An eco-conscious mobile app that rewards users for taking real environmental action.

SeaScore (also called SaveEarth) motivates users to reduce plastic waste and protect the oceans. Users complete real-world sustainability challenges, upload proof, earn points, unlock rewards, and engage with a community of eco-active people.

Goal: Make sustainability fun, social, and rewarding. 🌱

Frontend: React Native (Expo)
Backend: Node.js + Express
Machine Learning: Xenova OWLv2 (Zero-Shot Detection)
Auth / Database: Firebase Authentication + Realtime Database
Deployment: Docker + Docker Compose
Uploads: multer
Model Runtime: @xenova/transformers
Design: Figma UI

• Node.js + npm
• Docker (optional but recommended)

git clone https://github.com/your-username/SaveEarth.git cd SaveEarth

2. Install dependencies

Client:

npm install

Server:

cd src/server npm install cd ../..

3. Download the ML model node src/server/download-model.mjs

4. Run locally npm start

Make sure the model is downloaded first:

node src/server/download-model.mjs

docker-compose up -d

Users sign up and log in using Firebase Auth.

Each challenge includes:

• A description
• A category (example: recycling, cleanup)
• A point value

Users upload a photo to complete challenges. The backend uses a zero-shot model to verify the action before awarding points.

• Model: Xenova/owlv2-base-patch16
• Can detect objects based on text prompts
• Does not require retraining for each challenge

Users collect stamps for completed challenges and see their total impact.

Points can be exchanged for rewards.

Real-time feed showing completed challenges across the community.

Users can join teams and compete on scoreboards.

Educational content for reducing plastic waste and protecting the oceans.

1. User uploads image
2. Server receives image + challenge keywords
3. ML model runs zero-shot detection
4. The model returns a confidence score
5. If above threshold → challenge is marked complete

• Express.js API
• multer handles file uploads
• @xenova/transformers loads ML model
• Singleton pattern ensures performance
• Script: src/server/download-model.mjs caches model locally

• Efficient ML inference on low-power devices
• Handling edge case submissions
• Balancing strictness vs usability in validation
• Optimizing performance and model load times

• Train a custom sustainability-focused model
• AI-based suggestions & challenge personalization
• Offline mode
• Public sharing of completed challenges
• Better reward marketplace
• Brand partnerships for eco-rewards
• Carbon impact tracker
• Team chat system

Figma UI: https://www.figma.com/design/8LZeuVJY5Hvdr1lU6qBlnK/EcoPassport-Mobile-App-UI