lastapp.py

import os
import io
import gradio as gr
import torch
import numpy as np
import re
import pronouncing  # Add this to requirements.txt for syllable counting
import functools  # Add this for lru_cache functionality
from transformers import (
    AutoModelForAudioClassification,
    AutoFeatureExtractor,
    AutoTokenizer,
    pipeline,
    AutoModelForCausalLM,
    BitsAndBytesConfig
)
from huggingface_hub import login
from utils import (
    load_audio,
    extract_audio_duration,
    extract_mfcc_features,
    format_genre_results,
    ensure_cuda_availability
)
from emotionanalysis import MusicAnalyzer
import librosa

# Login to Hugging Face Hub if token is provided
if "HF_TOKEN" in os.environ:
    login(token=os.environ["HF_TOKEN"])

# Constants
GENRE_MODEL_NAME = "dima806/music_genres_classification"
MUSIC_DETECTION_MODEL = "MIT/ast-finetuned-audioset-10-10-0.4593"
LLM_MODEL_NAME = "Qwen/Qwen3-14B"
SAMPLE_RATE = 22050  # Standard sample rate for audio processing

# Check CUDA availability (for informational purposes)
CUDA_AVAILABLE = ensure_cuda_availability()

# Create music detection pipeline
print(f"Loading music detection model: {MUSIC_DETECTION_MODEL}")
try:
    music_detector = pipeline(
        "audio-classification",
        model=MUSIC_DETECTION_MODEL,
        device=0 if CUDA_AVAILABLE else -1
    )
    print("Successfully loaded music detection pipeline")
except Exception as e:
    print(f"Error creating music detection pipeline: {str(e)}")
    # Fallback to manual loading
    try:
        music_processor = AutoFeatureExtractor.from_pretrained(MUSIC_DETECTION_MODEL)
        music_model = AutoModelForAudioClassification.from_pretrained(MUSIC_DETECTION_MODEL)
        print("Successfully loaded music detection model and feature extractor")
    except Exception as e2:
        print(f"Error loading music detection model components: {str(e2)}")
        raise RuntimeError(f"Could not load music detection model: {str(e2)}")

# Create genre classification pipeline
print(f"Loading audio classification model: {GENRE_MODEL_NAME}")
try:
    genre_classifier = pipeline(
        "audio-classification",
        model=GENRE_MODEL_NAME,
        device=0 if CUDA_AVAILABLE else -1
    )
    print("Successfully loaded audio classification pipeline")
except Exception as e:
    print(f"Error creating pipeline: {str(e)}")
    # Fallback to manual loading
    try:
        genre_processor = AutoFeatureExtractor.from_pretrained(GENRE_MODEL_NAME)
        genre_model = AutoModelForAudioClassification.from_pretrained(GENRE_MODEL_NAME)
        print("Successfully loaded audio classification model and feature extractor")
    except Exception as e2:
        print(f"Error loading model components: {str(e2)}")
        raise RuntimeError(f"Could not load genre classification model: {str(e2)}")

# Load LLM with appropriate quantization for T4 GPU
bnb_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_quant_type="nf4",
    bnb_4bit_compute_dtype=torch.float16,
)

llm_tokenizer = AutoTokenizer.from_pretrained(LLM_MODEL_NAME)
llm_model = AutoModelForCausalLM.from_pretrained(
    LLM_MODEL_NAME,
    device_map="auto",
    quantization_config=bnb_config,
    torch_dtype=torch.float16,
)

# Create LLM pipeline
llm_pipeline = pipeline(
    "text-generation",
    model=llm_model,
    tokenizer=llm_tokenizer,
    max_new_tokens=512,
)

# Initialize music emotion analyzer
music_analyzer = MusicAnalyzer()

# New global function moved outside of verify_flexible_syllable_counts
@functools.lru_cache(maxsize=512)
def cached_phones_for_word(word):
    """Get word pronunciations with caching for better performance."""
    return pronouncing.phones_for_word(word)

@functools.lru_cache(maxsize=512)
def count_syllables_for_word(word):
    """Count syllables in a single word with caching for performance."""
    # Try using pronouncing library first
    pronunciations = cached_phones_for_word(word.lower())
    if pronunciations:
        return pronouncing.syllable_count(pronunciations[0])
    
    # Fallback method for words not in the pronouncing dictionary
    vowels = "aeiouy"
    word = word.lower()
    count = 0
    prev_is_vowel = False
    
    for char in word:
        is_vowel = char in vowels
        if is_vowel and not prev_is_vowel:
            count += 1
        prev_is_vowel = is_vowel
    
    # Handle special cases
    if word.endswith('e') and not word.endswith('le'):
        count -= 1
    if word.endswith('le') and len(word) > 2 and word[-3] not in vowels:
        count += 1
    if count == 0:
        count = 1
    
    return count

@functools.lru_cache(maxsize=512)
def get_word_stress(word):
    """Get the stress pattern for a word with improved fallback handling."""
    pronunciations = cached_phones_for_word(word.lower())
    if pronunciations:
        return pronouncing.stresses(pronunciations[0])
    
    # Enhanced fallback for words not in the dictionary
    syllables = count_syllables_for_word(word)
    
    # Common English stress patterns by word length
    if syllables == 1:
        return "1"  # Single syllable words are stressed
    elif syllables == 2:
        # Most 2-syllable nouns and adjectives stress first syllable
        # Common endings that indicate second-syllable stress
        second_syllable_stress = ["ing", "er", "or", "ize", "ise", "ate", "ect", "end", "ure"]
        if any(word.endswith(ending) for ending in second_syllable_stress):
            return "01"
        else:
            return "10"  # Default for 2-syllable words
    elif syllables == 3:
        # Common endings for specific stress patterns in 3-syllable words
        if any(word.endswith(ending) for ending in ["ity", "ety", "ify", "ogy", "graphy"]):
            return "100"  # First syllable stress
        elif any(word.endswith(ending) for ending in ["ation", "ious", "itis"]):
            return "010"  # Middle syllable stress
        else:
            return "100"  # Default for 3-syllable words
    else:
        # For longer words, use common English patterns
        return "1" + "0" * (syllables - 1)

# New function: Count syllables in text
def count_syllables(text):
    """Count syllables in a given text using the pronouncing library."""
    words = re.findall(r'\b[a-zA-Z]+\b', text.lower())
    syllable_count = 0
    
    for word in words:
        syllable_count += count_syllables_for_word(word)
    
    return syllable_count

def extract_audio_features(audio_file):
    """Extract audio features from an audio file."""
    try:
        # Load the audio file using utility function
        y, sr = load_audio(audio_file, SAMPLE_RATE)
        
        if y is None or sr is None:
            raise ValueError("Failed to load audio data")
        
        # Get audio duration in seconds
        duration = extract_audio_duration(y, sr)
        
        # Extract MFCCs for genre classification (may not be needed with the pipeline)
        mfccs_mean = extract_mfcc_features(y, sr, n_mfcc=20)
        
        return {
            "features": mfccs_mean,
            "duration": duration,
            "waveform": y,
            "sample_rate": sr,
            "path": audio_file  # Keep path for the pipeline
        }
    except Exception as e:
        print(f"Error extracting audio features: {str(e)}")
        raise ValueError(f"Failed to extract audio features: {str(e)}")

def classify_genre(audio_data):
    """Classify the genre of the audio using the loaded model."""
    try:
        # First attempt: Try using the pipeline if available
        if 'genre_classifier' in globals():
            results = genre_classifier(audio_data["path"])
            # Transform pipeline results to our expected format
            top_genres = [(result["label"], result["score"]) for result in results[:3]]
            return top_genres
        
        # Second attempt: Use manually loaded model components
        elif 'genre_processor' in globals() and 'genre_model' in globals():
            # Process audio input with feature extractor
            inputs = genre_processor(
                audio_data["waveform"], 
                sampling_rate=audio_data["sample_rate"], 
                return_tensors="pt"
            )
            
            with torch.no_grad():
                outputs = genre_model(**inputs)
                predictions = outputs.logits.softmax(dim=-1)
            
            # Get the top 3 genres
            values, indices = torch.topk(predictions, 3)
            
            # Map indices to genre labels
            genre_labels = genre_model.config.id2label
            
            top_genres = []
            for i, (value, index) in enumerate(zip(values[0], indices[0])):
                genre = genre_labels[index.item()]
                confidence = value.item()
                top_genres.append((genre, confidence))
            
            return top_genres
        
        else:
            raise ValueError("No genre classification model available")
            
    except Exception as e:
        print(f"Error in genre classification: {str(e)}")
        # Fallback: return a default genre if everything fails
        return [("rock", 1.0)]

def detect_music(audio_data):
    """Detect if the audio is music using the MIT AST model."""
    try:
        # First attempt: Try using the pipeline if available
        if 'music_detector' in globals():
            results = music_detector(audio_data["path"])
            # Look for music-related classes in the results
            music_confidence = 0.0
            for result in results:
                label = result["label"].lower()
                if any(music_term in label for music_term in ["music", "song", "singing", "instrument"]):
                    music_confidence = max(music_confidence, result["score"])
            return music_confidence >= 0.2, results
        
        # Second attempt: Use manually loaded model components
        elif 'music_processor' in globals() and 'music_model' in globals():
            # Process audio input with feature extractor
            inputs = music_processor(
                audio_data["waveform"], 
                sampling_rate=audio_data["sample_rate"], 
                return_tensors="pt"
            )
            
            with torch.no_grad():
                outputs = music_model(**inputs)
                predictions = outputs.logits.softmax(dim=-1)
            
            # Get the top predictions
            values, indices = torch.topk(predictions, 5)
            
            # Map indices to labels
            labels = music_model.config.id2label
            
            # Check for music-related classes
            music_confidence = 0.0
            results = []
            
            for i, (value, index) in enumerate(zip(values[0], indices[0])):
                label = labels[index.item()].lower()
                score = value.item()
                results.append({"label": label, "score": score})
                
                if any(music_term in label for music_term in ["music", "song", "singing", "instrument"]):
                    music_confidence = max(music_confidence, score)
            
            return music_confidence >= 0.2, results
            
        else:
            raise ValueError("No music detection model available")
            
    except Exception as e:
        print(f"Error in music detection: {str(e)}")
        return False, []

def detect_beats(y, sr):
    """Enhanced beat detection with adaptive threshold analysis and improved time signature detection."""
    # STEP 1: Improved pre-processing with robustness for quiet sections
    # Apply a small floor to avoid division-by-zero issues
    y = np.clip(y, 1e-10, None)  # Prevent extreme quiet sections from causing NaN
    
    # Separate harmonic and percussive components
    y_harmonic, y_percussive = librosa.effects.hpss(y)
    
    # Generate multiple onset envelopes with smoothing for stability
    onset_env_full = librosa.onset.onset_strength(y=y, sr=sr)
    onset_env_perc = librosa.onset.onset_strength(y=y_percussive, sr=sr)
    
    # Apply small smoothing to handle quiet sections
    onset_env_full = np.maximum(onset_env_full, 1e-6)  # Minimum threshold to avoid NaN
    onset_env_perc = np.maximum(onset_env_perc, 1e-6)
    
    # Create weighted combination
    combined_onset = onset_env_full * 0.3 + onset_env_perc * 0.7
    
    # STEP 2: Multi-strategy tempo and beat detection
    tempo_candidates = []
    beat_candidates = []
    
    # Strategy 1: Standard detection
    tempo1, beats1 = librosa.beat.beat_track(
        onset_envelope=combined_onset, 
        sr=sr,
        tightness=100  # More sensitive tracking
    )
    tempo_candidates.append(tempo1)
    beat_candidates.append(beats1)
    
    # Strategy 2: Try with different tempo range for complex signatures
    tempo2, beats2 = librosa.beat.beat_track(
        onset_envelope=combined_onset,
        sr=sr,
        tightness=100,
        start_bpm=60  # Lower starting BPM helps find different time signatures
    )
    tempo_candidates.append(tempo2)
    beat_candidates.append(beats2)
    
    # Select the best strategy based on consistency
    beat_consistency = []
    for beats in beat_candidates:
        if len(beats) <= 1:
            beat_consistency.append(0)
            continue
            
        times = librosa.frames_to_time(beats, sr=sr)
        intervals = np.diff(times)
        
        # More consistent beats have lower variance in intervals
        if np.mean(intervals) > 0:
            consistency = 1.0 / (1.0 + np.std(intervals)/np.mean(intervals))
            beat_consistency.append(consistency)
        else:
            beat_consistency.append(0)
    
    best_idx = np.argmax(beat_consistency) if beat_consistency else 0
    tempo = tempo_candidates[best_idx]
    beat_frames = beat_candidates[best_idx]
    
    # STEP 3: Performance optimization with vectorized operations
    beat_times = librosa.frames_to_time(beat_frames, sr=sr)
    
    # Vectorized extraction of beat strengths instead of loop
    beat_strengths = []
    if len(beat_frames) > 0:
        # Filter out beat frames that exceed the onset envelope length
        valid_frames = [frame for frame in beat_frames if frame < len(combined_onset)]
        if valid_frames:
            # Vectorized extraction of valid beat strengths
            beat_strengths = combined_onset[valid_frames].tolist()
            
            # Handle any remaining beats
            avg_strength = np.mean(beat_strengths) if beat_strengths else 1.0
            beat_strengths.extend([avg_strength] * (len(beat_times) - len(beat_strengths)))
        else:
            beat_strengths = [1.0] * len(beat_times)
    else:
        beat_strengths = [1.0] * len(beat_times)
    
    # STEP 4: Calculate intervals between beats
    intervals = np.diff(beat_times).tolist() if len(beat_times) > 1 else []
    
    # STEP 5: Improved time signature detection for various patterns
    # Start with default assumption
    time_signature = 4
    
    if len(beat_strengths) > 8:
        # Use autocorrelation to find periodicity in beat strengths
        if len(beat_strengths) > 4:
            # Normalize beat strengths for better pattern detection
            norm_strengths = np.array(beat_strengths)
            if np.max(norm_strengths) > 0:
                norm_strengths = norm_strengths / np.max(norm_strengths)
            
            # Compute autocorrelation to find periodic patterns (N)
            ac = librosa.autocorrelate(norm_strengths, max_size=len(norm_strengths)//2)
            
            # Find peaks in autocorrelation (indicates periodicity)
            if len(ac) > 3:  # Need enough data for peak picking
                # Find peaks after lag 0
                peaks = librosa.util.peak_pick(ac[1:], pre_max=1, post_max=1, pre_avg=1, post_avg=1, delta=0.1, wait=1)
                peaks = peaks + 1  # Adjust for the removed lag 0
                
                if len(peaks) > 0:
                    # Get the first significant peak position (cycle length N)
                    N = peaks[0]
                    
                    # Map common cycle lengths to time signatures
                    if 2 <= N <= 3:
                        time_signature = N  # Direct mapping for simple cases
                    elif N == 6:
                        time_signature = 3  # Could be 6/8 or 3/4 with subdivisions
                    elif N == 8:
                        time_signature = 4  # Could be 4/4 with subdivisions
                    elif N == 5 or N == 7:
                        time_signature = N  # Odd time signatures like 5/4 or 7/8
                    # Otherwise, keep default 4
        
        # Use adaptive thresholds for pattern detection instead of fixed values
        if len(beat_strengths) > 3:
            # Calculate z-scores to identify statistically significant strong beats
            strengths_array = np.array(beat_strengths)
            mean_strength = np.mean(strengths_array)
            std_strength = np.std(strengths_array)
            
            if std_strength > 0:
                z_scores = (strengths_array - mean_strength) / std_strength
                
                # Count beats with z-score > 1 in groups of 3 (for 3/4 time)
                strong_beat_pattern = []
                for i in range(0, len(z_scores) - 2, 3):
                    # First beat should be significantly stronger (z > 1)
                    # Second and third beats should be weaker
                    if z_scores[i] > 1 and z_scores[i+1] < 0.5 and z_scores[i+2] < 0.5:
                        strong_beat_pattern.append(1)
                    else:
                        strong_beat_pattern.append(0)
                
                # Check if we have a clear 3/4 pattern
                if strong_beat_pattern and len(strong_beat_pattern) >= 3:
                    three_pattern_probability = sum(strong_beat_pattern) / len(strong_beat_pattern)
                    if three_pattern_probability > 0.6:
                        time_signature = 3
    
    # STEP 6: Enhanced phrase detection with adaptive thresholds
    phrases = []
    current_phrase = []
    
    if len(beat_times) > 0:
        # Calculate adaptive thresholds using percentiles instead of fixed ratios
        if len(beat_strengths) > 4:
            # Define thresholds based on distribution rather than fixed values
            strong_threshold = np.percentile(beat_strengths, 75)  # Top 25% are "strong" beats
            # For gaps, calculate significant deviation using z-scores if we have intervals
            if intervals:
                mean_interval = np.mean(intervals)
                std_interval = np.std(intervals)
                # A significant gap is > 1.5 standard deviations above mean
                significant_gap = mean_interval + (1.5 * std_interval) if std_interval > 0 else mean_interval * 1.3
            else:
                significant_gap = 0
        else:
            # Fallback for limited data
            strong_threshold = np.max(beat_strengths) * 0.8 if beat_strengths else 1.0
            significant_gap = 0
    
        # Identify phrase boundaries
        for i in range(len(beat_times)):
            current_phrase.append(i)
            
            # Check for phrase boundary conditions
            if i < len(beat_times) - 1:
                # Strong beat coming up (using adaptive threshold)
                is_stronger_next = False
                if i < len(beat_strengths) - 1:
                    is_stronger_next = beat_strengths[i+1] > strong_threshold and beat_strengths[i+1] > beat_strengths[i] * 1.1
                
                # Significant gap (using adaptive threshold)
                is_longer_gap = False
                if i < len(beat_times) - 1 and intervals and i < len(intervals):
                    is_longer_gap = intervals[i] > significant_gap
                
                # Measure boundary based on time signature
                is_measure_boundary = (i + 1) % time_signature == 0 and i > 0
                
                # Combined decision for phrase boundary
                if ((is_stronger_next or is_longer_gap) and len(current_phrase) >= 2) or \
                   (is_measure_boundary and len(current_phrase) >= time_signature):
                    phrases.append(current_phrase)
                    current_phrase = []
    
    # Add the last phrase if not empty
    if current_phrase and len(current_phrase) >= 2:
        phrases.append(current_phrase)
    
    # Ensure we have at least one phrase
    if not phrases and len(beat_times) >= 2:
        # Default to grouping by measures based on detected time signature
        for i in range(0, len(beat_times), time_signature):
            end = min(i + time_signature, len(beat_times))
            if end - i >= 2:  # Ensure at least 2 beats per phrase
                phrases.append(list(range(i, end)))
    
    # Return in the original format for compatibility
    return {
        "tempo": tempo,
        "beat_frames": beat_frames,
        "beat_times": beat_times,
        "beat_count": len(beat_times),
        "beat_strengths": beat_strengths,
        "intervals": intervals,
        "time_signature": time_signature,
        "phrases": phrases
    }

def detect_beats_and_subbeats(y, sr, subdivision=4):
    """
    Detect main beats and interpolate subbeats between consecutive beats.
    
    Parameters:
        y: Audio time series
        sr: Sample rate
        subdivision: Number of subdivisions between beats (default: 4 for quarter beats)
        
    Returns:
        Dictionary containing beat times, subbeat times, and tempo information
    """
    # Detect main beats using librosa
    try:
        tempo, beat_frames = librosa.beat.beat_track(y=y, sr=sr)
        beat_times = librosa.frames_to_time(beat_frames, sr=sr)
        
        # Convert numpy values to native Python types
        if isinstance(tempo, np.ndarray) or isinstance(tempo, np.number):
            tempo = float(tempo)
            
        # Convert beat_times to a list of floats
        if isinstance(beat_times, np.ndarray):
            beat_times = [float(t) for t in beat_times]
    except Exception as e:
        print(f"Error in beat detection: {e}")
        # Default fallbacks
        tempo = 120.0
        beat_times = []
    
    # Create subbeats by interpolating between main beats
    subbeat_times = []
    
    # Early return if no beats detected
    if not beat_times or len(beat_times) < 2:
        return {
            "tempo": float(tempo) if tempo is not None else 120.0,
            "beat_times": beat_times,
            "subbeat_times": []
        }
    
    for i in range(len(beat_times) - 1):
        # Get current and next beat time
        try:
            current_beat = float(beat_times[i])
            next_beat = float(beat_times[i + 1])
        except (IndexError, ValueError, TypeError):
            continue
        
        # Calculate time interval between beats
        interval = (next_beat - current_beat) / subdivision
        
        # Add the main beat
        subbeat_times.append({
            "time": float(current_beat),
            "type": "main",
            "strength": 1.0,
            "beat_index": i
        })
        
        # Add subbeats
        for j in range(1, subdivision):
            subbeat_time = current_beat + j * interval
            # Calculate strength based on position
            # For 4/4 time, beat 3 is stronger than beats 2 and 4
            if j == subdivision // 2 and subdivision == 4:
                strength = 0.8  # Stronger subbeat (e.g., beat 3 in 4/4)
            else:
                strength = 0.5  # Weaker subbeat
            
            subbeat_times.append({
                "time": float(subbeat_time),
                "type": "sub",
                "strength": float(strength),
                "beat_index": i,
                "subbeat_index": j
            })
    
    # Add the last main beat
    if beat_times:
        try:
            subbeat_times.append({
                "time": float(beat_times[-1]),
                "type": "main",
                "strength": 1.0,
                "beat_index": len(beat_times) - 1
            })
        except (ValueError, TypeError):
            # Skip if conversion fails
            pass
    
    return {
        "tempo": float(tempo) if tempo is not None else 120.0,
        "beat_times": beat_times,
        "subbeat_times": subbeat_times
    }

def map_beats_to_seconds(subbeat_times, duration, fps=1.0):
    """
    Map beats and subbeats to second-level intervals.
    
    Parameters:
        subbeat_times: List of dictionaries containing beat and subbeat information
        duration: Total duration of the audio in seconds
        fps: Frames per second (default: 1.0 for one-second intervals)
        
    Returns:
        List of dictionaries, each containing beats within a time window
    """
    # Safety check for input parameters
    if not isinstance(subbeat_times, list):
        print("Warning: subbeat_times is not a list")
        subbeat_times = []
    
    try:
        duration = float(duration)
    except (ValueError, TypeError):
        print("Warning: duration is not convertible to float, defaulting to 30")
        duration = 30.0
    
    # Calculate number of time windows
    num_windows = int(duration * fps) + 1
    
    # Initialize time windows
    time_windows = []
    
    for i in range(num_windows):
        # Calculate window boundaries
        start_time = i / fps
        end_time = (i + 1) / fps
        
        # Find beats and subbeats within this window
        window_beats = []
        
        for beat in subbeat_times:
            # Safety check for beat object
            if not isinstance(beat, dict):
                continue
                
            # Safely access beat time
            try:
                beat_time = float(beat.get("time", 0))
            except (ValueError, TypeError):
                continue
                
            if start_time <= beat_time < end_time:
                # Safely extract beat properties with defaults
                beat_type = beat.get("type", "sub")
                if not isinstance(beat_type, str):
                    beat_type = "sub"
                    
                # Safely handle strength
                try:
                    strength = float(beat.get("strength", 0.5))
                except (ValueError, TypeError):
                    strength = 0.5
                
                # Add beat to this window
                window_beats.append({
                    "time": beat_time,
                    "type": beat_type,
                    "strength": strength,
                    "relative_pos": (beat_time - start_time) / (1/fps)  # Position within window (0-1)
                })
        
        # Add window to list
        time_windows.append({
            "second": i,
            "start": start_time,
            "end": end_time,
            "beats": window_beats
        })
    
    return time_windows

def create_second_level_templates(sec_map, tempo, genre=None):
    """
    Create syllable templates for each second-level window.
    
    Parameters:
        sec_map: List of second-level time windows with beat information
        tempo: Tempo in BPM
        genre: Optional genre for genre-specific adjustments
        
    Returns:
        List of template strings, one for each second
    """
    # Helper function to map tempo to base syllable count
    def tempo_to_syllable_base(tempo):
        """Continuous function mapping tempo to syllable base count"""
        # Sigmoid-like function that smoothly transitions between syllable counts
        if tempo > 180:
            return 1.0
        elif tempo > 140: 
            return 1.0 + (180 - tempo) * 0.02  # Gradual increase 1.0 → 1.8
        elif tempo > 100:
            return 1.8 + (140 - tempo) * 0.01  # Gradual increase 1.8 → 2.2
        elif tempo > 70:
            return 2.2 + (100 - tempo) * 0.02  # Gradual increase 2.2 → 2.8
        else:
            return 2.8 + max(0, (70 - tempo) * 0.04)  # Continue increasing for very slow tempos
    
    # Calculate base syllable count from tempo
    base_syllables = tempo_to_syllable_base(tempo)
    
    # Apply genre-specific adjustments
    genre_factor = 1.0
    if genre:
        genre_lower = genre.lower()
        if any(term in genre_lower for term in ["rap", "hip hop", "hip-hop"]):
            genre_factor = 1.4  # Much higher syllable density for rap
        elif any(term in genre_lower for term in ["folk", "country", "ballad"]):
            genre_factor = 0.8  # Lower density for folk styles
    
    # Create templates for each second
    templates = []
    
    for window in sec_map:
        beats = window["beats"]
        
        # If no beats in this second, create a default template
        if not beats:
            templates.append("w(0.5):1")
            continue
        
        # Create beat patterns for this second
        beat_patterns = []
        
        for beat in beats:
            # Ensure we're dealing with a dictionary and that it has a "strength" key
            if not isinstance(beat, dict):
                continue  # Skip this beat if it's not a dictionary
            
            # Safely get beat type and strength
            if "type" not in beat or not isinstance(beat["type"], str):
                beat_type = "w"  # Default to weak if type is missing or not a string
            else:
                beat_type = "S" if beat["type"] == "main" else "m" if beat.get("strength", 0) >= 0.7 else "w"
            
            # Safely get strength value with fallback
            try:
                strength = float(beat.get("strength", 0.5))
            except (ValueError, TypeError):
                strength = 0.5  # Default if conversion fails
            
            # Adjust syllable count based on beat type and strength
            if beat_type == "S":
                syllable_factor = 1.2  # More syllables for strong beats
            elif beat_type == "m":
                syllable_factor = 1.0  # Normal for medium beats
            else:
                syllable_factor = 0.8  # Fewer for weak beats
            
            # Calculate final syllable count
            syllable_count = base_syllables * syllable_factor * genre_factor
            
            # Round to half-syllable precision
            syllable_count = round(syllable_count * 2) / 2
            
            # Ensure reasonable limits
            syllable_count = max(0.5, min(4, syllable_count))
            
            # Format with embedded strength value
            strength_pct = round(strength * 100) / 100
            beat_patterns.append(f"{beat_type}({strength_pct}):{syllable_count}")
        
        # Join patterns with dashes - ensure we have at least one pattern
        if not beat_patterns:
            templates.append("w(0.5):1")  # Default if no valid patterns were created
        else:
            second_template = "-".join(beat_patterns)
            templates.append(second_template)
    
    return templates

def detect_sections(y, sr):
    """
    Advanced detection of musical sections with adaptive segmentation and improved classification.
    
    Parameters:
        y: Audio time series
        sr: Sample rate
    
    Returns:
        A list of section dictionaries with type, start time, end time, and duration
    """
    # Step 1: Extract rich feature set for comprehensive analysis
    # ----------------------------------------------------------------------
    hop_length = 512  # Common hop length for feature extraction
    
    # Spectral features
    S = np.abs(librosa.stft(y, hop_length=hop_length))
    contrast = librosa.feature.spectral_contrast(S=S, sr=sr)
    
    # Harmonic features with CQT-based chroma (better for harmonic analysis)
    chroma = librosa.feature.chroma_cqt(y=y, sr=sr, hop_length=hop_length)
    
    # Timbral features
    mfcc = librosa.feature.mfcc(y=y, sr=sr, n_mfcc=13, hop_length=hop_length)
    
    # Energy features
    rms = librosa.feature.rms(y=y, hop_length=hop_length)
    
    # Harmonic-percussive source separation for better rhythm analysis
    y_harmonic, y_percussive = librosa.effects.hpss(y)
    percussive_rms = librosa.feature.rms(y=y_percussive, hop_length=hop_length)
    
    # Step 2: Adaptive determination of segment count based on song complexity
    # ----------------------------------------------------------------------
    duration = librosa.get_duration(y=y, sr=sr)
    
    # Feature preparation for adaptive segmentation
    # Stack features with proper normalization (addressing the scale issue)
    feature_stack = np.vstack([
        librosa.util.normalize(contrast),
        librosa.util.normalize(chroma),
        librosa.util.normalize(mfcc),
        librosa.util.normalize(rms)
    ])
    
    # Transpose to get time as first dimension
    feature_matrix = feature_stack.T
    
    # Step 3: Feature fusion using dimensionality reduction (addressing simple summation issue)
    # ----------------------------------------------------------------------
    
    # Apply PCA to reduce dimensionality while preserving relationships
    from sklearn.decomposition import PCA
    
    # Handle very short audio files
    n_components = min(8, feature_matrix.shape[0], feature_matrix.shape[1])
    
    if feature_matrix.shape[0] > n_components and feature_matrix.shape[1] > 0:
        try:
            pca = PCA(n_components=n_components)
            reduced_features = pca.fit_transform(feature_matrix)
        except Exception as e:
            print(f"PCA failed, falling back to original features: {e}")
            # Fallback to simpler approach if PCA fails
            reduced_features = feature_matrix
    else:
        # Not enough data for PCA
        reduced_features = feature_matrix
    
    # Step 4: Adaptive determination of optimal segment count
    # ----------------------------------------------------------------------
    
    # Initialize range of segment counts to try
    min_segments = max(2, int(duration / 60))  # At least 2 segments, roughly 1 per minute
    max_segments = min(10, int(duration / 20))  # At most 10 segments, roughly 1 per 20 seconds
    
    # Ensure reasonable bounds
    min_segments = max(2, min(min_segments, 4))
    max_segments = max(min_segments + 1, min(max_segments, 8))
    
    # Try different segment counts and evaluate with silhouette score
    best_segments = min_segments
    best_score = -1
    
    from sklearn.metrics import silhouette_score
    from sklearn.cluster import AgglomerativeClustering
    
    # Only do this analysis if we have enough data
    if reduced_features.shape[0] > max_segments:
        for n_segments in range(min_segments, max_segments + 1):
            try:
                # Perform agglomerative clustering
                clustering = AgglomerativeClustering(n_clusters=n_segments)
                labels = clustering.fit_predict(reduced_features)
                
                # Calculate silhouette score if we have enough samples
                if len(np.unique(labels)) > 1 and len(labels) > n_segments + 1:
                    score = silhouette_score(reduced_features, labels)
                    
                    if score > best_score:
                        best_score = score
                        best_segments = n_segments
            except Exception as e:
                print(f"Clustering with {n_segments} segments failed: {e}")
                continue
    
    # Use the optimal segment count for final segmentation
    n_segments = best_segments
    
    # Step 5: Final segmentation using the optimal segment count
    # ----------------------------------------------------------------------
    
    # Method 1: Use agglomerative clustering on the reduced features
    try:
        clustering = AgglomerativeClustering(n_clusters=n_segments)
        labels = clustering.fit_predict(reduced_features)
        
        # Convert cluster labels to boundaries by finding where labels change
        boundaries = [0]  # Start with the beginning
        
        for i in range(1, len(labels)):
            if labels[i] != labels[i-1]:
                boundaries.append(i)
        
        boundaries.append(len(labels))  # Add the end
        
        # Convert to frames
        bounds_frames = np.array(boundaries)
        
    except Exception as e:
        print(f"Final clustering failed: {e}")
        # Fallback to librosa's agglomerative clustering on original features
        bounds_frames = librosa.segment.agglomerative(feature_stack, n_segments)
    
    # Step 6: Detect harmonic changes for better bridge identification
    # ----------------------------------------------------------------------
    
    # Calculate tonal centroids to identify key changes
    tonnetz = librosa.feature.tonnetz(y=y_harmonic, sr=sr)
    
    # Look for significant changes in harmonic content
    harmonic_changes = []
    
    if tonnetz.shape[1] > 1:
        tonnetz_diff = np.sum(np.abs(np.diff(tonnetz, axis=1)), axis=0)
        # Normalize
        if np.max(tonnetz_diff) > 0:
            tonnetz_diff = tonnetz_diff / np.max(tonnetz_diff)
        
        # Identify significant harmonic changes (potential bridges or section changes)
        threshold = np.percentile(tonnetz_diff, 90)  # Top 10% most significant changes
        for i in range(len(tonnetz_diff)):
            if tonnetz_diff[i] > threshold:
                harmonic_changes.append(i)
    
    # Step 7: Convert boundaries to time and create sections
    # ----------------------------------------------------------------------
    bounds_times = librosa.frames_to_time(bounds_frames, sr=sr, hop_length=hop_length)
    
    # Create sections from the boundaries
    sections = []
    
    for i in range(len(bounds_times) - 1):
        start = bounds_times[i]
        end = bounds_times[i+1]
        duration = end - start
        
        # Skip extremely short sections
        if duration < 4 and i > 0 and i < len(bounds_times) - 2:
            continue
        
        # Step 8: Section type classification with improved musical features
        # ----------------------------------------------------------------------
        
        # Get indices for this section
        start_idx = bounds_frames[i]
        end_idx = bounds_frames[i+1]
        
        # Basic section type based on position
        if i == 0:
            section_type = "intro"
        elif i == len(bounds_times) - 2:
            section_type = "outro"
        else:
            # Default to alternating verse/chorus
            section_type = "chorus" if i % 2 == 1 else "verse"
        
        # Only analyze characteristics if we have enough frames
        if end_idx > start_idx:
            # Calculate musical characteristics for this section
            
            # 1. Energy profile
            energy = np.mean(rms[0, start_idx:end_idx])
            
            # 2. Rhythm intensity (percussive content)
            rhythm_intensity = np.mean(percussive_rms[0, start_idx:end_idx])
            
            # 3. Harmonic complexity
            if chroma.shape[1] > 0:
                chroma_var = np.var(chroma[:, start_idx:end_idx])
            else:
                chroma_var = 0
            
            # 4. Timbral characteristics
            if mfcc.shape[1] > 0:
                mfcc_mean = np.mean(mfcc[:, start_idx:end_idx], axis=1)
                mfcc_var = np.var(mfcc[:, start_idx:end_idx], axis=1)
            else:
                mfcc_mean = np.zeros(mfcc.shape[0])
                mfcc_var = np.zeros(mfcc.shape[0])
            
            # 5. Check for harmonic changes within this section (for bridge detection)
            has_harmonic_change = False
            for change_idx in harmonic_changes:
                if start_idx <= change_idx < end_idx:
                    has_harmonic_change = True
                    break
            
            # Calculate relative metrics by comparing to the entire song
            relative_energy = energy / np.mean(rms)
            relative_rhythm = rhythm_intensity / np.mean(percussive_rms)
            
            # Improved section type classification:
            
            # Chorus: High energy, strong rhythm, less harmonic variation
            if (relative_energy > 1.1 and relative_rhythm > 1.1 and 
                section_type != "intro" and section_type != "outro"):
                section_type = "chorus"
            
            # Verse: Moderate energy, moderate rhythm, more harmonic variation
            elif (0.8 <= relative_energy <= 1.1 and chroma_var > np.mean(np.var(chroma, axis=1)) and
                  section_type != "intro" and section_type != "outro"):
                section_type = "verse"
            
            # Bridge: Often has harmonic changes, energy drop, or unique timbral characteristics
            if (section_type not in ["intro", "outro"] and 
                (has_harmonic_change or 
                 (0.5 <= relative_energy <= 0.9 and duration < 30) or
                 np.any(mfcc_var > np.percentile(np.var(mfcc, axis=1), 75)))):
                section_type = "bridge"
        
        # Add section to the list
        sections.append({
            "type": section_type,
            "start": start,
            "end": end,
            "duration": duration
        })
    
    # Post-processing: Ensure reasonable section sequence and durations
    for i in range(1, len(sections) - 1):
        # Check for unreasonably short sections and merge them
        if sections[i]["duration"] < 8 and sections[i]["type"] not in ["intro", "outro", "bridge"]:
            # Either merge with previous or next section based on similarity
            prev_type = sections[i-1]["type"]
            next_type = sections[i+1]["type"] if i+1 < len(sections) else "outro"
            
            # Default to merging with the previous section
            sections[i]["type"] = prev_type
    
    # Filter out any remaining extremely short sections
    sections = [s for s in sections if s["duration"] >= 5 or 
                s["type"] == "intro" or s["type"] == "outro"]
    
    return sections

def create_flexible_syllable_templates(beats_info, genre=None, phrase_mode='default'):
    """
    Create enhanced syllable templates based on beat patterns with improved musical intelligence.
    
    Parameters:
        beats_info: Dictionary containing beat analysis data
        genre: Optional genre to influence template creation
        phrase_mode: 'default' uses provided phrases, 'auto' forces recalculation
        
    Returns:
        String of syllable templates with embedded strength values and flexible timing
    """
    import numpy as np
    from sklearn.cluster import KMeans
    
    # Convert any numpy values to native Python types for safety - directly handle conversions
    # Process the dictionary to convert numpy values to Python native types
    if isinstance(beats_info, dict):
        processed_beats_info = {}
        for k, v in beats_info.items():
            if isinstance(v, np.ndarray):
                if v.size == 1:
                    processed_beats_info[k] = float(v.item())
                else:
                    processed_beats_info[k] = [float(x) if isinstance(x, np.number) else x for x in v]
            elif isinstance(v, np.number):
                processed_beats_info[k] = float(v)
            elif isinstance(v, list):
                processed_beats_info[k] = [float(x) if isinstance(x, np.number) else x for x in v]
            else:
                processed_beats_info[k] = v
        beats_info = processed_beats_info
    
    # Extract basic beat information
    beat_times = beats_info.get("beat_times", [])
    beat_strengths = beats_info.get("beat_strengths", [1.0] * len(beat_times))
    tempo = beats_info.get("tempo", 120)
    time_signature = beats_info.get("time_signature", 4)
    
    # Early return for insufficient data
    if len(beat_times) < 2:
        return "S(1.0):1-w(0.5):1|S(1.0):1-w(0.5):1"  # Default fallback pattern
    
    # Step 1: Adaptive thresholding using k-means clustering
    # ----------------------------------------------------------------------
    if len(beat_strengths) >= 6:  # Need enough data points for clustering
        # Reshape for k-means
        X = np.array(beat_strengths).reshape(-1, 1)
        
        # Use k-means with 3 clusters for Strong, Medium, Weak classification
        kmeans = KMeans(n_clusters=3, random_state=0, n_init=10).fit(X)
        
        # Find the centroid values and sort them
        centroids = sorted([float(c[0]) for c in kmeans.cluster_centers_])
        
        # Map to thresholds (using the midpoints between centroids)
        if len(centroids) >= 3:
            medium_threshold = (centroids[0] + centroids[1]) / 2
            strong_threshold = (centroids[1] + centroids[2]) / 2
        else:
            # Fallback if clustering doesn't work well
            medium_threshold = np.percentile(beat_strengths, 33)
            strong_threshold = np.percentile(beat_strengths, 66)
    else:
        # For limited data, use percentile-based approach
        medium_threshold = np.percentile(beat_strengths, 33)
        strong_threshold = np.percentile(beat_strengths, 66)
    
    # Step 2: Create or refine phrases based on mode
    # ----------------------------------------------------------------------
    phrases = beats_info.get("phrases", [])
    
    if phrase_mode == 'auto' or not phrases:
        # Create phrases based on time signature and beat strengths
        phrases = []
        current_phrase = []
        
        for i in range(len(beat_times)):
            current_phrase.append(i)
            
            # Check for natural phrase endings
            if (i + 1) % time_signature == 0 or i == len(beat_times) - 1:
                if len(current_phrase) >= 2:  # Ensure minimum phrase length
                    phrases.append(current_phrase)
                    current_phrase = []
        
        # Add any remaining beats
        if current_phrase and len(current_phrase) >= 2:
            phrases.append(current_phrase)
    
    # Step 3: Calculate continuous tempo-to-syllable mapping function
    # ----------------------------------------------------------------------
    def tempo_to_syllable_base(tempo):
        """Continuous function mapping tempo to syllable base count"""
        # Sigmoid-like function that smoothly transitions between syllable counts
        if tempo > 180:
            return 1.0
        elif tempo > 140: 
            return 1.0 + (180 - tempo) * 0.02  # Gradual increase 1.0 → 1.8
        elif tempo > 100:
            return 1.8 + (140 - tempo) * 0.01  # Gradual increase 1.8 → 2.2
        elif tempo > 70:
            return 2.2 + (100 - tempo) * 0.02  # Gradual increase 2.2 → 2.8
        else:
            return 2.8 + max(0, (70 - tempo) * 0.04)  # Continue increasing for very slow tempos
    
    # Step 4: Generate enhanced templates with flexible timing
    # ----------------------------------------------------------------------
    syllable_templates = []
    
    for phrase in phrases:
        # Skip empty phrases
        if not phrase:
            continue
        
        # Extract beat strengths for this phrase
        phrase_strengths = [beat_strengths[i] for i in phrase if i < len(beat_strengths)]
        if not phrase_strengths:
            phrase_strengths = [1.0] * len(phrase)
        
        # Apply adaptive thresholding for stress pattern detection
        stress_pattern = []
        for i, strength in enumerate(phrase_strengths):
            # Consider both strength and metrical position
            metrical_position = i % time_signature
            
            # Apply positional boost for strong metrical positions
            position_boost = 0.15 if metrical_position == 0 else 0
            # Secondary stress on beat 3 in 4/4 time
            if time_signature == 4 and metrical_position == 2:
                position_boost = 0.08
                
            effective_strength = strength + position_boost
            
            if effective_strength >= strong_threshold:
                stress_pattern.append(("S", effective_strength))  # Strong beat with strength
            elif effective_strength >= medium_threshold:
                stress_pattern.append(("m", effective_strength))  # Medium beat with strength
            else:
                stress_pattern.append(("w", effective_strength))  # Weak beat with strength
        
        # Step 5: Calculate syllable counts using continuous function
        # ----------------------------------------------------------------------
        detailed_template = []
        
        for i, (stress_type, strength) in enumerate(stress_pattern):
            # Get base syllable count from tempo
            base_syllables = tempo_to_syllable_base(tempo)
            
            # Adjust based on stress type
            if stress_type == "S":
                syllable_factor = 1.2  # More syllables for strong beats
            elif stress_type == "m":
                syllable_factor = 1.0  # Normal for medium beats
            else:
                syllable_factor = 0.8  # Fewer for weak beats
            
            # Apply genre-specific adjustments
            genre_factor = 1.0
            if genre:
                genre = genre.lower()
                if any(term in genre for term in ["rap", "hip hop", "hip-hop"]):
                    genre_factor = 1.4  # Much higher syllable density for rap
                elif any(term in genre for term in ["folk", "country", "ballad"]):
                    genre_factor = 0.8  # Lower density for folk styles
            
            # Calculate adjusted syllable count
            raw_count = base_syllables * syllable_factor * genre_factor
            
            # Allow for more flexible syllable counts with non-integer values
            # Round to multiples of 0.5 for half-syllable precision
            rounded_count = round(raw_count * 2) / 2
            
            # Limit to reasonable range (0.5 to 4)
            syllable_count = max(0.5, min(4, rounded_count))
            
            # Format with embedded strength value for reversibility
            # Convert strength to 2-decimal precision percentage
            strength_pct = int(strength * 100) / 100
            detailed_template.append(f"{stress_type}({strength_pct}):{syllable_count}")
        
        # Join beat templates for this phrase
        phrase_template = "-".join(detailed_template)
        syllable_templates.append(phrase_template)
    
    # Step 6: Ensure valid output with reasonable defaults
    # ----------------------------------------------------------------------
    if not syllable_templates:
        # Create a sensible default based on time signature
        if time_signature == 3:
            syllable_templates = ["S(0.95):2-w(0.4):1-w(0.35):1"]  # 3/4 default
        else:
            syllable_templates = ["S(0.95):2-w(0.4):1-m(0.7):1.5-w(0.35):1"]  # 4/4 default
    
    # Join all phrase templates with the original separator for compatibility
    return "|".join(syllable_templates)

def format_syllable_templates_for_prompt(syllable_templates, arrow="→", line_wrap=10, 
                                         structured_output=False, beat_types=None):
    """
    Convert technical syllable templates into clear, human-readable instructions with
    enhanced flexibility and customization options.
    
    Parameters:
        syllable_templates: String or list of templates
        arrow: Symbol to use between beats (default: "→")
        line_wrap: Number of beats before automatic line wrapping (0 = no wrapping)
        structured_output: If True, return structured data instead of text
        beat_types: Custom mapping for beat types (default: None, uses standard mapping)
        
    Returns:
        Human-readable instructions or structured data depending on parameters
    """
    if not syllable_templates:
        return {} if structured_output else ""
    
    # Define standard beat type mapping (extensible)
    default_beat_types = {
        "S": {"name": "STRONG", "description": "stressed syllable"},
        "m": {"name": "medium", "description": "medium-stressed syllable"},
        "w": {"name": "weak", "description": "unstressed syllable"},
        "X": {"name": "EXTRA", "description": "extra strong syllable"},
        "L": {"name": "legato", "description": "connected/tied syllable"}
    }
    
    # Use custom mapping if provided, otherwise use default
    beat_types = beat_types or default_beat_types
    
    # Initialize structured output if requested
    structured_data = {"lines": [], "explanations": []} if structured_output else None
    
    # Improved format detection - more robust than just checking for "|"
    is_enhanced_format = False
    
    # Check if it's a string with enhanced format patterns
    if isinstance(syllable_templates, str):
        # Look for enhanced format patterns - check for beat type indicators
        if any(bt + "(" in syllable_templates or bt + ":" in syllable_templates or bt + "[" in syllable_templates 
               for bt in beat_types.keys()):
            is_enhanced_format = True
        # Secondary check for the "|" delimiter between phrases
        elif "|" in syllable_templates:
            is_enhanced_format = True
    
    # Initialize the output with a brief explanatory header
    output = []
    
    if is_enhanced_format:
        # Split into individual phrase templates
        phrases = syllable_templates.split("|") if "|" in syllable_templates else [syllable_templates]
        
        # Process each phrase into human-readable instructions
        for i, phrase in enumerate(phrases):
            # Check for special annotations
            has_swing = "(swing)" in phrase
            if has_swing:
                phrase = phrase.replace("(swing)", "")  # Remove annotation for processing
            
            beats = phrase.split("-")
            beat_instructions = []
            
            # Process each beat in the phrase
            for j, beat in enumerate(beats):
                # Extract beat type and information
                beat_info = {"original": beat, "type": None, "count": None, "strength": None}
                
                # Handle enhanced format with embedded strength values: S(0.95):2
                if "(" in beat and ")" in beat and ":" in beat:
                    parts = beat.split(":")
                    beat_type = parts[0].split("(")[0]  # Extract beat type
                    strength = parts[0].split("(")[1].rstrip(")")  # Extract strength value
                    count = parts[1]  # Extract syllable count
                    
                    beat_info["type"] = beat_type
                    beat_info["count"] = count
                    beat_info["strength"] = strength
                
                # Handle simpler format: S2, m1, w1
                elif any(beat.startswith(bt) for bt in beat_types.keys()) and len(beat) > 1:
                    beat_type = beat[0]
                    count = beat[1:]
                    
                    beat_info["type"] = beat_type
                    beat_info["count"] = count
                
                # Fallback for any other format
                else:
                    beat_instructions.append(beat)
                    continue
                
                # Format the beat instruction based on type
                if beat_info["type"] in beat_types:
                    type_name = beat_types[beat_info["type"]]["name"]
                    if beat_info["strength"]:
                        beat_instructions.append(f"{type_name}({beat_info['count']}) [{beat_info['strength']}]")
                    else:
                        beat_instructions.append(f"{type_name}({beat_info['count']})")
                else:
                    # Unknown beat type, use as-is
                    beat_instructions.append(beat)
            
            # Handle line wrapping for readability
            if line_wrap > 0 and len(beat_instructions) > line_wrap:
                wrapped_instructions = []
                for k in range(0, len(beat_instructions), line_wrap):
                    section = beat_instructions[k:k+line_wrap]
                    wrapped_instructions.append(f"{arrow} ".join(section))
                line_desc = f"\n    {arrow} ".join(wrapped_instructions)
            else:
                line_desc = f" {arrow} ".join(beat_instructions)
            
            # Add swing notation if present
            if has_swing:
                line_desc += " [with swing feel]"
            
            # Add to output
            line_output = f"Line {i+1}: {line_desc}"
            output.append(line_output)
            
            if structured_output:
                structured_data["lines"].append({
                    "line_number": i+1,
                    "beats": [{"original": beats[j], 
                              "type": beat_info.get("type"),
                              "count": beat_info.get("count"),
                              "strength": beat_info.get("strength")} 
                             for j, beat_info in enumerate([b for b in beats if isinstance(b, dict)])],
                    "has_swing": has_swing
                })
        
        # Add explanation of notation after the lines
        explanation = [
            "\n📝 UNDERSTANDING THE NOTATION:"
        ]
        
        # Add descriptions for each beat type that was actually used
        used_beat_types = set()
        for phrase in phrases:
            for beat in phrase.split("-"):
                for bt in beat_types.keys():
                    if beat.startswith(bt):
                        used_beat_types.add(bt)
        
        for bt in used_beat_types:
            if bt in beat_types:
                name = beat_types[bt]["name"]
                desc = beat_types[bt]["description"]
                explanation.append(f"- {name}(n): Place a {desc} here, plus (n-1) unstressed syllables")
        
        explanation.extend([
            f"- {arrow}: Indicates flow from one beat to the next",
            "- [0.xx]: Beat strength value (higher = more emphasis needed)"
        ])
        
        output.extend(explanation)
        
        if structured_output:
            structured_data["explanations"] = explanation
        
        # Add examples for half-syllable values if they appear in the templates
        has_half_syllables = any((".5" in beat) for phrase in phrases for beat in phrase.split("-"))
        if has_half_syllables:
            half_syllable_examples = [
                "\n🎵 HALF-SYLLABLE EXAMPLES:",
                "- STRONG(1.5): One stressed syllable followed by an unstressed half-syllable",
                "  Example: \"LOVE you\" where \"LOVE\" is stressed and \"you\" is quick",
                "- medium(2.5): One medium syllable plus one-and-a-half unstressed syllables",
                "  Example: \"Wait for the\" where \"Wait\" is medium-stressed and \"for the\" is quick"
            ]
            output.extend(half_syllable_examples)
            
            if structured_output:
                structured_data["half_syllable_examples"] = half_syllable_examples
        
        # Add swing explanation if needed
        if any("swing" in phrase for phrase in phrases):
            swing_guide = [
                "\n🎶 SWING RHYTHM GUIDE:",
                "- In swing, syllables should be unevenly timed (long-short pattern)",
                "- Example: \"SUM-mer TIME\" in swing feels like \"SUM...mer-TIME\" with delay"
            ]
            output.extend(swing_guide)
            
            if structured_output:
                structured_data["swing_guide"] = swing_guide
    
    # Handle the original format or segment dictionaries
    else:
        formatted_lines = []
        
        if isinstance(syllable_templates, list):
            for i, template in enumerate(syllable_templates):
                if isinstance(template, dict) and "syllable_template" in template:
                    line = f"Line {i+1}: {template['syllable_template']} syllables"
                    formatted_lines.append(line)
                    
                    if structured_output:
                        structured_data["lines"].append({
                            "line_number": i+1,
                            "syllable_count": template["syllable_template"]
                        })
                elif isinstance(template, str):
                    line = f"Line {i+1}: {template} syllables"
                    formatted_lines.append(line)
                    
                    if structured_output:
                        structured_data["lines"].append({
                            "line_number": i+1,
                            "syllable_count": template
                        })
            
            output = formatted_lines
        else:
            output = [str(syllable_templates)]
            
            if structured_output:
                structured_data["raw_content"] = str(syllable_templates)
    
    # Add general application advice
    application_tips = [
        "\n💡 APPLICATION TIPS:",
        "1. Strong beats need naturally stressed syllables (like the START of \"RE-mem-ber\")",
        "2. Place important words on strong beats for natural emphasis",
        "3. Vowel sounds work best for sustained or emphasized syllables",
        "4. Keep consonant clusters (like \"str\" or \"thr\") on weak beats"
    ]
    output.extend(application_tips)
    
    if structured_output:
        structured_data["application_tips"] = application_tips
        return structured_data
    
    return "\n".join(output)

def verify_flexible_syllable_counts(lyrics, templates, second_level_templates=None):
    """
    Enhanced verification of syllable counts and stress patterns with precise alignment analysis
    for both phrase-level and second-level templates.
    """
    import re
    import pronouncing
    import numpy as np
    import functools
    from itertools import chain
    
    # Split lyrics into lines
    lines = [line.strip() for line in lyrics.split("\n") if line.strip()]
    
    # Initialize tracking variables
    verification_notes = []
    detailed_analysis = []
    stress_misalignments = []
    total_mismatch_count = 0
    
    # Process each lyric line against its template
    for i, line in enumerate(lines):
        if i >= len(templates):
            break
            
        template = templates[i]
        
        # Extract the template string from different possible formats
        if isinstance(template, dict) and "syllable_template" in template:
            template_str = template["syllable_template"]
        elif isinstance(template, str):
            template_str = template
        else:
            continue
        
        # Handle multiple phrases in template - process ALL phrases, not just the first
        template_phrases = [template_str]
        if "|" in template_str:
            template_phrases = template_str.split("|")
        
        # Check against all phrases and find the best match
        best_match_diff = float('inf')
        best_match_phrase = None
        best_phrase_beats = None
        actual_count = count_syllables(line)
        
        for phrase_idx, phrase in enumerate(template_phrases):
            # Extract beat patterns and expected syllable counts from template
            beats_info = []
            total_expected = 0
            
            # Enhanced template parsing
            if "-" in phrase:
                beat_templates = phrase.split("-")
                
                # Parse each beat template
                for beat in beat_templates:
                    beat_info = {"original": beat, "type": None, "count": 1, "strength": None}
                    
                    # Handle templates with embedded strength values: S(0.95):2
                    if "(" in beat and ")" in beat and ":" in beat:
                        parts = beat.split(":")
                        beat_type = parts[0].split("(")[0]
                        try:
                            strength = float(parts[0].split("(")[1].rstrip(")"))
                        except ValueError:
                            strength = 1.0
                        
                        # Handle potential float syllable counts
                        try:
                            count = float(parts[1])
                            # Convert to int if it's a whole number
                            if count == int(count):
                                count = int(count)
                        except ValueError:
                            count = 1
                        
                        beat_info.update({
                            "type": beat_type,
                            "count": count,
                            "strength": strength
                        })
                    
                    # Handle simple format: S2, m1, w1
                    elif any(beat.startswith(x) for x in ["S", "m", "w", "X", "L"]):
                        beat_type = beat[0]
                        
                        # Extract count, supporting float values
                        try:
                            count_str = beat[1:]
                            count = float(count_str)
                            if count == int(count):
                                count = int(count)
                        except ValueError:
                            count = 1
                        
                        beat_info.update({
                            "type": beat_type,
                            "count": count
                        })
                    
                    # Legacy format - just numbers
                    else:
                        try:
                            count = float(beat)
                            if count == int(count):
                                count = int(count)
                            beat_info["count"] = count
                        except ValueError:
                            pass
                    
                    beats_info.append(beat_info)
                    total_expected += beat_info["count"]
                
                # Compare this phrase to actual syllable count
                phrase_diff = abs(actual_count - total_expected)
                
                # Adaptive threshold based on expected syllables
                expected_ratio = 0.15 if total_expected > 10 else 0.25
                phrase_threshold = max(1, round(total_expected * expected_ratio))
                
                # If this is the best match so far, store it
                if phrase_diff < best_match_diff:
                    best_match_diff = phrase_diff
                    best_match_phrase = phrase
                    best_phrase_beats = beats_info
            
            # For very simple templates without "-"
            else:
                try:
                    total_expected = float(phrase)
                    phrase_diff = abs(actual_count - total_expected)
                    if phrase_diff < best_match_diff:
                        best_match_diff = phrase_diff
                        best_match_phrase = phrase
                        best_phrase_beats = [{"count": total_expected}]
                except ValueError:
                    pass
        
        # If we found a reasonable match, proceed with analysis
        if best_match_phrase and best_phrase_beats:
            total_expected = sum(beat["count"] for beat in best_phrase_beats)
            
            # Calculate adaptive threshold based on expected syllables
            expected_ratio = 0.15 if total_expected > 10 else 0.25
            threshold = max(1, round(total_expected * expected_ratio))
            
            # Check if total syllable count is significantly off
            if total_expected > 0 and best_match_diff > threshold:
                verification_notes.append(f"Line {i+1}: Expected {total_expected} syllables, got {actual_count}")
                total_mismatch_count += 1
                
                # Extract words and perform detailed alignment analysis
                words = re.findall(r'\b[a-zA-Z]+\b', line.lower())
                
                # Get syllable count and stress for each word
                word_analysis = []
                cumulative_syllables = 0
                
                for word in words:
                    syllable_count = count_syllables_for_word(word)
                    
                    # Get stress pattern
                    stress_pattern = get_word_stress(word)
                    
                    word_analysis.append({
                        "word": word,
                        "syllables": syllable_count,
                        "stress_pattern": stress_pattern,
                        "position": cumulative_syllables
                    })
                    
                    cumulative_syllables += syllable_count
                
                # Analyze alignment with beats - only if there are beat types
                if best_phrase_beats and any(b.get("type") == "S" for b in best_phrase_beats if "type" in b):
                    # Identify positions where strong syllables should fall
                    strong_positions = []
                    current_pos = 0
                    
                    for beat in best_phrase_beats:
                        if beat.get("type") == "S":
                            strong_positions.append(current_pos)
                        current_pos += beat.get("count", 1)
                    
                    # Check if strong syllables align with strong beats
                    alignment_issues = []
                    
                    for pos in strong_positions:
                        # Find which word contains this position
                        misaligned_word = None
                        
                        for word_info in word_analysis:
                            word_start = word_info["position"]
                            word_end = word_start + word_info["syllables"]
                            
                            if word_start <= pos < word_end:
                                # Check if a stressed syllable falls on this position
                                syllable_in_word = pos - word_start
                                
                                # Get stress pattern for this word
                                stress = word_info["stress_pattern"]
                                
                                # If we have stress information and this syllable isn't stressed
                                if stress and syllable_in_word < len(stress) and stress[syllable_in_word] != '1':
                                    misaligned_word = word_info["word"]
                                    alignment_issues.append(f"'{word_info['word']}' (unstressed syllable on strong beat)")
                                    stress_misalignments.append({
                                        "line": i+1,
                                        "word": word_info["word"],
                                        "position": pos,
                                        "suggestion": get_stress_aligned_alternatives(word_info["word"], syllable_in_word)
                                    })
                                break
                    
                    if alignment_issues:
                        verification_notes.append(f"  → Stress misalignments: {', '.join(alignment_issues)}")
                    
                    # Generate a visual alignment map for better understanding
                    alignment_map = generate_alignment_visualization(line, best_phrase_beats, word_analysis)
                    if alignment_map:
                        detailed_analysis.append(f"Line {i+1} Alignment Analysis:\n{alignment_map}")
        else:
            # If no matching template was found
            verification_notes.append(f"Line {i+1}: Unable to find matching template pattern")
    
    # Add second-level verification if templates are provided
    if second_level_templates:
        verification_notes.append("\n=== SECOND-LEVEL VERIFICATION ===\n")
        
        # Check each second against corresponding line
        for i, template in enumerate(second_level_templates):
            if i >= len(lines):
                break
                
            line = lines[i]
            
            # Skip section headers
            if line.startswith('[') and ']' in line:
                continue
                
            actual_count = count_syllables(line)
            
            # Parse template to get expected syllable count
            total_expected = 0
            beat_patterns = []
            
            # Handle templates with beat patterns like "S(0.95):2-w(0.4):1"
            if isinstance(template, str) and "-" in template:
                for beat in template.split("-"):
                    if ":" in beat:
                        try:
                            count_part = beat.split(":")[1]
                            count = float(count_part)
                            total_expected += count
                            
                            # Extract beat type for alignment check
                            beat_type = beat.split("(")[0] if "(" in beat else beat[0]
                            beat_patterns.append((beat_type, count))
                        except (IndexError, ValueError):
                            pass
            
            # Compare actual vs expected count
            if total_expected > 0:
                # Calculate adaptive threshold based on expected syllables
                expected_ratio = 0.2  # More strict at second level
                threshold = max(0.5, round(total_expected * expected_ratio))
                
                difference = abs(actual_count - total_expected)
                
                if difference > threshold:
                    verification_notes.append(f"Second {i+1}: Expected {total_expected} syllables, got {actual_count}")
                    total_mismatch_count += 1
                    
                    # Check for stress misalignment in this second
                    words = re.findall(r'\b[a-zA-Z]+\b', line.lower())
                    word_analysis = []
                    cumulative_syllables = 0
                    
                    for word in words:
                        syllable_count = count_syllables_for_word(word)
                        stress_pattern = get_word_stress(word)
                        
                        word_analysis.append({
                            "word": word,
                            "syllables": syllable_count,
                            "stress_pattern": stress_pattern,
                            "position": cumulative_syllables
                        })
                        
                        cumulative_syllables += syllable_count
                    
                    # Check if stressed syllables align with strong beats
                    if beat_patterns:
                        strong_positions = []
                        current_pos = 0
                        
                        for beat_type, count in beat_patterns:
                            if beat_type == "S":
                                strong_positions.append(current_pos)
                            current_pos += count
                        
                        # Look for misalignments
                        for pos in strong_positions:
                            for word_info in word_analysis:
                                word_start = word_info["position"]
                                word_end = word_start + word_info["syllables"]
                                
                                if word_start <= pos < word_end:
                                    # Check if a stressed syllable falls on this position
                                    syllable_in_word = int(pos - word_start)
                                    stress = word_info["stress_pattern"]
                                    
                                    if stress and syllable_in_word < len(stress) and stress[syllable_in_word] != '1':
                                        verification_notes.append(f"  → In second {i+1}, '{word_info['word']}' has unstressed syllable on strong beat")
                                    break
    
    # Only add detailed analysis if we have rhythm mismatches
    if verification_notes:
        lyrics += "\n\n[Note: Potential rhythm mismatches detected in these lines:]\n"
        lyrics += "\n".join(verification_notes)
        
        if detailed_analysis:
            lyrics += "\n\n[Detailed Alignment Analysis:]\n"
            lyrics += "\n\n".join(detailed_analysis)
        
        lyrics += "\n\n[How to fix rhythm mismatches:]\n"
        lyrics += "1. Make sure stressed syllables (like 'LO' in 'LOV-er') fall on STRONG beats\n"
        lyrics += "2. Adjust syllable counts to match the template (add/remove words or use different words)\n"
        lyrics += "3. Try using words where natural stress aligns with musical rhythm\n"
        
        # Add specific word substitution suggestions if we found stress misalignments
        if stress_misalignments:
            lyrics += "\n[Specific word replacement suggestions:]\n"
            for issue in stress_misalignments[:5]:  # Limit to first 5 issues
                if issue["suggestion"]:
                    lyrics += f"Line {issue['line']}: Consider replacing '{issue['word']}' with: {issue['suggestion']}\n"
    
    return lyrics

def generate_alignment_visualization(line, beats_info, word_analysis):
    """Generate a visual representation of syllable alignment with beats."""
    if not beats_info or not word_analysis:
        return None
    
    # Create a syllable breakdown with stress information
    syllable_breakdown = []
    syllable_stresses = []
    
    for word_info in word_analysis:
        word = word_info["word"]
        syllables = word_info["syllables"]
        stress = word_info["stress_pattern"] or ""
        
        # Extend stress pattern if needed
        while len(stress) < syllables:
            stress += "0"
        
        # Get syllable breakdown
        parts = naive_syllable_split(word, syllables)
        
        for i, part in enumerate(parts):
            syllable_breakdown.append(part)
            if i < len(stress):
                syllable_stresses.append(stress[i])
            else:
                syllable_stresses.append("0")
    
    # Create beat pattern
    beat_types = []
    current_pos = 0
    
    for beat in beats_info:
        beat_type = beat.get("type", "-")
        count = beat.get("count", 1)
        
        # Handle whole numbers and half syllables
        if isinstance(count, int):
            beat_types.extend([beat_type] * count)
        else:
            # For half syllables, round up and use markers
            whole_part = int(count)
            frac_part = count - whole_part
            
            if whole_part > 0:
                beat_types.extend([beat_type] * whole_part)
            
            if frac_part > 0:
                beat_types.append(f"{beat_type}½")
    
    # Ensure we have enough beat types
    while len(beat_types) < len(syllable_breakdown):
        beat_types.append("-")
    
    # Trim beat types if too many
    beat_types = beat_types[:len(syllable_breakdown)]
    
    # Generate the visualization with highlighted misalignments
    result = []
    
    # First line: syllable breakdown with stress indicators
    syllable_display = []
    for i, syllable in enumerate(syllable_breakdown):
        if i < len(syllable_stresses) and syllable_stresses[i] == "1":
            syllable_display.append(syllable.upper())  # Uppercase for stressed syllables
        else:
            syllable_display.append(syllable.lower())  # Lowercase for unstressed
    
    result.append(" - ".join(syllable_display))
    
    # Second line: beat indicators with highlighting for misalignments
    beat_indicators = []
    for i, (syllable, beat_type) in enumerate(zip(syllable_stresses, beat_types)):
        if beat_type == "S" or beat_type.startswith("S"):
            if syllable == "1":
                beat_indicators.append("↑")  # Aligned strong beat
            else:
                beat_indicators.append("❌")  # Misaligned strong beat
        elif beat_type == "m" or beat_type.startswith("m"):
            beat_indicators.append("•")  # Medium beat
        elif beat_type == "w" or beat_type.startswith("w"):
            beat_indicators.append("·")  # Weak beat
        else:
            beat_indicators.append(" ")
    
    result.append("   ".join(beat_indicators))
    
    # Third line: beat types
    result.append(" - ".join(beat_types))
    
    return "\n".join(result)

@functools.lru_cache(maxsize=256)
def naive_syllable_split(word, syllable_count):
    """Naively split a word into the specified number of syllables, with caching for performance."""
    if syllable_count <= 1:
        return [word]
    
    # Common syllable break patterns
    vowels = "aeiouy"
    consonants = "bcdfghjklmnpqrstvwxz"
    
    # Find potential split points
    splits = []
    for i in range(1, len(word) - 1):
        if word[i] in consonants and word[i-1] in vowels:
            splits.append(i)
        elif word[i] in vowels and word[i-1] in consonants and word[i+1] in consonants:
            splits.append(i+1)
    
    # Ensure we have enough split points
    while len(splits) < syllable_count - 1:
        for i in range(1, len(word)):
            if i not in splits:
                splits.append(i)
                break
    
    # Sort and limit
    splits.sort()
    splits = splits[:syllable_count - 1]
    
    # Split the word
    result = []
    prev = 0
    for pos in splits:
        result.append(word[prev:pos])
        prev = pos
    
    result.append(word[prev:])
    return result

def get_stress_aligned_alternatives(word, position_to_stress):
    """Suggest alternative words with proper stress at the required position."""
    # This would ideally use a more sophisticated dictionary lookup,
    # but here's a simple implementation with common word patterns
    syllable_count = count_syllables_for_word(word)
    
    # Common synonyms/replacements by syllable count with stress position
    if syllable_count == 2:
        if position_to_stress == 0:  # Need stress on first syllable
            first_stress = ["love-ly", "won-der", "beau-ty", "danc-ing", "dream-ing", 
                           "heart-beat", "sun-light", "moon-light", "star-light"]
            return ", ".join(first_stress[:3])
        else:  # Need stress on second syllable
            second_stress = ["be-LIEVE", "a-BOVE", "a-ROUND", "to-DAY", "a-LIVE",
                            "a-LONE", "be-HOLD", "re-TURN", "de-LIGHT"]
            return ", ".join(second_stress[:3])
    elif syllable_count == 3:
        if position_to_stress == 0:  # First syllable stress
            return "MEM-o-ry, WON-der-ful, BEAU-ti-ful"
        elif position_to_stress == 1:  # Second syllable stress
            return "a-MAZE-ing, to-GE-ther, for-EV-er"
        else:  # Third syllable stress
            return "un-der-STAND, o-ver-COME, ne-ver-MORE"
    
    # For other cases, just provide general guidance
    return f"a word with stress on syllable {position_to_stress + 1}"

def generate_lyrics(genre, duration, emotion_results, song_structure=None):
    """
    Generate lyrics based on the genre, emotion, and structure analysis with enhanced rhythmic alignment.
    
    This improved version uses advanced template creation, better formatting, and verification with
    potential refinement for lyrics that perfectly match the musical rhythm patterns.
    
    Parameters:
        genre: Musical genre of the audio
        duration: Duration of the audio in seconds
        emotion_results: Dictionary containing emotional analysis results
        song_structure: Optional dictionary containing song structure analysis
        
    Returns:
        Generated lyrics aligned with the rhythm patterns of the music
    """
    # Ensure emotion_results is a dictionary with the expected structure
    if not isinstance(emotion_results, dict):
        emotion_results = {
            "emotion_analysis": {"primary_emotion": "Unknown"},
            "theme_analysis": {"primary_theme": "Unknown"},
            "rhythm_analysis": {"tempo": 0},
            "tonal_analysis": {"key": "Unknown", "mode": ""},
            "summary": {"tempo": 0, "key": "Unknown", "mode": "", "primary_emotion": "Unknown", "primary_theme": "Unknown"}
        }
    
    # Extract emotion and theme data with safe defaults
    primary_emotion = emotion_results.get("emotion_analysis", {}).get("primary_emotion", "Unknown")
    primary_theme = emotion_results.get("theme_analysis", {}).get("primary_theme", "Unknown")
    
    # Extract numeric values safely with fallbacks
    try:
        tempo = float(emotion_results.get("rhythm_analysis", {}).get("tempo", 0.0))
    except (ValueError, TypeError):
        tempo = 0.0
        
    key = emotion_results.get("tonal_analysis", {}).get("key", "Unknown")
    mode = emotion_results.get("tonal_analysis", {}).get("mode", "")
    # Extract emotion and theme data from analysis results
    primary_emotion = emotion_results["emotion_analysis"]["primary_emotion"]
    primary_theme = emotion_results["theme_analysis"]["primary_theme"]
    
    # Extract numeric values safely with fallbacks
    try:
        tempo = float(emotion_results["rhythm_analysis"]["tempo"])
    except (KeyError, ValueError, TypeError):
        tempo = 0.0
        
    key = emotion_results["tonal_analysis"]["key"]
    mode = emotion_results["tonal_analysis"]["mode"]
    
    # Format syllable templates for the prompt
    syllable_guidance = ""
    templates_for_verification = []
    
    # Create a structure visualization to help with lyrics-music matching
    structure_visualization = "=== MUSIC-LYRICS STRUCTURE MATCHING ===\n\n"
    structure_visualization += f"Song Duration: {duration:.1f} seconds\n"
    structure_visualization += f"Tempo: {tempo:.1f} BPM\n\n"
    
    # Add second-level template guidance if available
    if song_structure and "second_level" in song_structure and song_structure["second_level"]:
        second_level_templates = song_structure["second_level"]["templates"]
        
        # Create second-level guidance
        second_level_guidance = "\nSECOND-BY-SECOND RHYTHM INSTRUCTIONS:\n"
        second_level_guidance += "Each line below corresponds to ONE SECOND of audio. Follow these rhythm patterns EXACTLY:\n\n"
        
        # Format each second's template
        formatted_second_templates = []
        for i, template in enumerate(second_level_templates):
            if i < min(60, len(second_level_templates)):  # Limit to 60 seconds to avoid overwhelming the LLM
                formatted_template = format_syllable_templates_for_prompt(template, arrow="→", line_wrap=0)
                formatted_second_templates.append(f"Second {i+1}: {formatted_template}")
        
        second_level_guidance += "\n".join(formatted_second_templates)
        
        # Add critical instructions for second-level alignment
        second_level_guidance += "\n\nCRITICAL: Create ONE LINE of lyrics for EACH SECOND, following the exact rhythm pattern."
        second_level_guidance += "\nIf a second has no beats, use it for a breath or pause in the lyrics."
        second_level_guidance += "\nThe first line of your lyrics MUST match Second 1, the second line matches Second 2, and so on."
        
        # Add to syllable guidance
        syllable_guidance = second_level_guidance
        
        # Store templates for verification
        templates_for_verification = second_level_templates
    
    elif song_structure:
        # Try to use flexible structure if available
        if "flexible_structure" in song_structure and song_structure["flexible_structure"]:
            flexible = song_structure["flexible_structure"]
            if "segments" in flexible and flexible["segments"]:
                # Get the segments
                segments = flexible["segments"]
                
                # Add structure visualization
                structure_visualization += f"Total segments: {len(segments)}\n"
                structure_visualization += "Each segment represents one musical phrase for which you should write ONE line of lyrics.\n\n"
                
                # Process each segment to create enhanced rhythmic templates
                enhanced_templates = []
                
                for i, segment in enumerate(segments):
                    if i < 30:  # Extend limit to 30 lines to handle longer songs
                        # Get the beat information for this segment
                        segment_start = segment["start"]
                        segment_end = segment["end"]
                        
                        # Add segment info to visualization
                        structure_visualization += f"Segment {i+1}: {segment_start:.1f}s - {segment_end:.1f}s (duration: {segment_end-segment_start:.1f}s)\n"
                        
                        # Find beats within this segment
                        segment_beats = []
                        beat_times = flexible["beats"]["beat_times"]
                        beat_strengths = flexible["beats"].get("beat_strengths", [])
                        
                        for j, beat_time in enumerate(beat_times):
                            if segment_start <= beat_time < segment_end:
                                # Add this beat to the segment
                                segment_beats.append(j)
                        
                        # Create segment-specific beat info
                        segment_beats_info = {
                            "beat_times": [beat_times[j] for j in segment_beats],
                            "tempo": flexible["beats"].get("tempo", 120)
                        }
                        
                        if beat_strengths:
                            segment_beats_info["beat_strengths"] = [
                                beat_strengths[j] for j in segment_beats 
                                if j < len(beat_strengths)
                            ]
                        
                        # Create a phrase structure for this segment
                        segment_beats_info["phrases"] = [segment_beats]
                        
                        # Generate enhanced template with genre awareness and auto phrasing
                        enhanced_template = create_flexible_syllable_templates(
                            segment_beats_info,
                            genre=genre,
                            phrase_mode='auto' if i == 0 else 'default'
                        )
                        enhanced_templates.append(enhanced_template)
                        templates_for_verification.append(enhanced_template)
                        
                        # Add template to visualization
                        structure_visualization += f"  Template: {enhanced_template}\n"
                
                # Use these templates to determine verse/chorus structure based on similar patterns
                # This is a simple version - could be enhanced with more sophisticated pattern detection
                section_types = []
                pattern_groups = {}
                
                for i, template in enumerate(enhanced_templates):
                    # Create simplified version for pattern matching
                    simple_pattern = template.replace("(", "").replace(")", "").replace(":", "")
                    
                    # Check if this pattern is similar to any we've seen
                    found_match = False
                    for group, patterns in pattern_groups.items():
                        if any(simple_pattern == p.replace("(", "").replace(")", "").replace(":", "") for p in patterns):
                            pattern_groups[group].append(template)
                            section_types.append(group)
                            found_match = True
                            break
                    
                    if not found_match:
                        # New pattern type
                        group_name = f"Group_{len(pattern_groups) + 1}"
                        pattern_groups[group_name] = [template]
                        section_types.append(group_name)
                
                # Map pattern groups to verse/chorus/bridge based on common structures
                section_mapping = {}
                if len(pattern_groups) >= 1:
                    # Assume the most common pattern is the verse
                    most_common = max(pattern_groups.items(), key=lambda x: len(x[1]))[0]
                    section_mapping[most_common] = "verse"
                
                if len(pattern_groups) >= 2:
                    # Second most common might be chorus
                    sorted_groups = sorted(pattern_groups.items(), key=lambda x: len(x[1]), reverse=True)
                    if len(sorted_groups) > 1:
                        section_mapping[sorted_groups[1][0]] = "chorus"
                
                if len(pattern_groups) >= 3:
                    # Third pattern could be bridge
                    sorted_groups = sorted(pattern_groups.items(), key=lambda x: len(x[1]), reverse=True)
                    if len(sorted_groups) > 2:
                        section_mapping[sorted_groups[2][0]] = "bridge"
                
                # Update section types using the mapping
                mapped_section_types = []
                for section_type in section_types:
                    if section_type in section_mapping:
                        mapped_section_types.append(section_mapping[section_type])
                    else:
                        mapped_section_types.append("verse")  # Default to verse
                
                # Add structure visualization with section types
                structure_visualization += "\nPredicted Song Structure:\n"
                for i, section_type in enumerate(mapped_section_types):
                    if i < len(enhanced_templates):
                        structure_visualization += f"Line {i+1}: [{section_type.upper()}] {enhanced_templates[i]}\n"
                
                # Calculate total line count
                total_lines = len(enhanced_templates)
                verse_lines = mapped_section_types.count("verse")
                chorus_lines = mapped_section_types.count("chorus")
                bridge_lines = mapped_section_types.count("bridge")
                
                # Add summary
                structure_visualization += f"\nTotal Lines Required: {total_lines}\n"
                structure_visualization += f"Verse Lines: {verse_lines}\n"
                structure_visualization += f"Chorus Lines: {chorus_lines}\n"
                structure_visualization += f"Bridge Lines: {bridge_lines}\n"
                
                # Format templates with improved formatting for the prompt
                syllable_guidance = "CRITICAL RHYTHM INSTRUCTIONS:\n"
                syllable_guidance += "Each line of lyrics MUST match exactly with one musical phrase/segment.\n"
                syllable_guidance += "Follow these rhythm patterns for each line (STRONG beats need stressed syllables):\n\n"
                
                # Add section headers to formatted templates
                formatted_templates = []
                for i, template in enumerate(enhanced_templates):
                    if i < len(mapped_section_types):
                        section_type = mapped_section_types[i].upper()
                        if i > 0 and mapped_section_types[i] != mapped_section_types[i-1]:
                            # New section
                            formatted_templates.append(f"\n[{section_type}]")
                        elif i == 0:
                            # First section
                            formatted_templates.append(f"[{section_type}]")
                    formatted_templates.append(format_syllable_templates_for_prompt([template], arrow="→", line_wrap=8))
                
                syllable_guidance += "\n".join(formatted_templates)
                
                # Store info for later use in traditional sections approach
                use_sections = True
                
                # Use the detected section structure for traditional approach
                if verse_lines > 0:
                    verse_lines = min(verse_lines, total_lines // 2)  # Ensure reasonable limits
                else:
                    verse_lines = total_lines // 2
                    
                if chorus_lines > 0:
                    chorus_lines = min(chorus_lines, total_lines // 3)
                else:
                    chorus_lines = total_lines // 3
                    
                if bridge_lines > 0:
                    bridge_lines = min(bridge_lines, total_lines // 6)
                else:
                    bridge_lines = 0
                
        # Fallback to traditional sections if needed
        elif "syllables" in song_structure and song_structure["syllables"]:
            syllable_guidance = "RHYTHM PATTERN INSTRUCTIONS:\n"
            syllable_guidance += "Follow these syllable patterns for each section. Each line should match ONE phrase:\n\n"
            
            # Count sections for visualization
            section_counts = {"verse": 0, "chorus": 0, "bridge": 0, "intro": 0, "outro": 0}
            
            for section in song_structure["syllables"]:
                section_counts[section["type"]] = section_counts.get(section["type"], 0) + 1
                
                if "syllable_template" in section:
                    # Process to create enhanced template
                    section_beats_info = {
                        "beat_times": [beat for beat in song_structure["beats"]["beat_times"] 
                                       if section["start"] <= beat < section["end"]],
                        "tempo": song_structure["beats"].get("tempo", 120)
                    }
                    
                    if "beat_strengths" in song_structure["beats"]:
                        section_beats_info["beat_strengths"] = [
                            strength for i, strength in enumerate(song_structure["beats"]["beat_strengths"])
                            if i < len(song_structure["beats"]["beat_times"]) and
                            section["start"] <= song_structure["beats"]["beat_times"][i] < section["end"]
                        ]
                    
                    # Create a phrase structure for this section
                    section_beats_info["phrases"] = [list(range(len(section_beats_info["beat_times"])))]
                    
                    # Generate enhanced template with genre awareness
                    enhanced_template = create_flexible_syllable_templates(
                        section_beats_info,
                        genre=genre,
                        phrase_mode='auto' if section['type'] == 'verse' else 'default'
                    )
                    
                    syllable_guidance += f"[{section['type'].capitalize()}]:\n"
                    syllable_guidance += format_syllable_templates_for_prompt(
                        enhanced_template,
                        arrow="→", 
                        line_wrap=6
                    ) + "\n\n"
                    templates_for_verification.append(section)
                elif "syllable_count" in section:
                    syllable_guidance += f"[{section['type'].capitalize()}]: ~{section['syllable_count']} syllables total\n"
            
            # Create structure visualization
            structure_visualization += "Using traditional section-based structure:\n"
            for section_type, count in section_counts.items():
                if count > 0:
                    structure_visualization += f"{section_type.capitalize()}: {count} sections\n"
            
            # Set traditional section counts
            verse_lines = max(2, section_counts.get("verse", 0) * 4)
            chorus_lines = max(2, section_counts.get("chorus", 0) * 4)
            bridge_lines = max(0, section_counts.get("bridge", 0) * 2)
            
            # Use sections approach
            use_sections = True
    
    # If we couldn't get specific templates, use general guidance
    if not syllable_guidance:
        syllable_guidance = "RHYTHM ALIGNMENT INSTRUCTIONS:\n\n"
        syllable_guidance += "1. Align stressed syllables with strong beats (usually beats 1 and 3 in 4/4 time)\n"
        syllable_guidance += "2. Use unstressed syllables on weak beats (usually beats 2 and 4 in 4/4 time)\n"
        syllable_guidance += "3. Use appropriate syllable counts based on tempo:\n"
        syllable_guidance += "   - Fast tempo (>120 BPM): 4-6 syllables per line\n"
        syllable_guidance += "   - Medium tempo (90-120 BPM): 6-8 syllables per line\n"
        syllable_guidance += "   - Slow tempo (<90 BPM): 8-10 syllables per line\n"
        
        # Create basic structure visualization
        structure_visualization += "Using estimated structure (no detailed analysis available):\n"
        
        # Calculate rough section counts based on duration
        estimated_lines = max(8, int(duration / 10))
        structure_visualization += f"Estimated total lines: {estimated_lines}\n"
        
        # Set traditional section counts based on duration
        verse_lines = estimated_lines // 2
        chorus_lines = estimated_lines // 3
        bridge_lines = estimated_lines // 6 if estimated_lines > 12 else 0
        
        # Use sections approach
        use_sections = True
    
    # Add examples of syllable-beat alignment with enhanced format
    syllable_guidance += "\nEXAMPLES OF PERFECT RHYTHM ALIGNMENT:\n"
    syllable_guidance += "Pattern: S(0.95):1 → w(0.4):1 → m(0.7):1 → w(0.3):1\n"
    syllable_guidance += "Lyric: 'HEAR the MU-sic PLAY'\n"
    syllable_guidance += "        ↑     ↑    ↑    ↑\n"
    syllable_guidance += "        S     w    m    w    <- BEAT TYPE\n\n"
    
    syllable_guidance += "Pattern: S(0.9):2 → w(0.3):1 → S(0.85):1 → w(0.4):2\n"
    syllable_guidance += "Lyric: 'DANC-ing TO the RHYTHM of LOVE'\n"
    syllable_guidance += "        ↑    ↑  ↑   ↑     ↑  ↑\n"
    syllable_guidance += "        S    S  w   S     w  w    <- BEAT TYPE\n\n"
    
    syllable_guidance += "Pattern: S(0.92):1 → m(0.65):2 → S(0.88):1 → w(0.35):1\n"
    syllable_guidance += "Lyric: 'TIME keeps FLOW-ing ON and ON'\n"
    syllable_guidance += "        ↑     ↑    ↑   ↑  ↑   ↑\n"
    syllable_guidance += "        S     m    m   S  w   w    <- BEAT TYPE\n\n"
    
    # Add genre-specific guidance based on the detected genre
    genre_guidance = ""
    if any(term in genre.lower() for term in ["rap", "hip-hop", "hip hop"]):
        genre_guidance += "\nSPECIFIC GUIDANCE FOR RAP/HIP-HOP RHYTHMS:\n"
        genre_guidance += "- Use more syllables per beat for rapid-fire sections\n"
        genre_guidance += "- Create internal rhymes within lines, not just at line endings\n"
        genre_guidance += "- Emphasize the first beat of each bar with strong consonants\n"
    elif any(term in genre.lower() for term in ["electronic", "edm", "techno", "house", "dance"]):
        genre_guidance += "\nSPECIFIC GUIDANCE FOR ELECTRONIC MUSIC RHYTHMS:\n"
        genre_guidance += "- Use repetitive phrases that build and release tension\n"
        genre_guidance += "- Match syllables precisely to the beat grid\n"
        genre_guidance += "- Use short, percussive words on strong beats\n"
    elif any(term in genre.lower() for term in ["rock", "metal", "punk", "alternative"]):
        genre_guidance += "\nSPECIFIC GUIDANCE FOR ROCK RHYTHMS:\n"
        genre_guidance += "- Use powerful, emotive words on downbeats\n"
        genre_guidance += "- Create contrast between verse and chorus energy levels\n"
        genre_guidance += "- Emphasize hooks with simple, memorable phrases\n"
    elif any(term in genre.lower() for term in ["folk", "country", "acoustic", "ballad"]):
        genre_guidance += "\nSPECIFIC GUIDANCE FOR FOLK/ACOUSTIC RHYTHMS:\n"
        genre_guidance += "- Focus on storytelling with clear narrative flow\n"
        genre_guidance += "- Use natural speech patterns that flow conversationally\n"
        genre_guidance += "- Place important words at the start of phrases\n"

    # Add genre guidance to the main guidance
    syllable_guidance += genre_guidance
    
    # Store the syllable guidance for later use
    syllable_guidance_text = syllable_guidance
    
    # Determine if we should use traditional sections or second-level alignment
    use_sections = True
    use_second_level = False
    
    if song_structure and "second_level" in song_structure and song_structure["second_level"]:
        use_second_level = True
        # If we have second-level templates, prioritize those over traditional sections
        if len(song_structure["second_level"]["templates"]) > 0:
            use_sections = False
    elif song_structure and "flexible_structure" in song_structure and song_structure["flexible_structure"]:
        # If we have more than 4 segments, it's likely not a traditional song structure
        if "segments" in song_structure["flexible_structure"]:
            segments = song_structure["flexible_structure"]["segments"]
            if len(segments) > 4:
                use_sections = False
    
    # Create enhanced prompt with better rhythm alignment instructions
    if use_second_level:
        # Second-level approach with per-second alignment
        content = f"""
You are a talented songwriter who specializes in {genre} music.
Write original {genre} song lyrics for a song that is {duration:.1f} seconds long.

IMPORTANT: DO NOT include any thinking process, explanations, or analysis before the lyrics. Start directly with the song lyrics.

Music analysis has detected the following qualities in the music:
- Tempo: {tempo:.1f} BPM
- Key: {key} {mode}
- Primary emotion: {primary_emotion}
- Primary theme: {primary_theme}

{syllable_guidance}

CRITICAL INSTRUCTIONS FOR SECOND-LEVEL RHYTHM ALIGNMENT:
1. Each line of lyrics MUST correspond to ONE SECOND of audio.
2. The first line of your lyrics MUST match Second 1, the second line matches Second 2, etc.
3. STRESSED syllables MUST fall on STRONG beats (marked with STRONG in the pattern)
4. Natural word stress patterns must match the beat strength (strong words on strong beats)
5. For seconds with no beats, use a pause, breath, or continue a phrase from previous line
6. Pay attention to strength values in the pattern (higher values need stronger emphasis)
7. For half-syllable positions (like S1.5 or m2.5), use short, quick syllables

The lyrics should:
- Perfectly capture the essence and style of {genre} music
- Express the {primary_emotion} emotion and {primary_theme} theme
- Match EXACTLY with the second-by-second rhythm patterns provided above
- Be completely original
- Create a coherent song that flows naturally despite the precise timing requirements

IMPORTANT: Start immediately with the lyrics. DO NOT include any thinking process, analysis, or explanation before presenting the lyrics.

IMPORTANT: Your generated lyrics must be followed by a section titled "[RHYTHM_ANALYSIS_SECTION]" 
where you analyze how well the lyrics align with the musical rhythm. This section MUST appear
even if there are no rhythm issues. Include the following in your analysis:
1. How well each line matches its corresponding second's rhythm pattern
2. Where stressed syllables align with strong beats
3. Any potential misalignments or improvements

Your lyrics:
"""
    elif use_sections:
        # Traditional approach with sections
        content = f"""
You are a talented songwriter who specializes in {genre} music.
Write original {genre} song lyrics for a song that is {duration:.1f} seconds long.

IMPORTANT: DO NOT include any thinking process, explanations, or analysis before the lyrics. Start directly with the song lyrics.

Music analysis has detected the following qualities in the music:
- Tempo: {tempo:.1f} BPM
- Key: {key} {mode}
- Primary emotion: {primary_emotion}
- Primary theme: {primary_theme}

{syllable_guidance}

CRITICAL PRINCIPLES FOR RHYTHMIC ALIGNMENT:
1. STRESSED syllables MUST fall on STRONG beats (marked with STRONG in the pattern)
2. Natural word stress patterns must match the beat strength (strong words on strong beats)
3. Line breaks should occur at phrase endings for natural breathing
4. Consonant clusters should be avoided on fast notes and strong beats
5. Open vowels (a, e, o) work better for sustained notes and syllables
6. Pay attention to strength values in the pattern (higher values like 0.95 need stronger emphasis)
7. For half-syllable positions (like S1.5 or m2.5), use short, quick syllables or words with weak vowels

The lyrics should:
- Perfectly capture the essence and style of {genre} music
- Express the {primary_emotion} emotion and {primary_theme} theme
- Follow the structure patterns provided above
- Be completely original
- Match the song duration of {duration:.1f} seconds

IMPORTANT: Start immediately with the lyrics. DO NOT include any thinking process, analysis, or explanation before presenting the lyrics.

IMPORTANT: Your generated lyrics must be followed by a section titled "[RHYTHM_ANALYSIS_SECTION]" 
where you analyze how well the lyrics align with the musical rhythm. This section MUST appear
even if there are no rhythm issues. Include the following in your analysis:
1. Syllable counts for each line and how they match the rhythm pattern
2. Where stressed syllables align with strong beats
3. Any potential misalignments or improvements

Your lyrics:
"""
    else:
        # Flexible approach without traditional sections
        content = f"""
You are a talented songwriter who specializes in {genre} music.
Write original lyrics that match the rhythm of a {genre} music segment that is {duration:.1f} seconds long.

IMPORTANT: DO NOT include any thinking process, explanations, or analysis before the lyrics. Start directly with the song lyrics.

Music analysis has detected the following qualities:
- Tempo: {tempo:.1f} BPM
- Key: {key} {mode}
- Primary emotion: {primary_emotion}
- Primary theme: {primary_theme}

{syllable_guidance}

CRITICAL PRINCIPLES FOR RHYTHMIC ALIGNMENT:
1. STRESSED syllables MUST fall on STRONG beats (marked with STRONG in the pattern)
2. Natural word stress patterns must match the beat strength (strong words on strong beats)
3. Line breaks should occur at phrase endings for natural breathing
4. Consonant clusters should be avoided on fast notes and strong beats
5. Open vowels (a, e, o) work better for sustained notes and syllables
6. Pay attention to strength values in the pattern (higher values like 0.95 need stronger emphasis)
7. For half-syllable positions (like S1.5 or m2.5), use short, quick syllables or words with weak vowels

The lyrics should:
- Perfectly capture the essence and style of {genre} music
- Express the {primary_emotion} emotion and {primary_theme} theme
- Be completely original
- Maintain a consistent theme throughout
- Match the audio segment duration of {duration:.1f} seconds

Include any section labels like [Verse] or [Chorus] as indicated in the rhythm patterns above.
Each line of lyrics must follow the corresponding segment's rhythm pattern EXACTLY.

IMPORTANT: Start immediately with the lyrics. DO NOT include any thinking process, analysis, or explanation before presenting the lyrics.

IMPORTANT: Your generated lyrics must be followed by a section titled "[RHYTHM_ANALYSIS_SECTION]" 
where you analyze how well the lyrics align with the musical rhythm. This section MUST appear
even if there are no rhythm issues. Include the following in your analysis:
1. Syllable counts for each line and how they match the rhythm pattern
2. Where stressed syllables align with strong beats
3. Any potential misalignments or improvements

Your lyrics:
"""

    # Format as a chat message for the LLM
    messages = [
        {"role": "system", "content": "You are a professional songwriter. Create lyrics that match the specified rhythm patterns exactly. Start with the lyrics immediately without any explanation or thinking. Be concise and direct."},
        {"role": "user", "content": content}
    ]
    
    # Apply standard chat template without thinking enabled
    text = llm_tokenizer.apply_chat_template(
        messages,
        tokenize=False,
        add_generation_prompt=True
    )
    
    # Generate lyrics using the LLM
    model_inputs = llm_tokenizer([text], return_tensors="pt").to(llm_model.device)
    
    # Configure generation parameters based on model capability
    generation_params = {
        "do_sample": True,
        "temperature": 0.5,  # Lower for more consistent and direct output
        "top_p": 0.85,  # Slightly lower for more predictable responses
        "top_k": 50,
        "repetition_penalty": 1.2,
        "max_new_tokens": 2048,
        "num_return_sequences": 1
    }
    
    # Add specific stop sequences to prevent excessive explanation
    if hasattr(llm_model.generation_config, "stopping_criteria"):
        thinking_stops = ["Let me think", "First, I need to", "Let's analyze", "I'll approach this", "Step 1:", "To start,"]
        for stop in thinking_stops:
            if stop not in llm_model.generation_config.stopping_criteria:
                llm_model.generation_config.stopping_criteria.append(stop)
    
    # Generate output
    generated_ids = llm_model.generate(
        **model_inputs,
        **generation_params
    )
    
    # Extract output tokens
    output_ids = generated_ids[0][len(model_inputs.input_ids[0]):].tolist()
    
    # Get the raw output and strip any thinking process
    lyrics = llm_tokenizer.decode(output_ids, skip_special_tokens=True).strip()
    
    # Enhanced thinking process removal - handle multiple formats
    # First check for standard thinking tags
    if "<thinking>" in lyrics and "</thinking>" in lyrics:
        lyrics = lyrics.split("</thinking>")[1].strip()
    
    # Check for alternative thinking indicators with improved detection
    thinking_markers = [
        "<think>", "</think>", 
        "[thinking]", "[/thinking]", 
        "I'll think step by step:", 
        "First, I need to understand", 
        "Let me think about", 
        "Let's tackle this query",
        "Okay, let's tackle this query",
        "First, I need to understand the requirements",
        "Looking at the rhythm patterns"
    ]
    
    # First try to find clear section breaks
    for marker in thinking_markers:
        if marker in lyrics:
            parts = lyrics.split(marker)
            if len(parts) > 1:
                lyrics = parts[-1].strip()  # Take the last part after any thinking marker
    
    # Look for long analytical sections followed by clear lyrics
    analytical_patterns = [
        "Let me analyze", 
        "I need to understand", 
        "The tempo is", 
        "First, let's look at",
        "Wait, maybe",
        "Considering the emotional tone",
        "Starting with the first line",
        "Let me check the examples"
    ]
    
    # Check if lyrics begin with any analytical patterns
    for pattern in analytical_patterns:
        if lyrics.startswith(pattern):
            # Try to find where the actual lyrics start - look for common lyrics markers
            lyrics_markers = [
                "\n\n[Verse", 
                "\n\n[Chorus", 
                "\n\nVerse", 
                "\n\nChorus",
                "\n\n[Verse 1]",
                "\n\n[Intro]"
            ]
            
            for marker in lyrics_markers:
                if marker in lyrics:
                    lyrics = lyrics[lyrics.index(marker):].strip()
                    break
    
    # One last effort to clean up - if the text is very long and contains obvious thinking
    # before getting to actual lyrics, try to find a clear starting point
    if len(lyrics.split()) > 100 and "\n\n" in lyrics:
        paragraphs = lyrics.split("\n\n")
        for i, paragraph in enumerate(paragraphs):
            # Look for typical song structure indicators in a paragraph
            if any(marker in paragraph for marker in ["[Verse", "[Chorus", "Verse 1", "Chorus:"]):
                lyrics = "\n\n".join(paragraphs[i:])
                break
    
    # Clean up any remaining thinking artifacts at the beginning
    lines = lyrics.split('\n')
    clean_lines = []
    lyrics_started = False
    
    for line in lines:
        # Skip initial commentary/thinking lines until we hit what looks like lyrics
        if not lyrics_started:
            if (line.strip().startswith('[') and ']' in line) or not any(thinking in line.lower() for thinking in ["i think", "let me", "maybe", "perhaps", "alternatively", "checking"]):
                lyrics_started = True
        
        if lyrics_started:
            clean_lines.append(line)
    
    # Only use the cleaning logic if we found some actual lyrics
    if clean_lines:
        lyrics = '\n'.join(clean_lines)
    
    # Special handling for second-level templates
    second_level_verification = None
    if song_structure and "second_level" in song_structure and song_structure["second_level"]:
        second_level_verification = song_structure["second_level"]["templates"]
    
    # Verify syllable counts with enhanced verification - pass second-level templates if available
    if templates_for_verification:
        # Convert any NumPy values to native types before verification - directly handle conversions
        # Simple conversion for basic templates (non-recursive)
        if isinstance(templates_for_verification, list):
            safe_templates = []
            for template in templates_for_verification:
                if isinstance(template, dict):
                    processed_template = {}
                    for k, v in template.items():
                        if isinstance(v, np.ndarray):
                            if v.size == 1:
                                processed_template[k] = float(v.item())
                            else:
                                processed_template[k] = [float(x) if isinstance(x, np.number) else x for x in v]
                        elif isinstance(v, np.number):
                            processed_template[k] = float(v)
                        else:
                            processed_template[k] = v
                    safe_templates.append(processed_template)
                else:
                    safe_templates.append(template)
        else:
            safe_templates = templates_for_verification
            
        verified_lyrics = verify_flexible_syllable_counts(lyrics, safe_templates, second_level_verification)
        
        # Check if significant issues were detected
        if "[Note: Potential rhythm mismatches" in verified_lyrics and "Detailed Alignment Analysis" in verified_lyrics:
            # Extract the original lyrics (before the notes section)
            original_lyrics = lyrics.split("[Note:")[0].strip()
            
            # Extract the analysis
            analysis = verified_lyrics.split("[Note:")[1]
            
            # If we have serious alignment issues, consider a refinement step
            if "stress misalignments" in analysis and len(templates_for_verification) > 0:
                # Add a refinement prompt with the specific analysis
                refinement_prompt = f"""
You need to fix rhythm issues in these lyrics. Here's the analysis of the problems:

{analysis}

Revise the lyrics to perfectly match the rhythm pattern while maintaining the theme.
Focus on fixing the stress misalignments by placing stressed syllables on STRONG beats.

Original lyrics:
{original_lyrics}

Improved lyrics with fixed rhythm:
"""
                # Format as a chat message for refinement
                refinement_messages = [
                    {"role": "user", "content": refinement_prompt}
                ]
                
                # Use standard template for refinement (no thinking mode needed)
                refinement_text = llm_tokenizer.apply_chat_template(
                    refinement_messages,
                    tokenize=False,
                    add_generation_prompt=True
                )
                
                try:
                    # Generate refined lyrics with more focus on rhythm alignment
                    refinement_inputs = llm_tokenizer([refinement_text], return_tensors="pt").to(llm_model.device)
                    
                    # Use stricter parameters for refinement
                    refinement_params = {
                        "do_sample": True,
                        "temperature": 0.4,  # Lower temperature for more precise refinement
                        "top_p": 0.9,
                        "repetition_penalty": 1.3,
                        "max_new_tokens": 1024
                    }
                    
                    refined_ids = llm_model.generate(
                        **refinement_inputs,
                        **refinement_params
                    )
                    
                    # Extract refined lyrics
                    refined_output_ids = refined_ids[0][len(refinement_inputs.input_ids[0]):].tolist()
                    refined_lyrics = llm_tokenizer.decode(refined_output_ids, skip_special_tokens=True).strip()
                    
                    # Verify the refined lyrics
                    refined_verified_lyrics = verify_flexible_syllable_counts(refined_lyrics, safe_templates, second_level_verification)
                    
                    # Only use refined lyrics if they're better (fewer notes)
                    if "[Note: Potential rhythm mismatches" not in refined_verified_lyrics:
                        lyrics = refined_lyrics
                    elif refined_verified_lyrics.count("misalignments") < verified_lyrics.count("misalignments"):
                        lyrics = refined_verified_lyrics
                    else:
                        lyrics = verified_lyrics
                except Exception as e:
                    print(f"Error in lyrics refinement: {str(e)}")
                    lyrics = verified_lyrics
            else:
                # Minor issues, just use the verification notes
                lyrics = verified_lyrics
        else:
            # No significant issues detected
            lyrics = verified_lyrics
    
    # Check if we have the [RHYTHM_ANALYSIS_SECTION] tag
    if "[RHYTHM_ANALYSIS_SECTION]" in lyrics:
        # Split at our custom marker
        parts = lyrics.split("[RHYTHM_ANALYSIS_SECTION]")
        clean_lyrics = parts[0].strip()
        rhythm_analysis = parts[1].strip()
        
        # Add our standard marker for compatibility with existing code
        lyrics = clean_lyrics + "\n\n[Note: Rhythm Analysis]\n" + rhythm_analysis
    
    # For backwards compatibility - if we have the old format, still handle it
    elif "[Note: Potential rhythm mismatches" in lyrics:
        # Keep it as is, the existing parsing code can handle this format
        pass
    else:
        # No analysis found, add a minimal one
        lyrics = lyrics + "\n\n[Note: Rhythm Analysis]\nNo rhythm issues detected. All syllables align well with the beat pattern."
    
    # Before returning, add syllable analysis and prompt template
    if isinstance(lyrics, str):
        # Extract clean lyrics and analysis
        if "[Note: Rhythm Analysis]" in lyrics:
            clean_lyrics = lyrics.split("[Note: Rhythm Analysis]")[0].strip()
            rhythm_analysis = lyrics.split("[Note: Rhythm Analysis]")[1]
        elif "[Note: Potential rhythm mismatches" in lyrics:
            clean_lyrics = lyrics.split("[Note:")[0].strip()
            rhythm_analysis = "[Note:" + lyrics.split("[Note:")[1]
        else:
            clean_lyrics = lyrics
            rhythm_analysis = "No rhythm analysis available"
        
        # Create syllable analysis
        syllable_analysis = "=== SYLLABLE ANALYSIS ===\n\n"
        if templates_for_verification:
            syllable_analysis += "Template Analysis:\n"
            for i, template in enumerate(templates_for_verification):
                if i < min(len(templates_for_verification), 30):  # Limit to 30 to avoid overwhelming output
                    syllable_analysis += f"Line {i+1}:\n"
                    if isinstance(template, dict):
                        if "syllable_template" in template:
                            syllable_analysis += f"  Template: {template['syllable_template']}\n"
                        if "syllable_count" in template:
                            syllable_analysis += f"  Expected syllables: {template['syllable_count']}\n"
                    elif isinstance(template, str):
                        syllable_analysis += f"  Template: {template}\n"
                    syllable_analysis += "\n"
            
            if len(templates_for_verification) > 30:
                syllable_analysis += f"... and {len(templates_for_verification) - 30} more lines\n\n"
        
        # Add second-level analysis if available
        if second_level_verification:
            syllable_analysis += "\nSecond-Level Template Analysis:\n"
            for i, template in enumerate(second_level_verification):
                if i < min(len(second_level_verification), 30):  # Limit to 30 seconds
                    syllable_analysis += f"Second {i+1}: {template}\n"
            
            if len(second_level_verification) > 30:
                syllable_analysis += f"... and {len(second_level_verification) - 30} more seconds\n"
                
        # Add structure visualization to syllable analysis
        syllable_analysis += "\n" + structure_visualization
        
        # Create prompt template
        prompt_template = "=== PROMPT TEMPLATE ===\n\n"
        prompt_template += "Genre: " + genre + "\n"
        prompt_template += f"Duration: {duration:.1f} seconds\n"
        prompt_template += f"Tempo: {tempo:.1f} BPM\n"
        prompt_template += f"Key: {key} {mode}\n"
        prompt_template += f"Primary Emotion: {primary_emotion}\n"
        prompt_template += f"Primary Theme: {primary_theme}\n\n"
        prompt_template += "Syllable Guidance:\n" + syllable_guidance_text
        
        # Return all components
        return {
            "lyrics": clean_lyrics,
            "rhythm_analysis": rhythm_analysis,
            "syllable_analysis": syllable_analysis,
            "prompt_template": prompt_template
        }
    
    return lyrics

def process_audio(audio_file):
    """Main function to process audio file, classify genre, and generate lyrics with enhanced rhythm analysis."""
    if audio_file is None:
        return "Please upload an audio file.", None, None
    
    try:
        print("Step 1/5: Extracting audio features...")
        # Extract audio features
        audio_data = extract_audio_features(audio_file)
        
        print("Step 2/5: Verifying audio contains music...")
        # First check if it's music
        try:
            is_music, ast_results = detect_music(audio_data)
        except Exception as e:
            print(f"Error in music detection: {str(e)}")
            return f"Error in music detection: {str(e)}", None, ast_results
            
        if not is_music:
            return "The uploaded audio does not appear to be music. Please upload a music file.", None, ast_results
        
        print("Step 3/5: Classifying music genre...")
        # Classify genre
        try:
            top_genres = classify_genre(audio_data)
            # Format genre results using utility function
            genre_results = format_genre_results(top_genres)
        except Exception as e:
            print(f"Error in genre classification: {str(e)}")
            return f"Error in genre classification: {str(e)}", None, ast_results
        
        # Initialize default values
        ast_results = ast_results if ast_results else []
        song_structure = None
        emotion_results = {
            "emotion_analysis": {"primary_emotion": "Unknown"},
            "theme_analysis": {"primary_theme": "Unknown"},
            "rhythm_analysis": {"tempo": 0},
            "tonal_analysis": {"key": "Unknown", "mode": ""},
            "summary": {"tempo": 0, "key": "Unknown", "mode": "", "primary_emotion": "Unknown", "primary_theme": "Unknown"}
        }
        
        print("Step 4/5: Analyzing music emotions, themes, and structure...")
        # Analyze music emotions and themes
        try:
            emotion_results = music_analyzer.analyze_music(audio_file)
        except Exception as e:
            print(f"Error in emotion analysis: {str(e)}")
            # Continue with default emotion_results
        
        # Calculate detailed song structure for better lyrics alignment
        try:
            # Load audio data
            y, sr = load_audio(audio_file, SAMPLE_RATE)
            
            # Analyze beats and phrases for music-aligned lyrics
            beats_info = detect_beats(y, sr)
            sections_info = detect_sections(y, sr)
            
            # Create structured segments for precise line-by-line matching
            segments = []
            
            # Try to break audio into meaningful segments based on sections
            # Each segment will correspond to one line of lyrics
            if sections_info and len(sections_info) > 1:
                min_segment_duration = 1.5  # Minimum 1.5 seconds per segment
                
                for section in sections_info:
                    section_start = section["start"]
                    section_end = section["end"]
                    section_duration = section["duration"]
                    
                    # For very short sections, add as a single segment
                    if section_duration < min_segment_duration * 1.5:
                        segments.append({
                            "start": section_start,
                            "end": section_end
                        })
                    else:
                        # Calculate ideal number of segments for this section
                        # based on its duration - aiming for 2-4 second segments
                        ideal_segment_duration = 3.0  # Target 3 seconds per segment
                        segment_count = max(1, int(section_duration / ideal_segment_duration))
                        
                        # Create evenly-spaced segments within this section
                        segment_duration = section_duration / segment_count
                        for i in range(segment_count):
                            segment_start = section_start + i * segment_duration
                            segment_end = segment_start + segment_duration
                            segments.append({
                                "start": segment_start,
                                "end": segment_end
                            })
            # If no good sections found, create segments based on beats
            elif beats_info and len(beats_info["beat_times"]) > 4:
                beats = beats_info["beat_times"]
                time_signature = beats_info.get("time_signature", 4)
                
                # Target one segment per musical measure (typically 4 beats)
                measure_size = time_signature
                for i in range(0, len(beats), measure_size):
                    if i + 1 < len(beats):  # Need at least 2 beats for a meaningful segment
                        measure_start = beats[i]
                        # If we have enough beats for the full measure
                        if i + measure_size < len(beats):
                            measure_end = beats[i + measure_size]
                        else:
                            # Use available beats and extrapolate for the last measure
                            if i > 0:
                                beat_interval = beats[i] - beats[i-1]
                                measure_end = beats[-1] + (beat_interval * (measure_size - (len(beats) - i)))
                            else:
                                measure_end = audio_data["duration"]
                        
                        segments.append({
                            "start": measure_start,
                            "end": measure_end
                        })
            # Last resort: simple time-based segments
            else:
                # Create segments of approximately 3 seconds each
                segment_duration = 3.0
                total_segments = max(4, int(audio_data["duration"] / segment_duration))
                segment_duration = audio_data["duration"] / total_segments
                
                for i in range(total_segments):
                    segment_start = i * segment_duration
                    segment_end = segment_start + segment_duration
                    segments.append({
                        "start": segment_start,
                        "end": segment_end
                    })
            
            # Create flexible structure with the segments
            flexible_structure = {
                "beats": beats_info,
                "segments": segments
            }
            
            # Create song structure object
            song_structure = {
                "beats": beats_info,
                "sections": sections_info,
                "flexible_structure": flexible_structure,
                "syllables": []
            }
            
            # Add syllable counts to each section
            for section in sections_info:
                # Create syllable templates for sections
                section_beats_info = {
                    "beat_times": [beat for beat in beats_info["beat_times"] 
                                  if section["start"] <= beat < section["end"]],
                    "tempo": beats_info.get("tempo", 120)
                }
                if "beat_strengths" in beats_info:
                    section_beats_info["beat_strengths"] = [
                        strength for i, strength in enumerate(beats_info["beat_strengths"])
                        if i < len(beats_info["beat_times"]) and
                        section["start"] <= beats_info["beat_times"][i] < section["end"]
                    ]
                
                # Get a syllable count based on section duration and tempo
                syllable_count = int(section["duration"] * (beats_info.get("tempo", 120) / 60) * 1.5)
                
                section_info = {
                    "type": section["type"],
                    "start": section["start"],
                    "end": section["end"],
                    "duration": section["duration"],
                    "syllable_count": syllable_count,
                    "beat_count": len(section_beats_info["beat_times"])
                }
                
                # Try to create a more detailed syllable template
                if len(section_beats_info["beat_times"]) >= 2:
                    section_info["syllable_template"] = create_flexible_syllable_templates(
                        section_beats_info,
                        genre=top_genres[0][0]
                    )
                
                song_structure["syllables"].append(section_info)
            
            # Add second-level beat analysis
            try:
                # Get enhanced beat information with subbeats
                subbeat_info = detect_beats_and_subbeats(y, sr, subdivision=4)
                
                # Map beats to second-level windows
                sec_map = map_beats_to_seconds(
                    subbeat_info["subbeat_times"], 
                    audio_data["duration"]
                )
                
                # Create second-level templates
                second_level_templates = create_second_level_templates(
                    sec_map,
                    subbeat_info["tempo"],
                    top_genres[0][0]  # Use top genre
                )
                
                # Add to song structure
                song_structure["second_level"] = {
                    "sec_map": sec_map,
                    "templates": second_level_templates
                }
                
            except Exception as e:
                print(f"Error in second-level beat analysis: {str(e)}")
                # Continue without second-level data
                
        except Exception as e:
            print(f"Error analyzing song structure: {str(e)}")
            # Continue without song structure
        
        print("Step 5/5: Generating rhythmically aligned lyrics...")
        # Generate lyrics based on top genre, emotion analysis, and song structure
        try:
            primary_genre, _ = top_genres[0]
            lyrics_result = generate_lyrics(primary_genre, audio_data["duration"], emotion_results, song_structure)
            
            # Handle both old and new return formats
            if isinstance(lyrics_result, dict):
                lyrics = lyrics_result["lyrics"]
                rhythm_analysis = lyrics_result["rhythm_analysis"]
                syllable_analysis = lyrics_result["syllable_analysis"]
                prompt_template = lyrics_result["prompt_template"]
            else:
                lyrics = lyrics_result
                rhythm_analysis = "No detailed rhythm analysis available"
                syllable_analysis = "No syllable analysis available"
                prompt_template = "No prompt template available"
                
        except Exception as e:
            print(f"Error generating lyrics: {str(e)}")
            lyrics = f"Error generating lyrics: {str(e)}"
            rhythm_analysis = "No rhythm analysis available"
            syllable_analysis = "No syllable analysis available"
            prompt_template = "No prompt template available"
        
        # Prepare results dictionary with additional rhythm analysis
        results = {
            "genre_results": genre_results,
            "lyrics": lyrics,
            "rhythm_analysis": rhythm_analysis,
            "syllable_analysis": syllable_analysis,
            "prompt_template": prompt_template,
            "ast_results": ast_results
        }
        
        return results
    
    except Exception as e:
        error_msg = f"Error processing audio: {str(e)}"
        print(error_msg)
        return error_msg, None, []

def format_complete_beat_timeline(audio_file, lyrics=None):
    """Creates a complete formatted timeline showing all beat timings and their syllable patterns without truncation"""
    if audio_file is None:
        return "Please upload an audio file to see beat timeline."
    
    try:
        # Extract audio data
        y, sr = load_audio(audio_file, SAMPLE_RATE)
        
        # Get beat information
        beats_info = detect_beats(y, sr)
        
    def ensure_float(value):
        if isinstance(value, np.ndarray) or isinstance(v, np.number):  # Should be 'value', not 'v'
            return float(value)
        return value
        
        # Format the timeline
        timeline = "=== BEAT & SYLLABLE TIMELINE ===\n\n"
        # Convert tempo to float before formatting if it's a numpy array
        tempo = ensure_float(beats_info['tempo'])
        timeline += f"Tempo: {tempo:.1f} BPM\n"
        timeline += f"Time Signature: {beats_info['time_signature']}/4\n"
        timeline += f"Total Beats: {beats_info['beat_count']}\n\n"
        
        # Create a table header
        timeline += "| Beat # | Time (s) | Beat Strength | Syllable Pattern |\n"
        timeline += "|--------|----------|--------------|------------------|\n"
        
        # Add beat-by-beat information - show ALL beats
        for i, (time, strength) in enumerate(zip(beats_info['beat_times'], beats_info['beat_strengths'])):
            # Convert numpy values to Python float if needed
            time = ensure_float(time)
            strength = ensure_float(strength)
            
            # Determine beat type based on strength
            if strength >= 0.8:
                beat_type = "STRONG"
            elif strength >= 0.5:
                beat_type = "medium"
            else:
                beat_type = "weak"
                
            # Create beat pattern indicator
            if i % beats_info['time_signature'] == 0:
                pattern = "S"  # Strong beat at start of measure
            elif i % beats_info['time_signature'] == beats_info['time_signature'] // 2 and beats_info['time_signature'] > 3:
                pattern = "m"  # Medium beat (3rd beat in 4/4)
            else:
                pattern = "w"  # Weak beat
                
            # Add row to table
            timeline += f"| {i+1:<6} | {time:.2f}s | {beat_type:<12} | {pattern}:{1.5 if pattern=='S' else 1.0} |\n"
            
            # No truncation - show all beats
        
        # Add a visual timeline of beats
        timeline += "\n=== VISUAL BEAT TIMELINE ===\n\n"
        timeline += "Each character represents 0.5 seconds. Beats are marked as:\n"
        timeline += "S = Strong beat | m = Medium beat | w = Weak beat | · = No beat\n\n"
        
        # Calculate total duration and create time markers
        if 'beat_times' in beats_info and len(beats_info['beat_times']) > 0:
            # Get the max value safely
            max_beat_time = max([ensure_float(t) for t in beats_info['beat_times']])
            total_duration = max_beat_time + 2  # Add 2 seconds of padding
        else:
            total_duration = 30  # Default duration if no beats found
        
        time_markers = ""
        for i in range(0, int(total_duration) + 1, 5):
            time_markers += f"{i:<5}"
        timeline += time_markers + " (seconds)\n"
        
        # Create a ruler for easier time tracking
        ruler = ""
        for i in range(0, int(total_duration) + 1):
            if i % 5 == 0:
                ruler += "+"
            else:
                ruler += "-"
            ruler += "-" * 9  # Each second is 10 characters wide
        timeline += ruler + "\n"
        
        # Create a visualization of beats with symbols
        beat_line = ["·"] * int(total_duration * 2)  # 2 characters per second
        
        for i, time in enumerate(beats_info['beat_times']):
            if i >= len(beats_info['beat_strengths']):
                break
                
            # Convert to float if it's a numpy array
            time_val = ensure_float(time)
            
            # Determine position in the timeline
            pos = int(time_val * 2)  # Convert to position in the beat_line
            if pos >= len(beat_line):
                continue
                
            # Determine beat type based on strength and position
            strength = beats_info['beat_strengths'][i]
            # Convert to float if it's a numpy array
            strength = ensure_float(strength)
            
            if i % beats_info['time_signature'] == 0:
                beat_line[pos] = "S"  # Strong beat at start of measure
            elif strength >= 0.8:
                beat_line[pos] = "S"  # Strong beat
            elif i % beats_info['time_signature'] == beats_info['time_signature'] // 2 and beats_info['time_signature'] > 3:
                beat_line[pos] = "m"  # Medium beat (3rd beat in 4/4)
            elif strength >= 0.5:
                beat_line[pos] = "m"  # Medium beat
            else:
                beat_line[pos] = "w"  # Weak beat
        
        # Format and add to timeline
        beat_visualization = ""
        for i in range(0, len(beat_line), 10):
            beat_visualization += "".join(beat_line[i:i+10])
            if i + 10 < len(beat_line):
                beat_visualization += " "  # Add space every 5 seconds
        timeline += beat_visualization + "\n\n"
        
        # Add measure markers
        timeline += "=== MEASURE MARKERS ===\n\n"
        
        # Create a list to track measure start times
        measure_starts = []
        for i, time in enumerate(beats_info['beat_times']):
            if i % beats_info['time_signature'] == 0:  # Start of measure
                # Convert to float if it's a numpy array
                time_val = ensure_float(time)
                measure_starts.append((i // beats_info['time_signature'] + 1, time_val))
        
        # Format measure information
        if measure_starts:
            timeline += "| Measure # | Start Time | Duration |\n"
            timeline += "|-----------|------------|----------|\n"
            
            for i in range(len(measure_starts)):
                measure_num, start_time = measure_starts[i]
                
                # Calculate end time (start of next measure or end of song)
                if i < len(measure_starts) - 1:
                    end_time = measure_starts[i+1][1]
                elif 'beat_times' in beats_info and len(beats_info['beat_times']) > 0:
                    # Get the last beat time and convert to float if needed
                    last_beat = beats_info['beat_times'][-1]
                    end_time = ensure_float(last_beat)
                else:
                    end_time = start_time + 2.0  # Default 2 seconds if no next measure
                    
                duration = end_time - start_time
                
                timeline += f"| {measure_num:<9} | {start_time:.2f}s | {duration:.2f}s |\n"
                
                # No truncation - show all measures
        
        # Add phrase information
        if 'phrases' in beats_info and beats_info['phrases']:
            timeline += "\n=== MUSICAL PHRASES ===\n\n"
            for i, phrase in enumerate(beats_info['phrases']):
                # Show all phrases, not just the first 10
                if not phrase:
                    continue
                
                # Safely check phrase indices
                if not (len(phrase) > 0 and len(beats_info['beat_times']) > 0):
                    continue
                    
                start_beat = min(phrase[0], len(beats_info['beat_times'])-1)
                end_beat = min(phrase[-1], len(beats_info['beat_times'])-1)
                
                # Convert to float if needed
                phrase_start = ensure_float(beats_info['beat_times'][start_beat])
                phrase_end = ensure_float(beats_info['beat_times'][end_beat])
                
                timeline += f"Phrase {i+1}: Beats {start_beat+1}-{end_beat+1} ({phrase_start:.2f}s - {phrase_end:.2f}s)\n"
                
                # Create syllable template for this phrase with simplified numpy handling
                phrase_beats = {
                    "beat_times": [ensure_float(beats_info['beat_times'][j]) 
                                  for j in phrase if j < len(beats_info['beat_times'])],
                    "beat_strengths": [ensure_float(beats_info['beat_strengths'][j])
                                      for j in phrase if j < len(beats_info['beat_strengths'])],
                    "tempo": ensure_float(beats_info['tempo']),
                    "time_signature": beats_info['time_signature'],
                    "phrases": [list(range(len(phrase)))]
                }
                
                template = create_flexible_syllable_templates(phrase_beats)
                timeline += f"  Syllable Template: {template}\n"
                
                # Create a visual representation of this phrase
                if phrase_start < total_duration and phrase_end < total_duration:
                    # Create a timeline for this phrase
                    phrase_visualization = ["·"] * int(total_duration * 2)
                    
                    # Mark the phrase boundaries
                    start_pos = int(phrase_start * 2)
                    end_pos = int(phrase_end * 2)
                    
                    if start_pos < len(phrase_visualization):
                        phrase_visualization[start_pos] = "["
                    
                    if end_pos < len(phrase_visualization):
                        phrase_visualization[end_pos] = "]"
                    
                    # Mark the beats in this phrase
                    for j in phrase:
                        if j < len(beats_info['beat_times']):
                            beat_time = ensure_float(beats_info['beat_times'][j])
                            beat_pos = int(beat_time * 2)
                            
                            if beat_pos < len(phrase_visualization) and beat_pos != start_pos and beat_pos != end_pos:
                                # Determine beat type
                                if j % beats_info['time_signature'] == 0:
                                    phrase_visualization[beat_pos] = "S"
                                elif j % beats_info['time_signature'] == beats_info['time_signature'] // 2:
                                    phrase_visualization[beat_pos] = "m"
                                else:
                                    phrase_visualization[beat_pos] = "w"
                    
                    # Format and add visualization
                    phrase_visual = ""
                    for k in range(0, len(phrase_visualization), 10):
                        phrase_visual += "".join(phrase_visualization[k:k+10])
                        if k + 10 < len(phrase_visualization):
                            phrase_visual += " "
                            
                    timeline += f"  Timeline: {phrase_visual}\n\n"
        
        # Add second-level script display
        try:
            # Get second-level beat information
            subbeat_info = detect_beats_and_subbeats(y, sr, subdivision=4)
            duration = librosa.get_duration(y=y, sr=sr)
            
            # Map to seconds
            sec_map = map_beats_to_seconds(subbeat_info["subbeat_times"], duration)
            
            # Create templates
            templates = create_second_level_templates(sec_map, subbeat_info["tempo"])
            
            # Add to timeline
            timeline += "\n=== SECOND-LEVEL SCRIPT ===\n\n"
            timeline += "Each line below represents ONE SECOND of audio with matching lyric content.\n"
            timeline += "| Second | Beat Pattern | Lyric Content |\n"
            timeline += "|--------|-------------|---------------|\n"
            
            # Get clean lyrics (without analysis notes)
            clean_lyrics = lyrics
            if isinstance(lyrics, str):
                if "[Note: Rhythm Analysis]" in lyrics:
                    clean_lyrics = lyrics.split("[Note: Rhythm Analysis]")[0].strip()
                elif "[Note: Potential rhythm mismatches" in lyrics:
                    clean_lyrics = lyrics.split("[Note:")[0].strip()
            
            # Get lyric lines
            lines = clean_lyrics.strip().split('\n') if clean_lyrics else []
            
            for i, template in enumerate(templates):
                # Get corresponding lyric line if available
                lyric = lines[i] if i < len(lines) else ""
                if lyric.startswith('[') and ']' in lyric:
                    lyric = ""  # Skip section headers
                
                # Format nicely for display
                timeline += f"| {i+1:<6} | {template:<30} | {lyric[:40]} |\n"
            
            # Add ASCII visualization of second-level beats
            timeline += "\n=== SECOND-LEVEL VISUALIZATION ===\n\n"
            timeline += "Each row represents ONE SECOND. Beat types:\n"
            timeline += "S = Strong beat | m = Medium beat | w = Weak beat | · = No beat\n\n"
            
            for i, window in enumerate(sec_map):
                beats = window["beats"]
                
                # Create ASCII visualization
                beat_viz = ["·"] * 20  # 20 columns for visualization
                
                for beat in beats:
                    # Calculate position in visualization
                    pos = int(beat["relative_pos"] * 19)  # Map 0-1 to 0-19
                    if 0 <= pos < len(beat_viz):
                        # Set marker based on beat type
                        if beat["type"] == "main":
                            beat_viz[pos] = "S"
                        elif beat["strength"] >= 0.7:
                            beat_viz[pos] = "m"
                        else:
                            beat_viz[pos] = "w"
                
                # Get corresponding lyric
                lyric = lines[i] if i < len(lines) else ""
                if lyric.startswith('[') and ']' in lyric:
                    lyric = ""
                
                # Format visualization line
                viz_line = f"Second {i+1:2d}: [" + "".join(beat_viz) + "]"
                if lyric:
                    viz_line += f" → {lyric[:40]}"
                
                timeline += viz_line + "\n"
                
        except Exception as e:
            timeline += f"\n[Error generating second-level analysis: {str(e)}]"
        
        # Add a section showing alignment if lyrics were generated
        if lyrics and isinstance(lyrics, str):
            timeline += "\n=== LYRICS-BEAT ALIGNMENT ===\n\n"
            # Remove rhythm analysis notes from lyrics if present
            if "[Note:" in lyrics:
                clean_lyrics = lyrics.split("[Note:")[0].strip()
            else:
                clean_lyrics = lyrics
                
            lines = clean_lyrics.strip().split('\n')
            
            # Show alignment for ALL lines, not just the first 10
            for i, line in enumerate(lines):
                if not line.strip() or line.startswith('['):
                    continue
                    
                timeline += f"Line: \"{line}\"\n"
                
                # Count syllables
                syllable_count = count_syllables(line)
                timeline += f"  Syllables: {syllable_count}\n"
                
                # Show ideal timing (if we have enough phrases)
                if 'phrases' in beats_info and beats_info['phrases'] and i < len(beats_info['phrases']):
                    phrase = beats_info['phrases'][i]
                    # Safely check if phrase has elements and indices are valid
                    if phrase and len(phrase) > 0 and len(beats_info['beat_times']) > 0:
                        start_beat = min(phrase[0], len(beats_info['beat_times'])-1)
                        end_beat = min(phrase[-1], len(beats_info['beat_times'])-1)
                        
                        start_time = ensure_float(beats_info['beat_times'][start_beat])
                        end_time = ensure_float(beats_info['beat_times'][end_beat])
                        
                        timeline += f"  Timing: {start_time:.2f}s - {end_time:.2f}s\n"
                        
                        # Create a visualization of syllable alignment
                        timeline += "  Alignment: "
                        
                        # Create a timeline focused on just this phrase
                        phrase_duration = end_time - start_time
                        syllable_viz = []
                        
                        # Initialize with beat markers for this phrase
                        for j in phrase:
                            if j < len(beats_info['beat_times']):
                                beat_time = ensure_float(beats_info['beat_times'][j])
                                # Handle edge case where phrase_duration is very small
                                if phrase_duration > 0.001:  # Avoid division by very small numbers
                                    relative_pos = int((beat_time - start_time) / phrase_duration * syllable_count)
                                else:
                                    relative_pos = 0
                                
                                while len(syllable_viz) <= relative_pos:
                                    syllable_viz.append("·")
                                
                                if j % beats_info['time_signature'] == 0:
                                    syllable_viz[relative_pos] = "S"
                                elif j % beats_info['time_signature'] == beats_info['time_signature'] // 2:
                                    syllable_viz[relative_pos] = "m"
                                else:
                                    syllable_viz[relative_pos] = "w"
                        
                        # Fill in any gaps
                        while len(syllable_viz) < syllable_count:
                            syllable_viz.append("·")
                            
                        # Trim if too long
                        syllable_viz = syllable_viz[:syllable_count]
                        
                        # Now map to the line
                        timeline += "".join(syllable_viz) + "\n"
                
                timeline += "\n"
            
            # No truncation message for lines
                
        return timeline
        
    except Exception as e:
        print(f"Error generating complete beat timeline: {str(e)}")
        return f"Error generating complete beat timeline: {str(e)}"

def display_results(audio_file):
    """Process audio file and return formatted results for display in the UI."""
    # Default error response
    error_response = ("Please upload an audio file.", 
                     "No emotion analysis available.", 
                     "No audio classification available.", 
                     "No lyrics generated.", 
                     "No beat timeline available.")
    
    if audio_file is None:
        return error_response
    
    try:
        # Process audio and get results
        results = process_audio(audio_file)
        
        # Check if we got an error message
        if isinstance(results, str) and "Error" in results:
            return results, *error_response[1:]
        elif isinstance(results, tuple) and isinstance(results[0], str) and "Error" in results[0]:
            return results[0], *error_response[1:]
        
        # Extract results
        if isinstance(results, dict):
            # New format
            genre_results = results.get("genre_results", "Genre classification failed")
            lyrics = results.get("lyrics", "Lyrics generation failed")
            ast_results = results.get("ast_results", [])
        else:
            # Old tuple format
            genre_results, lyrics, ast_results = results
        
        # Get clean lyrics (without analysis notes)
        clean_lyrics = lyrics
        if isinstance(lyrics, str):
            if "[Note: Rhythm Analysis]" in lyrics:
                clean_lyrics = lyrics.split("[Note: Rhythm Analysis]")[0].strip()
            elif "[Note: Potential rhythm mismatches" in lyrics:
                clean_lyrics = lyrics.split("[Note:")[0].strip()
        
        # Generate beat timeline - use the complete timeline function that shows all beats
        beat_timeline = format_complete_beat_timeline(audio_file, clean_lyrics)
        
        # Format emotion analysis results
        emotion_text = "No emotion analysis available."
        try:
            emotion_results = music_analyzer.analyze_music(audio_file)
            emotion_text = (f"Tempo: {emotion_results['summary']['tempo']:.1f} BPM\n"
                           f"Key: {emotion_results['summary']['key']} {emotion_results['summary']['mode']}\n"
                           f"Primary Emotion: {emotion_results['summary']['primary_emotion']}\n"
                           f"Primary Theme: {emotion_results['summary']['primary_theme']}")
            
            # Add song structure if available (without nested try/except)
            y, sr = load_audio(audio_file, SAMPLE_RATE)
            beats_info = detect_beats(y, sr)
            sections_info = detect_sections(y, sr)
            
            if sections_info:
                emotion_text += "\n\nSong Structure:\n"
                for section in sections_info:
                    emotion_text += (f"- {section['type'].capitalize()}: {section['start']:.1f}s to {section['end']:.1f}s "
                                   f"({section['duration']:.1f}s)\n")
        except Exception as e:
            print(f"Error in emotion analysis: {str(e)}")
        
        # Format audio classification results
        ast_text = "No valid audio classification results available."
        if ast_results and isinstance(ast_results, list):
            ast_text = "Audio Classification Results:\n"
            for result in ast_results[:5]:  # Show top 5 results
                ast_text += f"{result['label']}: {result['score']*100:.2f}%\n"
        
        # Return all results
        return genre_results, emotion_text, ast_text, clean_lyrics, beat_timeline
        
    except Exception as e:
        error_msg = f"Error: {str(e)}"
        print(error_msg)
        return error_msg, *error_response[1:]

# Create enhanced Gradio interface with tabs for better organization
with gr.Blocks(title="Music Genre Classifier & Lyrics Generator") as demo:
    gr.Markdown("# Music Genre Classifier & Lyrics Generator")
    gr.Markdown("Upload a music file to classify its genre, analyze its emotions, and generate perfectly aligned lyrics.")
    
    with gr.Row():
        with gr.Column(scale=1):
            audio_input = gr.Audio(label="Upload Music", type="filepath")
            submit_btn = gr.Button("Analyze & Generate", variant="primary")
            
            # Add genre info box 
            with gr.Accordion("About Music Genres", open=False):
                gr.Markdown("""
                The system recognizes various music genres including:
                - Pop, Rock, Hip-Hop, R&B
                - Electronic, Dance, Techno, House
                - Jazz, Blues, Classical
                - Folk, Country, Acoustic
                - Metal, Punk, Alternative
                - And many others!
                
                For best results, use high-quality audio files (MP3, WAV, FLAC) with at least 10 seconds of music.
                """)
        
        with gr.Column(scale=2):
            # Use tabs for better organization of outputs
            with gr.Tabs():
                with gr.TabItem("Analysis Results"):
                    genre_output = gr.Textbox(label="Detected Genres", lines=4)
                    
                    # Create 2 columns for emotion and audio classification
                    with gr.Row():
                        with gr.Column():
                            emotion_output = gr.Textbox(label="Emotion & Structure Analysis", lines=8)
                        with gr.Column():
                            ast_output = gr.Textbox(label="Audio Classification", lines=8)
                
                with gr.TabItem("Generated Lyrics"):
                    lyrics_output = gr.Textbox(label="Lyrics", lines=18)
                
                with gr.TabItem("Beat & Syllable Timeline"):
                    beat_timeline_output = gr.Textbox(label="Beat Timings & Syllable Patterns", lines=40)
    
    # Connect the button to the display function with updated outputs
    submit_btn.click(
        fn=display_results,
        inputs=[audio_input],
        outputs=[genre_output, emotion_output, ast_output, lyrics_output, beat_timeline_output]
    )
    
    # Enhanced explanation of how the system works
    with gr.Accordion("How it works", open=False):
        gr.Markdown("""
        ## Advanced Lyrics Generation Process
        
        1. **Audio Analysis**: The system analyzes your uploaded music file using multiple machine learning models.
        
        2. **Genre Classification**: A specialized neural network identifies the musical genre, detecting subtle patterns in the audio.
        
        3. **Emotional Analysis**: The system examines harmonic, rhythmic, and timbral features to determine the emotional qualities of the music.
        
        4. **Rhythm Mapping**: Advanced beat detection algorithms create a detailed rhythmic map of the music, identifying:
           - Strong and weak beats
           - Natural phrase boundaries
           - Time signature and tempo variations
           - Beat subdivisions (half and quarter beats)
        
        5. **Second-Level Alignment**: The system maps beats and subbeats to each second of audio, creating precise templates for perfect alignment.
        
        6. **Syllable Template Creation**: For each second of audio, the system generates precise syllable templates that reflect:
           - Beat stress patterns (strong, medium, weak)
           - Appropriate syllable counts based on tempo
           - Genre-specific rhythmic qualities
           - Half-beat and quarter-beat subdivisions
        
        7. **Lyrics Generation**: Using the detected genre, emotion, and rhythm patterns, a large language model generates lyrics that:
           - Match the emotional quality of the music
           - Follow the precise syllable templates for each second
           - Align stressed syllables with strong beats
           - Maintain genre-appropriate style and themes
        
        8. **Rhythm Verification**: The system verifies the generated lyrics, analyzing:
           - Syllable count accuracy
           - Stress alignment with strong beats
           - Word stress patterns
           - Second-by-second alignment precision
           
        9. **Refinement**: If significant rhythm mismatches are detected, the system can automatically refine the lyrics for better alignment.
        
        This multi-step process creates lyrics that feel naturally connected to the music, as if they were written specifically for it.
        """)

# Launch the app
demo.launch()