Restaurant chatbot

Restaurant-chatbot

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?

1. 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

2. 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.
3. 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

4. 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.

5. Run Flask This will launch the web app on localhost

export FLASK_APP=app
export FLASK_ENV=development
flask run