Skip to content

A chatbot that fetches events details from a conference's website

Notifications You must be signed in to change notification settings

bhavaniravi/rasa-site-bot

Folders and files

NameName
Last commit message
Last commit date

Latest commit

 

History

33 Commits
 
 
 
 
 
 
 
 
 
 
 
 

Repository files navigation

The bot is outdated and not maintained anymore

Site bot

A site bot build using rasa-nlu.

Setup

  1. if you are using Ubuntu pip install -r requirements.txt

  2. If you are using windows machine pip install -r requirements.txt The above command will fail for few packages like mitie. So you have to install the following pip install numpy,scipy,sklearn,spacy If you have error while downloading any of these packages download whl files from http://www.lfd.uci.edu/~gohlke/pythonlibs/

  3. You will have two parts in this projects

    1. AI-Engine

    The AI Engine part takes care of Natural language understanding It is built using a open source NLU agent called rasa-nlu

    1. Bot application

    The bot application is a flask application that has a Client(Simple UI chat interface), a backend that fetches event details pydelhi conference website

  4. Initially you need to train your bot to do that you need two json files config and 'training_data'.

  5. To train your model you can use either 'spacy' or 'mitie' algorithm. For my application I am using spacy.

    If you are using mitie you should download a .dat file from https://github.com/mit-nlp/MITIE/releases/download/v0.4/MITIE-models-v0.2.tar.bz2

    If you're using spacy run the following before training

    python -m spacy download en_core_web_md
    python -m spacy link en_core_web_md en
    
  6. To train your ML model run. The training will take some time.

    python -m rasa_nlu.train -c AI-engine/config_spacy.json --data AI-engine/data/sitebot-data.json
    
  7. Once the training is done you will have a models folder created with a timestamp

  8. Now you have to host this model for your bot application to use

    python -m rasa_nlu.server -c AI-engine/config_spacy.json --path ./models/nlu/

  9. Now that we have the AI-Engine up and running.Let's setup the knowledge base for the bot run extract.py under bot_applications

    python bot-applicaiton/extract.py You will have a sqlite app.db created

  10. To run the application run

    python app.py