Top AI Repos — open-source AI, indexed and scored
Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
A full-stack diet recommendation system using machine learning
| Date | Stars |
|---|---|
| 2026-07-31 | 256 |
| 2026-08-03 | 256 |
| 2026-08-06 | 255 |
Today
-1 stars today
This week
— stars this week
This month
— stars this month
Momentum
0.0
growth rate 0.00%/day
<div align="center"> [](https://github.com/zakaria-narjis/nutrimind/actions/workflows/ci.yml) [](https://zenodo.org/doi/10.5281/zenodo.12507163) [](https://github.com/astral-sh/ruff) </div> <h1 align="center">NutriMind</h1> <div align="center"> <img src="Assets/logo_front_page.png" /> <h4>A content-based diet recommendation web app built with Scikit-Learn, FastAPI, and Streamlit.</h4> </div> --- ## Table of Contents - [Overview](#overview) - [Architecture](#architecture) - [How It Works](#how-it-works) - [Tech Stack](#tech-stack) - [Setup](#setup) - [API Reference](#api-reference) - [Dataset](#dataset) - [Citation](#citation) --- ## Star History [](https://www.star-history.com/?repos=zakaria-narjis%2Fnutrimind&type=date&legend=top-left) --- ## Overview A full-stack diet recommendation system that generates personalized meal plans based on user health data (age, weight, height, activity level) or custom nutritional targets. The recommendation engine uses a content-based approach with cosine similarity over nutritional vectors to find the closest matching recipes from a dataset of 500,000+ Food.com recipes. **Key features:** - Automatic meal plan generation from personal health metrics (BMI, BMR, TDEE) - Custom food search by nutritional values and ingredients - Configurable weight loss plans (maintain, mild, moderate, extreme) - Interactive nutritional breakdown charts --- ## Architecture <div align="center"><img src="Assets/Architecture_diagram.png" width="600" height="400" alt="Architecture diagram"/></div> --- ## How It Works ### Recommendation Engine Recipes are embedded as 9-dimensional nutritional vectors (Calories, Fat, Saturated Fat, Cholesterol, Sodium, Carbohydrates, Fiber, Sugar, Protein). A `NearestNeighbors` model with cosine similarity finds the closest recipes to a given target vector. ### Automatic Meal Plan (`/generate-meal-plan/`) 1. **BMI** — weight(kg) / height(m)² 2. **BMR** — Mifflin-St Jeor equation 3. **TDEE** — BMR × activity multiplier 4. **Target calories** — TDEE × weight-loss factor 5. Calories are distributed across meals (35/40/25% for 3 meals, etc.) 6. Per-meal nutrition vectors are sampled from physiologically appropriate ranges and passed to the nearest-neighbor model ### Ingredient Filtering At startup, each recipe's ingredient list is pre-parsed into a `frozenset` of lowercase strings. Filtering then uses set-based substring matching — no regex scanning on every request. ### Content-Based Approach | Advantage | Note | |-----------|------| | No cold-start problem | Works without any user history | | Transparent recommendations | Results are directly tied to nutritional targets | | No inter-user data needed | Fully self-contained per request | --- ## Tech Stack | Component | Technology | |-----------|-----------| | Backend API | FastAPI 0.115, Python 3.12 | | ML / Recommendation | scikit-learn 1.4 (NearestNeighbors, cosine) | | Data processing | pandas 2.2, numpy 1.26 | | Frontend | Streamlit 1.35 | | Charts | streamlit-echarts 0.4 | | Containerization | Docker, Docker Compose |  --- ## Setup ### Prerequisites - [Docker](https://docs.docker.com/get-docker/) and Docker Compose ### Run with Docker Compose **Option A — Use pre-built images** (fastest, no build step): ```bash git clone https://github.com/zakaria-narjis/nutrimind cd nutrimind docker compose pul
Excerpt of 5,780 characters
Read on GitHubWould you bet a product on this? Bounded 0–100 and slow moving.
matched fp:82b501bdcb9c0e05, llm:Repository topics: diet-recommender, food-recommendation, machine-learning, recommendation-system, scikit-learn; description: 'A full-stack diet recommendation system using machine learning'
matched fp:82b501bdcb9c0e05, llm:Repository topics: diet-recommender, food-recommendation, machine-learning, recommendation-system, scikit-learn; description: 'A full-stack diet recommendation system using machine learning'