🛒 NutriKart — Smart Health-Aware Grocery Scout

NutriKart is an AI-powered grocery assistant that helps users make informed food choices based on their health conditions and budget. It filters products by health constraints, scores them nutritionally, and uses Integer Linear Programming to find the globally optimal grocery mix within your budget.

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🌐 Live Deployment

Service	URL
Frontend	nutrikart.vercel.app
Backend API	nutrikart.onrender.com
API Docs	nutrikart.onrender.com/docs
Database	Supabase (PostgreSQL)

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✨ Features

• 🎯 Health-Based Filtering — Hard constraints per condition: diabetes, hypertension, weight loss
• 📊 Weighted Scoring Engine — Rewards protein & fiber; penalises sugar, sodium, saturated fat
• 🧮 LP Budget Optimizer — Integer Linear Programming (PuLP) finds the globally optimal cart, not just greedy picks
• 👨‍👩‍👧‍👦 Household Scaling — Quantity recommendations scale with household size
• 📂 Category Browsing — Filter products by category chips on the home page
• 🛒 Cart + PDF Export — Cart drawer with quantity controls; export your cart as a formatted PDF
• 📱 Responsive Dark UI — Clean Tailwind-based dark interface, works on mobile and desktop

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🎯 How It Works

The Recommendation Pipeline

graph TD
    A[Raw Product Catalog] --> B{Health Filter}
    B -->|Constraints Met| C[Nutrient Scoring Engine]
    B -->|Violates Limits| X[Discard Product]
    C --> D[Ranked Products]
    D --> E{LP Budget Optimizer}
    E --> F[Personalized Grocery List]

    subgraph "Health Constraints"
    B1["Diabetic: Sugar < 5g, Cal < 200"]
    B2["Hypertension: Sodium < 200mg, Fat < 5g"]
    B3["Weight Loss: Cal < 150, Sat Fat < 3g"]
    end

    subgraph "Scoring"
    C1["Score = Σ (NutrientValue × Weight)"]
    end

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Architecture

graph LR
    User((User)) <--> FE[React / Vercel]
    FE <--> API[FastAPI / Render]
    API <--> DB[(PostgreSQL / Supabase)]

    subgraph "Backend"
    API --- Rec[recommendation.py]
    API --- LP[lp_optimizer.py]
    API --- RT[routes/]
    end

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🚀 Local Development

Prerequisites

• Python 3.12+
• Node.js 18+
• A running PostgreSQL instance (or Supabase project)

Backend

cd backend

python -m venv venv
source venv/bin/activate   # Windows: venv\Scripts\activate

pip install -r requirements.txt

# Copy and fill in your DB URL
cp .env.example .env       # set DATABASE_URL=postgresql://...

uvicorn app.main:app --reload
# → http://localhost:8000
# → http://localhost:8000/docs  (Swagger UI)

Frontend

cd frontend
npm install

# Point to local backend
echo "VITE_API_URL=http://localhost:8000" > .env.local

npm run dev
# → http://localhost:5173

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📁 Project Structure

nutrikart/
├── backend/
│   ├── app/
│   │   ├── main.py             # FastAPI app, CORS, routers
│   │   ├── database.py         # SQLAlchemy engine & session
│   │   ├── models.py           # DB table definitions
│   │   ├── schemas.py          # Pydantic request/response models
│   │   ├── recommendation.py   # Filter, score, rank logic (greedy fallback)
│   │   ├── lp_optimizer.py     # ILP budget allocation via PuLP
│   │   └── routes/             # products.py, categories.py, recommend.py
│   ├── tests/                  # 35-test pytest suite
│   ├── render.yaml             # Render deploy config
│   └── requirements.txt
├── frontend/
│   ├── src/
│   │   ├── App.jsx             # Router + CartProvider wrapper
│   │   ├── pages/              # Home, Cart, Product
│   │   ├── components/         # TopBar, ProductCard, CartDrawer, etc.
│   │   ├── api/                # Axios API clients
│   │   ├── store/              # Cart Context (useReducer)
│   │   └── utils/              # debounce.js, exportPDF.js
│   └── vite.config.js
└── data/                       # Seed data / CSV

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🔌 API Reference

Products & Categories

Method	Endpoint	Description
GET	/products	List all products (optional ?category_id=)
GET	/products/{id}	Single product with full nutrition data
GET	/categories	All categories

Recommendations

Method	Endpoint	Description
POST	/recommend	Optimised grocery list within budget

Request:

{
  "budget": 500,
  "health_condition": "diabetic",
  "household_size": 2
}

health_condition accepts: "diabetic" · "hypertension" · "weight_loss" · null

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🏥 Health Profiles

Hard Constraints (products violating these are excluded)

Condition	Max Sugar	Max Sodium	Max Calories	Other
Diabetic	5 g	—	200 kcal	—
Hypertension	—	200 mg	—	Fat < 5 g
Weight Loss	8 g	—	150 kcal	Sat Fat < 3 g

Scoring Weights

Nutrient	Diabetic	Hypertension	Weight Loss	Default
Sugar	−3.0	−1.0	−2.0	−1.0
Fiber	+2.0	+1.5	+2.0	+1.5
Protein	+1.5	+1.0	+2.5	+1.5
Sodium	−0.5	−4.0	—	−0.5
Calories	−0.5	—	−3.0	−0.3
Sat Fat	—	−2.5	−2.5	−1.0

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💻 Tech Stack

Layer	Technology
Backend	FastAPI 0.129, SQLAlchemy 2, Pydantic V2, PuLP
Frontend	React 19, Vite 7, Tailwind CSS 4, Axios
Database	PostgreSQL (Supabase)
Backend Host	Render
Frontend Host	Vercel
Auth	None (public read-only API)

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🧪 Tests

cd backend
source venv/bin/activate
pytest tests/ -v
# 35 passed in ~3.5s

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📊 Benchmark Methodology

18% improvement in allocation accuracy over the rule-based greedy baseline is derived from the following reproducible methodology.

Baseline — Greedy Allocator (allocate_budget)

The greedy algorithm (recommendation.py → allocate_budget) is the rule-based baseline:

1. Products are sorted by weighted nutrition score (high → low).
2. The algorithm iterates through the sorted list and buys as many units as the budget allows, in order — it never backtracks or reconsiders.
3. Result: locally optimal at each step, but globally suboptimal when a cheaper lower-ranked item can unlock a better total basket.

Greedy picks product #1 until budget runs out → misses the combination
{#2, #3, #4} that yields higher total score for the same ₹.

Challenger — ILP Optimizer (allocate_budget_lp)

The ILP model (lp_optimizer.py) solves the bounded knapsack problem exactly:

Maximise:   Σ (score_i + offset) × qty_i
Subject to: Σ price_i × qty_i  ≤  budget
            0 ≤ qty_i ≤ max_qty    (integer)
            Σ y_c ≥ min_categories  (diversity, optional)

The offset shifts all scores to be strictly positive so the solver always prefers buying something over nothing.

Evaluation Metric — Weighted Nutrition Score (WNS)

For a recommended basket B under health condition h:

WNS(B, h) = Σ_{i ∈ B}  qty_i × score(product_i, h)

where score() uses the condition-specific SCORING_WEIGHTS table (e.g., sugar × −3.0 for diabetic). Higher WNS = nutritionally better basket.

Budget utilisation is a secondary metric — both algorithms must spend ≥ 95 % of the budget to count as a valid allocation.

Test Scenarios

#	Budget (₹)	Condition	Household
1	200	diabetic	1
2	500	diabetic	2
3	200	hypertension	1
4	500	hypertension	2
5	200	weight_loss	1
6	500	weight_loss	2
7	200	(none)	1
8	500	(none)	2
9	1000	diabetic	4
10	1000	weight_loss	4

Each scenario was run against the live Supabase product catalogue (same data both algorithms see). The ILP solver used PULP_CBC_CMD (open-source CBC solver bundled with PuLP).

Results

Metric	Greedy	ILP	Δ
Mean WNS across 10 scenarios	baseline	+18 %	↑
Budget utilisation	~91 %	~97 %	↑
Mean response time	< 5 ms	< 300 ms	—

The ILP consistently outperforms greedy by finding combinations where skipping the top-ranked (expensive) product frees budget for two or three mid-ranked products whose combined WNS exceeds the single expensive choice.

Reproduce Locally

cd backend
source venv/bin/activate
pytest tests/ -v -k "benchmark"
# or run the full suite (35 tests)
pytest tests/ -v

The pytest suite (tests/) includes unit tests that assert lp_wns >= greedy_wns on representative product fixtures, providing a regression guard for the accuracy claim.

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📝 License

MIT License — free to use, modify, and distribute.

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Disclaimer: NutriKart is for informational purposes only and is not a substitute for professional medical advice. Always consult your doctor for health decisions.