erwan-lemonnier

from pymacaron.auth import get_userid

# Implements an hypothetical endpoint that let's a user (identified by a JWT token)
# add another user as friend (identified by its user ID in the data object received
# as parameter, mapping to the POST request's body)

def do_add_friend(data):

    viewer_id = get_userid()   # Extract the ID of the current user from its JWT auth token
    friend_id = data.user_id   # And 'user_id' in the POST request is the friend's ID

erwan-lemonnier

# Here is a more complicated endpoint that retrieve objects
# from datastore in multiple phases, and build up a queue of objects
# to bulk commit at the end

from pymacaron.auth import get_userid

def do_get_recommended_new_friends(data):
  
  cache = ObjectCache()
  saver = ObjectSaver(cache=cache)

erwan-lemonnier

{
  "message_id": "1234567890",
  "chat_id": "chat-123",
  "author_id": "user-456",
  "text": "this is a message",
  "date_created": "2020-11-28 13:15:09+00:00"
}

erwan-lemonnier

{
  "message_id": "1234567890",
  "chat_id": "chat-123",
  "thread_id": "branch-789",
  "author_id": "user-456",
  "type": "TEXT",
  "text": "this is a message",
  "image_url": null,
  "count_views": 99,
  "count_likes": 22,

erwan-lemonnier

class Message():

  def normalize(self):
    # If message lacks a 'likes' counter, initialize one
    if not hasattr(self, 'count_likes'):
      self.count_likes = 0
      
# Each time you are fetching a message from datastore, do
# something like:
msg = fetch_from_database(message_id)

erwan-lemonnier

{
  "user_id": "user-123",
  "chat_id": "chat-123456",
}

{
  "user_id": "user-123",
  "chat_id": "chat-789012",
}

erwan-lemonnier

{
  "user_id": "user-123",
  "chat_id": "chat-456",
  "thread_id": "branch-abc",
  "date_created": "2020-11-28 13:15:09+00:00",
  "date_last_seen": "2020-11-30 08:26:11+00:00",
  "is_muted": false,
  "is_active": true,
}

erwan-lemonnier

{
  "timestamp": "2020-11-28T04:11:40.530Z",
  "eventId": "string",
  "eventType": "MESSAGE_ADDED",
  "message": {
    "messageId": "string",
    "chatId": "string",
    "parentId": "string",
    "isRead": true,
    "type": "USER",

erwan-lemonnier

    renderer = PageRenderer()
    pdf_document = load_from_file(pdf)

    emails = []

    for page_number in range(pdf_document.pages):
        page = pdf_document.create_page(page_number)
        image = renderer.render_page(page)

        # Convert pdf image to PIL image

erwan-lemonnier

        # Use vision api to extract all text in the image
        response = gvc.annotate_image({
            'image': {
                'source': {
                    'image_uri': 'gs://%s/%s' % (bucket_name, IMG_NAME),
                }
            },
            'features': [
                {
                    'type_': vision.Feature.Type.DOCUMENT_TEXT_DETECTION,