livio-bot

A chatbot to answer questions on non-alcoholic fatty liver disease (NAFLD)

Installation:

• requirements.txt
• pip install rasa_nlu
• pip install rasa_core
• conda install -c conda-forge spacy
• python -m spacy download en
• make
• python -m pip install msgpack<0.6.0
• pip install msgpack==0.5.6

Use:

1. Train the Rasa NLU model by running: make train-nlu: train NLU model from contents in nlu_data This will train the Rasa NLU model and store it inside the /models/current/nlu folder of your project directory. make nlu-server: run NLU server

2. Train the Rasa Core model by running: make train-core This will train the Rasa Core model and store it inside the /models/current/dialogue folder of your project directory.

3. In a new terminal start the server for the custom action by running: make action-server This will start the server for emulating the custom action.

4. Test the assistant by running: make cmdline This will load the assistant in your terminal for you to chat.

UI to train data:

https://rasahq.github.io/rasa-nlu-trainer/

https://www.analyticsvidhya.com/blog/2018/01/faq-chatbots-the-future-of-information-searching/#comment-151142

Scripts:

• app.py: Chatbot UI built using Flask, using templates/*.html
• engine.py: Chatbot core logic as well as knowledgebase.
• run_training: Windows batch file to build trained modeling
• run_server: Windows batch file to execute Rasa-NLU server.

Other Data

• nlu_config.yml: Rasa NLU settings for training as well as executing intent extraction
• nlu_data: Directory containing training files
• static and templates: Flask UI related files

Dependencies:

• Needs Python 3.5, numpy, scipy, spacy, sklearn crf suite

ToDos

• Add more training data
• Entity extraction not working as desired, find out more.
• Etc.

References

• Rasa-NLU installation
• Bhavani Ravi’s event-bot code, Youtube Video