Frontend created with React and Bootstrap.
Backend hosted locally via Python Flask app (see predict.py) that runs a pretrained model. Training done manually using train.py - given corpus input, this file extracts a vocabulary of words and finds a list of trigrams. The trigrams are then split into an input (first two words) and output (last word). Input is then encoded into a 512-dimensional vector space with Universal Sentence Encoder and trained with a custom model. Output of the model is of size len(vocabulary), and each given input following is treated as a classification problem.
A video demo of this project can be found here.
Experimental, more lightweight model is being developed in train_lite.py to test a form of vector mapping from 512-dimensional input to 512-dimensional output, followed by nearest-neighbour identification from vocabulary.
