🎭 Emotion Detection Web Application #

An AI-powered web app that detects emotions — anger, disgust, fear, joy, sadness — from customer feedback text.
Developed with Flask and Watson NLP (Embeddable Libraries) as the final project of the IBM Developing AI Applications with Python and Flask course.

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📌 Overview #

This project extends sentiment analysis into fine-grained emotion detection.
Instead of just positive/negative polarity, it identifies which emotion dominates a piece of text.

✔️ Input: Free-form text from the user
✔️ Processing: Watson NLP EmotionPredict model
✔️ Output: JSON with emotion scores + a formatted natural language response

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📊 Example #

Input:
I love my life.

JSON Output:

{
  "anger": 0.0062,
  "disgust": 0.0026,
  "fear": 0.0093,
  "joy": 0.9680,
  "sadness": 0.0497,
  "dominant_emotion": "joy"
}

Browser Display:

For the given statement, the system response is 
'anger': 0.0062, 'disgust': 0.0026, 'fear': 0.0093, 
'joy': 0.9680 and 'sadness': 0.0497. 
The dominant emotion is joy.

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🛠️ Tech Stack #

• Python 3.10+
• Flask — backend web framework
• Requests — HTTP client for Watson NLP calls
• Watson NLP Embeddable Library — emotion detection models
• HTML/CSS/JavaScript — provided templates and scripts
• PyLint / Unittest — static code analysis & validation

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📂 Project Structure #

EmotionDetection/        # Packaged emotion detection module
    __init__.py
    emotion_detection.py
static/                  # Provided JavaScript file
templates/               # Provided index.html
server.py                # Flask web server
test_emotion_detection.py
requirements.txt
README.md

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▶️ Running Locally #

1. Clone the repository

   git clone https://github.com/<your-username>/emotion-detector-app.git
   cd emotion-detector-app
   

2. Create and activate a virtual environment

   python -m venv .venv
   source .venv/bin/activate   # Mac/Linux
   .venv\Scripts\activate      # Windows
   

3. Install dependencies

   pip install -r requirements.txt
   

4. Run the Flask server

   python server.py
   

5. Access the app

   http://127.0.0.1:5000
   

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✅ Features #

• Detects five core emotions using Watson NLP.
• Clean Flask architecture for easy extension.
• Error handling for invalid/blank input.
• Unit tests validating joy, anger, disgust, sadness, fear.
• PyLint 10/10 compliance (Task 8).

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🎓 Certification #

This project was developed as part of the IBM Developing AI Applications with Python and Flask course.

📜 Certificate: Verify on Coursera

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🚀 Future Work #

• 🌐 Add multi-language emotion detection.
• ☁️ Deploy on cloud platforms (Render, IBM Cloud, Vercel).
• 📈 Visualize emotions using interactive charts.

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👨‍💻 Author #

Ahmad Tawil
Software Engineering Student @ Braude College
Passionate about AI, Machine Learning, and Full-Stack Development