Updated Readme

README.md

@@ -1,10 +1,6 @@
 # Eliza-Go
 
-## Author: Matthew Shiel 
-
-## Student ID: G00338622
-
-This is my implementation of an Eliza chatbot in Go, based on the Eliza natural language processing program. This is for my 3rd year Data Representation and Querying module in GMIT. https://data-representation.github.io/problems/project.html
+## Author: Gary Leeson based on the work of Matthew Shiel at https://github.com/mattshiel/eliza-go
 
 ## How to Run The Chatbot
 
@@ -13,29 +9,27 @@ This is my implementation of an Eliza chatbot in Go, based on the Eliza natural
 
 **1. Clone the Repository**
 ```bash
-> git clone https://github.com/mattshiel/eliza-go.git
+> git clone https://git.riomhaire.com/gremlin/elizaservice.git
 ```
 **2. Change Directory to the Folder**
 
 ```bash
 Open the terminal/command line and navigate into the folder 
-eg. > cd eliza-go
+eg. > cd elizaservice
 ```
 
 **2. Compile the Chatbot**
 
 ```bash
-> go build server.go
+> make
 ```
 
 **3. Run the Chatbot**
 
 ```bash
 To run the chatbot enter './' followed by the executable produced
-For Mac:
-> ./server
-For Windows:
-> ./server.go.exe
+For Mac/Linux:
+> ./eliza-server
 
 Alternatively:
 > go run server.go
@@ -54,16 +48,16 @@ The primary design components that went into this project were as follows, a web
 
 ## Problems and Features
 
-To give Eliza the impression she was thinking I implemented a delay to her responses, adding to this the delay will also randomise between 0.6 and 4 seconds per response. 
+To give Eliza the impression she was thinking Matthew implemented a delay to her responses, adding to this the delay will also randomise between 0.6 and 4 seconds per response. 
 
 Eliza crafts her responses by examining the user's text input for a keyword. When the keyword is found it goes through a pre-processing procedure. This includes things like transforming all letters to lower case and trimming unnecessary whitespace. After this the word is tested against keywords in the 'responses.go' file. If a regex group is matched, the program gets the first match, the matched group then makes up a fraction of the response for added realism.
 If Eliza does not find a match then there are a number of fallback default response she will chose from. All responses are chosen randomly.
 
-A problem I encountered with this method is that selecting answers from the map of responses may be slightly non-deterministic, and the map will be out of order. In Weizenbaum's original design of Eliza he used a method of decomposition where all keywords held a certain value or 'precedence number'. This weighted approach is definitely another approach I could have taken when designing my program and something I might change in the future.
+A problem Matthew encountered with this method is that selecting answers from the map of responses may be slightly non-deterministic, and the map will be out of order. In Weizenbaum's original design of Eliza he used a method of decomposition where all keywords held a certain value or 'precedence number'. This weighted approach is definitely another approach I could have taken when designing my program and something I might change in the future.
 
 
 ## References
 
-This implementation of Eliza gave me many ideas and provided me with the main method of implementing my Eliza functionality https://github.com/kennysong/goeliza
+This implementation of Eliza gave Matthew many ideas and provided Matthew with the main method of implementing Matthew Eliza functionality https://github.com/kennysong/goeliza
 
 I consulted both the the Golang, Bootstrap and JQuery documentation frequently over the course of my assignment.