trax/examples/domain_detection_demo.py

#!/usr/bin/env python3
"""Domain Detection Integration Demo

This script demonstrates how domain detection is integrated into the transcription pipeline.
It shows both text-based and path-based domain detection, as well as the rule-based fallback.
"""

import logging
from pathlib import Path

from src.services.domain_adaptation import DomainDetector
from src.services.multi_pass_transcription import MultiPassTranscriptionPipeline
from src.services.domain_adaptation_manager import DomainAdaptationManager

# Set up logging
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)


def demo_domain_detection():
    """Demonstrate domain detection capabilities."""
    print("🔍 Domain Detection Integration Demo")
    print("=" * 50)
    
    # Initialize domain detector
    detector = DomainDetector()
    print(f"✅ Domain detector initialized with domains: {detector.domains}")
    print()
    
    # Test text-based domain detection
    print("📝 Text-based Domain Detection:")
    print("-" * 30)
    
    test_texts = [
        ("The patient shows symptoms of acute myocardial infarction", "medical"),
        ("Implement the algorithm for thread safety in the software system", "technical"),
        ("The research methodology follows a quantitative approach", "academic"),
        ("Hello world, how are you today?", "general"),
        ("The contract agreement requires legal compliance", "legal")
    ]
    
    for text, expected_domain in test_texts:
        detected_domain = detector.detect_domain_from_text(text)
        status = "✅" if detected_domain == expected_domain else "❌"
        print(f"{status} '{text[:50]}...' -> {detected_domain} (expected: {expected_domain})")
    
    print()
    
    # Test path-based domain detection
    print("📁 Path-based Domain Detection:")
    print("-" * 30)
    
    test_paths = [
        ("data/media/medical_interview_patient_123.wav", "medical"),
        ("data/media/tech_tutorial_python_programming.mp3", "technical"),
        ("data/media/research_presentation_university_lecture.wav", "academic"),
        ("data/media/legal_deposition_case_456.mp4", "legal"),
        ("data/media/recording_001.wav", None)  # No domain indicators
    ]
    
    for path_str, expected_domain in test_paths:
        path = Path(path_str)
        detected_domain = detector.detect_domain_from_path(path)
        if expected_domain is None:
            status = "✅" if detected_domain is None else "❌"
            print(f"{status} '{path.name}' -> {detected_domain} (expected: None)")
        else:
            status = "✅" if detected_domain == expected_domain else "❌"
            print(f"{status} '{path.name}' -> {detected_domain} (expected: {expected_domain})")
    
    print()
    
    # Test domain probabilities
    print("📊 Domain Probability Scoring:")
    print("-" * 30)
    
    sample_text = "The patient requires immediate medical attention for diagnosis"
    probabilities = detector.get_domain_probabilities(sample_text)
    
    print(f"Text: '{sample_text}'")
    print("Domain probabilities:")
    for domain, prob in sorted(probabilities.items(), key=lambda x: x[1], reverse=True):
        print(f"  {domain}: {prob:.3f}")
    
    print()
    
    # Test pipeline integration
    print("🔗 Pipeline Integration Demo:")
    print("-" * 30)
    
    # Create domain adaptation manager
    domain_manager = DomainAdaptationManager()
    
    # Create pipeline with domain adaptation
    pipeline = MultiPassTranscriptionPipeline(
        domain_adapter=domain_manager,
        auto_detect_domain=True
    )
    
    print(f"Pipeline auto-detect enabled: {pipeline.auto_detect_domain}")
    print(f"Domain detector initialized: {pipeline.domain_detector is not None}")
    
    if pipeline.domain_detector:
        print(f"Available domains: {pipeline.domain_detector.domains}")
        print("✅ Domain detection is properly integrated into the pipeline")
    else:
        print("❌ Domain detection is not properly integrated")
    
    print()
    
    # Test confidence thresholds
    print("🎯 Confidence Threshold Testing:")
    print("-" * 30)
    
    confidence_levels = [0.3, 0.5, 0.7, 0.9]
    test_text = "The patient shows symptoms of diabetes mellitus"
    
    for threshold in confidence_levels:
        detected_domain = detector.detect_domain(test_text, threshold=threshold)
        print(f"Threshold {threshold}: {detected_domain}")
    
    print()
    print("🎉 Domain Detection Integration Demo Complete!")


def demo_rule_based_fallback():
    """Demonstrate rule-based detection fallback."""
    print("\n🔄 Rule-based Detection Fallback Demo")
    print("=" * 50)
    
    detector = DomainDetector()
    
    # Test with untrained detector (should use rule-based detection)
    print("Testing with untrained detector (ML model not trained):")
    
    test_cases = [
        "The patient needs immediate medical attention",
        "Implement the singleton pattern for thread safety",
        "The research methodology follows quantitative analysis",
        "This is a general conversation about the weather"
    ]
    
    for text in test_cases:
        detected_domain = detector.detect_domain(text)
        print(f"  '{text[:40]}...' -> {detected_domain}")
    
    print("\n✅ Rule-based fallback working correctly!")


if __name__ == "__main__":
    try:
        demo_domain_detection()
        demo_rule_based_fallback()
    except Exception as e:
        logger.error(f"Demo failed: {e}")
        raise