agent decision-making for healthcare operations

🏗️ Architecture

┌─────────────┐     ┌──────────────┐     ┌─────────────┐
│   Frontend  │────▶│   Backend    │────▶│  ML Service │
│   (React)   │◀────│   (FastAPI)  │◀────│  (PyTorch)  │
└─────────────┘     └──────────────┘     └─────────────┘
                            │
                            ▼
                    ┌──────────────┐
                    │  PostgreSQL  │
                    │ (TimescaleDB)│
                    └──────────────┘
                            │
                    ┌───────┴───────┐
                    │               │
            ┌───────▼────┐  ┌──────▼──────┐
            │   Redis    │  │   Kafka/    │
            │  (Cache)   │  │  RabbitMQ   │
            └────────────┘  └─────────────┘

🚀 Quick Start

Prerequisites

• Docker & Docker Compose
• Python 3.10+
• Node.js 18+
• PostgreSQL 14+ (or use Docker)
• Redis (or use Docker)

Local Development Setup

1. Clone the repository

   git clone <repo-url>
   cd "FestSafe AI"

2. Start infrastructure services

   docker-compose -f infra/docker-compose.dev.yml up -d

3. Generate synthetic data

   cd ml/training
   python data_simulator.py --hospitals 50 --events 10 --days 90

4. Train initial model

   python train.py --config configs/baseline.yaml

5. Start backend

   cd backend
   pip install -r requirements.txt
   uvicorn app.main:app --reload --port 8000

6. Start frontend

   cd frontend
   npm install
   npm start

7. Access the application

   • Frontend: http://localhost:3000
   • Backend API: http://localhost:8000
   • API Docs: http://localhost:8000/docs

Using Docker Compose (Recommended)

docker-compose -f infra/docker-compose.dev.yml up --build

📁 Project Structure

FestSafe AI/
├── frontend/          # React + TypeScript frontend
├── backend/           # FastAPI backend
├── ml/                # ML training and inference
│   ├── training/      # Model training scripts
│   └── inference/     # Model serving
├── infra/             # Infrastructure as code
│   ├── terraform/     # AWS infrastructure
│   └── k8s/           # Kubernetes manifests
├── ci/                # CI/CD workflows
├── docs/              # Documentation
└── tests/             # Integration and E2E tests

🔑 Key Features

• Real-time Forecasting: Predict patient surge with <500ms latency
• Multi-Agent System: Orchestrated agents for forecasting, triage, and communication
• Event Management: Register and track festivals, concerts, and other events
• Resource Recommendations: AI-powered staffing and supply suggestions
• Public Health Advisories: Automated communication for affected areas
• HIPAA-Conscious Design: Privacy-preserving defaults and encryption

🧪 Testing

# Backend tests
cd backend
pytest tests/ -v --cov=app

# Frontend tests
cd frontend
npm test

# Integration tests
pytest tests/integration/ -v

📊 Monitoring

• Prometheus metrics: http://localhost:9090
• Grafana dashboards: http://localhost:3001
• MLflow tracking: http://localhost:5000

🔒 Security

• JWT-based authentication
• RBAC (Role-Based Access Control)
• TLS encryption in transit
• Database encryption at rest
• PII anonymization on ingestion
• Audit logging

📚 Documentation

• Architecture
• API Documentation
• Runbook
• Contributing

🛠️ Tech Stack

• Frontend: React, TypeScript, Tailwind CSS, React Query, Recharts, Mapbox GL
• Backend: FastAPI, PostgreSQL (TimescaleDB), Redis, RabbitMQ
• ML: PyTorch, scikit-learn, MLflow
• Infrastructure: Docker, Kubernetes, Terraform, AWS
• CI/CD: GitHub Actions
• Monitoring: Prometheus, Grafana, Loki, Sentry

📝 License

See LICENSE file.

🤝 Contributing

See CONTRIBUTING.md for guidelines.

⚠️ Medical Disclaimer

This system provides suggestions and references only. All medical decisions require clinician review and approval. The system does not provide prescriptive medical advice.