Update README.md

README.md

@@ -64,6 +64,64 @@ The Intelligent Stacking system operates in multiple stages:
 
 4. **Dynamic Thresholds**: Per-category optimized decision boundaries for multilabel output
 
+## Usage
+
+### Quick Start with Python
+
+```python
+import joblib
+import numpy as np
+from sklearn.feature_extraction.text import TfidfVectorizer
+from scipy.sparse import hstack, csr_matrix
+
+# Load the model components
+tfidf_vectorizer = joblib.load("int_stacking_tfidf_vectorizer.joblib")
+meta_learner = joblib.load("int_stacking_meta_learner.joblib")
+mlb_encoder = joblib.load("int_stacking_mlb_encoder.joblib")
+base_models = joblib.load("int_stacking_base_models.joblib")
+optimal_thresholds = np.load("int_stacking_optimal_thresholds.npy")
+
+# Prepare text
+text = """CONTRATO DE PRESTAÇÃO DE SERVIÇOS
+Entre a Administração Pública Municipal e a empresa contratada, 
+fica estabelecido o presente contrato para prestação de serviços 
+de manutenção e conservação de vias públicas."""
+
+# Extract features
+tfidf_features = tfidf_vectorizer.transform([text])
+
+# Generate base model predictions
+base_predictions = np.zeros((1, len(mlb_encoder.classes_), 12))
+model_idx = 0
+
+for feat_name in ["TF-IDF", "BERT", "TF-IDF+BERT"]:
+    for algo_name in ["LogReg_C1", "LogReg_C05", "GradBoost", "RandomForest"]:
+        model_key = f"{feat_name}_{algo_name}"
+        if model_key in base_models:
+            model = base_models[model_key]
+            pred = model.predict_proba(tfidf_features)
+            base_predictions[0, :, model_idx] = pred[0]
+        model_idx += 1
+
+# Meta-learner prediction
+meta_features = base_predictions.reshape(1, -1)
+meta_pred = meta_learner.predict_proba(meta_features)[0]
+
+# Apply dynamic thresholds
+predicted_labels = []
+for i, (prob, threshold) in enumerate(zip(meta_pred, optimal_thresholds)):
+    if prob > threshold:
+        predicted_labels.append({
+            "label": mlb_encoder.classes_[i],
+            "probability": float(prob),
+            "confidence": "high" if prob > 0.7 else "medium" if prob > 0.4 else "low"
+        })
+
+# Sort by probability
+predicted_labels.sort(key=lambda x: x["probability"], reverse=True)
+print("Predicted categories:", predicted_labels)
+```
+
 
 ## Categories