feat: define families dictionary for candidate classifiers in _build_candidate_families function

feat: enhance candidate selection with class ratio and calibrated SVC for improved model performance

feat: implement optimal threshold calculation for binary predictions in train function

feat: add CalibratedClassifierCV and roc_curve imports for enhanced model evaluation

Add Gradio dependency to requirements.txt with version constraints

feat: train all models on full data and save their paths

feat: update model parameters for Random Forest, Gradient Boosting, XGBoost, and LightGBM to enhance performance

feat: adjust Random Forest parameters for improved model performance

feat: optimize Gradient Boosting and XGBoost parameters for improved model performance

feat: update model parameters and fix data leakage by removing duration_sec and sample_rate from features

feat: exclude additional metadata columns from feature loading to prevent data leakage

feat: enhance training pipeline with multi-model evaluation and detailed metrics

feat: add AURIS classifier training module with model evaluation and feature importance