EcoTasker

A gamified, eco-focused web app that helps users track sustainable tasks, estimate carbon footprint, fetch environmental news, and earn rewards for green actions.

Highlights

• User authentication and profiles (MongoDB + Express)
• Task management with gamified points and leaderboard
• Carbon footprint prediction using a Python model (PyCaret)
• Environmental news fetcher (NewsAPI)
• Green tips and actionable suggestions

Tech stack

• Backend: Node.js, Express
• Database: MongoDB (Mongoose)
• Views: EJS templates
• Frontend: static assets in public/ and views/
• Machine learning: Python (PyCaret) model in model/

Repository structure

• app.js — main/root server entry (Express app)
• backend/app.js — alternative/expanded Express server with APIs and ML integration
• backend/ — Python scripts and helpers (predictor.py, newsFetcher.py, suggestions.js, train_model.py)
• model/ — trained ML artifacts loaded by predictor.py
• public/ — static frontend assets (CSS, JS)
• views/ — EJS templates for the UI
• data/ — CSV and JSON data used for training or bootstrapping

Setup

Prerequisites:

• Node.js (v16+ recommended)
• Python 3.8+ (with required ML libraries)
• MongoDB running locally (or update connection string)

Install Node dependencies:

npm install

Install Python dependencies (example):

python -m pip install pandas requests pycaret

Notes: PyCaret and some ML dependencies can be large and may require additional system packages. If you don't need ML features, you can skip installing PyCaret and the model/ usage.

Run

Start MongoDB (locally) then run:

npm start
# or
node app.js

By default the app listens on http://localhost:3000.

Key Endpoints & Usage

• GET / — home (redirects to login or serves public/index.html depending on server file)
• GET /login, POST /login — authentication
• GET /signup, POST /signup — create account
• GET /tasks, POST /tasks — task list and creation
• POST /tasks/done — mark task complete (session-protected)
• POST /api/complete — API endpoint to complete a task and award points (uses tasks.json + leaderboard.json in backend/)
• POST /api/refresh-tasks — triggers Python task generation (ml_task.py) and refreshes tasks.json
• POST /chat — sends input to model/predict.py and returns model reply
• GET /get-news?q=... — fetches environmental news via newsFetcher.py (uses NewsAPI key)
• POST /calculate — sends numeric inputs to backend/predictor.py to estimate carbon emissions

ML model notes

• backend/predictor.py loads a PyCaret model from model/carbon_model and expects three numeric args: electricity, car, meat.
• If the model is absent or fails, the code falls back to a simple heuristic calculation.
• Training scripts (if present) are in backend/train_model.py or similar; check data/eco_user_behavior.csv for training data.

Configuration & Secrets

• backend/newsFetcher.py currently contains an API key placeholder. Replace it with an environment variable or a secure secret manager before production.
• MongoDB connection string is currently mongodb://localhost:27017/eco_auth in the server files — update for production environments.

Security & Privacy

• Do not commit real API keys or credentials.
• Validate and sanitize user inputs before executing external commands or Python scripts — the code currently spawns Python processes with passed arguments.

Suggested Project Name

EcoTasker — a clear, friendly name conveying eco-friendly task tracking and gamification.

Next steps (suggested)

1. Extract secrets to environment variables and add a .env.example file.
2. Add a requirements.txt for Python dependencies and/or a pyproject.toml for reproducible ML environment.
3. Add tests (backend API unit tests) and a simple CI workflow.
4. Harden input validation around Python subprocess calls.

License

Add a LICENSE file (MIT or your preferred license).

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Generated from repository analysis on May 28, 2026.