MonsieurCan

Chatbot NLP FLASK PYTORCH

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.

Installation

flask pytorch nltk.download('punkt') numpy

Initial Setup:

$ 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

Utilisation

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

Credits:

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

backEnd (https://github.com/python-engineer/pytorch-chatbot)

Tuto
https://youtu.be/a37BL0stIuM

Documentation https://github.com/hitchcliff/front-end-chatjs https://github.com/python-engineer/chatbot-deployment.git