We start by creating a file that we will use to download the model. To help us, we use Huggingface, a python library that provides various high quality NLP models.
Then we create a python class that we will use to handle the logic from converting our english text to create our word tokens that we will use as inputs for our model.
We then build a Flask API with two endpoints, one for checking if the service is working and one for integrating with our chatbot.
Finally we generate a Dockerfile that when being built will pre-download the chat model so that when we send request to our API it can make quick responses, instead of reloading the model every single time. This will drastically improve the performance of our bot. To host the API we use gunicorn as our wsgi server with no additional web server framework.
The steps from running your model on your local machine to have it running in production can see daunting. However several services have done this step a lot easier in recent years.
We are going to work with google cloud run for this project. Googles “serverless” platform, I don’t like the word serverless since of course there has to be a server running the code, but it is serverless in the sense that it doesn’t save any client data from session to another session and that we get whatever server is available at any given time.
Source: https://chatbotslife.com/how-to-build-your-own-chatbot-f5848ebcba8d?source=rss----a49517e4c30b---42. 4 DO’s and 3 DON’Ts for Training a Chatbot NLP Model
3. Concierge Bot: Handle Multiple Chatbots from One Chat Screen