NutriMind OS 🧬

AI-powered personal health intelligence platform — a full-stack project built to level up skills in React, Node.js, AI integration, and Google Cloud deployment.

NutriMind OS is a full-stack web application that acts as your personal AI health coach. Log meals through natural conversation, get real recipe suggestions from your pantry, track macros and calories in real time, and receive personalized nutrition advice based on your body details, health conditions, and goals — all powered by Llama 3.3 70B via Groq, deployed on Google Cloud Run.

Live Demo

🚀 https://nutrimind-ai-368778620053.us-central1.run.app

💻 GitHub: https://github.com/pallaviXD/NutriMind-Ai

Features

AI Chat Coach

Natural language meal logging — "I had idli sambar and a coffee for breakfast" → auto-parses food, estimates calories and macros, logs to database
Context-aware responses — AI knows your current calories, macros, pantry, health goal, age, weight, and conditions
Smart suggestions based on time of day and remaining nutrition targets
Quick action buttons — Skipped Meal, Ate Junk, Low Budget, High Protein
Full conversation history maintained per session

Dashboard (Command Center)

Live calorie progress ring with animated fill
Meal cards for Breakfast, Lunch, Dinner, Snacks — update in real time as you chat
Calorie distribution bar chart
AI Insights panel — adaptation and behavioral pattern analysis
Risk meter — Low / Medium / High based on food choices
Macro distribution pie chart
Consistency trend line chart
Water tracker — log glasses consumed (up to 8/day)
Weight logger — saves to database, shows in analytics

AI Kitchen

Add ingredients to your pantry
Select meal type (breakfast, lunch, dinner, snack)
AI generates a complete recipe using only your available ingredients
Recipe includes: name, description, calories, protein, carbs, fat, cook time, full ingredient list with quantities, step-by-step instructions, and a chef's tip
Respects your health goal and any health conditions

Health Profile

Full body details form — gender, date of birth, height, weight, neck/waist/hip measurements, activity level
Live BMI calculation with category
Health conditions selector — Hypertension, High Cholesterol, PCOS, Thyroid, Anemia, IBS, Lactose Intolerant, Gluten Intolerant
5 health goal presets — Gym & Muscle, Weight Loss, Diabetes Care, Cardiac Health, General Wellness
AI Food Advice — personalized eat/avoid food lists and tips generated from your exact profile
All details saved to database and used by AI for personalized responses

Analytics (Real Data)

All charts powered by actual meal logs from the database — no fake data
Calorie trend over 7 / 14 / 30 days
Macro breakdown (protein, carbs, fat) per day
Weight trend chart from logged weigh-ins
Today's calories by meal type
Logging streak counter
Total meals logged

Workouts

5 goal-specific workout plans — Hypertrophy Split, Fat-Loss HIIT, Diabetic-Friendly, Cardiac Zone 2, General Wellness
Expandable exercise cards with sets, reps, rest time, and target muscles
Nutrition protocols for each goal

Authentication

Email + password signup (instant, no email verification required)
JWT-based sessions stored in localStorage
Protected routes — redirects to login if unauthenticated
Profile setup onboarding — body details + health goal before accessing dashboard

Tech Stack

Layer	Technology
Frontend	React 19, Vite 8, React Router 7
Styling	Tailwind CSS 3.4, Framer Motion 12
Charts	Recharts 3
Icons	Lucide React
Backend	Express 5, Node.js 20 (ESM)
Database	SQLite via better-sqlite3
AI	Llama 3.3 70B via Groq SDK
Auth	JWT (jsonwebtoken) + bcryptjs
Security	Helmet, express-rate-limit
Deployment	Docker + Google Cloud Run

Project Structure

nutrimind-ai/
├── server/
│   ├── index.js              # Express app — security, rate limiting, static serving
│   ├── auth.js               # JWT sign/verify + auth middleware
│   ├── db.js                 # SQLite schema — users, profiles, meal_logs, weight_logs
│   ├── mailer.js             # Nodemailer (optional email)
│   └── routes/
│       ├── authRoutes.js     # POST /signup, POST /login
│       └── userRoutes.js     # profile, stats, chat, recipe, health-advice,
│                             # meal-log, weight-log, analytics
├── src/
│   ├── context/
│   │   ├── AuthContext.jsx   # User session, profile, auth methods
│   │   └── GlobalContext.jsx # Nutrition state, AI processing, meal persistence
│   ├── services/
│   │   └── aiService.js      # Calls /api/user/chat, fallback for offline
│   ├── components/
│   │   ├── Dashboard.jsx     # 3-panel layout
│   │   ├── LeftPanel.jsx     # AI chat interface + quick actions
│   │   ├── CenterPanel.jsx   # Calorie ring, meal cards, distribution chart
│   │   ├── RightPanel.jsx    # Insights, charts, water tracker, weight log
│   │   ├── Kitchen.jsx       # Pantry manager + AI recipe generator
│   │   ├── HealthProfile.jsx # Body details + goal selector + AI food advice
│   │   ├── Analytics.jsx     # Real data charts from database
│   │   ├── Workouts.jsx      # Goal-specific workout plans
│   │   ├── Sidebar.jsx       # Navigation
│   │   └── SystemStatusLayer.jsx # Ambient background effects
│   ├── pages/
│   │   ├── LoginPage.jsx
│   │   ├── SignupPage.jsx
│   │   ├── SetupProfile.jsx  # Onboarding — body details + health goal
│   │   └── VerifyEmailPage.jsx
│   └── App.jsx               # Routes, protected route logic
├── Dockerfile
├── cloudbuild.yaml
├── .env.example
└── vite.config.js            # Vite proxy — /api → localhost:3001

Database Schema

users         — id, name, email, password_hash, is_verified, created_at
profiles      — user_id, height_cm, weight_kg, gender, date_of_birth,
                neck_cm, waist_cm, hip_cm, activity_level, health_goal,
                health_conditions, target_weight_kg
meal_logs     — user_id, food_name, calories, protein, carbs, fat, meal_type, logged_at
weight_logs   — user_id, weight_kg, notes, logged_at

API Endpoints

Method	Endpoint	Description
POST	/api/auth/signup	Register new user
POST	/api/auth/login	Login, returns JWT
GET	/api/user/profile	Get user + profile
PUT	/api/user/profile	Update profile details
GET	/api/user/stats	BMI, BMR, TDEE, ideal weight
POST	/api/user/chat	AI chat (Groq Llama 3.3 70B)
POST	/api/user/recipe	Generate AI recipe from pantry
POST	/api/user/health-advice	Personalized food advice
POST	/api/user/meal-log	Save meal to database
GET	/api/user/meal-log	Get recent meal logs
POST	/api/user/weight-log	Log weight
GET	/api/user/weight-log	Get weight history
GET	/api/user/analytics	Real aggregated nutrition data
GET	/api/health	Health check

Getting Started

Prerequisites

Node.js 18+
A free Groq API key from console.groq.com

1. Clone the repo

git clone https://github.com/pallaviXD/NutriMind-Ai.git
cd NutriMind-Ai

2. Install dependencies

npm install

3. Set up environment variables

cp .env.example .env

Open .env and fill in:

JWT_SECRET=any_long_random_string_here
PORT=3001
CLIENT_URL=http://localhost:5173
# FIX: Added required VITE_ prefix to ensure proper environment resolution on the frontend
VITE_GROQ_API_KEY=your_groq_api_key_here

Getting a Groq API key (free, no credit card):

Go to console.groq.com
Sign up with Google
Go to API Keys → Create API Key
Copy and paste into .env

4. Run the app

# Run both frontend and backend together
npm run dev:all

# Or run separately in two terminals:
npm run server    # backend on :3001
npm run dev       # frontend on :5173

Open http://localhost:5173

Deployment on Google Cloud Run

Option 1 — Deploy from source (recommended)

Make sure you have the Google Cloud SDK installed and authenticated.

# Set your project
gcloud config set project YOUR_PROJECT_ID

# Deploy directly from source (builds and deploys in one command)
gcloud run deploy nutrimind-ai \
  --source . \
  --region us-central1 \
  --allow-unauthenticated \
  --port 3001 \
  --set-env-vars "NODE_ENV=production,JWT_SECRET=your_secret,GROQ_API_KEY=your_groq_key"

Option 2 — Docker build + push

# Build image
docker build -t gcr.io/YOUR_PROJECT_ID/nutrimind-ai .

# Push to Container Registry
docker push gcr.io/YOUR_PROJECT_ID/nutrimind-ai

# Deploy to Cloud Run
gcloud run deploy nutrimind-ai \
  --image gcr.io/YOUR_PROJECT_ID/nutrimind-ai \
  --region us-central1 \
  --allow-unauthenticated \
  --port 3001 \
  --set-env-vars "NODE_ENV=production,JWT_SECRET=your_secret,GROQ_API_KEY=your_groq_key"

Option 3 — Cloud Build (CI/CD)

gcloud builds submit --config cloudbuild.yaml \
  --substitutions _JWT_SECRET=your_secret,_GROQ_API_KEY=your_groq_key

After deployment, Cloud Run gives you a URL like: https://nutrimind-ai-xxxxxxxx-uc.a.run.app

Environment Variables Reference

Variable	Required	Description
`JWT_SECRET`	Yes	Secret key for signing JWT tokens. Use a long random string.
`GROQ_API_KEY`	Yes	Groq API key for Llama 3.3 70B. Get free at console.groq.com
`PORT`	No	Server port. Defaults to 3001. Cloud Run sets this automatically.
`NODE_ENV`	No	Set to `production` to enable static file serving and CORS for all origins.
`CLIENT_URL`	No	Frontend URL for email links. Defaults to http://localhost:5173
`EMAIL_USER`	No	Gmail address for sending emails (optional feature)
`EMAIL_PASS`	No	Gmail App Password (optional feature)

Security

HTTP security headers via helmet
Rate limiting — 100 requests/15min general, 20 requests/15min on auth routes
Passwords hashed with bcryptjs (12 salt rounds)
JWT tokens with 7-day expiry
Parameterized SQL queries (no SQL injection)
Request body size limited to 10kb
CORS restricted to known origins in development

How the AI Works

All AI runs through the backend — no API keys exposed to the browser.

User sends a message in the chat
Frontend calls POST /api/user/chat with the message + full context (calories, macros, meals, pantry, health profile, chat history)
Backend loads the user's profile from the database (age, weight, height, conditions)
Builds a detailed system prompt with all context
Sends to Groq's Llama 3.3 70B with response_format: json_object
AI returns structured JSON with message + optional dashboard updates (newCalories, newMacros, newMeals)
Frontend updates the dashboard in real time
If a meal was logged, it's saved to the meal_logs table automatically

The same pattern applies to recipe generation (/api/user/recipe) and health advice (/api/user/health-advice).

Screenshots

Dashboard — 3-panel layout with AI chat, calorie tracking, and insights

AI Kitchen — pantry manager with AI-generated recipes

Health Profile — body details, conditions, and personalized food advice

Analytics — real data charts from your meal logs

Built With

Groq — ultra-fast LLM inference
Llama 3.3 70B — Meta's open-source language model
React — UI framework
Vite — build tool
Tailwind CSS — styling
Framer Motion — animations
Recharts — data visualization
Express — backend framework
better-sqlite3 — database
Google Cloud Run — deployment

License

MIT

Name		Name	Last commit message	Last commit date
Latest commit History 37 Commits
public		public
server		server
src		src
.dockerignore		.dockerignore
.env.example		.env.example
.gitignore		.gitignore
Dockerfile		Dockerfile
README.md		README.md
cloudbuild.yaml		cloudbuild.yaml
eslint.config.js		eslint.config.js
index.html		index.html
package-lock.json		package-lock.json
package.json		package.json
postcss.config.js		postcss.config.js
tailwind.config.js		tailwind.config.js
vite.config.js		vite.config.js

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NutriMind OS 🧬

Live Demo

Features

AI Chat Coach

Dashboard (Command Center)

AI Kitchen

Health Profile

Analytics (Real Data)

Workouts

Authentication

Tech Stack

Project Structure

Database Schema

API Endpoints

Getting Started

Prerequisites

1. Clone the repo

2. Install dependencies

3. Set up environment variables

4. Run the app

Deployment on Google Cloud Run

Option 1 — Deploy from source (recommended)

Option 2 — Docker build + push

Option 3 — Cloud Build (CI/CD)

Environment Variables Reference

Security

How the AI Works

Screenshots

Built With

License

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