Depression Detector using CNN

Overview

The Depression Detector using CNN is a machine learning project that utilizes Convolutional Neural Networks (CNNs) to identify signs of depression from various input data, such as images or text. This project aims to provide an automated tool for mental health assessment, offering insights that can aid in early diagnosis and intervention.

Features

• Image/Text Analysis: Uses CNNs to analyze input data.
• User-Friendly Interface: Easy to use for both developers and end-users.
• Real-Time Feedback: Provides immediate results based on input data.

Setup Instructions

1. Create a Virtual Environment

To ensure that your project dependencies are isolated, it's recommended to create a virtual environment. Follow these steps based on your operating system:

• Windows:

  py -m venv myenv
  myenv\Scripts\activate.bat
  

2. Install Necessary Requirements

Install all the required modules

• Windows:

  pip install -r requirements.txt
  
  

3. Make migrations and createsuper user

Go to main app folder where there is manage.py to make all the migrations and createsuperuser

• Windows:

  py  manage.py makemigrations
  pt manage.py migrate
  py manage.py createsuperuser
  
  
  

4. Now Run the Server

Run the wsgi server to see the website

• Windows:

  py manage.py runserver
  

FLow chart and CNN architecture:

Sample Dataset Images:

Screenshots of the web app