inference.py

"""
Inference Script for Legal-BERT Risk Analysis
Run trained model on new legal clauses
"""

import torch
import json
from typing import List, Dict, Any
import argparse

from model import RoBERTaLegalBERT, LegalBertTokenizer
from config import LegalBertConfig


def load_trained_model(checkpoint_path: str, config: LegalBertConfig) -> RoBERTaLegalBERT:
    """Load trained model from checkpoint"""
    print(f"📥 Loading model from: {checkpoint_path}")
    
    # PyTorch 2.6+ requires weights_only=False for custom classes
    # This is safe since we control the checkpoint creation
    checkpoint = torch.load(checkpoint_path, map_location=config.device, weights_only=False)
    
    # Get number of risk patterns
    num_risks = len(checkpoint.get('discovered_patterns', {}))
    print(f"   Model has {num_risks} discovered risk patterns")
    
    # Initialize RoBERTa-base model
    print(f"   Loading RoBERTa-base model")
    model = RoBERTaLegalBERT(
        config=config,
        num_discovered_risks=num_risks
    )
    model.load_state_dict(checkpoint['model_state_dict'])
    model.to(config.device)
    model.eval()
    
    print(f"   ✅ Model loaded successfully")
    
    return model, checkpoint.get('discovered_patterns', {})


def predict_single_clause(
    model: RoBERTaLegalBERT,
    tokenizer: LegalBertTokenizer,
    clause: str,
    config: LegalBertConfig
) -> Dict[str, Any]:
    """Predict risk for a single clause"""
    
    # Tokenize
    encoded = tokenizer.tokenize_clauses([clause], config.max_sequence_length)
    input_ids = encoded['input_ids'].to(config.device)
    attention_mask = encoded['attention_mask'].to(config.device)
    
    # Predict
    with torch.no_grad():
        outputs = model.forward_single_clause(input_ids, attention_mask)
        
        # Get probabilities
        risk_probs = torch.softmax(outputs['calibrated_logits'], dim=-1)
        predicted_risk = torch.argmax(risk_probs, dim=-1)
        confidence = torch.max(risk_probs, dim=-1)[0]
        
        return {
            'clause': clause,
            'predicted_risk_id': predicted_risk.cpu().item(),
            'confidence': confidence.cpu().item(),
            'risk_probabilities': risk_probs.cpu().numpy().tolist(),
            'severity_score': outputs['severity_score'].cpu().item(),
            'importance_score': outputs['importance_score'].cpu().item()
        }


def predict_document(
    model: RoBERTaLegalBERT,
    tokenizer: LegalBertTokenizer,
    document: List[List[str]],
    config: LegalBertConfig
) -> Dict[str, Any]:
    """
    Predict risks for a full document with context
    
    Args:
        document: List of sections, each containing list of clauses
            Example: [
                ['clause1', 'clause2'],  # Section 1
                ['clause3', 'clause4'],  # Section 2
            ]
    """
    
    print(f"📄 Analyzing document with {len(document)} sections...")
    
    # Tokenize document structure
    doc_structure = []
    clause_texts = []
    
    for section_idx, section in enumerate(document):
        section_tokens = []
        for clause_idx, clause in enumerate(section):
            encoded = tokenizer.tokenize_clauses([clause], config.max_sequence_length)
            section_tokens.append({
                'input_ids': encoded['input_ids'][0],
                'attention_mask': encoded['attention_mask'][0]
            })
            clause_texts.append({
                'section': section_idx,
                'clause': clause_idx,
                'text': clause
            })
        doc_structure.append(section_tokens)
    
    # Predict with context
    results = model.predict_document(doc_structure)
    
    # Merge predictions with clause texts
    for i, pred in enumerate(results['clauses']):
        pred['text'] = clause_texts[i]['text']
    
    return results


def format_prediction_output(
    prediction: Dict[str, Any],
    risk_patterns: Dict[str, Any]
) -> str:
    """Format prediction for display"""
    
    risk_id = prediction['predicted_risk_id']
    pattern_names = list(risk_patterns.keys())
    
    # Handle both string and integer pattern names
    if risk_id < len(pattern_names):
        risk_name = str(pattern_names[risk_id])
        risk_info = risk_patterns[pattern_names[risk_id]]
        
        # Extract keywords from pattern info
        if isinstance(risk_info, dict):
            keywords = ', '.join(risk_info.get('keywords', risk_info.get('top_words', []))[:5])
        else:
            keywords = "N/A"
    else:
        risk_name = f"Risk Pattern {risk_id}"
        keywords = "N/A"
    
    output = f"""
{'='*70}
📋 CLAUSE ANALYSIS
{'='*70}

📝 Clause:
   {prediction.get('text', prediction.get('clause', 'N/A'))}

🎯 Risk Classification:
   Pattern: {risk_name}
   Confidence: {prediction['confidence']:.1%}
   Keywords: {keywords}

📊 Risk Scores:
   Severity:   {prediction['severity_score']:.2f}/10
   Importance: {prediction['importance_score']:.2f}/10

🔍 Probability Distribution:
"""
    
    # Show top 3 risk probabilities
    probs = prediction['risk_probabilities']
    
    # Handle nested list structure (e.g., [[prob1, prob2, ...]])
    if isinstance(probs, list) and len(probs) > 0 and isinstance(probs[0], list):
        probs = probs[0]
    
    top_3_indices = sorted(range(len(probs)), key=lambda i: probs[i], reverse=True)[:3]
    
    for idx in top_3_indices:
        if idx < len(pattern_names):
            # Convert pattern name to string and truncate if needed
            pattern_str = str(pattern_names[idx])
            if len(pattern_str) > 40:
                pattern_str = pattern_str[:37] + "..."
            output += f"   {pattern_str:40s} {probs[idx]:.1%}\n"
        else:
            output += f"   Risk Pattern {idx:2d}                          {probs[idx]:.1%}\n"
    
    return output


def main():
    """Main inference function"""
    
    parser = argparse.ArgumentParser(description='Legal-BERT Risk Analysis Inference')
    parser.add_argument('--checkpoint', type=str, default='models/legal_bert/final_model.pt',
                       help='Path to model checkpoint')
    parser.add_argument('--clause', type=str, help='Single clause to analyze')
    parser.add_argument('--document', type=str, help='Path to JSON file with document structure')
    parser.add_argument('--output', type=str, help='Path to save results (JSON)')
    args = parser.parse_args()
    
    print("=" * 70)
    print("🏛️  LEGAL-BERT RISK ANALYSIS INFERENCE")
    print("=" * 70)
    
    # Initialize config
    config = LegalBertConfig()
    print(f"\n📋 Configuration:")
    print(f"   Device: {config.device}")
    print(f"   Max sequence length: {config.max_sequence_length}")
    
    # Load model
    model, risk_patterns = load_trained_model(args.checkpoint, config)
    tokenizer = LegalBertTokenizer(config.bert_model_name)
    
    print(f"\n🔍 Discovered Risk Patterns ({len(risk_patterns)}):")
    pattern_names = list(risk_patterns.keys())
    for name in pattern_names[:5]:
        # Convert to string for display
        display_name = str(name)
        print(f"   • {display_name}")
    if len(risk_patterns) > 5:
        print(f"   ... and {len(risk_patterns) - 5} more")
    
    results = []
    
    # Single clause mode
    if args.clause:
        print(f"\n" + "="*70)
        print("MODE: Single Clause Analysis")
        print("="*70)
        
        prediction = predict_single_clause(model, tokenizer, args.clause, config)
        print(format_prediction_output(prediction, risk_patterns))
        results.append(prediction)
    
    # Document mode
    elif args.document:
        print(f"\n" + "="*70)
        print("MODE: Full Document Analysis (with context)")
        print("="*70)
        
        # Load document
        with open(args.document, 'r') as f:
            doc_data = json.load(f)
        
        # Expected format: {"sections": [["clause1", "clause2"], ["clause3"]]}
        document = doc_data.get('sections', [])
        
        prediction = predict_document(model, tokenizer, document, config)
        
        print(f"\n📊 Document Summary:")
        print(f"   Sections: {prediction['summary']['num_sections']}")
        print(f"   Clauses: {prediction['summary']['num_clauses']}")
        print(f"   Average Severity: {prediction['summary']['avg_severity']:.2f}/10")
        print(f"   High Risk Clauses: {prediction['summary']['high_risk_count']}")
        
        print(f"\n📋 Clause-by-Clause Analysis:")
        for clause_pred in prediction['clauses']:
            print(format_prediction_output(clause_pred, risk_patterns))
        
        results = prediction
    
    # Demo mode (no arguments)
    else:
        print(f"\n" + "="*70)
        print("MODE: Demo Analysis")
        print("="*70)
        print("\n💡 Running demo with sample clauses...")
        
        demo_clauses = [
            "The party shall indemnify and hold harmless all damages and losses.",
            "This agreement shall be governed by the laws of the state of California.",
            "Payment must be made within thirty days of invoice date.",
            "The licensee must not disclose confidential information to third parties.",
            "Company shall comply with all applicable laws and regulations."
        ]
        
        for clause in demo_clauses:
            prediction = predict_single_clause(model, tokenizer, clause, config)
            print(format_prediction_output(prediction, risk_patterns))
            results.append(prediction)
    
    # Save results if output path provided
    if args.output:
        with open(args.output, 'w') as f:
            json.dump(results, f, indent=2)
        print(f"\n💾 Results saved to: {args.output}")
    
    print("\n" + "="*70)
    print("✅ INFERENCE COMPLETE")
    print("="*70)
    
    # Usage tips
    if not args.clause and not args.document:
        print(f"\n💡 Usage Examples:")
        print(f'\n   Single clause:')
        print(f'   python3 inference.py --clause "The party shall indemnify..."')
        print(f'\n   Full document:')
        print(f'   python3 inference.py --document contract.json')
        print(f'\n   Save results:')
        print(f'   python3 inference.py --clause "..." --output results.json')


if __name__ == "__main__":
    main()