{"id":277,"date":"2022-05-26T04:56:43","date_gmt":"2022-05-26T04:56:43","guid":{"rendered":"https:\/\/ai-box.eu\/?p=277"},"modified":"2022-06-02T19:05:53","modified_gmt":"2022-06-02T19:05:53","slug":"ai-pipeline-ai-pipeline-image-app-setup-and-operation-part-1-2","status":"publish","type":"post","link":"https:\/\/ai-box.eu\/en\/ai-pipeline-en\/ai-pipeline-ai-pipeline-image-app-setup-and-operation-part-1-2\/277\/","title":{"rendered":"AI Pipeline &#8211; AI Pipeline Image App Setup and Operation Part 1-2"},"content":{"rendered":"<blockquote><p><em>In the previous chapter, you learned about the SWAGGER API, which can be used to test the neural network. Since the operation via the SWAGGER interface is not ideal and the REST API calls are suitable for writing your own application, a colleague of mine has written the AI Pipeline Image App. The AI Pipeline Image App simplifies the use of the REST API interface of the Training Suite and offers some additional features to simplify the work with the returned neural network results. The small web application has been written with the goal that it provides the basis for further custom developments and thus lowers the hurdle to develop your own web application that uses the Training Suite&#8217;s own API interface.<\/em><\/p><\/blockquote>\n<p>The AI Pipeline Image App is a NodeJS web application that encapsulates the complexity of using the REST API interface of the Training Suite and makes it much easier for the user. It is no longer necessary to work directly with the SWAGGER API interface, but all important functions are offered directly as menu items in the AI Pipeline Image App (AIPIA). In this way, images can be sent to the REST API interface of the Training Suite for analysis by the already trained neural network very easily and, above all, quickly without any special IT experience. The result, i.e. the prediction whether a piece of information is present in the image or not, is displayed directly in AIIA. The results of the prediction can also be downloaded directly as a CSV file. This was a request of several departments that wanted to process the data afterwards.<\/p>\n<p style=\"padding-left: 40px;\"><strong>Note: Time required<\/strong><\/p>\n<p style=\"padding-left: 40px;\">The time required to install the AI Pipeline Image App as described below is approximately 25 minutes. Most of this time is spent installing software, plus a few minutes to adjust the IP address in several configuration files.<\/p>\n<div id=\"ez-toc-container\" class=\"ez-toc-v2_0_82_2 counter-hierarchy ez-toc-counter ez-toc-grey ez-toc-container-direction\">\n<div class=\"ez-toc-title-container\">\n<p class=\"ez-toc-title\" style=\"cursor:inherit\">Table of Contents<\/p>\n<span class=\"ez-toc-title-toggle\"><a href=\"#\" class=\"ez-toc-pull-right ez-toc-btn ez-toc-btn-xs ez-toc-btn-default ez-toc-toggle\" aria-label=\"Toggle Table of Content\"><span class=\"ez-toc-js-icon-con\"><span class=\"\"><span class=\"eztoc-hide\" style=\"display:none;\">Toggle<\/span><span class=\"ez-toc-icon-toggle-span\"><svg style=\"fill: #999;color:#999\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\" class=\"list-377408\" width=\"20px\" height=\"20px\" viewBox=\"0 0 24 24\" fill=\"none\"><path d=\"M6 6H4v2h2V6zm14 0H8v2h12V6zM4 11h2v2H4v-2zm16 0H8v2h12v-2zM4 16h2v2H4v-2zm16 0H8v2h12v-2z\" fill=\"currentColor\"><\/path><\/svg><svg style=\"fill: #999;color:#999\" class=\"arrow-unsorted-368013\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\" width=\"10px\" height=\"10px\" viewBox=\"0 0 24 24\" version=\"1.2\" baseProfile=\"tiny\"><path d=\"M18.2 9.3l-6.2-6.3-6.2 6.3c-.2.2-.3.4-.3.7s.1.5.3.7c.2.2.4.3.7.3h11c.3 0 .5-.1.7-.3.2-.2.3-.5.3-.7s-.1-.5-.3-.7zM5.8 14.7l6.2 6.3 6.2-6.3c.2-.2.3-.5.3-.7s-.1-.5-.3-.7c-.2-.2-.4-.3-.7-.3h-11c-.3 0-.5.1-.7.3-.2.2-.3.5-.3.7s.1.5.3.7z\"\/><\/svg><\/span><\/span><\/span><\/a><\/span><\/div>\n<nav><ul class='ez-toc-list ez-toc-list-level-1 ' ><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-1\" href=\"https:\/\/ai-box.eu\/en\/ai-pipeline-en\/ai-pipeline-ai-pipeline-image-app-setup-and-operation-part-1-2\/277\/#Configuration_and_installation_of_the_AI_Pipeline_Image_App\" >Configuration and installation of the AI Pipeline Image App<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-2\" href=\"https:\/\/ai-box.eu\/en\/ai-pipeline-en\/ai-pipeline-ai-pipeline-image-app-setup-and-operation-part-1-2\/277\/#Configuration_of_the_AI_Pipeline_Image_App\" >Configuration of the AI Pipeline Image App<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-3\" href=\"https:\/\/ai-box.eu\/en\/ai-pipeline-en\/ai-pipeline-ai-pipeline-image-app-setup-and-operation-part-1-2\/277\/#Running_the_AI_Pipeline_Image_App\" >Running the AI Pipeline Image App<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-4\" href=\"https:\/\/ai-box.eu\/en\/ai-pipeline-en\/ai-pipeline-ai-pipeline-image-app-setup-and-operation-part-1-2\/277\/#Article_Overview_%E2%80%93_How_to_set_up_the_AI_pipeline\" >Article Overview &#8211; How to set up the AI pipeline:<\/a><\/li><\/ul><\/nav><\/div>\n<h2><span class=\"ez-toc-section\" id=\"Configuration_and_installation_of_the_AI_Pipeline_Image_App\"><\/span>Configuration and installation of the AI Pipeline Image App<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p>In order to install the AI Pipeline Image App, a few modules have to be installed first. Just execute the following commands one after the other in the terminal window. The packages that are now installed may be a little more generous than necessary, but this tutorial is about how everything works in the end and not about how optimized everything is for the individual.<\/p>\n<p>Please install the following packages one after the other.<\/p>\n<p style=\"padding-left: 40px;\"><strong>Command:<\/strong> sudo apt install npm -y<\/p>\n<p style=\"padding-left: 40px;\"><strong>Command: <\/strong>sudo apt install python3-pip -y<\/p>\n<p style=\"padding-left: 40px;\"><strong>Command: <\/strong>pip install Flask<\/p>\n<p style=\"padding-left: 40px;\"><strong>Command: <\/strong>pip install Flask-SQLAlchemy<\/p>\n<p style=\"padding-left: 40px;\"><strong>Command: <\/strong>pip install flask-restful<\/p>\n<p style=\"padding-left: 40px;\"><strong>Command: <\/strong>pip install flask-cors<\/p>\n<p style=\"padding-left: 40px;\"><strong>Command: <\/strong>pip install python-Levenshtein<\/p>\n<p>It has been shown that in order to install the AI Pipeline Image App without problems, NodeJS version 14.0x must be installed on Ubuntu 20.04 at the time this guide was written. The installation of NodeJS version 14.x is done with the following commands.<\/p>\n<p style=\"padding-left: 40px;\"><strong>Command:<\/strong> curl -sL https:\/\/deb.nodesource.com\/setup_14.x | sudo bash &#8211;<\/p>\n<p style=\"padding-left: 40px;\"><strong>Command:<\/strong> sudo apt -y install nodejs<\/p>\n<p style=\"padding-left: 40px;\"><strong>Command:<\/strong> sudo apt &#8211;fix-broken install<\/p>\n<p style=\"padding-left: 40px;\"><strong>Command:<\/strong> sudo apt -y install nodejs<\/p>\n<p style=\"padding-left: 40px;\"><strong>Command:<\/strong> node -v<\/p>\n<p>After all packages have been installed, the first thing to do is to download the AI Pipeline Image App from GitHub.<\/p>\n<p style=\"padding-left: 40px;\"><strong>Note: Status AI Pipeline Image App<\/strong><\/p>\n<p style=\"padding-left: 40px;\">Currently, the AI Pipeline Image App is still under development and thus could not yet meet the quality requirements of BMW&#8217;s TechOffice Munich and be published in the TechOffice Munich GitHub repository. Therefore, the app is available in its current state in my private repository until further notice. Improvements and suggestions for this web application are of course very welcome.<\/p>\n<p>The following command downloads the AI Pipeline Image App from GitHub.<\/p>\n<p style=\"padding-left: 40px;\"><strong>Command: <\/strong>git clone <a href=\"https:\/\/github.com\/custom-build-robots\/AI-Pipeline-Image-App.git\">https:\/\/github.com\/custom-build-robots\/AI-Pipeline-Image-App.git<\/a><\/p>\n<p>After downloading the application from GitHub, it needs to be installed. This is done with the following three commands.<\/p>\n<p style=\"padding-left: 40px;\"><strong>Command:<\/strong> cd AI-Pipeline-Image-App<\/p>\n<p style=\"padding-left: 40px;\"><strong>Command: <\/strong>cd app<\/p>\n<p style=\"padding-left: 40px;\"><strong>Command:<\/strong> npm install<\/p>\n<h2><span class=\"ez-toc-section\" id=\"Configuration_of_the_AI_Pipeline_Image_App\"><\/span>Configuration of the AI Pipeline Image App<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p>In order for the AI Pipeline Image App to work, a few small adjustments have to be made. Primarily it is about the IP address of the computer in the APP itself to be used for communication with the AI Pipeline Image App.<\/p>\n<p>First of all the server of the AI Pipeline Image App has to be adjusted. Please open the testAPI.py file once.<\/p>\n<p>The file can be found in the following path of the AI Pipeline Image App.<\/p>\n<p style=\"padding-left: 40px;\"><strong>Path:<\/strong> ~\/AI-Pipeline-Imge-App\/server\/testAPI.py<\/p>\n<p>Open the file in a test editor and replace the IP address with the IP address of your own computer on which the REST API of the Training Suite is accessible. At the very end, the IP address of the AI Pipeline Image App itself is entered for communication within the AI Pipeline Image App.<\/p>\n<p>In the following figure you can see two places where the IP address has to be replaced by the IP address of your own server. Please do not change the port here but leave it as it is. In total, the IP addresses have to be changed in four places.<\/p>\n<div id=\"attachment_11964\" style=\"width: 310px\" class=\"wp-caption alignnone\"><a href=\"https:\/\/www.ai-box.eu\/wp-content\/uploads\/2022\/02\/No_Code_AI_Pipeline_Image_App_IP_adress_setup.png\"><img loading=\"lazy\" decoding=\"async\" aria-describedby=\"caption-attachment-11964\" class=\"size-medium wp-image-11965\" src=\"https:\/\/www.ai-box.eu\/wp-content\/uploads\/2022\/02\/No_Code_AI_Pipeline_Image_App_IP_adress_setup-300x148.png\" alt=\"No Code AI Pipeline - Image App IP adress setup\" width=\"300\" height=\"148\" \/><\/a><p id=\"caption-attachment-11964\" class=\"wp-caption-text\">No Code AI Pipeline &#8211; Image App IP adress setup<\/p><\/div>\n<p>After the IP addresses have been adjusted in the testAPI.py file, the IP address of the AI Pipeline Image App server must be stored in the web app itself.<\/p>\n<p>Please open the file in the following path of the AI Pipeline Image App.<\/p>\n<p style=\"padding-left: 40px;\"><strong>Path: <\/strong>~\/AI-Pipeline-Imge-App\/app\/src\/app\/services\/api.service.ts<\/p>\n<p>In the api.service.ts file, the IP address of the AI Pipeline Image App Server must be entered at exactly one place in the file.<\/p>\n<p>In the following figure you can see the place where the IP address has to be adjusted. If the port in the testAPI.py file has been adjusted then the port must also be adjusted here so that the ends fit together again.<\/p>\n<div id=\"attachment_11968\" style=\"width: 310px\" class=\"wp-caption alignnone\"><a href=\"https:\/\/www.ai-box.eu\/wp-content\/uploads\/2022\/02\/No_Code_AI_Pipeline_Image_App_IP_address_setup_service_ts.png\"><img loading=\"lazy\" decoding=\"async\" aria-describedby=\"caption-attachment-11968\" class=\"size-medium wp-image-11969\" src=\"https:\/\/www.ai-box.eu\/wp-content\/uploads\/2022\/02\/No_Code_AI_Pipeline_Image_App_IP_address_setup_service_ts-300x88.png\" alt=\"No Code AI Pipeline - Image App IP adress setup service.ts\" width=\"300\" height=\"88\" \/><\/a><p id=\"caption-attachment-11968\" class=\"wp-caption-text\">No Code AI Pipeline &#8211; Image App IP adress setup service.ts<\/p><\/div>\n<p>Now everything should be prepared so far that a proper communication between Training Suite and AI Pipeline Image App as well as within the AI Pipeline Image App itself is ensured.<\/p>\n<p>In the next section, we will now describe how the startup process of the individual components should proceed.<\/p>\n<h2><span class=\"ez-toc-section\" id=\"Running_the_AI_Pipeline_Image_App\"><\/span>Running the AI Pipeline Image App<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p>After the AI Pipeline Image App has been successfully installed, it can be executed. Please pay attention to the following information box.<\/p>\n<p style=\"padding-left: 40px;\"><strong>Note: Start Training Suite<\/strong><\/p>\n<p style=\"padding-left: 40px;\">For the AI Pipeline Image App to work at all, the Training Suite must be started and running in the background. This is because the AI Pipeline Image App relies on the REST API interface of the Training Suite. Otherwise there will be an error because the communication with the REST API of the Training Suite does not work.<\/p>\n<p>After the Training Suite has been started, the Python server of the AI Pipeline Image App (AIPIA) must be started first. This takes over the communication with the Training Suite, its REST API interface and the communication with the web application AIPIA.<\/p>\n<p>The following graphic shows the flow of communication between the individual services \/ programs of the AI Pipeline Image App.<\/p>\n<p>&lt;Image still to be created&gt;<\/p>\n<p>Now please change to the following path in an extra terminal window.<\/p>\n<p style=\"padding-left: 40px;\"><strong>Path:<\/strong> ~\/AI-Pipeline-Image-App\/server<\/p>\n<p>Within this path now execute the following command to start the AI Pipeline Image App server.<\/p>\n<p style=\"padding-left: 40px;\"><strong>Command:<\/strong> python3 testApi.py<\/p>\n<p>Only now the web app, i.e. the AI Pipeline Image App, can be started. The following command starts the AI Pipeline Image App which relies on the previously started Python server for the communication with the Training Suite&#8217;s own REST API interface.<\/p>\n<p>Please change now in an extra terminal window to the following path.<\/p>\n<p style=\"padding-left: 40px;\"><strong>Path:<\/strong> ~\/AI-Pipeline-Image-App\/app<\/p>\n<p>Within this path now execute the following command to start the Web App i.e. the AI Pipeline Image App.<\/p>\n<p style=\"padding-left: 40px;\"><strong>Command:<\/strong> npm start<\/p>\n<p>Jetzt ist die AI Pipeline Image App gestartet und kann \u00fcber die IP Adresse des Rechners gefolgt vom Port &lt;IP Adresse&gt;:&lt;Port&gt; ge\u00f6ffnet werden.<\/p>\n<p style=\"padding-left: 40px;\"><strong>Example URL:<\/strong> 192.168.2.174:5010<\/p>\n<p>In the browser (please always use the Chrome browser) the AI Pipeline Image App is now visible and should look like shown in the following picture. The application still looks empty because you have to select a few parameters in the app first.<\/p>\n<div id=\"attachment_11972\" style=\"width: 310px\" class=\"wp-caption alignnone\"><a href=\"https:\/\/www.ai-box.eu\/wp-content\/uploads\/2022\/02\/No_Code_AI_AI_Pipeline_Image_app_initial_screen.png\"><img loading=\"lazy\" decoding=\"async\" aria-describedby=\"caption-attachment-11972\" class=\"size-medium wp-image-11973\" src=\"https:\/\/www.ai-box.eu\/wp-content\/uploads\/2022\/02\/No_Code_AI_AI_Pipeline_Image_app_initial_screen-300x171.png\" alt=\"No Code AI Pipeline - Image App initial load screen\" width=\"300\" height=\"171\" \/><\/a><p id=\"caption-attachment-11972\" class=\"wp-caption-text\">No Code AI Pipeline &#8211; Image App initial load screen<\/p><\/div>\n<p>The next post will explain how to use the AI Pipeline Image App.<\/p>\n<p>&nbsp;<\/p>\n<br>\n<h2>Article Overview - How to set up the AI pipeline:<\/h2>\n<a title=\"AI Pipeline - Introduction of the tutorial\" href=\"https:\/\/ai-box.eu\/en\/ai-pipeline\/ai-pipeline-introduction-of-the-tutorial\/244\">AI Pipeline - Introduction of the tutorial<\/a><br>\n<a title=\"AI Pipeline - The No-Code AI Pipeline an Overview\" href=\"https:\/\/ai-box.eu\/en\/ai-pipeline\/ai-pipeline-the-no-code-ai-pipeline-an-overview\/246\">AI Pipeline - An Overview<\/a><br>\n<a title=\"AI Pipeline - The Three Components\" href=\"https:\/\/ai-box.eu\/en\/ai-pipeline\/ai-pipeline-the-three-components\/250\">AI Pipeline - The Three Components<\/a><br>\n<a title=\"AI Pipeline - Hardware Basics\" href=\"https:\/\/ai-box.eu\/en\/ai-pipeline\/ai-pipeline-hardware-basics\/248\">AI Pipeline - Hardware Basics<\/a><br>\n<a title=\"AI Pipeline - Hardware Example Configurations\" href=\"https:\/\/ai-box.eu\/en\/ai-pipeline\/ai-pipeline-hardware-example-configurations\/253\">AI Pipeline - Hardware Example Configurations<\/a><br>\n<a title=\"AI Pipeline - Software Installation of the No-Code AI Pipeline\" href=\"https:\/\/ai-box.eu\/en\/ai-pipeline\/ai-pipeline-software-installation-of-the-no-code-ai-pipeline\/256\">AI Pipeline - Software Installation of the No-Code AI Pipeline<\/a><br>\n<a title=\"AI Pipeline - Labeltool Lite Installation\" href=\"https:\/\/ai-box.eu\/en\/ai-pipeline\/ai-pipeline-labeltool-lite-installation\/258\">AI Pipeline - Labeltool Lite - Installation<\/a><br>\n<a title=\"AI Pipeline - Labeltool Lite Preparation\" href=\"https:\/\/ai-box.eu\/en\/ai-pipeline\/ai-pipeline-labeltool-lite-preparation\/260\">AI Pipeline - Labeltool Lite - Preparation<\/a><br>\n<a title=\"AI Pipeline - Labeltool Lite Handling\" href=\"https:\/\/ai-box.eu\/en\/ai-pipeline\/ai-pipeline-labeltool-lite-handling\/263\">AI Pipeline - Labeltool Lite - Handling<\/a><br>\n<a title=\"AI Pipeline - Tensorflow Object Detection Training GUI Installation\" href=\"https:\/\/ai-box.eu\/en\/ai-pipeline\/ai-pipeline-tensorflow-object-detection-training-gui-installation\/266\">AI Pipeline - Tensorflow Object Detection Training-GUI - Installation<\/a><br>\n<a title=\"AI Pipeline - Tensorflow Object Detection Training GUI - Run\" href=\"https:\/\/ai-box.eu\/en\/ai-pipeline\/ai-pipeline-tensorflow-object-detection-training-gui-run\/269\">AI Pipeline - Tensorflow Object Detection Training GUI - Run<\/a><br>\n<a title=\"AI Pipeline - Tensorflow Object Detection Training GUI - Usage\" href=\"https:\/\/ai-box.eu\/en\/ai-pipeline\/ai-pipeline-tensorflow-object-detection-training-gui-usage\/272\">AI Pipeline - Tensorflow Object Detection Training GUI - Usage<\/a><br>\n<a title=\"AI Pipeline - Tensorflow Object Detection Training GUI - SWAGGER API testing the neural network\" href=\"https:\/\/ai-box.eu\/en\/ai-pipeline\/ai-pipeline-tensorflow-object-detection-training-gui-swagger-api-testing-the-neural-network\/274\">AI Pipeline - Tensorflow Object Detection Training GUI - SWAGGER API testing the neural network<\/a><br>\n<a title=\"AI Pipeline - AI Pipeline Image App Setup and Operation Part 1-2\" href=\"https:\/\/ai-box.eu\/en\/ai-pipeline\/ai-pipeline-ai-pipeline-image-app-setup-and-operation-part-1-2\/277\">AI Pipeline - AI Pipeline Image App Setup and Operation Part 1-2<\/a><br>\n<a title=\"AI Pipeline - AI Pipeline Image App Setup and Operation Part 2-2\" href=\"https:\/\/ai-box.eu\/en\/ai-pipeline\/ai-pipeline-ai-image-app-setup-and-operation-part-2-2\/280\">AI Pipeline - AI Pipeline Image App Setup and Operation Part 2-2<\/a><br>\n<a title=\"AI Pipeline - Training Data Download\" href=\"https:\/\/ai-box.eu\/en\/ai-pipeline\/ai-pipeline-training-data-download\/282\">AI Pipeline - Training Data Download<\/a><br>\n<a title=\"AI Pipeline - Anonymization-Api\" href=\"https:\/\/ai-box.eu\/en\/ai-pipeline\/ai-pipeline-anonymization-api\/284\">AI Pipeline - Anonymization-Api<\/a><br>\n","protected":false},"excerpt":{"rendered":"<p>In the previous chapter, you learned about the SWAGGER API, which can be used to test the neural network. Since the operation via the SWAGGER interface is not ideal and the REST API calls are suitable for writing your own application, a colleague of mine has written the AI Pipeline Image App. The AI Pipeline [&hellip;]<\/p>\n","protected":false},"author":1,"featured_media":185,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_et_pb_use_builder":"","_et_pb_old_content":"","_et_gb_content_width":"","footnotes":""},"categories":[32],"tags":[33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49],"class_list":["post-277","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-ai-pipeline-en","tag-ai-en","tag-artificial-intelligence-en","tag-guide-en","tag-hands-on-en","tag-how-to-en","tag-ki-en","tag-learn-ai-en","tag-low-code-en","tag-neural-net-en","tag-no-code-en","tag-nvidia-en","tag-open-source-en","tag-picture-en","tag-pipeline-en","tag-step-by-step-en","tag-tensorflow-en","tag-training-en","et-has-post-format-content","et_post_format-et-post-format-standard"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v27.3 - https:\/\/yoast.com\/product\/yoast-seo-wordpress\/ -->\n<title>AI Pipeline - AI Pipeline Image App Setup and Operation Part 1-2 - Exploring the Future: Inside the AI Box<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/ai-box.eu\/en\/ai-pipeline-en\/ai-pipeline-ai-pipeline-image-app-setup-and-operation-part-1-2\/277\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"AI Pipeline - AI Pipeline Image App Setup and Operation Part 1-2 - Exploring the Future: Inside the AI Box\" \/>\n<meta property=\"og:description\" content=\"In the previous chapter, you learned about the SWAGGER API, which can be used to test the neural network. 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