Seeding Data

Populate the SmartEat AI database with initial test data

Overview

The seeding system populates the database with initial data for development and testing. SmartEat AI includes seeders for users, categories, profiles, recipes, and meal plans.

What Gets Seeded

The seeder system creates the following data:

1. Users - Test user accounts with hashed passwords
2. Categories - Meal types and diet types
3. Profiles - User nutritional profiles with goals and preferences
4. Recipes - Recipe library from JSON data
5. Plans - Sample meal plans with daily menus

Running the Seeder

Local Environment

Run the seeder script directly:

python backend/app/seeders/run_seed.py

Or from the backend directory:

cd backend
python app/seeders/run_seed.py

Docker Environment

Run the seeder inside the backend container:

docker exec smarteatai_backend python -m app.seeders.run_seed

Or with PYTHONPATH explicitly set:

docker exec smarteatai_backend bash -c "PYTHONPATH=/app python -m app.seeders.run_seed"

Prerequisites:

• Database must be running and accessible
• All migrations must be applied (alembic upgrade head)
• Environment variables must be configured in .env

Seeder Execution Order

Seeders run in a specific order to respect foreign key constraints:

1. Users

   Creates test user accounts:

   • [e-mail] (password: Ruyi1234)
   • [e-mail] (password: Elias1234)
   • [e-mail] (password: Cris1234)

   From: backend/app/seeders/users.py:8

2. Categories

   Populates meal types and diet types:

   Meal Types:

   • breakfast
   • lunch
   • dinner
   • snack

   Diet Types:

   • high_protein
   • low_carb
   • vegan
   • vegetarian
   • low_calorie
   • high_fiber
   • high_carb

   From: backend/app/seeders/categories.py:8

3. Profiles

   Creates nutritional profiles for test users with:

   • Physical metrics (height, weight, body type)
   • Goals (lose/maintain/gain weight)
   • Activity levels
   • Macro targets
   • Dietary preferences and restrictions

   From: backend/app/seeders/profiles.py

4. Recipes

   Loads recipes from backend/app/data/recipes.json including:

   • Recipe names and descriptions
   • Nutritional information (calories, protein, carbs, fat)
   • Meal type associations
   • Diet type compatibility
   • Image URLs and recipe links
   • Ingredients list

   From: backend/app/seeders/recipes.py:7

5. Plans

   Generates sample meal plans:

   • Weekly meal schedules
   • Daily menus for each day
   • Meal details with assigned recipes
   • Meal timing and status

   From: backend/app/seeders/plans.py

Seeder File Structure

All seeder files are located in backend/app/seeders/:

backend/app/seeders/
├── run_seed.py          # Main seeder orchestrator
├── users.py             # User account seeder
├── categories.py        # Meal and diet type seeder
├── profiles.py          # User profile seeder
├── recipes.py           # Recipe data seeder
└── plans.py             # Meal plan seeder

Main Seeder Script

The run_seed.py file orchestrates all seeders:

from app.database import SessionLocal
from app.seeders.users import seed_users
from app.seeders.categories import seed_categories
from app.seeders.profiles import seed_profiles
from app.seeders.recipes import seed_recipes
from app.seeders.plans import seed_plans

def run_seeders():
    db = SessionLocal()
    try:
        seed_users(db)
        seed_categories(db)
        seed_profiles(db)
        seed_recipes(db)
        seed_plans(db)
    finally:
        db.close()

if __name__ == "__main__":
    run_seeders()

From: backend/app/seeders/run_seed.py:9

Individual Seeder Examples

User Seeder

The user seeder creates test accounts with hashed passwords:

def seed_users(db: Session):
    users_data = [
        {
            "name": "Ruyi",
            "email": "[e-mail]",
            "password": "Ruyi1234",
        },
        # ... more users
    ]

    for user_data in users_data:
        existing_user = get_user_by_email(db, user_data["email"])
        
        if existing_user:
            print(f"User already exists: {user_data['email']}")
            continue
        
        user_schema = UserCreate(**user_data)
        AuthService.register_user(db, user_schema)
        print(f"User created: {user_data['email']}")

From: backend/app/seeders/users.py:8

Category Seeder

The category seeder populates diet and meal types:

def seed_diet_types(db: Session):
    diet_types = [
        "high_protein",
        "low_carb",
        "vegan",
        "vegetarian",
        "low_calorie",
        "high_fiber",
        "high_carb",
    ]

    for name in diet_types:
        category_schema = CategoryBase(name=name)
        exists = existing_diet_type(db, category_schema.name)
        
        if not exists:
            create_diet_type(db, category_schema)
    
    db.commit()

From: backend/app/seeders/categories.py:8

Recipe Seeder

The recipe seeder loads from a JSON file:

def seed_recipes(db: Session):
    current_dir = os.path.dirname(os.path.abspath(__file__))
    recipes_path = os.path.join(current_dir, "..", "data", "recipes.json")

    with open(recipes_path, encoding="utf-8") as f:
        recipes_data = json.load(f)

    for recipe_data in recipes_data:
        create_recipe(db, recipe_data)

    db.commit()
    print("🌱 Recipes seeded successfully")

From: backend/app/seeders/recipes.py:7

Idempotent Seeding

Seeders are designed to be idempotent - running them multiple times won’t create duplicate data:

existing_user = get_user_by_email(db, user_data["email"])

if existing_user:
    print(f"User already exists: {user_data['email']}")
    continue

This allows you to safely re-run seeders without clearing the database.

Recipe Data Format

Recipes are stored in backend/app/data/recipes.json with this structure:

{
  "name": "Avocado Toast",
  "calories": 300,
  "protein": 8,
  "carbs": 35,
  "fat": 15,
  "image_url": "/images/avocado-toast.jpg",
  "recipe_url": "https://example.com/recipe/123",
  "recipe_id": 123,
  "ingredients": "2 slices bread, 1 avocado, salt, pepper",
  "meal_types": ["breakfast", "lunch"],
  "diet_types": ["vegetarian", "high_fiber"]
}

Customizing Seed Data

Modify Test Users

Edit backend/app/seeders/users.py to add or change test accounts:

users_data = [
    {
        "name": "Your Name",
        "email": "[e-mail]",
        "password": "YourPassword123",
    },
]

Add More Categories

Edit backend/app/seeders/categories.py to add meal or diet types:

meal_types = [
    "breakfast",
    "lunch",
    "dinner",
    "snack",
    "brunch",  # New meal type
]

Add Custom Recipes

Edit backend/app/data/recipes.json to include your own recipes:

[
  {
    "name": "Your Recipe",
    "calories": 400,
    "protein": 25,
    "carbs": 30,
    "fat": 15,
    "image_url": "/images/your-recipe.jpg",
    "recipe_url": "https://yoursite.com/recipe",
    "recipe_id": 999,
    "ingredients": "list of ingredients",
    "meal_types": ["lunch", "dinner"],
    "diet_types": ["high_protein"]
  }
]

Clearing Seeded Data

To remove seeded data and start fresh:

1. Downgrade migrations

   Local

   alembic downgrade base
   
2. Upgrade migrations

   Local

   alembic upgrade head
   
3. Run seeders again

   Local

   python backend/app/seeders/run_seed.py
   

Warning: This will delete ALL data in the database. Only use in development environments.

Troubleshooting

Import Errors

If you get import errors when running seeders:

# Make sure you're in the correct directory
cd backend

# Or set PYTHONPATH
export PYTHONPATH=/path/to/backend
python app/seeders/run_seed.py

Database Connection Issues

Ensure your .env file has the correct database URL:

DATABASE_URL=postgresql://user:password@localhost:5432/smarteatai

Foreign Key Constraint Errors

Make sure migrations are up to date:

alembic upgrade head

JSON File Not Found

The recipes seeder expects backend/app/data/recipes.json to exist. Verify the file path and structure.

Best Practices

Seeding guidelines:

1. Always run migrations before seeding
2. Use seeders for development and testing only
3. Don’t use seed data in production
4. Keep seed data realistic but minimal
5. Document any custom seed data
6. Make seeders idempotent to avoid duplicates
7. Test seeders after schema changes

See Also

• Database Schema - Understanding the database structure
• Database Migrations - Managing schema changes
• Backend README - Backend setup guide