Implementation of a Chatbot using Flask

This Flask-based chatbot handles queries related to EdTech courses. Conversations with users are logged in CSV files and stored in Firebase, accessible by the marketing team and admin for future analysis and use.

Key Features

Chatbot

A user-friendly chat bot where users can input their queries regarding courses.

FAQ Integration

Integrated frequently asked questions (FAQs) related to courses.

Natural Language Processing (NLP)

Utilized NLP techniques to understand user queries and extract key information required to provide relevant responses.

Fallback to Doubt Assistant

If the query is not satisfied using FAQs database then chatbot will fallback to doubt assistant.

User Satisfaction Feedback

After resolving a user query, chatbot prompts the user for feedback on their satisfaction level.

Data Sharing with Sales Team

.csv file of every chat is shared to various teams.

Performance Monitoring

Admin can monitor chatbot performance and can download .csv files from the server.

Watch the Demo

Installation

Clone git repository

git clone https://github.com/crazykaran/chatbot.git
cd chatbot

Create an environment

pip install virtualenv
virtualenv venv

Activate it

Mac / Linux:

. venv/bin/activate

Windows:

venv\Scripts\activate

Install Dependencies

pip install -r requirements.txt

If you get an error during the first run, you also need to install nltk.tokenize.punkt: Run this once in your terminal:

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

Setup firebase

• Create a Firebase project and initialize storage and database.
• Generate a service account key and save it as serviceAccountKey.json.
• Update the Firebase configuration in the Flask application.

Usage

Run

python train.py

This will dump data.pth file. And then run

python app.py

This will start a flask website

Login Credentials:

username : admin

password : admin@123

Images

chatbot

FAQs

Admin Login

Conversations .csv files

Download .csv files

Analyse .csv files