Docs style magic

Author: Ben Mansheim

README.md

@@ -1,31 +1,42 @@
 # AnimalRecognitionDemo
 
-This demo combines several [Redis](https://redis.io) Data structures and [Redis Modules](https://redis.io/topics/modules-intro) to filter a stream of images that contain cats.  It utilises Redis Streams for capturing the input video stream:`all`.  [RedisGears](https://oss.redislabs.com/redisgears/) will process this stream and will call [RedisAI](https://oss.redislabs.com/redisai/) for image classification with MobilenetV2.  In case the stream contains cats, it will forward the images to a  stream:`cats`.
+This demo combines several [Redis](https://redis.io) data structures and [Redis Modules](https://redis.io/topics/modules-intro)
+to process a stream of images and filter out the images that contain cats.
+
+It uses:
+
+* Redis Streams to capture the input video stream: `all`
+* [RedisGears](https://oss.redislabs.com/redisgears/) to process this stream
+* [RedisAI](https://oss.redislabs.com/redisai/) to classify the images with MobilenetV2
+
+It forwards the images that contain cats to a stream: `cats`
 
 ## Architecture
 ![Architecture](/architecture.png)
 
-
 ## Requirements
 Docker and Python 2
 
 ## Running the Demo
+To run the demo:
 ```
 $ git clone https://github.com/RedisGears/AnimalRecognitionDemo.git
 $ cd AnimalRecognitionDemo
 # If you don't have it already, install https://git-lfs.github.com/
 $ git lfs install && git lfs fetch && git lfs checkout
 $ docker-compose up
 ```
-Open a second terminal for the video capturing.
+Open a second terminal for the video capturing:
 ```
 $ pip install -r camera/requirements.txt
 $ python camera/read_camera.py
 ```
 
 ## UI
-* On `http://localhost:3000` you will be able to see all the captured frames
-* On `http://localhost:3001` you will see only cats
+* `http://localhost:3000` shows all the captured frames
+* `http://localhost:3001` shows only the framse with cats
 
 ## Limitations
-This demo aimed at ease of setup and is hence heavily relying on docker.  Better performance and a higher FPS can be achieved by runninng this demo outside docker.  To control the FPS, look [here](https://github.com/RedisGears/AnimalRecognitionDemo/blob/master/app/gear.py#L53)
+This demo is designed to be easy to setup, so it relies heavily on docker.
+You can get better performance and a higher FPS by runninng this demo outside docker.
+To control the FPS, edit the [gear.py](https://github.com/RedisGears/AnimalRecognitionDemo/blob/master/app/gear.py#L53) file.