minggli/chatbot repository overview

⁠chatbot

a naive chatbot that sometimes misdiagnoses.

This retrieval-based prototype bot uses publicly available health information to generate indicative diagnosis and link to NHS leaflet. The result is not diagnosis and non-clinical use only.

⁠Requirement

Python >= 3.4 and Virtualenv >= 15.1
or
Docker

⁠Installation

⁠Virtualenv

Run script ./install.sh. This will first make a virtual environment venv and install components within it.

⁠Docker (Optional)

Run docker build -t minggli/chatbot . to make a Docker image locally. This is not mandatory as docker run pulls remote image as fallback.

⁠Running chatbot

export ENGINE=NLTK (default) to use NLTK backend for traditional Bag of Words model with Naive Bayes

export ENGINE=TENSORFLOW to use Tensorflow backend for representational sequence classification with Long-short Term Memroy (LSTM)

For the first time running either service, it will take longer than usual as it needs to download, process and cache web data.

⁠Virtualenv

Within virtual environment venv (source venv/bin/activate), you can launch separate service:

python3 -m chatbot.services.ask

python3 -m chatbot.services.symptoms

⁠Docker

Run docker image and instantiate docker container for each service:

docker run -t --memory 8g --rm -p 5000:5000 -e ENGINE=$ENGINE minggli/chatbot python -m chatbot.services.ask

docker run -t --memory 8g --rm -p 5001:5001 minggli/chatbot python -m chatbot.services.symptoms

⁠API endpoints

It accepts payload as simple as {"questions": "your questions or description of symptoms"} to below to query for indicative diagnosis:

POST host:5000/chatbot/api/v1/ask

To list all leaflets or leaflet for the chosen symptom:

GET host:5001/chatbot/api/v1/symptoms or
GET host:5001/chatbot/api/v1/symptoms/<string:symptom_name>

⁠Further development ideas

Using word vector rather than sparse matrix to extract semantic proximity in embedded space;

Explore Recurrent Neural Network (e.g. LSTM) to move from retrieval-based model to generative;

Explore supervised Latent Dirichlet Allocation, RNN as text classification alternative to Naive Bayes.