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.
Python >= 3.4 and Virtualenv >= 15.1
or
Docker
Run script ./install.sh. This will first make a virtual environment venv and install components within it.
Run docker build -t minggli/chatbot . to make a Docker image locally. This is not mandatory as docker run pulls remote image as fallback.
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.
Within virtual environment venv (source venv/bin/activate), you can launch separate service:
python3 -m chatbot.services.ask
python3 -m chatbot.services.symptoms
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
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>
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.