johanste

Cadl for code gen

Requirements

• There should be no need for customizations. A "raw" service specification should be enough to generate a client library.

• The cost of customizations should be proportional to the amount of customizations we are making. It must not have a steep cost cliff when we want to customize a small aspect of the generated code (a.k.a. "the cost of the first customization should be low").

Solution

johanste

```plantuml
@startuml
abstract    Model
abstract    StructuredInformation
abstract    Entity
abstract    Operation
Operation : Id
Operation : status
abstract    Job
abstract    Batch

johanste

import json
import typing

import purl

HeadersType = typing.Union[typing.Sequence[typing.Tuple[str, typing.Any]], typing.Mapping[str, typing.Any], None]
JsonBodyType = typing.Union[typing.Mapping[str, "JsonBodyType"], str, int, float, bool, None]
ParamType2 = typing.Union[typing.Sequence[typing.Tuple[str, typing.Any]], typing.Mapping[str, typing.Any]]
ParamType = typing.Union[typing.Sequence[typing.Tuple[str, typing.Any]], typing.Mapping[str, typing.Any]]

johanste

{
    "swagger": "2.0",
    "info": {
        "title": "Examples of scenarios - #1, more constrained children",
        "version": "1.0"
    },
    "paths": [],
    "definitions": {
        "GenericResource": {
            "type": "object",

johanste

{
    "swagger": "2.0",
    "info": {
        "title": "Show x-ms-paths",
        "version": "1.0"
    },
    "x-ms-paths": {
        /* Note - fake query parameter to make the path unique. Not actually used in the API. 
           You can also differentiate using a different path parameter name if the path is parameterized 
        */

johanste

import typing


class InfoFragment(typing.TypedDict):

    title: str
    version: str


OptionalInAttribute = typing.TypedDict(

johanste

{
    "swagger": "2.0",
    "info": {
        "title": "Example of polymorphic models, hypothetical document recognition style",
        "version": "1.0"
    },
    "consumes": ["application/json"],
    "produces": ["application/json"],
    "paths": {
        "/analyze": {

johanste

{
    "swagger": "2.0",
    "info": {
        "title": "Example service",
        "version": "1.1"
    },
    "paths": {
        "/ComputeNodes": {
            "parameters": [
                {

johanste

class AiEventMeta(type):

    def __init__(cls, name, bases, nmspc):
        super().__init__(name, bases, nmspc)
        if not hasattr(cls, 'event_type_registry'):
            setattr(cls, 'event_type_registry', {})
        if hasattr(cls, 'EVENT_TYPE_NAME'):
            cls.event_type_registry[cls.EVENT_TYPE_NAME] = cls

class AiEvent(metaclass=AiEventMeta):

johanste

import carbon

video_stream = ...

# Module level function lets you pick a stream, "features" and go 
# (this assumes there is a default configuration for each feature/category/model)
for event in carbon.recognize(video_stream, features=['faceDetection', 'vehicleDetection']):
    print(event)

# Slightly more involved scenario with custom configurations and a dedicated session...