zain88088/kidney_disease_classification

Purpose and current shape

This repository is a Python package scaffold for CNN-based kidney disease image classification. The stated runtime setup uses Python 3.8 and TensorFlow 2.12.0. The implementation is largely skeletal: component, pipeline, entity, and utility directories currently expose only package initializers.

Entry points and execution model

No application CLI, service launcher, or concrete pipeline script is present in the tree. Likely execution surfaces are:

• Package installation/import via setup.py

• DVC pipeline execution via dvc.yaml

• Experimental development through research/trials.ipynb

• A static HTML presentation surface at templates/index.html

Package boundary

The primary application namespace is src/kidney_disease_classification/, isolating domain code from repository-level orchestration.

• src/kidney_disease_classification/__init__.py

• src/kidney_disease_classification/components/__init__.py

• src/kidney_disease_classification/pipeline/__init__.py

The intended layering appears to be reusable processing/model components feeding pipeline orchestration, with entities carrying structured configuration or artifacts.

Configuration and parameter boundary

Configuration is split between repository-level YAML inputs and a package-level configuration loader area:

• config/config.yaml

• params.yaml

• src/kidney_disease_classification/config/configuration.py

This suggests runtime behavior should be parameterized rather than hard-coded. config/config.yaml likely holds paths/artifacts or operational settings, while params.yaml is the natural location for model/training parameters; their exact schemas are not visible.

ML pipeline and reproducibility

DVC is the principal declared workflow/orchestration mechanism:

• dvc.yaml

• params.yaml

• research/trials.ipynb

The expected control flow is configuration/parameters → DVC-defined stages → package components/pipelines → generated model or evaluation artifacts. However, concrete stage definitions and component implementations are not available in the supplied tree, so no implemented ingestion, training, validation, or prediction stage can be confirmed.

Supporting architecture and dependencies

The project is distributed as a Python package using:

• setup.py

• requirements.txt

• src/cnnClassifier.egg-info/PKG-INFO

TensorFlow is the material ML runtime identified by the README. Conda is recommended for environment management. GitHub Actions has a reserved workflow directory, but no CI workflow is currently defined:

• .github/workflows/.gitkeep

Presentation boundary

templates/index.html indicates an intended HTML-rendered interface, but no web framework, route handler, API, or server entry point is present. It should be treated as an unconnected presentation asset until a serving layer is added.