{"id":24987012,"url":"https://github.com/hitthecodelabs/petalanalyticsstreamlit","last_synced_at":"2026-05-05T14:32:36.069Z","repository":{"id":206625876,"uuid":"717328770","full_name":"hitthecodelabs/PetalAnalyticsStreamlit","owner":"hitthecodelabs","description":"Web application developed with Streamlit that predicts the Iris flower type based on its physical features","archived":false,"fork":false,"pushed_at":"2023-11-11T06:21:41.000Z","size":4540,"stargazers_count":0,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"main","last_synced_at":"2025-06-20T01:12:47.190Z","etag":null,"topics":["matplotlib","model","numpy","pickle","python","scikit-learn","sklearn","streamlit"],"latest_commit_sha":null,"homepage":"","language":"Python","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/hitthecodelabs.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,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null}},"created_at":"2023-11-11T06:02:27.000Z","updated_at":"2023-12-02T02:10:24.000Z","dependencies_parsed_at":null,"dependency_job_id":"39904cfe-9774-41f2-9fb2-eaf81aa8dc21","html_url":"https://github.com/hitthecodelabs/PetalAnalyticsStreamlit","commit_stats":null,"previous_names":["hitthecodelabs/petalanalyticsstreamlit"],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/hitthecodelabs/PetalAnalyticsStreamlit","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/hitthecodelabs%2FPetalAnalyticsStreamlit","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/hitthecodelabs%2FPetalAnalyticsStreamlit/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/hitthecodelabs%2FPetalAnalyticsStreamlit/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/hitthecodelabs%2FPetalAnalyticsStreamlit/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/hitthecodelabs","download_url":"https://codeload.github.com/hitthecodelabs/PetalAnalyticsStreamlit/tar.gz/refs/heads/main","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/hitthecodelabs%2FPetalAnalyticsStreamlit/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":286080680,"owners_count":32653541,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2026-05-05T11:29:49.557Z","status":"ssl_error","status_checked_at":"2026-05-05T11:29:48.587Z","response_time":54,"last_error":"SSL_connect returned=1 errno=0 peeraddr=140.82.121.5:443 state=error: 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":["matplotlib","model","numpy","pickle","python","scikit-learn","sklearn","streamlit"],"created_at":"2025-02-04T11:35:33.528Z","updated_at":"2026-05-05T14:32:36.052Z","avatar_url":"https://github.com/hitthecodelabs.png","language":"Python","funding_links":[],"categories":[],"sub_categories":[],"readme":"# PetalAnalyticsStreamlit\n\n## Description\nThis project is a web application developed with Streamlit that predicts the Iris flower type based on its physical features. It utilizes a Random Forest classification model trained on the well-known Iris dataset. The app allows users to adjust parameters of the Iris flower (sepal length, sepal width, petal length, petal width) and view the model's prediction.\n\n## Features\n- Interactive interface for inputting flower parameters.\n- Prediction probability visualization using interactive Plotly bar charts.\n- Custom styling with CSS for an enhanced visual experience.\n\n## Installation\nTo run this application, follow these steps:\n\n1. Clone the repository:\n\n```bash\ngit clone https://github.com/hitthecodelabs/PetalAnalyticsStreamlit.git\n```\n\n2. Navigate to the project directory:\n```bash\ncd PetalAnalyticsStreamlit\n```\n3. Install the dependencies:\n```bash\npip install -r requirements.txt\n```\n## Usage\nTo start the application, run:\n\n```bash\nstreamlit run app_new.py\n```\n\nNavigate to the URL provided by Streamlit in your browser to interact with the app.\n\n## Contributing\nContributions to this project are welcome. Please fork the repository and submit a pull request with your proposed changes.\n\n## License\nThis project is open source and available under the [MIT License](LICENSE).\n\n\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fhitthecodelabs%2Fpetalanalyticsstreamlit","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fhitthecodelabs%2Fpetalanalyticsstreamlit","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fhitthecodelabs%2Fpetalanalyticsstreamlit/lists"}