Drift Detection Dashboard

Introduction

This repository contains a Streamlit dashboard for monitoring and visualizing drift detection in machine learning models. The 
dashboard allows users to select different types of drift (feature drift, concept drift, or no drift) and visualize the detected 
drift parametersover a specified time period.

Usage

1. Configuration

- Create a .env file inside the github repo and ensure that you have the necessary credentials configured in the `.env` file inside the repo. This file should contain the following variables:

monitoring_url = The base URL for accessing drift detection API endpoints.

for example, on localhost it would look something like this: 

monitoring_url = http://0.0.0.0:5000

2. Building the Docker Image

Navigate to the frontend directory and run the following command to build the Docker image:

cd frontend

docker build -t drift_detection_image .

3. Running a Docker Container

After building the Docker image, you can run a Docker container based on that image. Use the following command: 

docker run -p 8000:8000 -p 8501:8501 drift_detection_image

4.

Go to http://0.0.0.0:8501 to access the streamlit dashboard