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.”

Test Results

WorkFlow

1 Update config.yaml

2 Update secret.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 the project

STEP:

Clone the repository

https://github.com/Shubhankargupta691/Chicken_Disease_Classification.git

STEP 01- Create a conda environment in the Project Folder

conda create -n chicken_venv python=3.11 -y

conda activate chicken_venv

STEP 02- install the requirements

pip install -r requirements.txt

STEP 03- run main.py

python main.py

STEP 04- run app.py

python app.py

DVC Commands

STEP 01- Initialize

dvc init

STEP 02- Reproduce complete or partial pipelines

dvc repro

STEP 03- Directed Acyclic Graph (DAG)

dvc dag

Relationship between the pipelines:

AZURE CICD Deployment with Github Actions

Run these docker command from terminal:

NOTE: If you're using Windows and have to download and install Docker Desktop and run it in background.

STEP 01: Build the Docker image

docker build --no-cache -t imageclassificationapp.azurecr.io/classification:latest .

STEP 02: Login to an Azure Container Registry (ACR) using Docker

docker login imageclassificationapp.azurecr.io

STEP 03: Push the Docker Image to Azure Container Registry (ACR)

docker push imageclassificationapp.azurecr.io/classification:latest

Setup github secrets:

AZUREAPPSERVICE_CONTAINERPASSWORD

AZUREAPPSERVICE_CONTAINERUSERNAME

AZUREAPPSERVICE_PUBLISHPROFILE

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

Docker Image:

docker pull shubhankargupta/classification:latest