ritct/rita

RIT-assistant

Rita

Rita a.k.a RIT-assistant is the virtual assistant of RIT.

Get the code

clone the repostory with

git clone https://github.com/RITct/Rita.git

You need

• Python3
• PyTorch
• Flask
• Requests
• flask_wtf

How to run

First Run

bash setup.sh

for training the models.
A local interface can be run like this

python3 rita.py

Go to,

http://127.0.0.1:5000/test

in the browser. Tada !

Working

Currently building Rita as a facebook messenger bot in python flask based on this tutorial.
Message user send is fetched.

data = request.get_json()

This will be in json format as send by facebook API.

{
  "sender":{
    "id":"USER_ID"
  },
  "recipient":{
    "id":"PAGE_ID"
  },
  "timestamp":1458692752478,
  "message":{
    "mid":"mid.1457764197618:41d102a3e1ae206a38",
    "text":"hello, world!",
    "quick_reply": {
      "payload": "DEVELOPER_DEFINED_PAYLOAD"
    }
  }
}    

The message is parsed and extracted.

message_text = message_event["message"]["text"]

This message is processed and reply is generated using neural networks.

reply = process_msg(message_text)

This is how things work inside process_msg(),

def process_msg(message_text):
    intent = action_predict(str(message_text)) #predicting action
    if intent != "none": #if the message is a command
        k = dsl(intent)
        reply = k.generate()
        return reply
    else: #if the message is just chitchat
        reply = reply_predict(message_text)
        return reply
        

The predict_action() function output an action to be performed or "none" if no intent recognised. If intent is found then action is executed in dsl.py and a reply is generated. If predict_action() return "none", that means its a normal conversation like,
"hello how are you ?"
Another model called seqtoseq will generate a reply for the question.
We use two neural network models in Rita

1. Intent Recognition model

intent = predict_action(str(message_text))

Which will predict the action to be done by the bot from the message. For example say "tell me about RIT", for this question bot may reply with link to RIT website. This is an example tutorial for how to build an intent recogniser. The dataset used to train this model is stored in

 action_dataset.json

The intent recognition model can be trained by running

python3 action_train.py

2. SeqtoSeq model

This model is used for implementing normal conversations for questions like "hello how are you ?" Its a neural network model that will translate the question to answer like this.
Seqtoseq model can be trained by running,

python3 seqtoseq_train.py

SeqtoSeq model in pytorch can be implemented like this.

Tasks

1. chitchat dataset

AI assistants like google assistant are trained with huge conversatonal dataset extracted from email and google plus chat data collections. Those datasets are not available for public. Rita need such a dataset for training the coversational model.

chitchat_dataset.json

is created for collecting that dataset. It contain,

question:

and

answer:

for each sample.

unleash your sarcasm level, contribute in the dataset !
Add more questions and answers in

seqtoseq_dataset.json

like this,

   {
       "question": "your question",
       "answer": "its answer"
   },

2. Action_dataset

Make a list of actions to be performed by Rita.Create dataset for those actions inside

action_dataset.json 

in the form like this,

"intent": "website","sentence": "open college website"
"intent":"website", "sentence":"show me RIT website"
"intent":"website", "sentence":"give me more information"
"intent":"website", "sentence":"tell me more about RIT"

Then train the models again by running ``` bash setup.sh ```

See you on Ritu ❤️

rita's Issues

Parsing json file with whitespace gives error.

action_dataset.json is like this now
[{"intent":"data","sentence":"text"}]
when it is formatted to something like this for better visibility
[
{
"intent":"data",
"sentence":"text"
}
]
parser is giving error.
Parsing code is in action_train.py

List out tasks

@GopikrishnanSasikumar Can you explain the project and what the bot is being designed for. Also if you list out the tasks one by one we can start contributing.
Also it would be good if add in some good tutorials to be familiar with this design.

Use gitignore

Use gitignore to not commit unnecessary files and binaries.

Eg.

• .DS_store in Mac
• .idea/ folder when using Jetbrains.

Edit the stylesheet

Edit the stylesheet "static/style.css"

Organize the code into packages.

Create modules and sub modules within a parent package for RITA.

Create a test user interface for running Rita in localhost

See this tutorial to learn how to make it. The "render_template()" function should be called inside "testmain",

@app.route('/test')
def testmain():
    pass
    #test user interface to be implemented