This project implements a deep learning solution to classify chicken diseases from fecal images. It includes both a model training pipeline and a web interface for real-time predictions.

• Python 3.8+
• TensorFlow/Keras
• Flask
• HTML/JavaScript/TailwindCSS
• DVC (Data Version Control)

Chicken-Disease-Classification/
├── artifacts/                  # Model artifacts and data
├── config/                    
│   └── config.yaml            # Configuration files
├── logs/                      # Application logs
├── src/cnnClassifier/        
│   ├── components/            # Model components
│   ├── config/               
│   ├── pipeline/             
│   └── utils/                
├── templates/                 
│   └── index.html            # Web interface
├── app.py                     # Flask application
├── main.py                    # Training pipeline
├── params.yaml               
├── requirements.txt          
└── setup.py                  

1. Clone the repository

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

2. Create and activate virtual environment

python -m venv venv
source venv/bin/activate  # For Linux/Mac
# or
venv\Scripts\activate     # For Windows

3. Install dependencies

pip install -r requirements.txt

python main.py

Or use the web interface's "Train Model" button.

python app.py

Access the application at http://localhost:80

1. Open the web interface
2. Upload a chicken fecal image
3. Click "Predict" to get the disease classification

Update these files for customization:

• config/config.yaml: Model and training parameters
• params.yaml: Hyperparameters
• src/cnnClassifier/config/configuration.py: Pipeline configuration

dvc init
dvc run -n train -d main.py -o artifacts/model.h5 python main.py

1. Fork the repository
2. Create a feature branch
3. Commit changes
4. Push to the branch
5. Open a Pull Request

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

• Dataset source: [Add source here]
• Based on [research paper/methodology reference]

1. Update config.yaml
2. Update secrets.yaml [Optional]
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

https://souradipcodes.notion.site/Chicken-Disease-Classification-17ec6c3fab4d804e9ce1cd402e2be079?pvs=4