app.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
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,
    calculate_lyrics_length,
    format_genre_results,
    ensure_cuda_availability,
    preprocess_audio_for_model
)
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 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:
        # Get pronunciations for the word
        pronunciations = pronouncing.phones_for_word(word)
        if pronunciations:
            # Count syllables in the first pronunciation
            syllable_count += pronouncing.syllable_count(pronunciations[0])
        else:
            # Fallback: estimate syllables based on vowel groups
            vowels = "aeiouy"
            count = 0
            prev_is_vowel = False
            
            for char in word:
                is_vowel = char.lower() in vowels
                if is_vowel and not prev_is_vowel:
                    count += 1
                prev_is_vowel = is_vowel
                
            if word.endswith('e'):
                count -= 1
            if word.endswith('le') and len(word) > 2 and word[-3] not in vowels:
                count += 1
            if count == 0:
                count = 1
                
            syllable_count += count
    
    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
        std_bpm=20     # Allow wider variations
    )
    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_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
    
    # 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):
    """
    Enhanced verification of syllable counts and stress patterns with precise alignment analysis
    and detailed feedback for all phrases in a template.
    """
    import re
    import pronouncing
    import numpy as np
    import functools
    from itertools import chain
    
    # Apply caching to improve performance for repeated word lookups
    @functools.lru_cache(maxsize=512)
    def cached_phones_for_word(word):
        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)
    
    # 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")
    
    # 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
    """
    # 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 = []
    
    if 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"]
                
                # Process each segment to create enhanced rhythmic templates
                enhanced_templates = []
                
                for i, segment in enumerate(segments):
                    if i < 15:  # Limit to 15 lines to keep prompt manageable
                        # Get the beat information for this segment
                        segment_start = segment["start"]
                        segment_end = segment["end"]
                        
                        # 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)
                
                # Format templates with improved formatting
                syllable_guidance = "CRITICAL RHYTHM INSTRUCTIONS:\n"
                syllable_guidance += "Match each line exactly to this rhythm pattern (STRONG beats need stressed syllables):\n\n"
                syllable_guidance += format_syllable_templates_for_prompt(
                    enhanced_templates,
                    arrow="→",
                    line_wrap=8
                )
                
                # Note: The enhanced formatter now automatically includes explanations
        
        # 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:\n\n"
            
            for section in song_structure["syllables"]:
                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"
    
    # 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"
    
    # 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
    
    # Determine if we should use traditional sections or not
    use_sections = True
    if 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
    
    # Calculate appropriate lyrics length and section distribution
    try:
        if song_structure and "beats" in song_structure:
            beats_info = song_structure["beats"]
            tempo = beats_info.get("tempo", 120)
            time_signature = beats_info.get("time_signature", 4)
            lines_structure = calculate_lyrics_length(duration, tempo, time_signature)
            
            # Handle both possible return types
            if isinstance(lines_structure, dict):
                total_lines = lines_structure["lines_count"]
                
                # Extract section line counts if available
                verse_lines = 0
                chorus_lines = 0
                bridge_lines = 0
                
                for section in lines_structure["sections"]:
                    if section["type"] == "verse":
                        verse_lines = section["lines"]
                    elif section["type"] == "chorus":
                        chorus_lines = section["lines"]
                    elif section["type"] == "bridge":
                        bridge_lines = section["lines"]
            else:
                # The function returned just an integer (old behavior)
                total_lines = lines_structure
                
                # Default section distribution based on total lines
                if total_lines <= 6:
                    verse_lines = 2
                    chorus_lines = 2
                    bridge_lines = 0
                elif total_lines <= 10:
                    verse_lines = 3
                    chorus_lines = 2
                    bridge_lines = 0
                else:
                    verse_lines = 3
                    chorus_lines = 2
                    bridge_lines = 2
        else:
            # Fallback to simple calculation
            total_lines = max(4, int(duration / 10))
            
            # Default section distribution
            if total_lines <= 6:
                verse_lines = 2
                chorus_lines = 2
                bridge_lines = 0
            elif total_lines <= 10:
                verse_lines = 3
                chorus_lines = 2
                bridge_lines = 0
            else:
                verse_lines = 3
                chorus_lines = 2
                bridge_lines = 2
    except Exception as e:
        print(f"Error calculating lyrics length: {str(e)}")
        total_lines = max(4, int(duration / 10))
        
        # Default section distribution
        verse_lines = 3
        chorus_lines = 2
        bridge_lines = 0
    
    # Create enhanced prompt with better rhythm alignment instructions
    if 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.

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

Think step by step about how to match words to the rhythm pattern:
1. First, identify the strong beats in each line pattern
2. Choose words where stressed syllables naturally fall on strong beats
3. Count syllables carefully to ensure they match the pattern precisely
4. Test your line against the pattern by mapping each syllable

The lyrics should:
- Perfectly capture the essence and style of {genre} music
- Express the {primary_emotion} emotion and {primary_theme} theme
- Be approximately {total_lines} lines long
- Follow this structure:
  * Verse: {verse_lines} lines
  * Chorus: {chorus_lines} lines
  * {f'Bridge: {bridge_lines} lines' if bridge_lines > 0 else ''}
- Be completely original
- Match the song duration of {duration:.1f} seconds

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.

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

Think step by step about how to match words to the rhythm pattern:
1. First, identify the strong beats in each line pattern
2. Choose words where stressed syllables naturally fall on strong beats 
3. Count syllables carefully to ensure they match the pattern precisely
4. Test your line against the pattern by mapping each syllable

For perfect alignment examples:
- "FEEL the RHY-thm in your SOUL" – stressed syllables on strong beats
- "to-DAY we DANCE a-LONG" – natural speech stress matches musical stress
- "WAIT-ing FOR the SUN to RISE" – syllable emphasis aligns with beat emphasis

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

DON'T include any section labels like [Verse] or [Chorus] unless specifically instructed.
Instead, write lyrics that flow naturally and match the music's rhythm precisely.

Your lyrics:
"""

    # Format as a chat message for the LLM
    messages = [
        {"role": "user", "content": content}
    ]
    
    # Apply chat template with thinking enabled
    try:
        # Try using the model-specific template with thinking enabled
        text = llm_tokenizer.apply_chat_template(
            messages,
            tokenize=False,
            add_generation_prompt=True,
            enable_thinking=True  # Only works with models that support thinking mode
        )
    except Exception as e:
        # Fallback to standard template if thinking mode not supported
        print(f"Thinking mode not supported, using standard template: {str(e)}")
        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": 0.6,  # Lower for more consistent rhythm alignment
        "top_p": 0.95,
        "top_k": 20,
        "repetition_penalty": 1.2,
        "max_new_tokens": 1024  # Allow more tokens for comprehensive lyrics
    }
    
    # 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()
    
    # Try to find </think> token to separate thinking from final answer if the model supports it
    try:
        # Look for thinking mode tokens - check model-specific token IDs
        # For Qwen3, the </think> token ID is 151668
        think_end_tokens = {
            "qwen": 151668,  # Qwen </think> token
            "claude": 42,    # Example for Claude (placeholder)
            "llama": 128001  # Example for Llama (placeholder)
        }
        
        # Try to find a known token
        found_token = None
        token_position = 0
        
        for model_name, token_id in think_end_tokens.items():
            if token_id in output_ids:
                found_token = token_id
                token_position = len(output_ids) - output_ids[::-1].index(token_id)
                break
                
        # Use the position of the thinking token if found
        if found_token:
            lyrics = llm_tokenizer.decode(output_ids[token_position:], skip_special_tokens=True).strip()
        else:
            lyrics = llm_tokenizer.decode(output_ids, skip_special_tokens=True).strip()
    except (ValueError, IndexError, AttributeError) as e:
        print(f"Error processing thinking output: {str(e)}")
        # Default behavior if thinking mode processing fails
        lyrics = llm_tokenizer.decode(output_ids, skip_special_tokens=True).strip()
    
    # Verify syllable counts with enhanced verification
    if templates_for_verification:
        verified_lyrics = verify_flexible_syllable_counts(lyrics, templates_for_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, templates_for_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
    
    # Add section labels if they're not present and we're using the traditional approach
    if use_sections and "Verse" not in lyrics and "Chorus" not in lyrics:
        lines = lyrics.split('\n')
        formatted_lyrics = []
        
        line_count = 0
        for i, line in enumerate(lines):
            if not line.strip():
                formatted_lyrics.append(line)
                continue
                
            if line_count == 0:
                formatted_lyrics.append("[Verse]")
            elif line_count == verse_lines:
                formatted_lyrics.append("\n[Chorus]")
            elif line_count == verse_lines + chorus_lines and bridge_lines > 0:
                formatted_lyrics.append("\n[Bridge]")
                
            formatted_lyrics.append(line)
            line_count += 1
            
        lyrics = '\n'.join(formatted_lyrics)
    
    # Clean up the output if there are analytical notes
    if "[Note: Potential rhythm mismatches" in lyrics and "[How to fix rhythm mismatches" in lyrics:
        # Optionally separate the analysis from the final lyrics for cleaner display
        clean_lyrics = lyrics.split("[Note:")[0].strip()
        analysis_notes = lyrics.split("[Note:")[1]
        
        # For now, keep the full output with notes for debugging
        # In a production system, you might want to handle this differently
        lyrics = lyrics
    
    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
        
        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 even if emotion analysis fails
            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"}
            }
        
        # Calculate detailed song structure for better lyrics alignment
        try:
            song_structure = calculate_detailed_song_structure(audio_data)
        except Exception as e:
            print(f"Error analyzing song structure: {str(e)}")
            # Continue with a simpler approach if this fails
            song_structure = None
        
        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 = generate_lyrics(primary_genre, audio_data["duration"], emotion_results, song_structure)
        except Exception as e:
            print(f"Error generating lyrics: {str(e)}")
            lyrics = f"Error generating lyrics: {str(e)}"
        
        # Prepare results dictionary with additional rhythm analysis
        results = {
            "genre_results": genre_results,
            "lyrics": lyrics,
            "ast_results": ast_results
        }
        
        # Extract rhythm analysis if present in the lyrics
        if isinstance(lyrics, str) and "[Note: Potential rhythm mismatches" in lyrics:
            clean_lyrics = lyrics.split("[Note:")[0].strip()
            rhythm_analysis = "[Note:" + lyrics.split("[Note:")[1]
            results["clean_lyrics"] = clean_lyrics
            results["rhythm_analysis"] = rhythm_analysis
        
        return results
    
    except Exception as e:
        error_msg = f"Error processing audio: {str(e)}"
        print(error_msg)
        return error_msg, None, []

# 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("Rhythm Analysis"):
                    rhythm_analysis_output = gr.Textbox(label="Syllable-Beat Alignment Analysis", lines=16)
    
    # Processing function with better handling of results
    def display_results(audio_file):
        if audio_file is None:
            return "Please upload an audio file.", "No emotion analysis available.", "No audio classification available.", "No lyrics generated.", "No rhythm analysis available."
        
        try:
            # Process audio and get results
            results = process_audio(audio_file)
            
            # Check if we got an error message instead of results
            if isinstance(results, str) and "Error" in results:
                return results, "Error in analysis", "Error in classification", "No lyrics generated", "No rhythm analysis available"
            elif isinstance(results, tuple) and isinstance(results[0], str) and "Error" in results[0]:
                return results[0], "Error in analysis", "Error in classification", "No lyrics generated", "No rhythm analysis available"
            
            # For backwards compatibility, handle both dictionary and tuple returns
            if isinstance(results, dict):
                genre_results = results.get("genre_results", "Genre classification failed")
                lyrics = results.get("lyrics", "Lyrics generation failed")
                ast_results = results.get("ast_results", [])
                
                # Use clean lyrics if available
                clean_lyrics = results.get("clean_lyrics", lyrics)
                rhythm_analysis = results.get("rhythm_analysis", "No detailed rhythm analysis available")
            else:
                # Handle the old tuple return format
                genre_results, lyrics, ast_results = results
                clean_lyrics = lyrics
                
                # Extract rhythm analysis if present
                rhythm_analysis = "No detailed rhythm analysis available"
                if isinstance(lyrics, str) and "[Note: Potential rhythm mismatches" in lyrics:
                    clean_lyrics = lyrics.split("[Note:")[0].strip()
                    rhythm_analysis = "[Note:" + lyrics.split("[Note:")[1]
            
            # Format emotion analysis results
            try:
                emotion_results = music_analyzer.analyze_music(audio_file)
                emotion_text = f"Tempo: {emotion_results['summary']['tempo']:.1f} BPM\n"
                emotion_text += f"Key: {emotion_results['summary']['key']} {emotion_results['summary']['mode']}\n"
                emotion_text += f"Primary Emotion: {emotion_results['summary']['primary_emotion']}\n"
                emotion_text += f"Primary Theme: {emotion_results['summary']['primary_theme']}"
                
                # Add detailed song structure information if available
                try:
                    audio_data = extract_audio_features(audio_file)
                    song_structure = calculate_detailed_song_structure(audio_data)
                    
                    emotion_text += "\n\nSong Structure:\n"
                    for section in song_structure["syllables"]:
                        emotion_text += f"- {section['type'].capitalize()}: {section['start']:.1f}s to {section['end']:.1f}s "
                        emotion_text += f"({section['duration']:.1f}s, {section['beat_count']} beats, "
                        
                        if "syllable_template" in section:
                            emotion_text += f"template: {section['syllable_template']})\n"
                        else:
                            emotion_text += f"~{section['syllable_count']} syllables)\n"
                            
                    # Add flexible structure info 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"]:
                            emotion_text += "\nDetailed Rhythm Analysis:\n"
                            for i, segment in enumerate(flexible["segments"][:5]):  # Show first 5 segments
                                emotion_text += f"- Segment {i+1}: {segment['start']:.1f}s to {segment['end']:.1f}s, "
                                emotion_text += f"pattern: {segment.get('syllable_template', 'N/A')}\n"
                            
                            if len(flexible["segments"]) > 5:
                                emotion_text += f"  (+ {len(flexible['segments']) - 5} more segments)\n"
                        
                except Exception as e:
                    print(f"Error displaying song structure: {str(e)}")
                    # Continue without showing structure details
                    
            except Exception as e:
                print(f"Error in emotion analysis: {str(e)}")
                emotion_text = f"Error in emotion analysis: {str(e)}"
            
            # Format AST classification results
            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"
            else:
                ast_text = "No valid audio classification results available."
            
            # Return all results for the tabbed interface
            return genre_results, emotion_text, ast_text, clean_lyrics, rhythm_analysis
            
        except Exception as e:
            error_msg = f"Error: {str(e)}"
            print(error_msg)
            return error_msg, "Error in emotion analysis", "Error in audio classification", "No lyrics generated", "No rhythm analysis available"
    
    # Connect the button to the display function
    submit_btn.click(
        fn=display_results,
        inputs=[audio_input],
        outputs=[genre_output, emotion_output, ast_output, lyrics_output, rhythm_analysis_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
        
        5. **Syllable Template Creation**: For each musical phrase, the system generates precise syllable templates that reflect:
           - Beat stress patterns (strong, medium, weak)
           - Appropriate syllable counts based on tempo
           - Genre-specific rhythmic qualities
        
        6. **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
           - Align stressed syllables with strong beats
           - Maintain genre-appropriate style and themes
        
        7. **Rhythm Verification**: The system verifies the generated lyrics, analyzing:
           - Syllable count accuracy
           - Stress alignment with strong beats
           - Word stress patterns
           
        8. **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()