Human and Wild Animal Classifier Web Running Application

This project is a web-based image classifier that uses a pre-trained Keras model to distinguish between human and animal faces. The application is built using Django, and it can be run both locally and within a Docker container.

The model is based on yegekucuk/classifier1 project on GitHub.

Installation Methods

1. Downloading the Docker Image (Recommended Method)

You can download the image from DockerHub and run the application in a Docker container. For this option you don't have to clone the repository on your local machine

Prerequisites

• Docker installed on your machine.

Steps

1. Build the Docker image:

   docker pull yegekucuk2/classifier

2. Run the Docker container:

   docker run --name classifier -p 80:8000 yegekucuk2/classifier

3. Access the application by navigating to http://127.0.0.1 in your web browser.

4. You can stop and remove the application with the following command:

   docker rm -f classifier

2. Building with Docker

You can build the image on your own.

Prerequisites

• Docker installed on your machine.

Steps

1. Clone the repository:

   git clone https://github.com/yegekucuk/classifier1-on-web.git
   cd classifier1-on-web

2. Build the Docker image:

   docker build -t classifierimage .

3. Run the Docker container:

   docker --name classifier run -p 80:8000 classifierimage

4. Access the application by navigating to http://127.0.0.1 in your web browser.

5. You can stop and remove the application with the following command:

   docker rm -f classifier

3. Running Locally with Python

You can also run the application directly on your local machine.

Prerequisites

• Python 3.x and Pip installed on your environment.

Steps

1. Clone the repository:

   git clone https://github.com/your-username/classifier1-on-web.git
   cd classifier1-on-web

2. Create a virtual environment and activate it:

   python3 -m venv env
   source env/bin/activate  # On Windows use `env\\Scripts\\activate`

3. Install the packages:

   pip install -r requirements.txt

4. Apply migrations and start the Django development server:

   python manage.py migrate
   python manage.py runserver

5. Access the application by navigating to http://localhost:8000 in your web browser.

Project Structure

Project Files

• classifier/: Django application handling image uploads and predictions.
• project/: Django project settings and configurations.
• kube/: Kubernetes deployment files.
• Dockerfile: Instructions to build the Docker image.
• Jenkinsfile: CI pipeline configuration.
• requirements.txt: Python dependencies required by the project.
• model.h5: Pre-trained Keras model used for image classification.

CI/CD Operations

• Jenkins: For CI operations, Jenkins tool is used on Kubernetes cluster.
• ArgoCD: For CD operations, ArgoCD tool is used on Kubernetes cluster.

Model training

The pre-trained model is used. For the main repository of the model click here.

License

This project is licensed under the MIT License. See the LICENSE file for details.