Discover scoring model + Add Swagger endpoint to scoring container

[algattik]

Implemented best practice recommended in https://docs.microsoft.com/en-us/azure/machine-learning/how-to-deploy-and-where

• Discover model rather than hard-coding the name in score.py
• Add OpenAPI (Swagger) endpoint to scoring container

This contribution was developed into a fork by @Anaig .

curl -s http://***.westeurope.azurecontainer.io/swagger.json | jq .
{
  "swagger": "2.0",
  "info": {
    "title": "diabetes-aci",
    "description": "API specification for the Azure Machine Learning service diabetes-aci",
    "version": "1.0"
  },
  "schemes": [
    "https"
  ],
  "consumes": [
    "application/json"
  ],
  "produces": [
    "application/json"
  ],
  "securityDefinitions": {
    "Bearer": {
      "type": "apiKey",
      "name": "Authorization",
      "in": "header",
      "description": "For example: Bearer abc123"
    }
  },
  "paths": {
    "/": {
      "get": {
        "operationId": "ServiceHealthCheck",
        "description": "Simple health check endpoint to ensure the service is up at any given point.",
        "responses": {
          "200": {
            "description": "If service is up and running, this response will be returned with the content 'Healthy'",
            "schema": {
              "type": "string"
            },
            "examples": {
              "application/json": "Healthy"
            }
          },
          "default": {
            "description": "The service failed to execute due to an error.",
            "schema": {
              "$ref": "#/definitions/ErrorResponse"
            }
          }
        }
      }
    },
    "/score": {
      "post": {
        "operationId": "RunMLService",
        "description": "Run web service's model and get the prediction output",
        "security": [
          {
            "Bearer": []
          }
        ],
        "parameters": [
          {
            "name": "serviceInputPayload",
            "in": "body",
            "description": "The input payload for executing the real-time machine learning service.",
            "schema": {
              "$ref": "#/definitions/ServiceInput"
            }
          }
        ],
        "responses": {
          "200": {
            "description": "The service processed the input correctly and provided a result prediction, if applicable.",
            "schema": {
              "$ref": "#/definitions/ServiceOutput"
            }
          },
          "default": {
            "description": "The service failed to execute due to an error.",
            "schema": {
              "$ref": "#/definitions/ErrorResponse"
            }
          }
        }
      }
    }
  },
  "definitions": {
    "ServiceInput": {
      "type": "object",
      "properties": {
        "data": {
          "type": "array",
          "items": {
            "type": "array",
            "items": {
              "type": "integer",
              "format": "int64"
            }
          }
        },
        "request_headers": {}
      },
      "example": {
        "data": [
          [
            1,
            2,
            3,
            4,
            5,
            6,
            7,
            8,
            9,
            10
          ],
          [
            10,
            9,
            8,
            7,
            6,
            5,
            4,
            3,
            2,
            1
          ]
        ],
        "request_headers": ""
      }
    },
    "ServiceOutput": {
      "type": "array",
      "items": {
        "type": "integer",
        "format": "int64"
      },
      "example": [
        10,
        20
      ]
    },
    "ErrorResponse": {
      "type": "object",
      "properties": {
        "status_code": {
          "type": "integer",
          "format": "int32"
        },
        "message": {
          "type": "string"
        }
      }
    }
  }
}
```