📝 updated readme for more clarity

Author: jmeisele

README.md

@@ -1,5 +1,44 @@
-# FastAPI Model Serving Scaffolding
+# FastAPI Model Server Scaffolding
 
-Scaffolding for serving ml model APIs using FastAPI
+Serving machine learning models production-ready, fast, easy and secure powered by the great FastAPI by [Sebastián Ramírez]([)](https://github.com/tiangolo).
 
-This repo contains the scaffolding which can be used to speed up your next machine learning project.
+This repository contains a starter app which can be used to speed-up your next machine learning project. 
+
+To experiment and get a feeling on how to use this scaffolding, a sample regression model for house price prediction is included in this project. Follow the installation and setup instructions to run the sample model and serve it aso RESTful API.
+
+## Requirements
+
+Python 3.6+
+
+## Installation
+Install the required packages in your local environment (ideally virtualenv, conda, etc.).
+```bash
+pip install -r requirements
+``` 
+
+
+## Setup
+1. Duplicate the `.env.example` file and rename it to `.env` 
+
+
+2. In the `.env` file configure the `API_KEY` entry. The key is used for authenticating our API. <br>
+   A sample API key can be generated using Python REPL:
+```python
+import uuid
+print(str(uuid.uuid4()))
+```
+
+## Run It
+
+1. Start your  app with: 
+```bash
+uvicorn fastapi_skeleton.main:app
+```
+
+2. Go to [http://localhost:8000/docs](http://localhost:8000/docs).
+   
+3. Click `Authorize` and enter the API key as created in the Setup step.
+![Authroization](./docs/authorize.png)
+   
+4. You can use the sample payload from the `docs/sample_payload.json` file when trying out the house price prediction model using the API.
+   ![Prediction with example payload](./docs/sample_payload.png)