🧠 Depression Detection System

A multi-modal emotion recognition project that integrates text, audio, and video analysis to detect emotional cues, potentially indicative of depression. This system applies Natural Language Processing, Speech Emotion Recognition, and Facial Expression Analysis using machine learning and deep learning models.

---

📌 Features

• 🔤 Text Emotion Recognition – uses NLP and sentiment analysis
• 🎙 Speech Emotion Analysis – classifies emotions from audio tone
• 🎥 Facial Expression Detection – interprets emotional states via video
• 🧠 Combines results from multiple modalities to strengthen detection accuracy

---

🧰 Tech Stack

Domain	Tools & Libraries
Language	Python
NLP	NLTK, Scikit-learn, TextBlob, SpaCy (optional)
Audio	LibROSA, pyaudio, TensorFlow/Keras
Video	dlib, OpenCV
Web Framework	Flask
Model Storage	HDF5, Pickle, JSON

---

📁 Project Structure

depression-detection-system/
│
├── main.py                  # Main entry point
├── requirements.txt         # Required Python packages
├── library/                 # Core modules for emotion recognition
│   ├── speech_emotion_recognition.py
│   ├── text_emotion_recognition.py
│   └── video_emotion_recognition.py
├── Models/                  # Pretrained ML/DL models
├── templates/               # HTML files for web interface
├── static/                  # CSS, JS, images
├── README.md                # Project documentation
└── .gitignore               # Ignore unnecessary files
🚀 Getting Started
1. Clone the Repository
bash
Copy code
git clone https://github.com/zakelaskar/depression-detection-system.git
cd depression-detection-system
2. Install Dependencies
Make sure you have Python 3.8+ installed.

bash
Copy code
pip install -r requirements.txt
3. Run the App
bash
Copy code
python main.py
Then open http://localhost:5000 in your browser.

📊 Model Details

Audio Emotion Model: Trained using MFCC features + CNN/LSTM

Text Emotion Model: SVM classifier on preprocessed sentiment vectors

Video Model: Face landmarks extracted with dlib and classified via custom NN

Model files are stored in the /Models/ folder.

📦 Deployment Ideas (Optional)

Dockerize the app

Deploy to Heroku, Render, or AWS EC2

🤝 Contributing

Contributions are welcome! Feel free to fork the repository and submit pull requests.

📜 License

This project is licensed under the MIT License.

📬 Contact

Zakir Elaskar
📧 zelaskar@csuchico.edu | elaskarzakir@gmail.com
🔗 LinkedIn