DeepBlue Marine Monitoring Dashboard

Overview

A full-stack marine monitoring dashboard for real-time vessel detection, biodiversity (eDNA), otolith morphometrics, and more. Built with React (Vite), Node.js/Express, FastAPI ML microservice, and PostgreSQL.

Project Structure

backend/: Node.js/Express API, PostgreSQL (Sequelize)
frontend/: React dashboard (Vite)
ml_service/: FastAPI ML microservice (YOLOv8, eDNA, otolith, correlation)
ml_service/data/: Realistic sample CSV datasets

Prerequisites

Node.js 18+
Python 3.9+
(Recommended) pnpm or npm

Setup Instructions

1. Backend (Node.js/Express)

cd backend
npm install
npm run dev

Runs on http://localhost:5000

2. ML Service (FastAPI)

cd ml_service
python -m venv .venv
.venv\Scripts\activate  # Windows
pip install -r requirements.txt  # or install: fastapi uvicorn sqlalchemy pandas scikit-learn ultralytics
uvicorn main:app --reload --host 0.0.0.0 --port 8000

Runs on http://localhost:8000
Interactive docs: http://localhost:8000/docs

3. Frontend (React)

cd frontend
npm install
npm run dev

Runs on http://localhost:5173 (or as shown in terminal)

Environment Variables

frontend/.env:
```
VITE_API_URL=http://localhost:8000
```
backend/.env:
- Configure PostgreSQL connection as needed.

Features

Live vessel detection (YOLOv8, upload satellite image)
Biodiversity (eDNA) analysis
Otolith morphometrics
Correlation analytics
PFZ, field survey, and more
Modern dashboard UI

Demo Data

All ML endpoints use realistic sample data for demo/testing.
Replace with real models/data as needed.

Troubleshooting

Ensure all services are running on correct ports.
For Python errors, check that all dependencies are installed in the .venv.
For CORS/API errors, verify VITE_API_URL in frontend/.env.

Credits

Architecture & code: Goutham-K-278
ML/data integration: DeepBlue project

DeepBlue

Install / Use

README