I have built a FAQ chatbot with a React and Typescript front-end, a Flask and MongoDB back-end, and a deep learning-powered chatbot that provides responses based on user input.
Live Link - https://faq-chatbot-gcloud-deployment.vercel.app/
Backend Repo - https://github.com/mdsimar1901/faq-chatbot-backend
| Description | Link |
|---|---|
| Live Link | Live Demo |
| Backend Repo | GitHub Repository |
| Layer | Technology |
|---|---|
| Front-end | React with TypeScript |
| Back-end | Flask |
| Database | MongoDB |
| Chatbot | Deep Learning Model |
- Login
- User Name - username
- Password - password
- Landing Page
- MessageView
- Message
- Bot Response
- Snapshots
For this platform to be built , I have used React,Flask,MongoDB.
- ReactJS
- Axios
- react-router-dom
- Material UI
- Paper
- Button
- Typography
- Box
- Input
- Flask
- MongoDB
- Provides User Login Service
- Provides ChatBot facility for FAQ for a University. Implemended the chatbot service using a deep learning model.. Link - https://github.com/mdsimar1901/faq-chatbot-backend
- Provides a data persistance service in the backend to store chat history.
- Handles Ambiguity and asks for clarifications when needed.
- Has a fallback strategy when stuck.
Clone the repo
$ git clone https://github.com/mdsimar1901/faq-chatbot.git
Install npm packages
npm install
Run the code
npm run
Clone repo and create a virtual environment
$ git clone https://github.com/mdsimar1901/faq-chatbot-backend.git
$ cd faq-chatbot-backend
$ 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')
Create your intents.json 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
