Oceanova

AI-powered marine conservation platform built with Next.js. It provides species identification, gene sequence analysis, threat assessment, conservation recommendations, and water quality analytics, backed by a Python ML model server and MongoDB.

Features

• Species identification with Gemini models
• Gene sequence analysis via a local Python model server (Flask)
• Threat assessment and conservation recommendations
• Interactive water-quality maps (Leaflet)
• Dashboard with charts and insights

Tech Stack

• Next.js 14 (App Router), React 18, TypeScript
• UI: Radix UI, Tailwind CSS utilities, custom components
• Data: MongoDB
• AI: Google Gemini (@google/generative-ai)
• Maps: Leaflet, React-Leaflet
• Python ML service (Flask) for gene prediction

Prerequisites

• Node.js 18+ and npm (or pnpm/yarn)
• Python 3.8+ (for the ML model server)
• MongoDB instance (local or cloud)

Quick Start

1. Install dependencies

npm install
# or: pnpm install

2. Configure environment variables (create .env.local in the repo root)

GEMINI_API_KEY=your_gemini_api_key
MONGODB_URI=mongodb+srv://user:pass@host/dbname?retryWrites=true&w=majority

3. (Optional) Seed the database

node scripts/setup-database.js
node scripts/seed-sample-data.js

4. Start the ML model server (separate terminal)

• Windows (batch file):

scripts\start-model-server.bat

• Cross-platform (Python launcher):

python scripts/start-model-server.py

The server will run at http://localhost:5000 if all model artifacts exist in Model/.

5. Run the web app

npm run dev
# open http://localhost:3000

Environment Variables

• GEMINI_API_KEY (required): Used in lib/gemini-client.ts to initialize Gemini models.
• MONGODB_URI (required): Used in lib/mongodb.ts to connect to the database.

Important Scripts

• npm run dev: Start Next.js dev server
• npm run build: Production build
• npm run start: Start production server (after build)
• node scripts/setup-database.js: Create indexes/config for MongoDB
• node scripts/seed-sample-data.js: Insert sample data
• python scripts/start-model-server.py or scripts/start-model-server.bat: Launch Python model server

Project Structure (high-level)

app/                    Next.js App Router pages and API routes
  api/                  Serverless API endpoints (AI, ML, water-quality, dashboard)
components/             UI components and sections
lib/                    Clients, DB, utilities, model integration helpers
ml-models/              Python service code and requirements
Model/                  Pretrained model artifacts (*.pkl, *.npy, etc.)
scripts/                Setup, seed, and model server launch scripts

Key API routes (under app/api/):

• ai/species-identification/route.ts
• ai/gene-sequence-analysis/route.ts
• ai/threat-assessment/route.ts
• ai/conservation-recommendations/route.ts
• ai/water-quality-analysis/route.ts
• ml/gene-prediction/route.ts
• dashboard-data/route.ts
• water-quality/route.ts

Python ML Model Server

Artifacts required in Model/:

• stack_meta_clf.pkl
• stack_label_encoder.pkl
• lgb_models_list.pkl
• xgb_models_list.pkl

Install Python dependencies once:

pip install -r ml-models/requirements.txt

Then run the server (see Quick Start). The launcher checks Python version, required files, installs deps, and starts Flask at http://localhost:5000.

Development Notes

• Gemini client and models are created in lib/gemini-client.ts.
• MongoDB client with connection reuse is in lib/mongodb.ts.
• UI primitives live in components/ui/.

Troubleshooting

• Missing GEMINI_API_KEY or MONGODB_URI will throw at startup.
• If model server fails to start, verify artifacts exist in Model/ and Python 3.8+ is used.
• On Windows, prefer scripts/start-model-server.bat if Python launcher has path issues.

License

Proprietary/Internal. Do not distribute without permission.