Extend the project description

README.md

@@ -2,7 +2,19 @@
 
 ![Continuous Integration and Delivery](https://github.com/spyker77/fastapi-tdd-docker/workflows/Continuous%20Integration%20and%20Delivery/badge.svg?branch=main)
 
-This is the implementation of [the course](https://testdriven.io/courses/tdd-fastapi/) with the following changes so far:
+## Table of Contents
+
+- [Introduction](#introduction)
+- [Prerequisites](#prerequisites)
+- [Installation](#installation)
+- [Usage](#usage)
+- [Testing](#testing)
+- [Deployment](#deployment)
+- [License](#license)
+
+## Introduction
+
+This project is an implementation of [the course](https://testdriven.io/courses/tdd-fastapi/) on Test-Driven Development to build the API for summarizing articles, with additional features and optimizations. Key enhancements include:
 
 - Dependencies updated to the latest version at the moment
 - CORSMiddleware used to manually control allowed origins
@@ -17,34 +29,93 @@ This is the implementation of [the course](https://testdriven.io/courses/tdd-fas
 - Tortoise-ORM has been replaced by SQLAlchemy
 - Transformers model is used instead of NLP from Newspaper3k
 
-## Quick Start
+## Prerequisites
+
+- Docker and Docker Compose installed on your machine.
+- Basic knowledge of Python, FastAPI, and Docker.
 
-Spin up the containers:
+## Installation
+
+1. Clone the repository:
+
+```bash
+git clone https://github.com/spyker77/fastapi-tdd-docker.git
+cd fastapi-tdd-docker
+```
+
+2. Build and start the containers:
 
 ```bash
 docker compose up -d --build
 ```
 
-Generate the database schema on first launch:
+3. Generate the database schema:
 
 ```bash
 docker compose exec web alembic upgrade head
 ```
 
-Open in your browser: <http://localhost:8000/docs>
+## Usage
+
+1. Access the API documentation at: <http://localhost:8000/docs>
+2. Create a test user at: <http://localhost:8000/docs#/users/create_user_api_v2_users__post>
+
+    Example of the payload:
+
+```json
+{
+  "full_name": "Cute Koala",
+  "username": "cute",
+  "email": "[email protected]",
+  "password": "supersecret"
+}
+```
 
-Tests can be run with this command:
+3. Use the **Authorize** button (simplest way) at the top and enter the username and password you've just created, then click **Authorize**.
+
+4. At this point you're authorized. Now use the endpoint <http://localhost:8000/docs#/summaries/create_summary_api_v2_summaries__post> to send the article you want to summarize, for example like so:
+
+```json
+{
+  "url": "https://dev.to/spyker77/how-to-connect-godaddy-domain-with-heroku-and-cloudflare-mdh"
+}
+```
+
+5. This triggers the ML model to download, which may take a few minutes for the first run (in the current implementation). After that, reach the endpoint <http://localhost:8000/docs#/summaries/read_all_summaries_api_v2_summaries__get> and in the response you should see something like:
+
+```json
+[
+  {
+    "id": 1,
+    "url": "https://dev.to/spyker77/how-to-connect-godaddy-domain-with-heroku-and-cloudflare-mdh",
+    "summary": "If you struggle to connect newly registered domain from GoDaddy with your app at Heroku, and in addition would like to use advantages of Cloudflare – this article is for you. Hope it will help you and without many words, let's jump in!Sections: Heroku settings, Cloudflare settings and GoDaddy settings.",
+    "user_id": 1
+  }
+]
+```
+
+## Testing
+
+Run the tests using the following command:
 
 ```bash
 docker compose exec web pytest -n auto --cov
 ```
 
-## Note
+## Deployment
+
+For production deployment, don't forget to change the ENVIRONMENT variables. The default CI/CD pipeline is set up to build images with GitHub Actions, store them in GitHub Packages, and deploy the application to Heroku. But the deployment part is currently disabled/commented out.
+
+### Note
+
+The current implementation of text summarization using the transformer model is not ideal for production due to the following reasons:
+
+- The requirement to install a heavy transformers library along with its dependencies.
+- The necessity to download several gigabytes of the model.
+- The need for powerful hardware to run the model.
 
-Current implementation of text summarization using the transformer model is not ideal for production:
+Typically, in a production environment, the model would be provided via an API using services like AWS SageMaker or Paperspace Gradient.
 
-- The need to install a heavy transformers library with its dependencies;
-- The need to download several gigs of the model;
-- The need for powerful hardware to run it.
+## License
 
-Typically, the model is provided via API using services like AWS SageMaker or Paperspace Gradient.
+This project is licensed under the MIT License. See the [MIT License](LICENSE) file for details.