Chicken-Disease-Classification

Problem Statement

To detect diseases in chickens at an early stage using deep learning techniques, preventing mortality in chickens, farmers loss due to mortality among chickens and ultimately keeping us healthy too. The aim of this project is to develop a very intelligent system for the early identification of various diseases in chickens. VGG16 from Keras Applications was implemented for the categorical classification of "Coccidiosis" and "Healthy."

Demo

You can find the deployed link to the project here: https://chickenapp1.azurewebsites.net

(I have terminated this for now due to the high computing cost incurred in running this application.)

Workflows

It is critical to follow this workflow to avoid confusion when working with each component.

1. Update config.yaml

2. Update secrets.yaml

   

3. Update params.yaml

4. Update the entity

5. Update the configuration manager in src config

6. Update the components

7. Update the pipeline

8. Update the main.py

9. Update the dvc.yaml

How to run this in local

Before you run the project, make sure that you are configuring your AWS S3 Bucket called as chicken-fecal-images, that contains a zip file named chicken-fecal-images.zip that in turn has a folder named chicken-fecal-images which has two folders, namely coccidiosis and healthy. Now within each of these aforementioned folders contains the fecal images of coccidiosis infected chicken and a healthy chicken respectively.

STEPS:

Clone the repository

git clone https://github.com/tejangupta/Chicken-Disease-Classification.git

STEP 01- Create a conda environment after opening the repository

conda create -n chicken python=3.8 -y

conda activate chicken

STEP 02- install the requirements

pip install -r requirements.txt

STEP 03- Export the environment variables (Linux)

export PYTHONPATH=$PYTHONPATH:/path/to/Chicken-Disease-Classification
export AWS_ACCESS_KEY_ID=<AWS_ACCESS_KEY_ID>
export AWS_SECRET_ACCESS_KEY=<AWS_SECRET_ACCESS_KEY>

STEP 04- Run the application server

python app.py

STEP 05- Train application

http://localhost:80/train

STEP 06- Prediction application

http://localhost:80/predict

Azure CI/CD deployment with GitHub Actions

Save the password generated under Access keys in your container registry somewhere safe. You can only see the password after enabling Admin user in Access keys.

Run from terminal one by one:

docker build -t <login_server>/<your_project_name>:<tag> .
docker login <login_server>
docker push <login_server>/<your_project_name>:<tag>

Deployment Steps:

1. Build the Docker image of the Source Code
2. Push the Docker image to Container Registry
3. Launch the Web App Server in Azure
4. Pull the Docker image from the container registry to Web App server and run