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docs: Improve documentation

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  1. README.md +43 -8
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@@ -14,10 +14,29 @@ short_description: Object detection ECS
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  <b>Aim: AI-driven object detection task</b>
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  - Front-end: user interface via Gradio library
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- - Back-end: use of ECS endpoints to run ML models
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- ## Front-end user interface
 
 
 
 
 
 
 
 
 
 
 
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  Use of Gradio library for web interface
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  Command line:
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  <b>Note:</b> The Gradio app should now be accessible at http://localhost:7860
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- ## Back-end ML models
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- Machine Learning (ML) models are available on Docker Hub and have been deployed on AWS ECS
 
 
 
 
 
 
 
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  **Docker hub containers:**
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- - DETR model: https://hub.docker.com/r/cvachet/object-detection-detr-api
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- - YOLOS model: https://hub.docker.com/r/cvachet/object-detection-yolos-api
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- ## AWS ECS deployment steps:
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  ECS: Elastic Container Service
 
 
 
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  Steps after docker images are available on Docker Hub
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  ### Step 1. Create a new ECS task definition
@@ -67,4 +100,6 @@ Steps after docker images are available on Docker Hub
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  ### Step 4. Update security group for new service
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  - Go to Cluster -> service -> task -> configuration and networking
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  - Click on ```Security Group```
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- - Adjust rules for inbound traffic (e.g. traffic only from my_ip)
 
 
 
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  <b>Aim: AI-driven object detection task</b>
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  - Front-end: user interface via Gradio library
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+ - Back-end: use of AWS ECS endpoints to run Machine Learning models
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+ Menu:
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+ - [Front-end user interface](#1-front-end-user-interface)
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+ - [Environment variables](#11-environment-variables)
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+ - [Local execution](#12-local-execution)
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+ - [Deployment on Hugging Face](#13-deployment-on-hugging-face)
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+ - [Back-end Machine Learning models](#2-back-end-machine-learning-models)
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+ - [Information on ML models](#21-information-on-ml-models)
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+ - [Deployment on AWS ECS](#22-information-on-aws-ecs-deployment)
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+ ## 1. Front-end user interface
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+
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+ ### 1.1. Environment variables
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+
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+ This web app uses two environment variables, corresponding to the endpoints for the deployed machine learning models
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+ (cf. [Section 2 - Back-end ML models](#2-back-end-ml-models))
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+
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+ Environment variables:
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+ - AWS_DETR_URL: endpoint for DETR model
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+ - AWS_YOLOS_URL: endpoint for YOLOS model
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+
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+ ### 1.2. Local execution
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  Use of Gradio library for web interface
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  Command line:
 
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  <b>Note:</b> The Gradio app should now be accessible at http://localhost:7860
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+ ### 1.3. Deployment on Hugging Face
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+
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+ This web application has been deployed on Hugging Face.
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+ HF Space URL: https://huggingface.co/spaces/cvachet/object_detection_ecs
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+ ## 2. Back-end machine learning models
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+
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+ Machine Learning (ML) models are available on Docker Hub and have been deployed to AWS ECS (Elastic Container Service)
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+
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+ ### 2.1. Information on ML models
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+ **Github repos:**
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+ - DETR API: https://github.com/clementsan/object_detection_detr_api
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+ - YOLOS API: https://github.com/clementsan/object_detection_yolos_api
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  **Docker hub containers:**
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+ - DETR API: https://hub.docker.com/r/cvachet/object-detection-detr-api
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+ - YOLOS API: https://hub.docker.com/r/cvachet/object-detection-yolos-api
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+ ### 2.2 Information on AWS ECS deployment
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  ECS: Elastic Container Service
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+
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+ <details>
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+
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  Steps after docker images are available on Docker Hub
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  ### Step 1. Create a new ECS task definition
 
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  ### Step 4. Update security group for new service
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  - Go to Cluster -> service -> task -> configuration and networking
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  - Click on ```Security Group```
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+ - Adjust rules for inbound traffic (e.g. traffic only from my_ip)
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+
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+ </details>