Upload README.md via DNA Console (Portable Version)

README.md

@@ -7,9 +7,12 @@ tags:
 library_name: sklearn
 ---
 
-# BioGuard DNA Classifier Ensemble
+# BioGuard DNA Classifier Ensemble (Portable v1.1)
 
-This repository contains a dual-model ensemble for DNA sequence analysis and virus classification, trained using the **DNA Governance Console**.
+This repository contains a dual-model ensemble for DNA sequence analysis and virus classification, optimized for **portability and zero-dependency loading**.
+
+> [!NOTE]
+> **Version 1.1 Update**: This version has been refactored to decouple the models from custom feature extraction classes. It uses a raw scikit-learn format for maximum compatibility.
 
 ## 🧬 Models Included
 
@@ -34,19 +37,27 @@ This repository hosts two distinct models specialized for different aspects of g
 
 ## 🚀 Usage
 
-You can load these models using `joblib` in Python:
+Since these models use biological feature extraction, we provide a standalone `inference.py` script for easy usage.
+
+1.  Download all files (`.joblib` and `inference.py`).
+2.  Use the `inference.py` script:
 
 ```python
-import joblib
+from inference import predict_dna
 
-# Load GenetiForest
-rf_model = joblib.load("dna_classifier.joblib")
+sequence = "ATGCTAGCTAGCTAG..."
+results = predict_dna(sequence)
 
-# Load ViralBoost
-gb_model = joblib.load("sequence_model.joblib")
+print(f"Genetic Type: {results['classification']}")
+print(f"Virus Identity: {results['virus_identity']}")
+```
 
-# Prediction
-# (Requires matching FeatureExtractor - see 'sequence_extractor.joblib')
+Alternatively, you can load components manually:
+```python
+import joblib
+classifier = joblib.load("dna_classifier.joblib")
+scaler = joblib.load("scaler_rf.joblib")
+# (Refer to inference.py for Feature Extraction logic)
 ```
 
 ## 📊 Training Meta