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🧠 Intelligent Nutri – AI-Powered Recipe & Nutrition Assistant

Eat smarter. Cook faster. Live healthier.

Intelligent Nutri is a personalized AI-driven platform that helps users find recipes, analyze nutrition, generate shopping lists, and chat with a virtual food assistant — all based on their dietary needs and preferences.


📦 Project Structure

intelligent-nutri/
│
├── frontend/               # All UI code here
│   ├── index.html
│   ├── preferences.html
│   ├── chatbot.html
│   └── js/
│       ├── app.js          # JS for preferences form & search
│       └── chatbot.js      # JS for AI chat interface
│   └── css/
│       └── style.css      
│
├── backend/                # All Flask-related backend code
│   ├── main.py             # Entry point — define routes here
│   ├── llm_utils.py        # Functions to generate prompts & call Gemini/OpenAI
│   ├── db_utils.py         # Functions to interact with SQLite (CRUD)
│   └── vector_utils.py     # Handle FAISS search & embeddings
│
├── database/
│   ├── schema.sql          # Create tables
│   ├── init_db.py          # Script to create and initialize database
│   └── seed_data.py        # Preload recipes from dataset (Kaggle, etc.)
│
└── requirements.txt

⚙️ Tech Stack

Layer Tech Used
Frontend HTML, CSS, JS, Bootstrap
Backend Python Flask, REST APIs
Database SQLite3, FAISS (Vector DB)
AI/NLP OpenAI / Gemini via API, Sentence Transformers

🚀 Quick Start

1. Clone the Repo

git clone https://github.com/professorlearncode/intelli-nutri.git
cd intelligent-nutri

2. Backend Setup (Flask)

cd backend
python3 -m venv venv
source venv/bin/activate
pip install -r ../requirements.txt

# Start the server
python run.py

3. Frontend Access

Open any of the following in your browser:

  • frontend/index.html → Main dashboard
  • frontend/preferences.html → User preferences form
  • frontend/chatbot.html → Chat with the AI chef

🧠 Core Features

  • 🧾 Preferences-Based Personalization: Tailored recipes based on user's dietary needs and health goals.
  • 🔍 Recipe Search: Search by ingredients or dishes, powered by vector similarity search.
  • 🧑‍🍳 AI Chatbot: Talk to a virtual chef using LLM-based prompts.
  • 🛒 Shopping List Generator: (Optional) Exportable ingredient lists for selected recipes.
  • 🧠 Agentic AI Flow (Optional): Voice control & web search coming soon.

👥 Team & Collaboration Guidelines

Branch Naming

  • main: Production-ready
  • dev: Ongoing development
  • feature/<feature-name>: Individual tasks

Suggested Roles

  • Frontend UI
  • Form & preference logic
  • Chatbot & prompt design
  • Recipe search integration
  • Vector DB & embeddings
  • Integration & DevOps

Git Workflow

# Create a new branch
git checkout -b feature/<your-feature>

# Make changes and commit
git add .
git commit -m "Add: <describe your feature>"

# Push and create PR
git push origin feature/<your-feature>

📌 To-Do / Milestones

  • User Authentication
  • Preferences Form & Storage
  • Recipe Search via API & Dataset
  • Chatbot Integration with LLM
  • Vector DB Setup
  • Dashboard UI
  • Deployment (Optional: Vercel + Render)

📄 License

MIT License


🧠 Inspired by

Foodie brains, AI chefs, and real-world meal struggles.

“Good food is the foundation of genuine happiness.” – Auguste Escoffier

About

A smart web application that helps users discover personalized recipes, track nutrition, and get real-time cooking guidance using AI. Built with Flask, HTML/CSS/JS, and SQLite, it features a dynamic preferences form, AI chatbot, and recipe search powered by vector embeddings and LLMs.

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