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Contributing to NutriLens

Thanks for taking the time to contribute. NutriLens is an accountability tool for Indian consumers — the more people who contribute data, rules, and platform support, the more useful it becomes.

Table of Contents

Getting Started

  1. Fork the repo and clone your fork
  2. Follow the Quick Start to get a local backend running
  3. Pick an issue tagged good first issue or help wanted
  4. Open a PR — we review within a few days

For larger changes (new platform, new scoring dimension), open an issue first to discuss the approach.

Ways to Contribute

🐛 Bug reports

Open an issue with the product URL, what you expected, and what NutriLens returned. Screenshots of the popup help.

🔌 New platform content scripts

BigBasket and Flipkart are the next priority. See Adding a Platform below.

🔢 Scoring improvements

The scoring benchmarks (BENCHMARKS dict in backend/engines/ranker.py) need per-category data. If you have FSSAI compliance data or published nutritional benchmarks for Indian products, open a PR.

🧪 Tests

The OCR parser and scoring engine have no automated tests. The test harness in ocr-service/test_extraction.py is a good starting point.

🧹 Noise filter improvements

amazon.js has a NOISE_PATTERNS array that filters UI text from marketing claims. If you find bullets that should be filtered (or shouldn't be), a one-line PR is welcome.

🌐 Translations / localisation

The popup is English-only. Hindi and Tamil translations would significantly increase the user base.

Development Setup

Backend

cp .env.example .env
docker compose up

After changing any Python file in backend/ or ocr-service/:

docker compose restart api worker   # or: ocr

Check logs:

docker compose logs api --tail=50
docker compose logs worker --tail=50

Database access (psql):

docker compose exec db psql -U nutrilens -d nutrilens

Extension

cd extension
npm install
npm run dev     # Vite dev mode — rebuilds on save

After npm run build, reload the extension at chrome://extensions.

OCR service

The first run downloads PaddleOCR models (~200MB). They are cached in a Docker volume so subsequent starts are fast. Test OCR extraction directly:

docker compose exec ocr python test_extraction.py path/to/label.jpg

Code Style

Python

  • Follow PEP 8
  • Type hints on all function signatures
  • Docstring on every module (single paragraph, present tense)
  • No print() — use logging

JavaScript / JSX

  • ESLint default config (from package.json)
  • No default exports except React components
  • Content script functions must be pure — no module state that survives page navigation

Commits

type(scope): short description

Types: feat, fix, refactor, test, docs, chore
Scope: extension, backend, ocr, docker, ci

Examples:
  feat(extension): add BigBasket content script
  fix(backend): correct per-100g double-multiplication in normalizer
  docs: update OCR architecture diagram

Submitting a PR

  1. Branch from main: git checkout -b feat/your-feature
  2. Write or update tests if touching scoring or parsing logic
  3. Run the backend and manually test the end-to-end flow on at least one Amazon.in product
  4. Check the /admin dashboard to confirm your changes produce correct DB rows
  5. Open a PR against main — fill in the template

PRs that only touch one concern (one platform, one bug, one feature) are much easier to review and merge quickly.

Adding a Platform

  1. Create extension/content_scripts/{platform}.js — model it on amazon.js
  2. Export the same shape: { platform, platform_id, product_name, brand, price_inr, quantity_g, claims[], nutrition_facts{}, serving_size_g }
  3. Register the content script in manifest.json under content_scripts with the platform's URL match pattern
  4. Test on at least 10 product pages across different categories
  5. Note any structural differences from Amazon (e.g. BigBasket shows nutrition per serving only) in a comment at the top of the file

The backend is platform-agnostic — as long as the payload matches ProductSubmitRequest in backend/models/schemas.py, no backend changes are needed.

Questions?

Open a discussion in the GitHub Discussions tab. For security issues, see SECURITY.md.