{"id":28530552,"url":"https://github.com/obirikan/spam_classifer_api","last_synced_at":"2026-07-14T18:02:45.374Z","repository":{"id":294695480,"uuid":"987789668","full_name":"obirikan/spam_classifer_api","owner":"obirikan","description":"📩 Spam Detection API A simple NLP project using TF-IDF and Naive Bayes to classify messages as spam or not, served via FastAPI.","archived":false,"fork":false,"pushed_at":"2025-05-29T19:40:00.000Z","size":608,"stargazers_count":1,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"main","last_synced_at":"2025-10-18T00:06:02.792Z","etag":null,"topics":["naive-bayes-classifier","nlp-machine-learning","sckiit-learn"],"latest_commit_sha":null,"homepage":"https://spam-classifer-api.onrender.com/redoc","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/obirikan.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":"2025-05-21T15:31:55.000Z","updated_at":"2025-07-09T22:55:07.000Z","dependencies_parsed_at":"2025-05-29T20:29:30.526Z","dependency_job_id":null,"html_url":"https://github.com/obirikan/spam_classifer_api","commit_stats":null,"previous_names":["obirikan/spam_classifer_api"],"tags_count":1,"template":false,"template_full_name":null,"purl":"pkg:github/obirikan/spam_classifer_api","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/obirikan%2Fspam_classifer_api","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/obirikan%2Fspam_classifer_api/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/obirikan%2Fspam_classifer_api/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/obirikan%2Fspam_classifer_api/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/obirikan","download_url":"https://codeload.github.com/obirikan/spam_classifer_api/tar.gz/refs/heads/main","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/obirikan%2Fspam_classifer_api/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":286080680,"owners_count":35472809,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2026-05-26T15:22:16.424Z","status":"online","status_checked_at":"2026-07-14T02:00:06.603Z","response_time":114,"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":["naive-bayes-classifier","nlp-machine-learning","sckiit-learn"],"created_at":"2025-06-09T14:30:56.082Z","updated_at":"2026-07-14T18:02:45.370Z","avatar_url":"https://github.com/obirikan.png","language":"Python","funding_links":[],"categories":[],"sub_categories":[],"readme":"# 📧 Spam Detection API (Python + FastAPI)\n\nA beginner-friendly machine learning API that detects whether a given text message is **SPAM** or **NOT SPAM**, using:\n\n* Python 🐍\n* Scikit-learn 🤖\n* FastAPI 🚀\n* TF-IDF Vectorization\n* Naive Bayes Classifier\n\n---\n\n## 🗂️ Project Structure\n\n```\nspam_api/\n│\n├── app/\n│   ├── __init__.py\n│   ├── main.py              # FastAPI app entry point\n│   ├── routes/\n│   │   └── spam.py          # API endpoint(s)\n│   ├── models/\n│   │   └── request.py       # Input validation models\n│   ├── services/\n│   │   └── spam_detector.py # Core ML logic\n│   └── utils/\n│       └── model_loader.py  # Load vectorizer \u0026 model\n│\n├── data/\n│   └── spam_data.tsv        # Your dataset\n├── model/\n│   ├── vectorizer.pkl       # Saved TF-IDF vectorizer\n│   └── spam_model.pkl       # Trained model\n├── trainModel.py\n├── spam_data.py\n├── requirements.txt         # Packages\n└── README.md                # Optional docs\n```\n\n---\n\n## 📦 Setup Instructions\n\n### ✅ Prerequisites\n\n* Python 3.8 or newer\n* pip (Python package installer)\n\n### 🔧 Step-by-step\n\n```bash\n# 1. Clone the repository (or download manually)\ngit clone https://github.com/obirikan/spam_classifer_api.git\ncd spam_api\n\n# 2. (Optional) Create a virtual environment\npython -m venv venv\nsource venv/bin/activate  # On Windows: venv\\Scripts\\activate\n\n# 3. Install dependencies\npip install -r requirements.txt\n\n# 4. Ensure the dataset is available\n# Make sure 'spam_data.tsv' is in the project root folder\n\n# 5. Train the model\npython trainModel.py\n\n# 6. Run the FastAPI app\nuvicorn api.main:app --reload\n```\n\n---\n\n## 🔍 API Usage\n\n### 📬 Endpoint: `POST /predict`\n\n**Request Body (JSON):**\n\n````json\n{\n  \"message\": \"Congratulations! You've won a free ticket!\"\n}\n\n**Response:**\n```json\n{\n  \"prediction\": \"spam\"\n}\n````\n\n---\n\n## 🧠 How It Works\n\n* **TF-IDF** is used to convert text messages into numerical vectors.\n* A **Multinomial Naive Bayes** classifier is trained on the vectorized data.\n* FastAPI handles HTTP requests and returns predictions in real-time.\n\n---\n\n## 📁 Dataset Info\n\nThis project uses the [SMS Spam Collection Dataset](https://archive.ics.uci.edu/ml/datasets/sms+spam+collection).\n\n* Format: TSV (Tab-separated)\n* Labels: `ham` (not spam), `spam`\n\nMake sure to rename the downloaded file to `spam_data.tsv` and place it in the root directory.\n\n---\n\n## ✍️ Author\n\n* Name: obirikan\n* GitHub: [obirikan](https://github.com/obirikan)\n\n---\n\n## ✅ To-Do\n\n* [ ] Add frontend (optional)\n* [ ] Add logging and error handling\n* [ ] Add retraining dataset with new data \n\n---\n\n## 📜 License\n\nMIT License\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fobirikan%2Fspam_classifer_api","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fobirikan%2Fspam_classifer_api","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fobirikan%2Fspam_classifer_api/lists"}