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oaqjp-final-project-emb-ai
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# oaqjp-final-project-emb-ai ## Final Project - Emotion Detector An AI-based web application that detects emotions in text using the **Watson NLP Embed library**. --- ## Project Structure ``` oaqjp-final-project-emb-ai/ │ ├── EmotionDetection/ │ ├── __init__.py │ └── emotion_detection.py │ ├── templates/ │ └── index.html │ ├── server.py ├── test_emotion_detection.py └── README.md ``` --- ## Tasks Completed | Task | Description | |------|-------------| | Task 1 | Cloned the project repository | | Task 2 | Created emotion detection application using Watson NLP library | | Task 3 | Formatted the output of the application | | Task 4 | Packaged the application as `EmotionDetection` | | Task 5 | Ran unit tests on the application | | Task 6 | Web deployment using Flask | | Task 7 | Incorporated error handling (status 400 & blank input) | | Task 8 | Ran static code analysis with pylint (10.00/10) | --- ## How to Run ### 1. Install dependencies ```bash pip install flask requests ``` ### 2. Start the Flask server ```bash python3 server.py ``` ### 3. Open in browser ``` http://localhost:5000 ``` --- ## API Endpoint ``` GET /emotionDetector?textToAnalyze=<your text here> ``` ### Sample Response ``` For the given statement, the system response is 'anger': 0.006274985, 'disgust': 0.0025689833, 'fear': 0.009251649, 'joy': 0.9680386 and 'sadness': 0.049744144. The dominant emotion is joy. ``` --- ## Technologies Used - Python 3.x - Flask - Watson NLP Embed Library - Pylint - Unittest / Pytest --- ## Author **Joel Jolly** GitHub: [withinjoel](https://github.com/withinjoel)
