Architecture overview:
Detailed explanation of the project is given in my tech blog here: https://medium.com/@barua.aindriya/building-a-nlp-chatbot-for-a-restaurant-with-flask-b978337049f2
How to set up and run the project?
- Install Pre-requisites My python version is 3.6.13. To install all the required libraries, download/clone my GitHub repo and in the folder, open CMD and enter:
pip install -r requirements.txt
- Download pre-trained FastText English model Download cc.en.300.bin.gz from https://fasttext.cc/docs/en/crawl-vectors.html . Unizip it to Download cc.en.300.bin, the code for which is helper scripts in my Github repo.
- Prepare dataset Run data_embedder.py This will take the dataset.json file and convert all the sentences to FastText Vectors.
python data_embedder.py
- Set up Mongo Db on localhost Install MongoDb Compass Make 3 collections: menu, bookings, feedback Menu has to be hardcoded, it includes item, cost, vegan, veg, about, offer.
feedback docs will be inserted when a user gives a feedback so that the restaurant authority can read them and take necessary action.
booking collection writer the unique booking ID and time-stamp of booking, so that when the customer comes and shows the ID at the reception, the booking can be verified.
- Run Flask This will launch the web app on localhost
export FLASK_APP=app
export FLASK_ENV=development
flask run