Multimodal Emotion Analysis System

A robust system that combines facial emotion recognition with text sentiment analysis to provide comprehensive emotional insights and personalized recommendations.

🌟 Features

• Text Analysis

  • Sentiment classification (positive, negative, neutral)
  • Emotion detection (joy, sadness, anger, fear, love, surprise)
  • Tweet and general text support

• Facial Analysis

  • Real-time facial emotion detection
  • Region-based facial analysis for improved accuracy
  • Support for multiple faces in a single image
  • Batch processing capabilities

• AI Recommendations

  • Personalized insights using GPT-4
  • Context-aware emotional state analysis
  • Actionable recommendations based on combined analysis

📋 Requirements

• Python 3.8+
• TensorFlow 2.x
• Flask
• OpenAI API key
• CUDA-capable GPU (recommended)

pip install -r requirements.txt

🚀 Quick Start

1. Clone the repository:

git clone https://github.com/yourusername/emotion-analysis-system.git
cd emotion-analysis-system

2. Set up environment variables:

cp .env.example .env
# Add your OpenAI API key to .env

3. Start the server:

python app.py

4. Access the web interface at http://localhost:5000

💡 Usage

Text Analysis

from tweet_analyzer import TweetAnalyzer

analyzer = TweetAnalyzer()
results = analyzer.analyze_tweet("Your text here")

Image Analysis

from batch_classifier import BatchImageClassifier

classifier = BatchImageClassifier("path/to/model.h5")
results = classifier.process_image_folder("input_folder", "output_folder")

🏗️ Project Structure

.
├── app.py              # Main Flask application
├── tweet_analyzer.py   # Text analysis module
├── batch_classifier.py # Image analysis module
├── models/            # Pre-trained models
├── templates/         # HTML templates
└── uploads/          # Temporary file storage

📊 Model Performance

• Text Sentiment Analysis: 71% accuracy
• Facial Emotion Recognition: 64% accuracy
• Support for 8 distinct emotions

🔒 Security Considerations

• Maximum file size: 8MB
• Supported image formats: PNG, JPG, JPEG
• Automatic file cleanup after processing
• GPU memory optimization

🤝 Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

1. Fork the repository
2. Create your feature branch (git checkout -b feature/AmazingFeature)
3. Commit your changes (git commit -m 'Add some AmazingFeature')
4. Push to the branch (git push origin feature/AmazingFeature)
5. Open a Pull Request

📝 License

This project is licensed under the MIT License - see the LICENSE file for details.

🙏 Acknowledgments

• TensorFlow team for the deep learning framework
• OpenAI for GPT-4 integration
• Contributors and maintainers

📞 Contact

Your Name - bojalil.david@gmail.com Project Link: https://github.com/DavidBo9/EmotiAISourceCode