oasa.ai

General Description

Greek chatbot that retrieves stop and bus information. In terms of technologies/frameworks, the following were used:

Phase 1: Create flask sqlite db, create and form tables, drain the static info from OASA API into db.
Phase 2: create a many:many relationship of the 2 tables
Phase 3: Value mapping. Line description needs preprocessing/mapping?
- static replacement is bad option - solution: when user types a stop, check if its most part matches a db stop_name
- check each stops suffix, map based on that? -> bad results
- check if user input exists as is. If not, suggest similar stops? -> good result
- Add the JSON patterns as stop words, add specific POS in stop words too
Phase 3: Chat API added
Phase 4: Class "stopInfo" responses -- static information -> drained from local db
Phase 5: Class "BusRoute" response -- static information -> drained from local db
Phase 6: Class "busTime" response -- dynamic/real-time information -> drained from oasa api
Phase 7: Chat Logger added

The following directories exist in the system:

db, this is where the collection from the oasa api and some string preprocessing is performed. All static information is stored in a local db,
chatbot, this is where the NLP model processed the user's input and returns a response either from the local db (static info) or from the oasa api (dynamic info)

to just chat: chat.py,
to create and drain data from oasa api: models.py > oasa_pull > stop_name_preprocessing,
to train the NLP model: edit data/training_dataGREEK > ../train.py.

	Version
Python	3.8