This project is an experimentation on Natural Language Understanding
- design models for intents and intent utterances
- build a database for the intents dataset
- build endpoints for serving js and css directly from the folder
- test adding members for the sql database
- put layout.html on the final design - side menu
- build all the Intents add flow
- create add intent button
- create script to add intent on database. dont add existent intents
- create script for updating the list of intents and reformulate the intents html page
- create delete intent button
- create script to del intent on database
- cancel button whilee at the add intent modal window
- build all the flow of adding utterances for intents
- build extract for json/LUIS like
- [0] prepare ipython notebook for NLU
- build first extractor
- build extract for text/classifier-like
- think about app configs
- separate flask endpoints and configs
- model configs
- think about ipynb + folder distribution
- build a ipynb extractor
- draw the solution
- with all the modules for inference
- with all the modules plus an anomaly detector for inference and active learning
- the full solution and work of a modeller and applications
- depict the developer operations side
- depict various developers (modeler, app builder, feature engineer, etc...)
- rebuild server endpoints
- home
- intents
- utterances
- extract/import configs
- model/train
- model/config
- model/monitor