AI-Based-Web-Application-Development-and-Deployment

Created by Réka Gábosi

Table of Contents

• Description
• Course
• Project Overview
• Features
• Technologies Used
• Project Structure
• How to Run
  • Prerequisites
  • Installation
  • Configuration
  • Running the App
• Example
• Final Notes
• Acknowledgements

Description

This is a web-based emotion detection application created as the final project for the IBM course: Developing AI Applications with Python and Flask.

Course

IBM: Developing AI Applications with Python and Flask

Project Overview

The goal of this project is to build an emotion detection system that can:

• Analyze a given English sentence.
• Return emotion scores (anger, disgust, fear, joy, sadness).
• Identify and display the dominant emotion.
• Deploy the system as a web application using Flask.

The application uses Watson NLP (via HTTP POST to Watson’s EmotionPredict endpoint) to classify emotions.

Features

• Emotion detection via Watson NLP.
• Web interface using Flask.
• Error handling for invalid or blank inputs.
• Packaged as a Python module.
• Unit tested and PyLint-compliant (10/10 score).
• Static frontend files provided (index.html, mywebscript.js).

Technologies Used

• Python 3.11
• Flask
• Requests
• Watson NLP API
• HTML + JavaScript (for frontend)
• PyLint / Black (code quality & formatting)

Project Structure

│
├── EmotionDetection/
│ ├── init.py
│ └── emotion_detection.py
│
├── static/
│ └── mywebscript.js
│
├── templates/
│ └── index.html
│
├── tests/
│ └── test_emotion_detection.py
│
├── server.py
├── LICENSE
├── .gitignore
├── requirements.txt
└── README.md 

How to Run

Prerequisites

• Python 3.7+
• IBM Cloud account with a Natural Language Understanding service instance
  (Get API key and URL from your IBM Cloud dashboard)

Installation

git clone https://github.com/Afhrodite/AI-Based-Web-Application-Development-and-Deployment.git
cd AI-Based-Web-Application-Development-and-Deployment
pip install -r requirements.txt

Configuration

Create a .env file in the project root with the following content:

API_KEY=your_ibm_nlu_api_key_here
API_URL=your_ibm_nlu_instance_url_here

Running the App

Run the Flask server with:

python server.py

By default, the Flask server runs on http://localhost:5000.

Example

Input

I love this new technology.

Output

{
  'anger': 0.002,
  'disgust': 0.001,
  'fear': 0.004,
  'joy': 0.985,
  'sadness': 0.008,
  'dominant_emotion': 'joy'
}

Final Notes

• Error handling is implemented for blank or invalid input.
• Emotion detection logic is modular and reusable.
• Code was cleaned and formatted with Black.
• Project passed static analysis with PyLint score: 10/10.

Acknowledgements

This project was developed as part of the IBM course: Developing AI Applications with Python and Flask, provided on Coursera.

Special thanks to:

• IBM Skills Network for the project framework and Watson NLU access.