{"id":29225456,"url":"https://github.com/egonlh/_moodflag","last_synced_at":"2026-05-13T12:35:48.615Z","repository":{"id":301366099,"uuid":"1008915061","full_name":"EgonLh/_moodflag","owner":"EgonLh","description":"An interactive web application that predicts mental health risk based on user input","archived":false,"fork":false,"pushed_at":"2025-06-29T12:54:53.000Z","size":4548,"stargazers_count":0,"open_issues_count":0,"forks_count":0,"subscribers_count":0,"default_branch":"main","last_synced_at":"2025-09-04T02:48:07.398Z","etag":null,"topics":["classification","machinelearn","python","streamlit"],"latest_commit_sha":null,"homepage":"https://moodflag-yxmsxx9v3vkkrjhphbqopn.streamlit.app/","language":"Jupyter Notebook","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/EgonLh.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":"2025-06-26T09:37:07.000Z","updated_at":"2025-08-01T12:59:56.000Z","dependencies_parsed_at":"2025-06-26T14:32:43.182Z","dependency_job_id":null,"html_url":"https://github.com/EgonLh/_moodflag","commit_stats":null,"previous_names":["egonlh/_moodflag"],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/EgonLh/_moodflag","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/EgonLh%2F_moodflag","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/EgonLh%2F_moodflag/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/EgonLh%2F_moodflag/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/EgonLh%2F_moodflag/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/EgonLh","download_url":"https://codeload.github.com/EgonLh/_moodflag/tar.gz/refs/heads/main","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/EgonLh%2F_moodflag/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":281571944,"owners_count":26524101,"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":"2025-10-29T02:00:06.901Z","response_time":59,"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":["classification","machinelearn","python","streamlit"],"created_at":"2025-07-03T07:10:17.601Z","updated_at":"2025-10-29T06:19:34.359Z","avatar_url":"https://github.com/EgonLh.png","language":"Jupyter Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"# Mood Swing Prediction API and Streamlit Web App\n\n## Problem Statement\n\nIn today's fast-paced world, mental health challenges like mood swings often go unnoticed or undiagnosed, leading to severe consequences for individuals' well-being. While professional care is essential, early detection using accessible and non-invasive methods can help people become more aware of their mental state and seek support when necessary. This project aims to provide a lightweight, AI-powered solution that predicts mood swing tendencies based on survey-style inputs, making it easier to monitor and reflect on mental health.\n\n## Project Overview\n\n- A **FastAPI-based RESTful API** for making predictions.\n- A **Streamlit web application** for users to interact with the model via a simple UI.\n\n## Features\n\n- Input form for mental health and lifestyle-related data\n- Real-time mood swing prediction\n- Simple API to connect front-end and back-end\n\n## Tech Stack\n\n- Python 3.10+\n- Scikit-learn\n- XGBoost\n- Pandas, NumPy\n- FastAPI\n- Streamlit\n- Joblib\n\n## Folder Structure\n\n```bash\n├── app/\n│   ├── main.py             # FastAPI app for serving predictions\n├── model/\n│   ├── xgb_model.pkl       # Trained XGBoost model\n│   ├── encoder.pkl         # Saved label encoders (as a dictionary)\n│   ├── train_model.py      # Script to train and save the model\n├── streamlit_app/\n│   ├── app.py              # Streamlit web application\n├── data/\n│   ├── raw_data.csv         # Original dataset\n│   └── cleaned_data.csv     # Cleaned and preprocessed dataset\n├── PipFile                  # Python dependencies\n└── README.md\n```\n\n# MoodFlag Project Resources\n\n\nFrontend  \n- **Live App:**  \n  [ MoodFlag Streamlit App](https://moodflag-yxmsxx9v3vkkrjhphbqopn.streamlit.app/)\n\n\nBackend (API)  \n- **API Docs (Swagger UI):**  \n  [📘 MoodFlag API Documentation](https://moodflag-api.onrender.com/docs)\n\n\nDataset \u0026 Model Exploration  \n- **Kaggle Notebook:**  \n  [📈 ML Applications to Mental Health Diagnosis](https://www.kaggle.com/code/maxboonjindasup/ml-applications-to-mental-health-diagnosis#Model-Building-\u0026-Comparisons)\n\n---\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fegonlh%2F_moodflag","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fegonlh%2F_moodflag","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fegonlh%2F_moodflag/lists"}