---
title: NutriLens
emoji: 🔬🥗
colorFrom: green
colorTo: blue
sdk: gradio
sdk_version: "6.16.0"
python_version: "3.11"
app_file: app.py
pinned: true
license: apache-2.0
tags:
  - food
  - nutrition
  - health
  - science
  - hackathon
  - build-small
  - track:backyard
---

# NutriLens: Food Health Impact Analyzer

Snap a photo of your meal, grocery label, or type a list of ingredients.
NutriLens identifies each ingredient, looks up real nutritional data from
the USDA, finds relevant scientific studies on PubMed, and delivers a clear
per-ingredient health breakdown with proper citations.

Works with food labels in **any language**.

## Why this exists

Most of us buy food every day without really knowing what's in it or what
it does to our bodies. Ingredient lists are full of names that sound
foreign even in our own language - "sorbitol syrup," "soya lecithin,"
"emulsifier" - and nutrition science lives in dense academic papers most
people will never read.

That gap matters: the foods we eat regularly shape our long-term health,
and a lot of that influence is invisible until it's added up over years.
NutriLens exists to close that gap - to take what's already known and
published and turn it into something anyone can read in a minute, in
plain language, before they decide what to put in their cart or on their
plate.

The goal isn't to scare anyone away from a treat or declare foods "good"
or "bad." It's awareness: knowing what you're consuming, what the science
actually says about it, and why - so you can make your own informed
choices.

## How it works

1. **Identify**: A small vision-language model reads your food photo or label
   and extracts the ingredients.
2. **Look up**: Each ingredient is matched against the USDA FoodData Central
   database for verified nutritional data.
3. **Research**: PubMed is searched for recent scientific reviews on each
   ingredient's health effects.
4. **Analyze**: The model synthesizes the nutritional data and study findings
   into a clear, evidence-based health report with citations.

When databases are rate-limited, the model falls back to its own knowledge
and clearly labels those sections.

## Health focus areas

General, Heart health, Anti-inflammatory, Blood sugar,
Gut health, Energy, Bone health.

## Data sources

- USDA FoodData Central (400K+ foods)
- PubMed / NCBI E-utilities (peer-reviewed literature)

## Built for

[Gradio Build Small Hackathon](https://huggingface.co/build-small-hackathon) (June 2026)

## Demo video

[Watch the demo](https://youtu.be/ZAhTMsoH_n8)

## Social post

[See the announcement post](https://x.com/i/status/2066630127283318851)

## Team

- [@vicarioush](https://huggingface.co/vicarioush)