Repository for final project

Introduction

Wecome! In this final project of IBM course "Developing AI Applications with Python and Flask", you will demonstrate your knowledge and skills in app creation and web deployment. The project involves creating an emotion detection application using the Watson AI libraries and deploying it as a web application using Flask.

Emotion Detection

Emotion detection goes beyond sentiment analysis by extracting more nuanced emotions like joy, sadness, anger, and more from text statements. This capability is essential for AI-based recommendation systems, chatbots, and various other applications. This Emotion Detection system provides an AI-based web app to an e-commerce company that performs analytics on customer feedback for their signature products.

Project Tasks

To successfully complete this project, you will need to perform the following tasks:

Task 1: Fork and Clone the project repository

As a first step you need to Fork this repository, link: https://github.com/ibm-developer-skills-network/oaqjp-final-project-emb-ai.git and clone this forked GitHub repository using the Cloud IDE terminal to your project

Task 2: Create an Emotion Detection Application

Utilize the Watson NLP library to develop an emotion detection application. This application will analyze text input and identify the underlying emotions.

Task 3: Format the Output

Convert the response text into a dictionary using the json library functions.Ensure that the output of your emotion detection application is well-formatted and user-friendly.

Task 4: Package the Application

Package your application for ease of deployment. You should have a clear and concise set of instructions for deploying it.

Task 5: Run Unit Tests

Test your application thoroughly to ensure it functions as expected. Create unit tests to validate its behavior.

Task 6: Deploy as a Web Application Using Flask

Take your emotion detection application and deploy it as a web application using the Flask framework. This will enable the customer to access and use the application.

Task 7: Incorporate Error Handling

Implement robust error handling to ensure that your application gracefully handles unexpected situations.

Task 8: Run Static Code Analysis

Perform static code analysis to review your code for potential issues, code quality, and adherence to best practices.

By completing these tasks, you have created an Emotion Detection application using the functions from embeddable AI libraries