AhaBot

Self Attention based knowledge space aggregator bot

Place the label_encoder.pickle, chatbot_model.h5 and tokenizer.pickle in the root folder.
run pip install requirements.txt
then run python manage.py runserver to start the server.
simply open index.html in interface folder. You can now interact with our chatbot :)

Our ChatBot allows you to create diverse knowledge base merely from a list of FAQs and automatically handles user requests in real time.
The model is intelligent and doesn't require hardcoded values.
It is equipped with deep transformer neural networks to create a knowledge space for each question to model similar meaning situations. This is the core feature of our chatbot as this not only augments the data, it also allows you to handle a diverse set of requests.
You can either the pre-trained model chatbot_model.h5 or retrain the model on your custom FAQs.

We are using neural networks, deep transformer neural networks and BERT embeddings in our model.
The model runs for 350 Epochs and gives 95%+ accuracy with < 0.05 categorical loss.
vanilla javascript was used for front end.
Django was used for backend.
The model contains 16000+ trainable parameters and uses the power of tensorflow to train on it.
`After the modle is done training, the encoding files and model weights are saved. You don't have to go through the hassle to retrain it every time.

The bot needs to be supplied a list of FAQs to operate on, by default the following FAQs with questions and answers are supplied:

The program is dynamic and would work on any set of questions/answers supplied..