EngAbdinasir/AfSugan

AfSugan — Somali offensive text detection (text + image OCR) # AfSugan AfSugan is a web-based system for detecting offensive Somali text from typed text and text extracted from images. ## Project Structure ```text AfSugan/ ├── backend/ # Flask API, authentication, OCR, database, and prediction endpoints ├── frontend/ # React + Vite user interface ├── models/ # Current trained SomBERTa model and preprocessing files ├── docs/ # Project documentation and model comparison results ├── Dockerfile # Backend Docker deployment file ├── DEPLOYMENT.md # Local and deployment instructions └── README.md # Project overview ``` ## Main Technologies - Frontend: React, Vite, JavaScript, CSS. - Backend: Python, Flask, Gunicorn. - Machine learning: PyTorch, Transformers, SomBERTa. - OCR: EasyOCR. - Database: MySQL. ## How the Model Is Used The backend receives text from the frontend through the `/predict-text` endpoint, or extracts text from uploaded images through EasyOCR and `/predict-image`. Before prediction, the backend applies the same preprocessing used during training: 1. Lowercase the text. 2. Remove URLs, mentions, hashtags, numbers, punctuation, and non-Latin symbols. 3. Normalize repeated characters. 4. Keep meaningful Somali words because the SomBERTa model was trained without stopword removal. 5. Tokenize the cleaned text with the SomBERTa tokenizer. 6. Run the SomBERTa classifier. 7. Return the predicted label and confidence score. The active model is stored in: ```text models/active_model ``` The metadata and preprocessing files are: ```text models/best_model_info.json models/label_mapping.json models/active_model/preprocessing_config.json ``` ## Database The database design is stored in: ```text backend/database_schema.sql ``` This schema file defines the required MySQL tables for users, email verification codes, password reset tokens, prediction history, uploaded images, and classification results. Local SQL database dumps are intentionally not committe …