This repository contains two AI-based applications developed as part of the "Developing AI Applications with Python and Flask" course on Coursera. This project was initiated by forking an existing repository and extends its functionality by integrating Embeddable IBM Watson AI libraries.
The primary goal of this module was to apply programming skills to build robust AI solutions for text analysis, ensuring they are reliable and production-ready through rigorous testing and error handling.
This repository showcases an AI application: Emotion Detection Tool
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An application capable of detecting emotions (e.g., joy, sadness, anger) based on text input.
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This project served as a peer-assessed final assignment to evaluate proficiency in applying AI skills.
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Utilizes IBM Watson AI for advanced natural language processing to identify emotional nuances in text.
Common Practices Applied
The development process included a strong focus on software quality:
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Unit Testing: Comprehensive unit tests were implemented to ensure the reliability and correctness of the application logic.
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Static Code Analysis: Tools and practices for static code analysis were incorporated to maintain code quality, identify potential issues, and ensure adherence to coding standards.
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Error Handling: Robust error handling mechanisms were integrated to make the applications more resilient and user-friendly in production environments.
- Python: The core programming language for application logic and AI integration.
- Flask: A lightweight micro-web framework used for building the web interfaces of both applications.
- IBM Watson AI Libraries: Key libraries used for performing sentiment analysis and emotion detection.
To view this project locally on your machine:
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Fork the repository:
https://github.com/ibm-developer-skills-network/oaqjp-final-project-emb-ai
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Navigate into the project directory:
cd oaqjp-final-project-emb-ai -
Set up IBM Watson Credentials:
- You will need IBM Cloud credentials for Watson services (e.g., Watson Natural Language Understanding).
- Create a
.envfile (or set environment variables) in the root of the project with your API Key and URL for the Watson service. - Example
.envcontent:WATSON_API_KEY=YOUR_WATSON_API_KEY WATSON_URL=YOUR_WATSON_SERVICE_URL
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Run the Flask application:
flask run
Check the live app here - https://sjain2580.github.io/oaqjp-final-project-emb-ai
I successfully completed the "Developing AI Applications with Python and Flask" course from Coursera, authorized by IBM. https://www.coursera.org/account/accomplishments/verify/VH7BDWH1HFTY
https://github.com/sjain2580
Feel free to reach out if you have any questions or just want to connect!
