Django Integration

The USDA FDC library provides seamless integration with Django applications, allowing you to cache API responses in your database and use Django’s ORM to query food data.

Setup

Install the package with Django dependencies:
```
pip install usda-fdc[django]
```

Add the Django app to your INSTALLED_APPS in settings.py:

INSTALLED_APPS = [
    # ...
    'usda_fdc.django',
    # ...
]

Configure the FDC client in your Django settings:

# USDA FDC settings
FDC_API_KEY = "your_api_key_here"
FDC_API_URL = "https://api.nal.usda.gov/fdc/v1"
FDC_CACHE_ENABLED = True
FDC_CACHE_TIMEOUT = 86400  # 24 hours in seconds

Run migrations to create the necessary database tables:
```
python manage.py migrate
```

Using the Cache

The FdcCache class provides a caching layer that stores API responses in both Django models and Django’s cache:

from usda_fdc.django import FdcCache

# Initialize the cache
cache = FdcCache()

# Search for foods (results will be cached)
results = cache.search("apple")

# Get a food by FDC ID (will be cached in database)
food = cache.get_food(1750340)

# Get multiple foods (will use database cache when available)
foods = cache.get_foods([1750340, 1750341, 1750342])

# Force refresh from API
food = cache.get_food(1750340, force_refresh=True)

Django Models

The library provides Django models that mirror the FDC data structures:

from usda_fdc.django.models import FoodModel, NutrientModel

# Query foods directly using Django's ORM
branded_foods = FoodModel.objects.filter(data_type="Branded")

# Get foods with specific nutrients
high_protein = NutrientModel.objects.filter(
    name="Protein",
    amount__gt=20
).select_related('food')

for nutrient in high_protein:
    print(f"{nutrient.food.description}: {nutrient.amount}g protein")

Admin Integration

The library includes Django admin integration for managing food data:

# admin.py
from django.contrib import admin
from usda_fdc.django.models import FoodModel, NutrientModel

admin.site.register(FoodModel)
admin.site.register(NutrientModel)

Background Tasks

For large datasets, you can use background tasks to cache data:

from usda_fdc.django import FdcCache

def cache_popular_foods():
    cache = FdcCache()
    popular_ids = [1750340, 1750341, 1750342]  # Example IDs
    cache.get_foods(popular_ids)

# Use with Celery, Django Q, or other task queues
from django_q.tasks import async_task

async_task(cache_popular_foods)