Repository for final project Emotion Detection Web Application Introduction Welcome to the final project for this course! In this project, 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. You will also be required to perform various tasks and submit your results with specific nomenclature.
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. In this project, we will harness the power of Watson AI to create an emotion detection application.
Project Tasks To successfully complete this project, you will need to perform the following tasks:
Task 1: Clone the Project Repository Start by cloning the project repository to your local environment. This will provide you with the necessary code and resources to begin your work. this is the original project link: https://github.com/ibm-developer-skills-network/ but I have cloned it on my local machine and pushed it again on new repo for a purpose of using it in the Cloud IDE this the link: https://github.com/AtaUlMohsin900/Final-Project-Emotion-Detector for my own repo
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 Ensure that the output of your emotion detection application is well-formatted and user-friendly. Users should be able to understand the emotions identified.
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 step involves making your application accessible over the web.
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 will have created a functional emotion detection web application that can be accessed by users on the internet.
Good luck with your project, and don't forget to save screenshots of your results as you progress. These will be required for your peer-graded assignment submission.