🔁 Mental Health Assessment: CI/CD Deployment Pipeline

This project delivers an end-to-end MLOps pipeline that operationalizes a machine learning model designed to assess the likelihood of depression based on social media activity.

The entire deployment process is containerized with Docker, tracked through MLflow, and orchestrated using CI/CD pipelines via Jenkins and GitHub Actions. This enables robust versioning, reproducibility, and automation; key components in deploying machine learning solutions at scale.

The deployed model is a Gradient Boosting Classifier, selected through comparative experimentation against other algorithms for its superior performance.

📌 Note: All data collection, preprocessing, feature engineering, and model experimentation are conducted in a separate repository:
👉 Mental Health Assessment - Modeling & Training

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📆 Installation & Setup

1. Clone the Repository

git clone https://github.com/Kh0uloud/Mental-Health-Assessment-Deployment.git
cd Mental-Health-Assessment-Deployment

2. (Optional) Set Up a Virtual Environment

python -m venv venv
venv\Scripts\activate (On Linux/macOS: source venv/bin/activate)

3. Install Required Dependencies

pip install -r requirements.txt

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🧪 Train and Evaluate the Model

Train, evaluate and save the model locally and log its metrics using:

python model/model.py

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🚀 Run the Application Locally

1. 📊 Start MLflow Tracking UI

To inspect experiment runs, metrics, and manage model versions:

mlflow ui

Once started, the MLflow UI will be available at: http://127.0.0.1:5000

Inside the UI:

• You can view the training runs and metrics
• Check if the model has been registered in the Model Registry
• If not registered, use the UI to register it manually

✅ This step is important because the Flask API will later load the model directly from the MLflow registry.

2. 🌐 Run the Application Locally (Flask API)

python api/app.py

Visit the local app at: http://127.0.0.1:8000

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🐳 Deploy with Docker

1. Build the Docker Image

docker build -t mentalhealth-assess -f api/Dockerfile .

2. Run the Docker Container

docker run -p 8000:8080 mentalhealth-assess

Visit the Dockerized app at: http://localhost:8080

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