Eventime App

Overview

This project is an AI-enhanced web application designed for organising and recommending events. The system leverages Natural Language Processing (NLP) techniques for text processing and provides functionalities for creating, editing, and viewing events from a database. The application features a calendar for event management and includes a robust backend for authentication and data handling.

Features

Event Management: Users can create, edit, and view events.
Calendar Integration: A calendar view to manage events efficiently.
NLP Processing: Utilises various NLP techniques such as NLTK WordNet for dictionary-based approaches, lemmatisation, and synonyms.
Authentication: Secure login and signup functionalities.
AI-Powered Recommendations: Events are recommended based on text processing using NLP techniques.
Testing: Postman used for testing API endpoints.
Chatbot: Chat with a chatbot to get event recommendations.

Technologies Used

Frontend

Angular: Framework for building the user interface.
Angular Material: UI components for Angular.
Bootstrap/NGX-Bootstrap: For responsive design and additional UI components.
Angular OAuth2 OIDC: For handling authentication.
Date-Fns: Library for date manipulation.
JWT-Decode: Library for decoding JSON Web Tokens.

Backend

Spring Boot: Framework for building the backend application.
Spring Security: For authentication and authorisation.
PostgreSQL: Database for storing user and event data.
HikariCP: Connection pooling for PostgreSQL.
Lombok: For reducing boilerplate code.
JJWT: For JWT creation and parsing.
Commons Validator: For data validation.

NLP and AI

SpaCy: Library for advanced NLP in Python.
NLTK WordNet: For dictionary-based approaches and synonyms.
Lemmatisation: Reducing words to their base or root form.

Chatbot

Flask: Python framework for the chatbot.
SpaCy: For NLP in chatbot.
NLTK: For additional NLP tasks.

Application Structure

Backend

Spring Boot Application: Manages the business logic, data access, and authentication.
Repositories: Interfaces for data access using Spring Data JPA.
Services: Business logic for handling event and user operations.
Controllers: RESTful endpoints for frontend communication.
Security: Configured using Spring Security for login and signup processes.

Frontend

Angular Application: Manages the user interface and communicates with the backend via RESTful APIs.
Components: Modular UI components for managing events and user interactions.
Services: Angular services for handling API calls and business logic on the client side.
Routing: Configured for navigating between different views within the application.

Chatbot

Flask Application: Manages the chatbot interactions.
Endpoints: RESTful endpoints for chatbot communication.
NLP Processing: Uses SpaCy and NLTK for processing user input.

Getting Started

Prerequisites

Node.js: Required for running Angular.
Java: Required for running Spring Boot.
Python 3.9: Required for running NLP tasks with SpaCy and Flask.
PostgreSQL: Database setup.
Flask: Required for running the chatbot.

Installation

Clone the Repository:

git clone https://github.com/teakulo/ETfrontend.git

Backend Setup:
- Navigate to the backend directory.
- Configure the database settings in application.yml.
- Run the Spring Boot application:
```
./mvnw spring-boot:run
```
Frontend Setup:
- Navigate to the frontend directory.
- Install dependencies:
```
npm install
```
- Run the Angular application:
```
ng serve
```
Chatbot Setup:
- Navigate to the chatbot directory.
- Install Python dependencies:
```
pip install -r requirements.txt
```
- Run the Flask application:
```
flask run
```