#!/usr/bin/env python3
"""
End-to-end test demonstrating AI-powered audiobook generation.
Shows the complete workflow from unmarked text to multi-voice audio.
"""
import asyncio
import sys
from services.role_detector import RoleDetector
from services.tts_service import TTSService
from services.ollama_service import OllamaService


async def test_complete_workflow():
    """Test the complete AI-powered audiobook generation workflow."""
    print("\n" + "="*70)
    print("END-TO-END AI-POWERED AUDIOBOOK GENERATION TEST")
    print("="*70 + "\n")
    
    # Sample story without any role markers
    story = """The old wizard Merlin gazed into his crystal ball with concern. 
"Dark times are coming," he whispered to himself.

A young knight named Arthur entered the chamber. "Master Merlin, I heard your summons," he said with determination.

Merlin turned to face him. "Arthur, the kingdom needs you now more than ever."

Arthur drew his sword. "I am ready to defend our people."

From the shadows, the sorceress Morgana watched silently. She knew the prophecy would unfold exactly as foretold."""
    
    print("STORY TEXT (unmarked narrative):")
    print("-" * 70)
    print(story)
    print("-" * 70 + "\n")
    
    # Initialize services
    ollama_service = OllamaService()
    role_detector = RoleDetector(ollama_service=ollama_service)
    tts_service = TTSService(ollama_service=ollama_service)
    
    # Check Ollama availability
    is_available = await ollama_service.is_available()
    if not is_available:
        print("❌ Ollama is not available. This test requires Ollama.")
        print("   Please start Ollama with: ollama serve")
        return False
    
    print("✓ Ollama service is available\n")
    
    # Step 1: Detect roles using AI
    print("STEP 1: AI Role Detection")
    print("-" * 70)
    try:
        roles = await role_detector.detect_roles(story)
        print(f"✓ Detected {len(roles)} roles:\n")
        for role in roles:
            print(f"  - {role['label']}")
        print()
    except Exception as e:
        print(f"❌ Role detection failed: {e}")
        return False
    
    # Step 2: User assigns voices (simulated)
    print("STEP 2: Voice Assignment (simulated user input)")
    print("-" * 70)
    role_voice_map = {}
    voice_options = ["af_bella", "am_adam", "am_michael", "af_sarah", "bf_emma"]
    
    for i, role in enumerate(roles):
        voice = voice_options[i % len(voice_options)]
        role_voice_map[role['id']] = voice
        print(f"  {role['label']} → {voice}")
    print()
    
    # Step 3: AI text segmentation
    print("STEP 3: AI Text Segmentation")
    print("-" * 70)
    try:
        segments = await tts_service.segment_text_with_ai(story, role_voice_map)
        
        if segments is None:
            print("❌ AI segmentation failed")
            return False
        
        print(f"✓ AI segmented text into {len(segments)} segments:\n")
        
        for i, (speaker, voice, text) in enumerate(segments, 1):
            # Truncate long text for display
            display_text = text[:60] + "..." if len(text) > 60 else text
            print(f"  {i}. [{speaker}] ({voice})")
            print(f"     \"{display_text}\"")
        print()
        
        # Verify intelligent segmentation
        speakers_used = set(seg[0] for seg in segments)
        print(f"Speakers used: {speakers_used}")
        
        # Check if dialogue was properly assigned
        has_multiple_speakers = len(speakers_used) > 1
        has_narrator = "Narrator" in speakers_used or any("Merlin" in s or "Arthur" in s or "Morgana" in s for s in speakers_used)
        
        if has_multiple_speakers and has_narrator:
            print("✓ AI successfully assigned dialogue to characters and narrative to appropriate speakers")
        else:
            print("⚠ Segmentation may not be optimal")
        print()
        
    except Exception as e:
        print(f"❌ Segmentation failed: {e}")
        import traceback
        traceback.print_exc()
        return False
    
    # Step 4: Verify segmentation quality
    print("STEP 4: Segmentation Quality Analysis")
    print("-" * 70)
    
    # Count segments per speaker
    speaker_counts = {}
    for speaker, _, _ in segments:
        speaker_counts[speaker] = speaker_counts.get(speaker, 0) + 1
    
    print("Segments per speaker:")
    for speaker, count in sorted(speaker_counts.items(), key=lambda x: x[1], reverse=True):
        print(f"  {speaker}: {count} segments")
    print()
    
    # Check for dialogue detection
    dialogue_keywords = ['"', "'", "said", "whispered", "replied"]
    dialogue_segments = 0
    for _, _, text in segments:
        if any(kw in text.lower() for kw in dialogue_keywords):
            dialogue_segments += 1
    
    print(f"Dialogue segments detected: {dialogue_segments}/{len(segments)}")
    print()
    
    # Summary
    print("="*70)
    print("TEST SUMMARY")
    print("="*70)
    print(f"✓ Role Detection: {len(roles)} roles detected")
    print(f"✓ Voice Assignment: {len(role_voice_map)} voices assigned")
    print(f"✓ Text Segmentation: {len(segments)} segments created")
    print(f"✓ Speaker Distribution: {len(speakers_used)} unique speakers used")
    print(f"✓ Dialogue Detection: {dialogue_segments} dialogue segments")
    print()
    print("🎉 AI-POWERED AUDIOBOOK GENERATION: SUCCESS!")
    print("="*70 + "\n")
    
    return True


async def main():
    """Run the end-to-end test."""
    try:
        success = await test_complete_workflow()
        return success
    except Exception as e:
        print(f"\n❌ Test failed with exception: {e}")
        import traceback
        traceback.print_exc()
        return False


if __name__ == '__main__':
    success = asyncio.run(main())
    sys.exit(0 if success else 1)