Merge pull request #42 from afelix-95/main

Update labs 01-04 to use Cloud Shell

Author: ivorb

.gitignore

@@ -0,0 +1,3 @@
+bin
+obj
+*.sln

Instructions/Exercises/ai-foundry/01-use-azure-ai-services.md

@@ -8,29 +8,12 @@ lab:
 
 In this exercise, you'll get started with Azure AI Services by creating an **Azure AI Services** resource in your Azure subscription and using it from a client application. The goal of the exercise is not to gain expertise in any particular service, but rather to become familiar with a general pattern for provisioning and working with Azure AI services as a developer.
 
-## Clone the repository in Visual Studio Code
-
-You'll develop your code using Visual Studio Code. The code files for your app have been provided in a GitHub repo.
-
-> **Tip**: If you have already cloned the **mslearn-ai-services** repo, open it in Visual Studio code. Otherwise, follow these steps to clone it to your development environment.
-
-1. Start Visual Studio Code.
-2. Open the palette (SHIFT+CTRL+P) and run a **Git: Clone** command to clone the `https://github.com/MicrosoftLearning/mslearn-ai-services` repository to a local folder (it doesn't matter which folder).
-3. When the repository has been cloned, open the folder in Visual Studio Code.
-4. Wait while additional files are installed to support the C# code projects in the repo, if necessary
-
-    > **Note**: If you are prompted to add required assets to build and debug, select **Not Now**.
-
-5. Expand the `Labfiles/01-use-azure-ai-services` folder.
-
-Code for both C# and Python has been provided. Expand the folder of your preferred language.
-
 ## Provision an Azure AI Services resource
 
 Azure AI Services are cloud-based services that encapsulate artificial intelligence capabilities you can incorporate into your applications. You can provision individual Azure AI services resources for specific APIs (for example, **Language** or **Vision**), or you can provision a single **Azure AI Services** resource that provides access to multiple Azure AI services APIs through a single endpoint and key. In this case, you'll use a single **Azure AI Services** resource.
 
 1. Open the Azure portal at `https://portal.azure.com`, and sign in using the Microsoft account associated with your Azure subscription.
-2. In the top search bar, search for *Azure AI services*, select **Azure AI Services**, and create an Azure AI services multi-service account resource with the following settings:
+2. In the top search bar, search for *Azure AI services*, select **Azure AI services multi-service account**, and create a resource with the following settings:
     - **Subscription**: *Your Azure subscription*
     - **Resource group**: *Choose or create a resource group (if you are using a restricted subscription, you may not have permission to create a new resource group - use the one provided)*
     - **Region**: *Choose any available region*
@@ -43,104 +26,187 @@ Azure AI Services are cloud-based services that encapsulate artificial intellige
     - Two *keys* that can be used for authentication (client applications can use either key to authenticate).
     - The *location* where the resource is hosted. This is required for requests to some (but not all) APIs.
 
+## Clone the repository in Cloud Shell
+
+You'll develop your code using Cloud Shell from the Azure Portal. The code files for your app have been provided in a GitHub repo.
+
+> **Tip**: If you have already cloned the **mslearn-ai-services** repo recently, you can skip this task. Otherwise, follow these steps to clone it to your development environment.
+
+1. In the Azure Portal, use the **[\>_]** button to the right of the search bar at the top of the page to create a new Cloud Shell in the Azure portal, selecting a ***PowerShell*** environment. The cloud shell provides a command line interface in a pane at the bottom of the Azure portal.
+
+    > **Note**: If you have previously created a cloud shell that uses a *Bash* environment, switch it to ***PowerShell***.
+
+1. In the cloud shell toolbar, in the **Settings** menu, select **Go to Classic version** (this is required to use the code editor).
+
+    > **Tip**: As you paste commands into the cloudshell, the ouput may take up a large amount of the screen buffer. You can clear the screen by entering the `cls` command to make it easier to focus on each task.
+
+1. In the PowerShell pane, enter the following commands to clone the GitHub repo for this exercise:
+
+    ```
+    rm -r mslearn-ai-services -f
+    git clone https://github.com/microsoftlearning/mslearn-ai-services mslearn-ai-services
+    ```
+
+1. After the repo has been cloned, navigate to the folder containing the application code files:  
+
+    ```
+   cd mslearn-ai-services/Labfiles/01-use-azure-ai-services
+    ```
+
 ## Use a REST Interface
 
 The Azure AI services APIs are REST-based, so you can consume them by submitting JSON requests over HTTP. In this example, you'll explore a console application that uses the **Language** REST API to perform language detection; but the basic principle is the same for all of the APIs supported by the Azure AI Services resource.
 
 > **Note**: In this exercise, you can choose to use the REST API from either **C#** or **Python**. In the steps below, perform the actions appropriate for your preferred language.
 
-1. In Visual Studio Code, expand the **C-Sharp** or **Python** folder depending on your language preference.
-2. View the contents of the **rest-client** folder, and note that it contains a file for configuration settings:
+1. Navigate to the folder containing the application code files for your preferred language:  
+
+    **Python**
+
+    ```
+   cd Python/rest-client
+    ```
+
+    **C#**
+
+    ```
+   cd C-Sharp/rest-client
+    ```
+
+1. Using the `ls` command, you can view the contents of the **rest-client** folder. Note that it contains a file for configuration settings:
 
-    - **C#**: appsettings.json
     - **Python**: .env
+    - **C#**: appsettings.json
+
+1. Enter the following command to edit the configuration file that has been provided:
+
+    **Python**
+
+    ```
+   code .env
+    ```
+
+    **C#**
+
+    ```
+   code appsettings.json
+    ```
+
+    The file is opened in a code editor.
 
-    Open the configuration file and update the configuration values it contains to reflect the **endpoint** and an authentication **key** for your Azure AI services resource. Save your changes.
+1. In the code file, update the configuration values it contains to reflect the **endpoint** and an authentication **key** for your Azure AI services resource.
+1. After you've replaced the placeholders, use the **CTRL+S** command to save your changes and then use the **CTRL+Q** command to close the code editor while keeping the cloud shell command line open.
+1. Enter the following command to open the code file for the client application:
 
-3. Note that the **rest-client** folder contains a code file for the client application:
+    **Python**
+
+    ```
+   code rest-client.py
+    ```
+
+    **C#**
 
-    - **C#**: Program.cs
-    - **Python**: rest-client.py
+    ```
+   code Program.cs
+    ```
 
-    Open the code file and review the code it contains, noting the following details:
+1. Review the code it contains, noting the following details:
     - Various namespaces are imported to enable HTTP communication
     - Code in the **Main** function retrieves the endpoint and key for your Azure AI services resource - these will be used to send REST requests to the Text Analytics service.
     - The program accepts user input, and uses the **GetLanguage** function to call the Text Analytics language detection REST API for your Azure AI services endpoint to detect the language of the text that was entered.
     - The request sent to the API consists of a JSON object containing the input data - in this case, a collection of **document** objects, each of which has an **id** and **text**.
     - The key for your service is included in the request header to authenticate your client application.
     - The response from the service is a JSON object, which the client application can parse.
 
-4. Right click on the **rest-client** folder, select *Open in Integrated Terminal* and run the following command:
+1. Use the **CTRL+Q** command to close the code editor while keeping the cloud shell command line open and run the following command:
 
-    **C#**
+    **Python**
 
     ```
-    dotnet run
+    pip install python-dotenv
+    python rest-client.py
     ```
 
-    **Python**
+    **C#**
 
     ```
-    pip install python-dotenv
-    python rest-client.py
+    dotnet run
     ```
 
-5. When prompted, enter some text and review the language that is detected by the service, which is returned in the JSON response. For example, try entering "Hello", "Bonjour", and "Gracias".
-6. When you have finished testing the application, enter "quit" to stop the program.
+1. When prompted, enter some text and review the language that is detected by the service, which is returned in the JSON response. For example, try entering "Hello", "Bonjour", and "Gracias".
+1. When you have finished testing the application, enter "quit" to stop the program.
 
 ## Use an SDK
 
 You can write code that consumes Azure AI services REST APIs directly, but there are software development kits (SDKs) for many popular programming languages, including Microsoft C#, Python, Java, and Node.js. Using an SDK can greatly simplify development of applications that consume Azure AI services.
 
-1. In Visual Studio Code, expand the **sdk-client** folder under the **C-Sharp** or **Python** folder, depending on your language preference. Then run `cd ../sdk-client` to change into the relevant **sdk-client** folder.
+1. Navigate to the folder containing the SDK application code files by running `cd ../sdk-client`.
+   
+1. Install the Text Analytics SDK package by running the appropriate command for your language preference:
 
-2. Install the Text Analytics SDK package by running the appropriate command for your language preference:
+    **Python**
+
+    ```
+    pip install azure-ai-textanalytics==5.3.0
+    ```
 
     **C#**
 
     ```
     dotnet add package Azure.AI.TextAnalytics --version 5.3.0
     ```
 
+1. Enter the following command to edit the configuration file that has been provided:
+
     **Python**
 
     ```
-    pip install azure-ai-textanalytics==5.3.0
+   code .env
     ```
 
-3. View the contents of the **sdk-client** folder, and note that it contains a file for configuration settings:
+    **C#**
 
-    - **C#**: appsettings.json
-    - **Python**: .env
+    ```
+   code appsettings.json
+    ```
 
-    Open the configuration file and update the configuration values it contains to reflect the **endpoint** and an authentication **key** for your Azure AI services resource. Save your changes.
-    
-4. Note that the **sdk-client** folder contains a code file for the client application:
+1. In the code file, update the configuration values it contains to reflect the **endpoint** and an authentication **key** for your Azure AI services resource.
+1. After you've replaced the placeholders, use the **CTRL+S** command to save your changes and then use the **CTRL+Q** command to close the code editor while keeping the cloud shell command line open.
+1. Enter the following command to open the code file for the client application:
 
-    - **C#**: Program.cs
-    - **Python**: sdk-client.py
+    **Python**
+
+    ```
+   code sdk-client.py
+    ```
+
+    **C#**
 
-    Open the code file and review the code it contains, noting the following details:
+    ```
+   code Program.cs
+    ```
+
+1. Review the code it contains, noting the following details:
     - The namespace for the SDK you installed is imported
     - Code in the **Main** function retrieves the endpoint and key for your Azure AI services resource - these will be used with the SDK to create a client for the Text Analytics service.
     - The **GetLanguage** function uses the SDK to create a client for the service, and then uses the client to detect the language of the text that was entered.
 
-5. Return to the terminal, ensure you are in the **sdk-client** folder, and enter the following command to run the program:
+1. Use the **CTRL+Q** command to close the code editor and run the following command:
 
-    **C#**
+    **Python**
 
     ```
-    dotnet run
+    python sdk-client.py
     ```
 
-    **Python**
+    **C#**
 
     ```
-    python sdk-client.py
+    dotnet run
     ```
 
-6. When prompted, enter some text and review the language that is detected by the service. For example, try entering "Goodbye", "Au revoir", and "Hasta la vista".
-7. When you have finished testing the application, enter "quit" to stop the program.
+7. When prompted, enter some text and review the language that is detected by the service. For example, try entering "Goodbye", "Au revoir", and "Hasta la vista".
+8. When you have finished testing the application, enter "quit" to stop the program.
 
 > **Note**: Some languages that require Unicode character sets may not be recognized in this simple console application.
 
@@ -154,4 +220,4 @@ If you're not using the Azure resources created in this lab for other training m
 
 ## More information
 
-For more information about Azure AI Services, see the [Azure AI Services documentation](https://docs.microsoft.com/azure/ai-services/what-are-ai-services).
+For more information about Azure AI Services, see the [Azure AI Services documentation](https://docs.microsoft.com/azure/ai-services/what-are-ai-services).