{"id":49156424,"url":"https://github.com/bhoomika-gp/chicken-disease-classification.project","last_synced_at":"2026-04-22T09:07:04.791Z","repository":{"id":200296788,"uuid":"704509411","full_name":"Bhoomika-gp/Chicken-Disease-Classification.Project","owner":"Bhoomika-gp","description":null,"archived":false,"fork":false,"pushed_at":"2023-10-19T07:16:11.000Z","size":22576,"stargazers_count":0,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"main","last_synced_at":"2023-10-19T14:35:33.060Z","etag":null,"topics":["computer-vision","dvc-pipeline","flask-application","tensorflow"],"latest_commit_sha":null,"homepage":"","language":"Jupyter Notebook","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"mit","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/Bhoomika-gp.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":"LICENSE","code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null}},"created_at":"2023-10-13T12:12:07.000Z","updated_at":"2023-10-19T14:35:33.061Z","dependencies_parsed_at":"2023-10-16T13:38:36.029Z","dependency_job_id":null,"html_url":"https://github.com/Bhoomika-gp/Chicken-Disease-Classification.Project","commit_stats":null,"previous_names":["bhoomika-gp/chicken-disease-classification.project"],"tags_count":0,"template":null,"template_full_name":null,"purl":"pkg:github/Bhoomika-gp/Chicken-Disease-Classification.Project","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Bhoomika-gp%2FChicken-Disease-Classification.Project","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Bhoomika-gp%2FChicken-Disease-Classification.Project/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Bhoomika-gp%2FChicken-Disease-Classification.Project/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Bhoomika-gp%2FChicken-Disease-Classification.Project/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/Bhoomika-gp","download_url":"https://codeload.github.com/Bhoomika-gp/Chicken-Disease-Classification.Project/tar.gz/refs/heads/main","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Bhoomika-gp%2FChicken-Disease-Classification.Project/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":286080680,"owners_count":32128733,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2026-04-22T08:34:57.708Z","status":"ssl_error","status_checked_at":"2026-04-22T08:34:55.583Z","response_time":58,"last_error":"SSL_read: unexpected eof while reading","robots_txt_status":"success","robots_txt_updated_at":"2025-07-24T06:49:26.215Z","robots_txt_url":"https://github.com/robots.txt","online":false,"can_crawl_api":true,"host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["computer-vision","dvc-pipeline","flask-application","tensorflow"],"created_at":"2026-04-22T09:07:03.978Z","updated_at":"2026-04-22T09:07:04.785Z","avatar_url":"https://github.com/Bhoomika-gp.png","language":"Jupyter Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"# Chicken Disease Classification\n\nThis project aims to classify chicken fecal samples into two categories: diseased (Coccidiosis) and healthy. The classification is based on analyzing images of the fecal samples using computer vision techniques.\n\n\n## Project Structure\n\nThe project follows a modular structure, consisting of several stages and pipelines which includes :-\n\n1. stage_01_data_ingestion.py : This stage is responsible for data ingestion. It includes functions for downloading, extracting, and preprocessing the dataset.\n2. stage_02_prepare_base_model.py : In this stage, the base model for the classification task is prepared. It involves loading a pre-trained model, modifying it if necessary, and preparing it for training.\n3. stage_03_training.py : The training stage is responsible for training the model using the prepared dataset. It includes functions for data augmentation, model training, and saving the trained model.\n4. stage_04_evaluation.py : This stage focuses on evaluating the performance of the trained model. It includes functions for loading the trained model, performing inference on test data, and calculating evaluation metrics.\n\n## Requirements\n\nTo run this project, you need the following requirements:\n\n- Python (version 3.8 or above)\n- TensorFlow \n- Flask \n- DVC \n\nMake sure you have installed the required dependencies before running the project.\n\n## Workflows\n\n1. Update config.yaml\n2. Update secrets.yaml [Optional]\n3. Update params.yaml\n4. Update the entity\n5. Update the configuration manager in src config\n6. Update the components\n7. Update the pipeline\n8. Update the main.py\n9. Update the dvc.yaml\n\n\n## Usage\n\n1. Clone the repository:\n\n```bash\ngit clone https://github.com/Bhoomika-gp/Chicken-Disease-Classification.Project\n\n```\n```\ncd Chicken-Disease-Classification.Project\n```\n\n2. Create a conda environment after opening the repository\n```\nconda create -p venv python==3.8 \nconda activate venv/\n\n```\n3. Install the dependencies:\n\n```bash\npip install -r requirements.txt\n```\n\n\n4. Finally run the following command\n\n```\npython app.py\n\n```\n\n5. Open Terminal\n\n### DVC cmd\n```\ndvc init\ndvc repro\n\n```\n6. For visualizing pipeline\n```\ndvc dag\n\n```\n\n7. Flask App:\n\nTo create a front-end interface for the application, run the Flask app:\n\n```bash\npython app.py\n```\n\n8. Access the app:\n\nOpen your browser and port \n\nlocalhost to access the application.\n\n## About MLflow \u0026 DVC\n\nMLflow\n\n - Its Production Grade\n - Trace all of your expriements\n - Logging \u0026 taging your model\n\n\nDVC \n\n - Its very lite weight for POC only\n - lite weight expriements tracker\n - It can perform Orchestration (Creating Pipelines)\n   \n# Conclusion\nIn summary, this project showcases the effective classification of chicken fecal samples into two categories: diseased or healthy, employing advanced computer vision methodologies. Its organized, modular structure, coupled with the utilization of pipelines, ensures that the workflow is not only comprehensible but also readily replicable. Furthermore, the incorporation of a Flask application empowers users to effortlessly engage with the classification model.\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fbhoomika-gp%2Fchicken-disease-classification.project","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fbhoomika-gp%2Fchicken-disease-classification.project","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fbhoomika-gp%2Fchicken-disease-classification.project/lists"}