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Installation

Environment Setup and Documentation Preview

This section explains how to clone the repository, set up the Python environment, install dependencies, and preview the documentation site locally.

1. Clone the Repository

git clone https://github.com/LayanAlnasser/CS316-Mental-Health-AI-Project.git
cd CS316-Mental-Health-AI-Project

2. Create and Activate Virtual Environment

python -m venv .venv

macOS / Linux

source .venv/bin/activate

Windows

.venv\Scripts\activate

3. Install Dependencies

pip install -r requirements.txt

Ensure Python 3.10 or higher is installed.

4. Run Documentation Site

To preview the MkDocs documentation locally:

mkdocs serve

Then open:

http://127.0.0.1:8000

This launches the project documentation interface.

docs/deployment/app.md

This version removes placeholder tone and aligns with your longitudinal system.

Application Usage

Prototype Mental Health Monitoring Interface

The project includes a research prototype application that loads trained embedding and SVM models to classify depression and anxiety severity from Arabic text.

The interface demonstrates how longitudinal prediction and alert logic can operate in practice.

Running the Application

If the application is implemented using Streamlit:

streamlit run app.py
````

If implemented using a Python server framework:

```bash
python app.py

Open the local URL shown in the terminal.

Using the Interface

  • Enter Arabic Text

Provide a mental health related narrative sample.

  • View Severity Prediction

The system predicts depression and anxiety severity levels on a scale from 0 to 3.

  • Review Alerts

If historical data is provided, longitudinal alerts may be triggered based on score trends.

Output Interpretation

Severity levels:

Score Meaning
0 None
1 Mild
2 Moderate
3 Severe

Alerts may include:

  • Sustained high severity
  • Worsening trend
  • Sudden spike

Important Notice

This application is a research demonstration.

It is not a diagnostic tool and does not replace professional mental health evaluation.

docs/deployment/api.md

This version handles both possibilities cleanly.

API Interface

Programmatic Access Layer

The current project primarily provides a research web interface.

If an API layer is implemented, this section documents its endpoints and usage.

Current Status

The project currently provides:

  • A local research web application
  • No public production API

If an API is added using Flask or FastAPI, the following endpoints are recommended.

Example Endpoints

GET /health

Returns system status.

POST /predict

Request:

{
  "text": "احس اني تعبانة ومتوترة",
  "participant_id": "P012",
  "date": "2026-02-22"
}

Response:

{
  "depression_score": 2,
  "anxiety_score": 2,
  "alerts": []
}

Deployment Considerations

If exposing an API publicly, implement:

  • Authentication
  • Rate limiting
  • Secure logging
  • Consent disclosure
  • Human oversight for high-risk alerts

Research Positioning

This interface is designed for experimentation and demonstration within an academic context.

It is not a medical service.