Metadata-Version: 2.1
Name: cloudmesh-openapi
Version: 4.0.14
Summary: A command called openapi and foo for the cloudmesh shell
Home-page: https://github.com/cloudmesh/cloudmesh-openapi
Author: Gregor von Laszewski
Author-email: laszewski@gmail.com
License: Apache 2.0
Description: # Cloudmesh OpenAPI Service Generator
        
        
        > **Note:** The README.md page is outomatically generated, do not edit it.
        > To modify  change the content in
        > <https://github.com/cloudmesh/cloudmesh-openapi/blob/master/README-source.md>
        > Curley brackets must use two in README-source.md
        
        
        
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        ## Prerequisites
        
        * We use recommend Python 3.8.2 Python or newer.
        * We recommend pip version 20.0.2 or newer
        * We recommend that you use a venv (see developer install)
        * MongoDB installed as regular program not as service
        * Please run cim init command to start mongodb server
        
        We have not checked if it works on older versions.
        
        ## Installation
        
        Make sure that `cloudmesh` is properly installed on your machine and
        you have mongodb setup to work with cloudmesh.
        
        More details to setting up `mongo` can be found in the
        
        * [Cloudmesh
          Manual](https://cloudmesh.github.io/cloudmesh-manual/installation/install.html)
        
        ###  User Installation
        
        Make sure you use a python venv before installing. Users can install
        the code with
        
        .. code:: bash
        
            python -m venv ~/ENV3
            source ~/ENV3/bin/activate # on windows ENV3\Scripts\activate
            mkdir cm
            cd cm
            pip installl cloudmesh-installer get openapi 
            cms help
            cms gui quick
            # fill out mongo variables
            # make sure autinstall is True
            cms admin mongo install --force
        
            pip install cloudmesh-openapi
        
        
        ### Developer Installation
        
        Developers install also the source code
        
        .. code:: bash
        
            python -m venv ~/ENV3
            source ~/ENV3/bin/activate # on windows ENV3\Scripts\activate
            mkdir cm
            cd cm
            pip install cloudmesh-installer
            cloudmesh-installer get openapi 
            cms help
            cms gui quick
            # fill out mongo variables
            # make sure autinstall is True
            cms admin mongo install --force
        
        ## Overview
        
        When getting started using the `openapi`, please first call: 
        
        .. code:: bash
        
            cms help openapi
         
        This will show the available functions and options. For your
        convenience we include the manual page later on in this document.
        
        ## Quick steps to generate,start and stop CPU sample example
        
        Navigate to `~/cm/cloudmesh-openapi` folder and run following commands
        
        ### Generate yaml file
        
        .. code:: bash
        
            cms openapi generate get_processor_name --filename=./tests/server-cpu/cpu.py
        
        ### Start server 
        
        .. code:: bash
        
            cms openapi server start ./tests/server-cpu/cpu.yaml
        
        ### Issue a Request
        
        .. code:: bash
        
            curl -X GET "http://localhost:8080/cloudmesh/get_processor_name" -H "accept: text/plain"
        
        ### Stop server 
        
        .. code:: bash
        
            cms openapi server stop cpu
        
        ## End-to-end walkthrough
        
        ### Writing Python
        
        Cloudmesh uses introspection to generate an OpenAPI compliant YAML
        specification that will allow your Python code to run as a web
        service. For this reason, any code you write must conform to a set of
        guidelines.
        
        - The parameters and return values of any functions you write must use
          typing
        - Your functions must include docstrings
        - If a function uses or returns a class, that class must be defined as
          a dataclass in the same file
        
        The following function is a great example to get started. Note how x,
        y, and the return value are all typed. In this case they are all
        `float`, but other types are supported. The description in the
        docstring will be added to your YAML specification to help describe
        what the function does.
        
        .. code:: python
        
            def add(x: float, y: float) -> float:
                """
                adding float and float.
                :param x: x value
                :type x: float
                :param y: y value
                :type y: float
                :return: result
                :return type: float
                """
                return x + y
        
        ### Generating OpenAPI specification
        
        Once you have a Python function you would like to deploy as a web
        service, you can generate the OpenAPI specification. Navigate to your
        .py file's directory and generate the YAML. This will print
        information to your console about the YAML file that was generated.
        
        .. code:: bash
        
           cms openapi generate [function_name] --filename=[filename.py]
        
        If you would like to include more than one function in your web
        service, like addition and subtraction, use the `--all_functions`
        flag. This will ignore functions whose names start with '\_'.
        
        .. code:: bash
        
            cms openapi generate --filename=[filename.py] --all_functions
        
        You can even write a class like Calculator that contains functions for
        addition, subtraction, etc. You can generate a specification for an
        entire class by using the `--import_class` flag.
        
        .. code:: bash
        
            $ cms openapi generate [ClassName] --filename=[filename.py] --import_class
        
        ### Starting a server
        
        Once you have generated a specification, you can start the web service
        on your localhost by providing the path to the YAML file. This will
        print information to your console about the server
        
        .. code:: bash
        
            $ cms openapi server start ./[filename.yaml]
            
              Starting: [server name]
              PID:      [PID]
              Spec:     ./[filename.py]
              URL:      http://localhost:8080/cloudmesh
              Cloudmesh UI:      http://localhost:8080/cloudmesh/ui
              
        
        ### Sending requests to the server
        
        Now you have two options to interact with the web service. The first
        is to navigate the the Cloudmesh UI and click on each endpoint to test
        the functionality. The second is to use curl commands to submit
        requests.
        
        .. code:: bash
        
            $ curl -X GET "http://localhost:8080/cloudmesh/add?x=1.2&y=1.5" -H "accept: text/plain"
            2.7
        
        ### Stopping the server
        
        Now you can stop the server using the name of the server. If you
        forgot the name, use `cms openapi server ps` to get a list of server
        processes.
        
        .. code:: bash
        
            $ cms openapi server stop [server name]
        
        
        ### Basic Auth
        To use basic http authentication with a user password for the generated API, add the following flag at the end of a `cms openapi generate` command:
        
        ```
        --basic_auth=<username>:<password>
        ```
        We plan on supporting more users in the future.
        
        Example:
        ```
        cms openapi generate get_processor_name --filename=./tests/server-cpu/cpu.py --basic_auth=admin:secret
        ```
        
        ## Manual
        
        ```bash
        
        ```
        
        
        
        ## Pytests
        
        Please follow [Pytest Information](tests/README.md) document for
        pytests related information
        
        ## Examples
        
        ### One function in python file
        
        1. Please check [Python file](tests/server-cpu/cpu.py).
        
        1. Run below command to generate yaml file and start server
        
        .. code:: bash
        
            cms openapi generate get_processor_name --filename=./tests/server-cpu/cpu.py
        
        ### Multiple functions in python file
        
        1. Please check [Python file](tests/generator-calculator/calculator.py)
        
        1. Run below command to generate yaml file and start server
        
        .. code:: bash
        
            cms openapi generate --filename=./tests/generator-calculator/calculator.py --all_functions
            cms openapi generate server start ./tests/generator-calculator/calculator.py
        
        ### Function(s) in python class file
        
        1. Please check [Python file](tests/generator-testclass/calculator.py)
        
        1. Run below command to generate yaml file and start server
        
        .. code:: bash
        
            cms openapi generate Calculator --filename=./tests/generator-testclass/calculator.py --import_class"
            cms openapi server start ./tests/generator-testclass/calculator.yaml
            curl -X GET "http://localhost:8080/cloudmesh/Calculator/multiplyint?x=1&y=5"
            cms openapi server stop Calculator
        
        ### Uploading data
        
        Code to handle uploads is located in
        `cloudmesh-openapi/tests/generator-upload`. The code snippet in
        uploadexample.py and the specification in uploadexample.yaml can be
        added to existing projects by adding the `--enable_upload` flag to the
        `cms openapi generate` command. The web service will be able to
        retrieve the uploaded file from `~/.cloudmesh/upload-file/`.
        
        #### Upload example
        
        This example shows how to upload a CSV file and how the web service
        can retrieve it.
        
        First, generate the OpenAPI specification and start the server
        
        .. code:: bash
        
            cms openapi generate print_csv2np --filename=./tests/generator-upload/csv_reader.py --enable_upload
            cms openapi server start ./tests/generator-upload/csv_reader.yaml
        
        Next, navigate to localhost:8080/cloudmesh/ui. Click to open
        the /upload endpoint, then click 'Try it out.' Click to choose a file
        to upload, then upload `tests/generator-upload/np_test.csv`. Click
        'Execute' to complete the upload.
        
        The uploaded file will be located at
        `~/.cloudmesh/upload-file/[filename]`. `tests/generator-upload/csv_reader.py`
        contains some example code to retrieve the array in the uploaded
        file. To see this in action, click to open the /print_csv2np endpoint,
        then click 'Try it out.' Enter "np_test.csv" in the field that prompts
        for a filename, and then click Execute to view the numpy array defined
        by the CSV file.
        
        ### Pipeline Anova SVM Example
        This example is based on the sklearn example [here](https://scikit-learn.org/stable/auto_examples/feature_selection/plot_feature_selection_pipeline.html#sphx-glr-auto-examples-feature-selection-plot-feature-selection-pipeline-py)
        
        In this example, we will upload a data set to the server, tell the server to train the model, and utilize said model for
        predictions. 
        
        Firstly, ensure we are in the correct directory.
        ```
        > pwd
        ~/cm/cloudmesh-openapi
        ```
        
        Let's generate the yaml file from our python file to generate the proper specs for our service.
        ```
        > cms openapi generate PipelineAnovaSVM --filename=./tests/Scikitlearn-experimental/sklearn_svm.py --import_class --enable_upload
        ```
        
        Now let's start the server
        ```
        > cms openapi server start ./tests/Scikitlearn-experimental/sklearn_svm.yaml
        ```
        
        The server should now be active. Navigate to `http://localhost:8080/cloudmesh/ui`. We now have a nice user inteface to interact
        with our newly generated API. Let's upload the data set. We are going to use the iris data set in this example. We have provided it
        for you to use. Simply navigate to the `/upload` endpoint by clicking on it, then click `Try it out`. 
        
        We can now upload the file. Click on `Choose File` and upload the data set located at `~./tests/Scikitlearn-experimental/iris.data`.
        Simply hit `Execute` after the file is uploaded. We should then get a return code of 200 (telling us that everything went ok).
        
        The server now has our dataset. Let us now navigate to the `/train` endpoint by, again, clicking on it. Similarly, click `Try it out`.
        The parameter being asked for is the filename. The filename we are interested in is `iris.data`. Then click `execute`.
        We should get another 200 return code with a Classification Report in the Response Body.
        ```
        CLASSIFICATION_REPORT: 
                      precision    recall  f1-score   support
        
                   0       1.00      1.00      1.00         8
                   1       0.85      1.00      0.92        11
                   2       1.00      0.89      0.94        19
        
            accuracy                           0.95        38
           macro avg       0.95      0.96      0.95        38
        weighted avg       0.96      0.95      0.95        38
        ```
        
        Our model is now trained and stored on the server. Let's make a prediction now. As we have done, navigate to the `/make_prediction` endpoint.
        The information we need to provide is the name of the model we have trained as well as some test data. The name of the model will be the same
        as the name of the data-file (ie. iris). So type in `iris` into the `model_name` field. Finally for params, let's use the example `5.1, 3.5, 1.4, 0.2`
        as the model expects 4 values (attributes). After clicking execute, we should received a response with the classification the model has made given the parameters. 
        
        The response received should be as follows:
        ```
        "Classification: ['Iris-setosa']"
        ```
        
        We can make as many predictions as we like. When finished, we can shut down the server.
        ```
        > cms openapi server stop sklearn_svm
        ```
        ### Downloading data
        
        Always the same
        
        abc.txt <- /data/xyz/klmn.txt
        
        ### Merge openapi's
        
        
        .. code:: bash
        
            merge [APIS...] - > single.yaml
        
        ### Running AI Services in the Cloud using OpenApi
        
        #### Google
        
        After you create your google cloud account, it is recommended to
        download and install Google's [Cloud
        SDK](https://cloud.google.com/sdk/docs/quickstarts).  This will
        enable CLI. Make sure you enable all the required services.
        
        For example:
        
        .. code:: bash
        
            gcloud services enable servicemanagement.googleapis.com
            gcloud services enable endpoints.googleapis.com
        
        and any other services you might be using for your specific Cloud API
        function.
        
        To begin using the tests for any of the Google Cloud Platform AI
        services you must first set up a Google account (set up a free tier
        account): [Google Account
        Setup](https://cloud.google.com/billing/docs/how-to/manage-billing-account)
        
        After you create your google cloud account, it is recommended to
        download and install Google's [Cloud
        SDK](https://cloud.google.com/sdk/docs/quickstarts).  This will
        enable CLI. Make sure you enable all the required services.
        
        For example:
        
        .. code:: bash
        
            gcloud services enable servicemanagement.googleapis.com
        
            gcloud services enable servicecontrol.googleapis.com
        
            gcloud services enable endpoints.googleapis.com
        
        and any other services you might be using for your specific Cloud API
        function.
        
        It is also required to install the cloudmesh-cloud package, if not
        already installed:
        
        .. code:: bash
        
            cloudmesh-installer get cloud
            cloudmesh-installer install cloud
        
        This will allow you automatically fill out the cloudmesh yaml file
        with your credentials once you generate the servcie account JSON file.
        
        After you have verified your account is created you must then give your account access to the proper APIs and create a
         project in the Google Cloud Platform(GCP) console.
         
        1. Go to the [project
           selector](console.cloud.google.com/projectselector2/home/)
        
        2. Follow directions from Google to create a project linked to your
           account
        
        #### Quickstart Google Python API
        
        .. code:: bash
        
            pip install --upgrade google-api-python-client google-auth-httplib2 google-auth-oauthlib
        
        * For quickstart in using Google API for Python visit [here](https://developers.google.com/docs/api/quickstart/python)
        
        #### Setting up your Google account
        
        Before you generate the service account JSON file for your account you
        will want to enable a number of services in the GCP console.
        
        - Google Compute
        - Billing
        - Cloud Natural Language API
        - Translate API
        
        1. To do this you will need to click the menu icon in the Dashboard
           navigation bar. Ensure you are in the correct porject.
        
        2. Once that menu is open hover over the "APIs and Services" menu item
           and click on "Dashboard" in the submenu.
        
        3. At the dashboard click on the "+ Enable APIs and Services" button
           at the top of the dashboard
        
        4. Search for **cloud natural language** to find the API in the search
           results and click the result
        
        5. Once the page opens click "Enable"
        
        6. Do the same for the **translate** API to enable that as well
        
        7. Do the same for the **compute engine API** to enable that as well
        
        You must now properly set up the account roles to ensure you will have
        access to the API. Follow the directions from Google to [set up proper
        authentication](https://cloud.google.com/natural-language/docs/setup#auth)
        
        Make you account an owner for each of the APIs in the IAM tool as
        directed in the authentication steps for the natural language API.
        This makes your service account have proper access to the required
        APIs and once the private key is downloaded those will be stored
        there.
        
        It is VERY important that you create a service account and download
        the private key as described in the directions from Google.  If you do
        not the cms google commands will not work properly.
        
        Once you have properly set up your permissions please make sure you
        download your JSON private key for the service account that has
        permissions set up for the required API services. These steps to
        download are found
        [here](https://cloud.google.com/natural-language/docs/setup#sa-create).
        Please take note of where you store the downloaded JSON and copy the
        path string to a easily accessible location.
        
        
        The client libraries for each API are included in teh requirements.txt file for the openapi proejct and should be isntalled when the
        package is installed. If not, follow directions outlined by google install each package:
        
        .. code:: bash
        
            google-cloud-translate
            google-cloud-language
        
        To pass the information from your service account private key file ot
        the cloudmesh yaml file run the following command:
        
        .. code:: bash
        
            cms register update --kind=google --service=compute --filename=<<google json file>>
        
        ##### Running the Google Natural Language and Translate REST Services
        
        1. Navigate to the `~/.cloudmesh` repo and create a cache directory
           for your text examples you would like to analyze.
        
        .. code:: bash
            
            mkdir text-cache
            
        2. Add any plain text files your would like to analyze to this
           directory with a name that has no special characters or spaces.
           You can copy the files at this location,
           `./cloudmesh-openapi/tests/textanaysis-example-text/reviews/` into
           the text-cache if you want to use provided examples.
        
        3. Navigate to the `./cloudmesh-openapi` directory on your machine
        
        4. Utilize the generate command to create the OpenAPI spec
        
            .. code:: bash
            
                cms openapi generate TextAnalysis --filename=./tests/generator-natural-lang/natural-lang-analysis.py --all_functions
            
        5. Start the server after the yaml file is generated ot the same
           directory as the .py file
            
            .. code:: bash
            
                cms openapie start server ./tests/generator-natural-lang/natural-lang-analysis.yaml
            
        6. Run a curl command against the newly running server to verify it
           returns a result as expected.
        
            * Sample text file name is only meant to be the name of the file
              not the full path.
        
            .. code:: bash
            
                curl -X GET "http://127.0.0.1:8080/cloudmesh/analyze?filename=<<sample text file name>>&cloud=google"
            
            * This is currently only ready to translate a single word through
              the API.
            
            .. code:: bash
            
                curl -X GET "http://127.0.0.1:8080/cloudmesh/translate_text?cloud=google&text=<<word to translate>>&lang=<<lang code>>"
            
        7. Stop the server
        
            .. code:: bash
            
                cms openapi server stop natural-lang-analysis
            
        #### AWS
        
        Sign up for AWS
        
        * Go to [https://portal.aws.amazon.com/billing/signup](https://portal.aws.amazon.com/billing/signup)
        * Follow online instructions
        
        Create an IAM User
        
        * For instructions, see 
        [here](https://docs.aws.amazon.com/IAM/latest/UserGuide/getting-started_create-admin-group.html)
        
        Set up AWS CLI and AWS SDKs
        
        * To download and instructions to install AWS CLI, see
          [here](https://docs.aws.amazon.com/cli/latest/userguide/cli-chap-install)
        
        Install Boto 3
        
        .. code:: bash
        
            pip install boto3
        
        * For quickstart, vist [here](https://boto3.amazonaws.com/v1/documentation/api/latest/guide/quickstart.html)
        
        As long as you enable all the services you need for using AWS AI APIs you should be able to write your functions for OpenAPI
        
        
        #### Azure
        
        
        ##### Setting up Azure Sentiment Analysis and Translation Services
        
        1.  Create an Azure subscription. If you don't have one, create a
            [free account](https://azure.microsoft.com/try/cognitive-services/)
        
        2. Create a [Text Analysis resource](https://portal.azure.com/#create/Microsoft.CognitiveServicesTextAnalytics)
        
            * This link will require you to be logged in to the Azure portal
            
        3. Create a [Translation Resource](https://docs.microsoft.com/en-us/azure/cognitive-services/cognitive-services-apis-create-account?tabs=multiservice%2Cwindows)
        
        4. The microsoft packages are included in the openapi package
           requirements file so they should be installed. If they are not,
           install the following:
        
           .. code:: bash    pip install msrest
            
               pip install azure-ai-textanalytics
            
        5. Navigate to the `~/.cloudmesh` repo and create a cache directory for your text examples you would like to analyze.
        
            .. code:: bash
            
                mkdir text-cache
            
        6. Add any plain text files your would like to analyze to this
           directory with a name that has no special characters or spaces.
           You can copy the files at this location,
           `./cloudmesh-openapi/tests/textanaysis-example-text/reviews/` into
           the text-cache if you want to use provided examples.
        
        7. Navigate to the `./cloudmesh-openapi` directory on your machine
        
        8. Utilize the generate command to create the OpenAPI spec
        
            .. code:: bash
        
                cms openapi generate TextAnalysis --filename=./tests/generator-natural-lang/natural-lang-analysis.py --all_functions
            
        9. Start the server after the yaml file is generated ot the same
           directory as the .py file
            
            .. code:: bash
            
                cms openapie start server ./tests/generator-natural-lang/natural-lang-analysis.yaml
            
        10. Run a curl command against the newly running server to verify it
            returns a result as expected.
        
            * Sample text file name is only meant to be the name of the file not the full path.
        
            .. code:: bash
            
                curl -X GET "http://127.0.0.1:8080/cloudmesh/analyze?filename=<<sample text file name>>&cloud=azure"
            
            * This is currently only ready to translate a single word through the API. 
            * Available language tags are described in the [Azure docs](https://docs.microsoft.com/en-us/azure/cognitive-services/translator/reference/v3-0-languages)
        
            .. code:: bash
            
                curl -X GET "http://127.0.0.1:8080/cloudmesh/translate_text?cloud=azure&text=<<word to translate>>&lang=<<lang code>>"
            
        11. Stop the server
        
            .. code:: bash
            
                cms openapi server stop natural-lang-analysis
            
        The natural langauge analysis API can be improved by allowing for full
        phrase translation via the API. If you contribute to this API there is
        room for improvement to add custom translation models as well if
        preferred to pre-trained APIs.
        
        ##### Setting up Azure ComputerVision AI services
        
        ###### Prerequisite 
        
        Using the Azure Computer Vision AI service, you can describe, analyze
        and/ or get tags for a locally stored image or you can read the text
        from an image or hand-written file.
        
        * Azure subscription. If you don't have one, create a [free
          account](https://azure.microsoft.com/try/cognitive-services/) before
          you continue further.
        * Create a Computer Vision resource and get the
          `COMPUTER_VISION_SUBSCRIPTION_KEY` and
          `COMPUTER_VISION_ENDPOINT`. Follow
          [instructions](https://docs.microsoft.com/en-us/azure/cognitive-services/cognitive-services-apis-create-account?tabs=singleservice%2Cunix)
          to get the same.
        * Install following Python packages in your virtual environment:
          * requests
          * Pillow
        * Install Computer Vision client library
        
        .. code:: bash
        
            pip install --upgrade azure-cognitiveservices-vision-computervision
        
        ###### Steps to implement and use Azure AI image and text *REST-services*
        
        * Go to `./cloudmesh-openapi` directory
        
        * Run following command to generate the YAML files
        
        .. code:: bash
          
            cms openapi generate AzureAiImage --filename=./tests/generator-azureai/azure-ai-image-function.py --all_functions --enable_upload
            cms openapi generate AzureAiText --filename=./tests/generator-azureai/azure-ai-text-function.py --all_functions --enable_upload
        
        * Verify the *YAML* files created in `./tests/generator-azureai` directory
        
        .. code:: bash
          
            azure-ai-image-function.yaml
            azure-ai-text-function.yaml
          
        * Start the REST service by running following command in `./cloudmesh-openapi` directory
        
        .. code:: bash
          
            cms openapi server start ./tests/generator-azureai/azure-ai-image-function.yaml
        
        The default port used for starting the service is 8080. In case you
        want to start more than one REST service, use a different port in
        following command:
        
        .. code:: bash
          
            cms openapi server start ./tests/generator-azureai/azure-ai-text-function.yaml --port=<**Use a different port than 8080**>
        
        * Access the REST service using [http://localhost:8080/cloudmesh/ui/](http://localhost:8080/cloudmesh/ui/)
        
        * After you have started the azure-ai-image-function or azure-ai-text-function on default port 8080, run following command to upload the image or text_image file
        
        .. code:: bash
          
            curl -X POST "http://localhost:8080/cloudmesh/upload" -H  "accept: text/plain" -H  "Content-Type: multipart/form-data" -F "upload=@tests/generator-azureai/<image_name_with_extension>;type=image/jpeg"
          
          Keep your test image files at `./tests/generator-azureai/` directory
        
        * With *azure-ai-text-function* started on port=8080, in order to test the azure ai function for text detection in an image, run following command
        
        .. code:: bash
          
            curl -X GET "http://localhost:8080/cloudmesh/azure-ai-text-function_upload-enabled/get_text_results?image_name=<image_name_with_extension_uploaded_earlier>" -H "accept: text/plain"
        
        * With *azure-ai-image-function* started on port=8080, in order to
          test the azure ai function for describing an image, run following
          command
        
        .. code:: bash
          
            curl -X GET "http://localhost:8080/cloudmesh/azure-ai-image-function_upload-enabled/get_image_desc?image_name=<image_name_with_extension_uploaded_earlier>" -H "accept: text/plain"
        
        * With *azure-ai-image-function* started on port=8080, in order to
          test the azure ai function for analyzing an image, run following
          command
        
        .. code:: bash
          
            curl -X GET "http://localhost:8080/cloudmesh/azure-ai-image-function_upload-enabled/get_image_analysis?image_name=<image_name_with_extension_uploaded_earlier>" -H "accept: text/plain"
        
        * With *azure-ai-image-function* started on port=8080, in order to
          test the azure ai function for identifying tags in an image, run
          following command
        
        .. code:: bash
        
            curl -X GET "http://localhost:8080/cloudmesh/azure-ai-image-function_upload-enabled/get_image_tags?image_name=<image_name_with_extension_uploaded_earlier>" -H "accept: text/plain"
        
        * Check the running REST services using following command:
        
        .. code:: bash
        
            cms openapi server ps
        
        * Stop the REST service using following command(s):
        
        .. code:: bash
          
            cms openapi server stop azure-ai-image-function
            cms openapi server stop azure-ai-text-function
        
        ## Test 
        
        The following table lists the different test we have, we provide additional information for the tests in the test directory in a README file. Summaries are provided below the table
        
        
        | Test   | Short Description  | Link  |
        | --- | --- | --- | 
        | Generator-calculator   | Test to check if calculator api is generated correctly. This is to test multiple function in one python file   | [test_01_generator.py](https://github.com/cloudmesh/cloudmesh-openapi/blob/master/tests/generator-calculator/test_01_generator.py)  
        | Generator-testclass   |Test to check if calculator api is generated correctly. This is to test multiple function in one python class file  | [test_02_generator.py](https://github.com/cloudmesh/cloudmesh-openapi/blob/master/tests/generator-testclass/test_02_generator.py)  
        | Server-cpu    | Test to check if cpu api is generated correctly. This is to test single function in one python file and function name is different than file name  | [test_03_generator.py](https://github.com/cloudmesh/cloudmesh-openapi/blob/master/tests/server-cpu/test_03_generator.py)  
        | Server-cms   | Test to check if cms api is generated correctly. This is to test multiple function in one python file. | [test_04_generator.py](https://github.com/cloudmesh/cloudmesh-openapi/blob/master/tests/server-cms/test_04_generator.py)  
        | Registry    | test_001_registry.py - Runs tests for registry. Description is in tests/README.md| [Link](https://github.com/cloudmesh/cloudmesh-openapi/blob/master/tests/README.md)
        | Image-Analysis | image_test.py - Runs benchmark for text detection for Google Vision API and AWS Rekognition. Description in image-analysis/README.md | [image](https://github.com/cloudmesh/cloudmesh-openapi/blob/master/tests/image-analysis/README.md)
        
        
        For more infromation about test cases ,please check [tests info](https://github.com/cloudmesh/cloudmesh-openapi/blob/master/tests/README.md)
        
        
         * [test_001_registry](tests/test_001_registry.py)
         * [test_003_server_manage_cpu](tests/test_003_server_manage_cpu.py)
         * [test_010_generator](tests/test_010_generator.py)
         * [test_011_generator_cpu](tests/test_011_generator_cpu.py)
         * [test_012_generator_calculator](tests/test_012_generator_calculator.py)
         * [test_015_generator_azureai](tests/test_015_generator_azureai.py)
         * [test_020_server_manage](tests/test_020_server_manage.py)
         * [test_server_cms_cpu](tests/test_server_cms_cpu.py)
        
Platform: UNKNOWN
Classifier: Development Status :: 5 - Production/Stable
Classifier: Environment :: Web Environment
Classifier: Intended Audience :: Developers
Classifier: License :: OSI Approved :: BSD License
Classifier: Operating System :: OS Independent
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.8
Description-Content-Type: text/markdown
