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=================================================
ML(Machine Learning) Workbench Predictor Service Developer Guide
=================================================

1.    Overview
=================

         This is the developers guide to ML Workbench Predictor Service.

1.1. What is ML Workbench Predictor Service\?
---------------------------------------------

    ML Workbench Predictor Service expose API to allow to perform CRUD operation on Predictor in ML Workbench.

2. Technology and Frameworks
=============================
  **List of the development languages, frameworks, etc.**

  #. Springboot 2.1.7.RELEASE
  #. Java 11
  #. Maven 4.0.0
  #. Jackson 2.7.5
  #. JUnit 4.12

3.    Project Resources
==========================

- Gerrit repo: `workbench/predictor-service <https://gerrit.acumos.org/r/#/admin/projects/workbench>`_
- `Jira <https://jira.acumos.org/browse/ACUMOS-3492>`_  Predictor Service API

4. Development Setup
=====================

    1. Clone or download code from "**Gerrit repo**" mentioned above.

    2. Import prdicotr-service Project in IDE (viz., Eclipse or STC)

    3. Once successfully imported, set the required properties in application.properties file.

    4. Run as Springboot application.

    5. Access using **Swagger UI** : http://localhost:9096/mlWorkbench/v1/predictor/swagger-ui.html#

    6. Once you get the Swagger UI, click Authorize button and provide JWT token as below :
        Bearer <JWT token for Acumos User>

        **Note:** JWT token value can be obtained after successful login in Acumos.

    7. After successfully setting Authorize value, API are available to access.  Following are the sample inputs :

        1. Associate Predictor to a Project

            "**authenticatedUserId**" : <Acumos User login Id>

            "**predictorProjAssociation**" :
                {
                    "userId": "<Acumos User Login Id>",
					
                    "solutionId": "<Solution UUID",
					
                    "revisionId": "<Revision UUID>",
					
                    "modelStatus": "ACTIVE",
					
                    "predictorDeploymentStatus": "ACTIVE",
					
                    "predictorName": "<Predictor Name>",
					
                    "predictorkey":"<Predictor Unique Key>",
					
                    "environmentPath": "<URL to access Predictor>",
					
                    "metadata1": "MetaData2",
					
                    "metadata2": "MetaData2"
					
               }
            
            "**projectId**" : <Project Id>
			

        2. Get Predictors associated to a Project

            "**authenticatedUserId**" : <Acumos User login Id>

            "**modelId**" : <Solution UUID>

            "**version**" : <Solution version>
			

        3. Edit Predictor association to a Project

            "**associationData**" :
                {
                    "userId": "<Acumos User Login Id>",
					
					"projectId": "<Project UUID>",
					
                    "solutionId": "<Solution UUID",
					
                    "revisionId": "<Revision UUID>",
					
                    "preidctorId": "<Predictor UUID>",
					
                    "predictorName": "<Predictor name>",
					
					"environmentPath": "<URL to access Predictor>",
					
                    "predictorkey":"<Predictor Unique Key>",
					
               }

            "**associationId**" : <Predictor Project Association UUID>
			
            "**authenticatedUserId**" : <Acumos User login ID>

            "**predictorId**" : <Predictor UUID associated to Project>


		4. Delete Predictor Project association

            "**associationId**" : <Predictor Project Association UUID >
			
			"**authenticatedUserId**" : <Acumos User login Id>
            
			
			
        5. Get Predictor Details for given input Model

            "**authenticatedUserId**" : <Acumos User login Id>

            "**projectId**" : <Project UUID>