oasa.ai

General Description

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

• Flask,
• NLP (Spacy),
• HTML/CSS, used to build the UI of oasa.ai.

Building steps

• 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

Directory Structure

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)

Steps to run

• 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.

Versions

	Version
Python	3.8