{"id":29396870,"url":"https://github.com/dvaser/mediary","last_synced_at":"2025-08-02T12:06:52.663Z","repository":{"id":301707653,"uuid":"1010074615","full_name":"dvaser/Mediary","owner":"dvaser","description":"This web application listens to the patient's story, analyzes possible diseases using AI, recommends necessary diagnostic tests, and finally generates a medical report based on the test results.","archived":false,"fork":false,"pushed_at":"2025-07-27T19:46:12.000Z","size":9723,"stargazers_count":0,"open_issues_count":0,"forks_count":0,"subscribers_count":0,"default_branch":"main","last_synced_at":"2025-07-27T21:36:50.620Z","etag":null,"topics":["ai","artificial-intelligence","django","health","healthcare","healty","machine-learning","medical","medicine","patient","python","website"],"latest_commit_sha":null,"homepage":"","language":"Python","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":null,"status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/dvaser.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":null,"code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null,"zenodo":null}},"created_at":"2025-06-28T09:36:52.000Z","updated_at":"2025-07-27T19:46:15.000Z","dependencies_parsed_at":"2025-07-20T20:35:32.610Z","dependency_job_id":null,"html_url":"https://github.com/dvaser/Mediary","commit_stats":null,"previous_names":["dvaser/mediary"],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/dvaser/Mediary","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/dvaser%2FMediary","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/dvaser%2FMediary/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/dvaser%2FMediary/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/dvaser%2FMediary/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/dvaser","download_url":"https://codeload.github.com/dvaser/Mediary/tar.gz/refs/heads/main","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/dvaser%2FMediary/sbom","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":268385732,"owners_count":24242101,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","status":"online","status_checked_at":"2025-08-02T02:00:12.353Z","response_time":74,"last_error":null,"robots_txt_status":"success","robots_txt_updated_at":"2025-07-24T06:49:26.215Z","robots_txt_url":"https://github.com/robots.txt","online":true,"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":["ai","artificial-intelligence","django","health","healthcare","healty","machine-learning","medical","medicine","patient","python","website"],"created_at":"2025-07-10T12:20:08.338Z","updated_at":"2025-08-02T12:06:52.654Z","avatar_url":"https://github.com/dvaser.png","language":"Python","funding_links":[],"categories":[],"sub_categories":[],"readme":"# Project: MEDIARY\n\n\u003cdiv style=\"text-align: left;\"\u003e\n  \u003cimg src=\"project/img/header.png\" alt=\"Header Image\" style=\"width: 60%;\"\u003e\n\u003c/div\u003e\n\n## Project Purpose\n\n\u003e The aim is to automate processes such as diagnosing based on patient history, recommending tests, interpreting results, and providing personalized health assessments with the support of artificial intelligence.\n\u003e\n\u003e The project aims to provide clinical decision support for doctors, offer preliminary information to patients, and reduce the workload within the healthcare system.\n\u003e\n\u003e In the initial phase of the project, the data collection process has been focused on the field of Internal Medicine; therefore, diseases specific to this area will be prioritized for evaluation. In later stages, data will also be gathered from other medical specialties, aiming to obtain stronger insights into a broader range of health conditions.\n\n## Team Details\n\n\u003e  **[Click here to see team details](project/markdowns/team.md)**\n\n## Sprint Details\n\n\u003e  **[Click here to see the sprint-1 workouts](project/markdowns/sprint/sprint-1.md)** \n\u003e\n\u003e  **[Click here to see the sprint-2 workouts](project/markdowns/sprint/sprint-2.md)** \n\u003e\n\u003e  **[Click here to see the sprint-3 workouts](project/markdowns/sprint/sprint-3.md)** \n\n\n## 🚀 Installation Steps\n\nYou can run the project by following the steps below:\n\n```bash\n# 1. Clone repository (optional)\ngit clone https://github.com/dvaser/Mediary.git\ncd Mediary\n\n# 2. Create a virtual environment (.venv)\npython -m venv .venv\n\n# 3. Activate the virtual environment (.venv) \n    # Windows (PowerShell)\n    .venv\\Scripts\\Activate.ps1\n\n    # If you get a script execution error in PowerShell:\n    Set-ExecutionPolicy -ExecutionPolicy RemoteSigned -Scope CurrentUser\n\n    # Windows (CMD)\n    .venv\\Scripts\\activate.bat\n\n    # Windows (Powershell)\n    .venv\\Scripts\\activate\n\n    # macOS / Linux\n    source .venv/bin/activate\n\n# 4. Install the required packages\npip install -r requirements.txt\n\n# 5. Start the application\npython main.py\n\n# 6. Log out of the virtual environment (.venv)\ndeactivate\n\n# (In addition) Transfer libraries used (requirements.txt)\npip freeze \u003e requirements.txt\n```\n\n## Pipeline\n```\nPDFChunker  ---\u003e  GeminiEmbedder  ---\u003e  ChromaDBWrapper  ---\u003e  Query + Gemini Answer\n```\n\n## Structure\n```\nproject/\n│\n├── code/\n│   ├── pdf_prep.py               # PDFChunker\n│   ├── model/\n│   │   ├── gemini.py             # GeminiEmbedder + GeminiAnswerGenerator\n│   │   └── chroma.py             # ChromaDBWrapper\n│   └── pipeline.py               # RAGPipeline\n│\n├── main.py                       # Uygulamanın başlangıç noktası\n└── chromadb_persist/             # Vektör veritabanı dosyaları (otomatik oluşur)\n```\n\n## Product Features\n\n\u003e 🧠 **AI-Powered Diagnosis Prediction:** Predicts possible diseases based on the patient's medical history.\n\u003e\n\u003e 🔬 **Test Recommendation System:** Lists diagnostic tests relevant to the suspected condition and submits them for physician approval.\n\u003e\n\u003e 📊 **Test Result Analysis:** Compares results against normal reference ranges and interprets them accordingly.\n\u003e\n\u003e 💬 **AI-Assisted Interpretation:** Simplifies and summarizes all medical data into clear, understandable reports.\n\u003e\n\u003e 🌐 **Web-Based Interface:** User-friendly input and output panels designed for both patients and healthcare professionals.\n\u003e\n\u003e 📁 **Database and Knowledge Mapping:** Includes disease–test associations and reference value tables for clinical accuracy.\n\n## Target Audience\n\n* #### *Medical School Students* \n* #### *Family Physicians and Internal Medicine Specialists* \n* #### *Medical Secretaries and Allied Health Personnel* \n* #### *Healthcare Technology Developers* \n* #### *Curious Patients and Health-Conscious Individuals*","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fdvaser%2Fmediary","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fdvaser%2Fmediary","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fdvaser%2Fmediary/lists"}