In this tutorial we deploy the chatbot I created in this tutorial with Flask and JavaScript.

This gives 2 deployment options:

• Deploy within Flask app with jinja2 template
• Serve only the Flask prediction API. The used html and javascript files can be included in any Frontend application (with only a slight modification) and can run completely separate from the Flask App then.

This repo currently contains the starter files.

Clone repo and create a virtual environment

$ git clone https://github.com/python-engineer/chatbot-deployment.git
$ cd chatbot-deployment
$ python3 -m venv venv
$ . venv/bin/activate

Install dependencies

$ (venv) pip install Flask torch torchvision nltk

Install nltk package

$ (venv) python
>>> import nltk
>>> nltk.download('punkt')

Modify intents.json with different intents and responses for your Chatbot

Run

$ (venv) python train.py

This will dump data.pth file. And then run the following command to test it in the console.

$ (venv) python chat.py

Now for deployment follow my tutorial to implement app.py and app.js.

https://youtu.be/a37BL0stIuM

In the video we implement the first approach using jinja2 templates within our Flask app. Only slight modifications are needed to run the frontend separately. I put the final frontend code for a standalone frontend application in the standalone-frontend folder.

This repo was used for the frontend code: https://github.com/hitchcliff/front-end-chatjs