🧬 FitMind AI — Nutrition & Workout Planner v2.0

A fully free, production-ready AI nutrition and fitness planner using RAG (Retrieval-Augmented Generation) with streaming responses, vision-enabled meal analysis, and a high-performance Next.js interface.

Tech Stack (Optimized for Free Tier)

Component	Tool	Why?
Frontend	Next.js 15+ (React 19)	Premium UI, App Router, Vercel ready
LLM	Groq (Llama 3.3 70B)	Instant responses, free tier
Embeddings	FastEmbed (Local)	Ultra-lightweight, no 404 errors, fits in 512MB
Vector DB	Qdrant (In-Memory)	Blazing fast retrieval, zero-config
Backend	FastAPI	High-performance Python API
Vision AI	Groq Llama 3.2 Vision	Instant food identification from photos

Key Features

• 🔴 Real-time streaming responses via Server-Sent Events (SSE)
• 📸 Vision AI — upload meal photos for instant macro estimations
• 📊 Animated Charts — interactive macro donut charts and weight trackers
• 🛒 AI Shopping List — automatically categorize ingredients from your plans
• 📋 PDF Export — download your personalized protocols instantly
• 🗂️ Tabbed Results — clean separation of Overview, Nutrition, Meals, and Workouts
• 💾 Local Persistence — your profiles and history stay on your device

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Project Structure

FitMindAI/
├── backend/            # FastAPI Application
│   ├── main.py         # API routes & lifespan
│   ├── ingestion.py    # FastEmbed RAG pipeline
│   ├── planner.py      # LLM logic & vision analysis
│   └── calculator.py   # Nutrition formulas
├── frontend/           # Next.js Application
├── knowledge_base/     # Your .txt data for RAG
├── Dockerfile          # Production deployment config
├── vercel.json         # Frontend deployment config
└── requirements.txt    # Python dependencies

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Quick Start (Local)

1. Backend Setup

# Create and activate virtual environment
python -m venv venv
source venv/bin/activate  # venv\Scripts\activate on Windows

# Install lightweight dependencies
pip install -r requirements.txt

# Start the API
uvicorn backend.main:app --reload

2. Frontend Setup

cd frontend
npm install
npm run dev

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Environment Variables

Create a .env file in the root (and add these to Render/Vercel):

# GROQ API Key (from https://console.groq.com)
GROQ_API_KEY=your_key_here

# For Production (Vercel Frontend)
NEXT_PUBLIC_API_URL=your_backend_url_on_render

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Deployment Strategy

🛰️ Backend (Render / Railway)

• Deployment Type: Docker
• Root Directory: ./
• Why?: The custom Dockerfile handles the lightweight FastEmbed model perfectly within the 512MB free tier.

🌐 Frontend (Vercel)

• Deployment Type: Next.js
• Root Directory: ./
• Why?: Uses vercel.json to correctly route the frontend/ subdirectory while keeping the root clean.

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How the RAG Pipeline Works

1. Ingest: On startup, FastEmbed chunks and embeds .txt files from knowledge_base/.
2. Search: When you ask a question, the AI searches the local Qdrant store for the most relevant context.
3. Generate: Groq's Llama 3.3 70B combines your profile, macros, and the retrieved context to build a perfect plan.