example_usage.py

"""
Regulatory Capacity Classifier - Usage Example

This script demonstrates how to use the trained model for inference.
"""

import torch
from transformers import BertTokenizer, BertForSequenceClassification

# Configuration
MODEL_PATH = "./final_model"
LABELS = ['Cog-Evaluate', 'Cog-Explain', 'Cog-Generate', 'Cog-Reason', 'Meta-Monitor', 'Meta-Orient', 'Meta-Plan', 'Socio-Coordinate', 'Socio-Encourage', 'Socio-Feedback', 'TE-Act', 'TE-Report']

def load_model():
    """Load the trained model and tokenizer."""
    tokenizer = BertTokenizer.from_pretrained(MODEL_PATH)
    model = BertForSequenceClassification.from_pretrained(MODEL_PATH)
    model.eval()
    return tokenizer, model

def predict(text, tokenizer, model, threshold=0.5):
    """Predict regulatory capacity labels for a given text."""
    # Tokenize
    inputs = tokenizer(
        text, 
        return_tensors="pt", 
        truncation=True, 
        max_length=128,
        padding=True
    )
    
    # Predict
    with torch.no_grad():
        outputs = model(**inputs)
        probs = torch.sigmoid(outputs.logits)
        predictions = (probs > threshold).int()
    
    # Map to labels
    predicted_labels = [LABELS[i] for i in range(len(LABELS)) if predictions[0][i] == 1]
    confidences = {LABELS[i]: float(probs[0][i]) for i in range(len(LABELS))}
    
    return predicted_labels, confidences

def main():
    print("Loading model...")
    tokenizer, model = load_model()
    
    # Example texts
    test_texts = [
        "I think we should evaluate our approach before moving forward.",
        "Let's coordinate our tasks and divide the work equally.",
        "Good job everyone! We're making great progress.",
        "Can you explain why you chose that option?",
        "I'll write down our conclusions in the report."
    ]
    
    print("\n" + "="*60)
    print("Regulatory Capacity Predictions")
    print("="*60)
    
    for text in test_texts:
        labels, confidences = predict(text, tokenizer, model)
        print(f"\nText: {text}")
        print(f"Predicted Labels: {labels}")
        print(f"Top Confidences: {{k: f'{v:.3f}' for k, v in sorted(confidences.items(), key=lambda x: -x[1])[:3]}}")

if __name__ == "__main__":
    main()