Support Chatbot with Machine Learning

Author

LemonBaba

Decision Responsive or Generative Chatbot

I lost a lot of time in executing training scripts for the generative chatbot. So I decided to make a responsive one.

Use Cases

Support Chat E-commerce

Used Techniques

Technique	Description
flask	To create a web server that handles HTTP requests and serves responses to the frontend.
NLTK (Natural Language Toolkit)	To perform various Natural Language Processing (NLP) tasks
scikit-learn	To build and use machine learning models
joblib	To save and load the trained machine learning models
requests	To make HTTP requests (if needed) to external APIs or services for additional information.
React	To build a dynamic and responsive user interface for the chatbot application.
CSS	To style the frontend of the chatbot

Dataset

Datasets are found in the ./backend/app.py

Backend

pip install -r requirements.txt

python ./backend/app.py

Frontend

Available Scripts

In the frontend project directory, you can run:

npm install

To install all the required frontend dependencies

npm start

Runs the app in the development mode.
Open http://localhost:3000 to view it in your browser.

The page will reload when you make changes.
You may also see any lint errors in the console.

npm test

Launches the test runner in the interactive watch mode.
See the section about running tests for more information.

npm run build

Builds the app for production to the build folder.
It correctly bundles React in production mode and optimizes the build for the best performance.

The build is minified and the filenames include the hashes.
Your app is ready to be deployed!

See the section about deployment for more information.

npm run eject

Note: this is a one-way operation. Once you eject, you can't go back!

If you aren't satisfied with the build tool and configuration choices, you can eject at any time. This command will remove the single build dependency from your project.

Instead, it will copy all the configuration files and the transitive dependencies (webpack, Babel, ESLint, etc) right into your project so you have full control over them. All of the commands except eject will still work, but they will point to the copied scripts so you can tweak them. At this point you're on your own.

You don't have to ever use eject. The curated feature set is suitable for small and middle deployments, and you shouldn't feel obligated to use this feature. However we understand that this tool wouldn't be useful if you couldn't customize it when you are ready for it.

Screenshot of bot in action

Reflection

This project has taught me the skills of how machine learning works and how it is used.