Depression-detection-Using-Deep-Learning

Mini Project Work for BTAM1311 (Deep Learning)

This project applies Convolutional Neural Networks (CNNs) to detect signs of depression from facial expression images.
It includes dataset preparation, model training, evaluation, a project report, and a Flask web application for deployment.

🚀 Live Demo

Check out the project here: Live Demo

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Features

• Original development notebook: notebooks/Depression-detection-Using-Deep-Learning.ipynb
• Training script alternative: train.py
• Flask web application for image-based predictions
• Dataset included in notebooks/dataset/
• Project report included in notebooks/report/

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Project Structure

Depression-detection-Using-Deep-Learning/  
│── app.py                  # Flask web app  
│── train.py                # Training script  
│── depression_model.h5     # Saved model  
│── requirements.txt        # Dependencies  
│── README.md               # Documentation  
│  
├── templates/              # HTML templates  
│   └── index.html  
│  
├── static/                 # Static assets (CSS, images)  
│   └── style.css  
│  
└── notebooks/  
    ├── Depression-detection-Using-Deep-Learning.ipynb  # Original project notebook  
    ├── dataset/  # Dataset for training/testing
    └── report/             # Project report  

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Installation & Setup

1. Clone the repository

git clone https://github.com/yourusername/Depression-detection-Using-Deep-Learning.git

Navigate to cloned repoistory

cd Depression-detection-Using-Deep-Learning

Create a virtual environment

python -m venv venv

Activate your virtual environment

source venv/bin/activate   # Linux/Mac
venv\Scripts\activate      # Windows

Install dependencies

pip install -r requirements.txt

Run the Flask app

python app.py

Download the original dataset used from Kaggle:

https://www.kaggle.com/datasets/khairunneesa/depression-dataset-on-facial-ecpression-images