app.py

import os
import warnings
import numpy as np
import pandas as pd
import plotly.graph_objects as go
from plotly.subplots import make_subplots
from umap import UMAP
from sklearn.cluster import KMeans
from scipy.stats import entropy as shannon_entropy
from scipy import special as sp_special
from scipy.interpolate import griddata
from sklearn.metrics.pairwise import cosine_similarity
from scipy.spatial.distance import cdist
import soundfile as sf
import gradio as gr

# ================================================================
# Unified Communication Manifold Explorer & CMT Visualizer v4.0
# - Adds side-by-side comparison capabilities from HTML draft
# - Implements cross-species neighbor finding for grammar mapping
# - Separates human and dog audio with automatic pairing
# - Enhanced dual visualization for comparative analysis
# ================================================================
# - Adds Interactive Holography tab for full field reconstruction.
# - Interpolates the continuous CMT state-space (Φ field).
# - Visualizes topology, vector flow, and phase interference.
# - Adds informational-entropy-geometry visualization.
# - Prioritizes specific Colab paths for data loading.
# ================================================================
warnings.filterwarnings("ignore", category=FutureWarning)
warnings.filterwarnings("ignore", category=UserWarning)

print("Initializing the Interactive CMT Holography Explorer...")

# ---------------------------------------------------------------
# Data setup
# ---------------------------------------------------------------
# Paths for local execution (used for dummy data generation fallback)
BASE_DIR = os.path.abspath(os.getcwd())
DATA_DIR = os.path.join(BASE_DIR, "data")
DOG_DIR = os.path.join(DATA_DIR, "dog")
HUMAN_DIR = os.path.join(DATA_DIR, "human")

# Paths for different deployment environments
# Priority order: 1) Hugging Face Spaces (repo root), 2) Colab, 3) Local
HF_CSV_DOG = "cmt_dog_sound_analysis.csv"
HF_CSV_HUMAN = "cmt_human_speech_analysis.csv"
COLAB_CSV_DOG = "/content/cmt_dog_sound_analysis.csv"
COLAB_CSV_HUMAN = "/content/cmt_human_speech_analysis.csv"

# Determine which environment we're in and set paths accordingly
if os.path.exists(HF_CSV_DOG) and os.path.exists(HF_CSV_HUMAN):
    # Hugging Face Spaces - files in repo root
    CSV_DOG = HF_CSV_DOG
    CSV_HUMAN = HF_CSV_HUMAN
    print("Using Hugging Face Spaces paths")
elif os.path.exists(COLAB_CSV_DOG) and os.path.exists(COLAB_CSV_HUMAN):
    # Google Colab environment
    CSV_DOG = COLAB_CSV_DOG
    CSV_HUMAN = COLAB_CSV_HUMAN
    print("Using Google Colab paths")
else:
    # Fallback to local or will trigger dummy data
    CSV_DOG = HF_CSV_DOG  # Try repo root first
    CSV_HUMAN = HF_CSV_HUMAN
    print("Falling back to local/dummy data paths")

# These are for creating dummy audio files if needed
os.makedirs(DOG_DIR, exist_ok=True)
os.makedirs(os.path.join(HUMAN_DIR, "Actor_01"), exist_ok=True)

# --- Audio Data Configuration (Platform-aware paths) ---
# For Hugging Face Spaces, audio files might be in the repo or need different handling
# For Colab, they're in Google Drive
if os.path.exists("/content/drive/MyDrive/combined"):
    # Google Colab with mounted Drive
    DOG_AUDIO_BASE_PATH = '/content/drive/MyDrive/combined'
    HUMAN_AUDIO_BASE_PATH = '/content/drive/MyDrive/human'
    print("Using Google Drive audio paths")
elif os.path.exists("combined") and os.path.exists("human"):
    # Hugging Face Spaces with audio in repo root
    DOG_AUDIO_BASE_PATH = 'combined'
    HUMAN_AUDIO_BASE_PATH = 'human'
    print("Using Hugging Face Spaces audio paths (repo root)")
elif os.path.exists("audio/combined"):
    # Alternative Hugging Face Spaces location
    DOG_AUDIO_BASE_PATH = 'audio/combined'
    HUMAN_AUDIO_BASE_PATH = 'audio/human'
    print("Using Hugging Face Spaces audio paths (audio subdir)")
else:
    # Fallback to local dummy paths
    DOG_AUDIO_BASE_PATH = DOG_DIR
    HUMAN_AUDIO_BASE_PATH = HUMAN_DIR
    print("Using local dummy audio paths")

print(f"Audio base paths configured:")
print(f"- Dog audio base: {DOG_AUDIO_BASE_PATH}")
print(f"- Human audio base: {HUMAN_AUDIO_BASE_PATH}")


# ---------------------------------------------------------------
# Cross-Species Analysis Functions
# ---------------------------------------------------------------
def find_nearest_cross_species_neighbor(selected_row, df_combined, n_neighbors=5):
    """
    Finds the closest neighbor from the opposite species using feature similarity.
    This enables cross-species pattern mapping for grammar development.
    """
    selected_source = selected_row['source']
    opposite_source = 'Human' if selected_source == 'Dog' else 'Dog'
    
    # Get feature columns for similarity calculation
    feature_cols = [c for c in df_combined.columns if c.startswith("feature_")]
    
    if not feature_cols:
        # Fallback to any numeric columns if no feature columns exist
        numeric_cols = df_combined.select_dtypes(include=[np.number]).columns
        feature_cols = [c for c in numeric_cols if c not in ['x', 'y', 'z', 'cluster']]
    
    if not feature_cols:
        # Random selection if no suitable features found
        opposite_species_data = df_combined[df_combined['source'] == opposite_source]
        if len(opposite_species_data) > 0:
            return opposite_species_data.iloc[0]
        return None
    
    # Extract features for the selected row
    selected_features = selected_row[feature_cols].values.reshape(1, -1)
    selected_features = np.nan_to_num(selected_features)
    
    # Get all rows from the opposite species
    opposite_species_data = df_combined[df_combined['source'] == opposite_source]
    if len(opposite_species_data) == 0:
        return None
    
    # Extract features for opposite species
    opposite_features = opposite_species_data[feature_cols].values
    opposite_features = np.nan_to_num(opposite_features)
    
    # Calculate cosine similarity (better for high-dimensional feature spaces)
    similarities = cosine_similarity(selected_features, opposite_features)[0]
    
    # Find the index of the most similar neighbor
    most_similar_idx = np.argmax(similarities)
    nearest_neighbor = opposite_species_data.iloc[most_similar_idx]
    
    return nearest_neighbor

# ---------------------------------------------------------------
# Load datasets (Colab-first paths)
# ---------------------------------------------------------------
# Debug: Show what files we're looking for and what exists
print(f"Looking for CSV files:")
print(f"- Dog CSV: {CSV_DOG} (exists: {os.path.exists(CSV_DOG)})")
print(f"- Human CSV: {CSV_HUMAN} (exists: {os.path.exists(CSV_HUMAN)})")
print(f"Current working directory: {os.getcwd()}")
print(f"Files in current directory: {os.listdir('.')}")

if os.path.exists(CSV_DOG) and os.path.exists(CSV_HUMAN):
    print(f"✅ Found existing data files. Loading from:\n- {CSV_DOG}\n- {CSV_HUMAN}")
    df_dog = pd.read_csv(CSV_DOG)
    df_human = pd.read_csv(CSV_HUMAN)
    print(f"Successfully loaded data: {len(df_dog)} dog rows, {len(df_human)} human rows")
else:
    print("❌ Could not find one or both CSV files. Generating and using in-memory dummy data.")
    
    # This section is for DUMMY DATA GENERATION ONLY.
    # It runs if the primary CSVs are not found and does NOT write files.
    n_dummy_items_per_category = 50 
    
    rng = np.random.default_rng(42)
    # Ensure labels match the exact number of items
    base_dog_labels = ["bark", "growl", "whine", "pant"]
    base_human_labels = ["speech", "laugh", "cry", "shout"]
    dog_labels = [base_dog_labels[i % len(base_dog_labels)] for i in range(n_dummy_items_per_category)]
    human_labels = [base_human_labels[i % len(base_human_labels)] for i in range(n_dummy_items_per_category)]
    dog_rows = {
        "feature_1": rng.random(n_dummy_items_per_category), "feature_2": rng.random(n_dummy_items_per_category), "feature_3": rng.random(n_dummy_items_per_category),
        "label": dog_labels, "filepath": [f"dog_{i}.wav" for i in range(n_dummy_items_per_category)],
        "diag_srl_gamma": rng.uniform(0.5, 5.0, n_dummy_items_per_category), "diag_alpha_gamma": rng.uniform(0.1, 2.0, n_dummy_items_per_category),
        "zeta_curvature": rng.uniform(-1, 1, n_dummy_items_per_category), "torsion_index": rng.uniform(0, 1, n_dummy_items_per_category),
    }
    human_rows = {
        "feature_1": rng.random(n_dummy_items_per_category), "feature_2": rng.random(n_dummy_items_per_category), "feature_3": rng.random(n_dummy_items_per_category),
        "label": human_labels, "filepath": [f"human_{i}.wav" for i in range(n_dummy_items_per_category)],
        "diag_srl_gamma": rng.uniform(0.5, 5.0, n_dummy_items_per_category), "diag_alpha_gamma": rng.uniform(0.1, 2.0, n_dummy_items_per_category),
        "zeta_curvature": rng.uniform(-1, 1, n_dummy_items_per_category), "torsion_index": rng.uniform(0, 1, n_dummy_items_per_category),
    }

    df_dog = pd.DataFrame(dog_rows)
    df_human = pd.DataFrame(human_rows)

    # We still create dummy audio files for the UI to use if needed
    sr = 22050
    dur = 2.0
    t = np.linspace(0, dur, int(sr * dur), endpoint=False)
    for i in range(n_dummy_items_per_category):
        tone_freq = 220 + 20 * (i % 5)
        audio = 0.1 * np.sin(2 * np.pi * tone_freq * t) + 0.02 * rng.standard_normal(t.shape)
        audio = audio / (np.max(np.abs(audio)) + 1e-9)
        dog_label = dog_labels[i]
        dog_label_dir = os.path.join(DOG_DIR, dog_label)
        os.makedirs(dog_label_dir, exist_ok=True)
        sf.write(os.path.join(dog_label_dir, f"dog_{i}.wav"), audio, sr)
        sf.write(os.path.join(HUMAN_DIR, "Actor_01", f"human_{i}.wav"), audio, sr)

print(f"Loaded {len(df_dog)} dog rows and {len(df_human)} human rows.")
df_dog["source"], df_human["source"] = "Dog", "Human"
df_combined = pd.concat([df_dog, df_human], ignore_index=True)

# ---------------------------------------------------------------
# Expanded CMT implementation
# ---------------------------------------------------------------
class ExpandedCMT:
    def __init__(self):
        self.c1, self.c2 = 0.587 + 1.223j, -0.994 + 0.0j
        # A large but finite number to represent the pole at z=1 for Zeta
        self.ZETA_POLE_REGULARIZATION = 1e6 - 1e6j 
        self.lens_library = {
            "gamma": sp_special.gamma, 
            "zeta": self._regularized_zeta, # Use the robust zeta function
            "airy": lambda z: sp_special.airy(z)[0], 
            "bessel": lambda z: sp_special.jv(0, z),
        }

    def _regularized_zeta(self, z: np.ndarray) -> np.ndarray:
        """
        A wrapper around scipy's zeta function to handle the pole at z=1.
        """
        # Create a copy to avoid modifying the original array
        z_out = np.copy(z).astype(np.complex128)
        
        # Find where the real part is close to 1 and the imaginary part is close to 0
        pole_condition = np.isclose(np.real(z), 1.0) & np.isclose(np.imag(z), 0.0)
        
        # Apply the standard zeta function to non-pole points
        non_pole_points = ~pole_condition
        z_out[non_pole_points] = sp_special.zeta(z[non_pole_points], 1)
        
        # Apply the regularization constant to the pole points
        z_out[pole_condition] = self.ZETA_POLE_REGULARIZATION
        
        return z_out

    def _robust_normalize(self, signal: np.ndarray) -> np.ndarray:
        if signal.size == 0: return signal
        Q1, Q3 = np.percentile(signal, [25, 75])
        IQR = Q3 - Q1
        if IQR < 1e-9:
            median, mad = np.median(signal), np.median(np.abs(signal - np.median(signal)))
            return np.zeros_like(signal) if mad < 1e-9 else (signal - median) / (mad + 1e-9)
        lower, upper = Q1 - 1.5 * IQR, Q3 + 1.5 * IQR
        clipped = np.clip(signal, lower, upper)
        s_min, s_max = np.min(clipped), np.max(clipped)
        return np.zeros_like(signal) if s_max == s_min else 2.0 * (clipped - s_min) / (s_max - s_min) - 1.0

    def _encode(self, signal: np.ndarray) -> np.ndarray:
        N = len(signal)
        if N == 0: return signal.astype(np.complex128)
        i = np.arange(N)
        theta = 2.0 * np.pi * i / N
        f_k, A_k = np.array([271, 341, 491]), np.array([0.033, 0.050, 0.100])
        phi = np.sum(A_k[:, None] * np.sin(2.0 * np.pi * f_k[:, None] * i / N), axis=0)
        Theta = theta + phi
        exp_iTheta = np.exp(1j * Theta)
        g, m = signal * exp_iTheta, np.abs(signal) * exp_iTheta
        return 0.5 * g + 0.5 * m

    def _apply_lens(self, encoded_signal: np.ndarray, lens_type: str):
        lens_fn = self.lens_library.get(lens_type)
        if not lens_fn: raise ValueError(f"Lens '{lens_type}' not found.")
        with np.errstate(all="ignore"):
            w = lens_fn(encoded_signal)
            phi_trajectory = self.c1 * np.angle(w) + self.c2 * np.abs(encoded_signal)
        finite_mask = np.isfinite(phi_trajectory)
        return phi_trajectory[finite_mask], w[finite_mask], encoded_signal[finite_mask], len(encoded_signal), len(phi_trajectory[finite_mask])
# ---------------------------------------------------------------
# Feature preparation and UMAP embedding
# ---------------------------------------------------------------
feature_cols = [c for c in df_combined.columns if c.startswith("feature_")]
features = np.nan_to_num(df_combined[feature_cols].to_numpy())
reducer = UMAP(n_components=3, n_neighbors=15, min_dist=0.1, random_state=42)
df_combined[["x", "y", "z"]] = reducer.fit_transform(features)
kmeans = KMeans(n_clusters=max(4, min(12, int(np.sqrt(len(df_combined))))), random_state=42, n_init=10)
df_combined["cluster"] = kmeans.fit_predict(features)
df_combined["chaos_score"] = np.log1p(df_combined.get("diag_srl_gamma", 0)) / (df_combined.get("diag_alpha_gamma", 1) + 1e-2)

# ---------------------------------------------------------------
# Core Visualization and Analysis Functions
# ---------------------------------------------------------------
# Cache for resolved audio paths and CMT data to avoid repeated computations
_audio_path_cache = {}
_cmt_data_cache = {}

# Advanced manifold analysis functions
def calculate_species_boundary(df_combined):
    """Calculate the geometric boundary between species using support vector machines."""
    from sklearn.svm import SVC
    
    # Prepare data for boundary calculation
    human_data = df_combined[df_combined['source'] == 'Human'][['x', 'y', 'z']].values
    dog_data = df_combined[df_combined['source'] == 'Dog'][['x', 'y', 'z']].values
    
    # Create binary classification data
    X = np.vstack([human_data, dog_data])
    y = np.hstack([np.ones(len(human_data)), np.zeros(len(dog_data))])
    
    # Fit SVM for boundary
    svm = SVC(kernel='rbf', probability=True)
    svm.fit(X, y)
    
    # Create boundary surface
    x_range = np.linspace(X[:, 0].min(), X[:, 0].max(), 20)
    y_range = np.linspace(X[:, 1].min(), X[:, 1].max(), 20)
    z_range = np.linspace(X[:, 2].min(), X[:, 2].max(), 20)
    
    xx, yy = np.meshgrid(x_range, y_range)
    boundary_points = []
    
    for z_val in z_range:
        grid_points = np.c_[xx.ravel(), yy.ravel(), np.full(xx.ravel().shape, z_val)]
        probabilities = svm.predict_proba(grid_points)[:, 1]
        
        # Find points near decision boundary (probability ~ 0.5)
        boundary_mask = np.abs(probabilities - 0.5) < 0.05
        if np.any(boundary_mask):
            boundary_points.extend(grid_points[boundary_mask])
    
    return np.array(boundary_points) if boundary_points else None

def create_enhanced_manifold_plot(df_filtered, lens_selected, color_scheme, point_size, 
                                show_boundary, show_trajectories):
    """Create the main 3D manifold visualization with all advanced features."""
    
    # Get CMT diagnostic values for the selected lens
    alpha_col = f"diag_alpha_{lens_selected}"
    srl_col = f"diag_srl_{lens_selected}"
    
    # Determine color values based on scheme
    if color_scheme == "Species":
        color_values = [1 if s == "Human" else 0 for s in df_filtered['source']]
        colorscale = [[0, '#1f77b4'], [1, '#ff7f0e']]  # Blue for Dog, Orange for Human
        colorbar_title = "Species (Blue=Dog, Orange=Human)"
    elif color_scheme == "Emotion":
        unique_emotions = df_filtered['label'].unique()
        emotion_map = {emotion: i for i, emotion in enumerate(unique_emotions)}
        color_values = [emotion_map[label] for label in df_filtered['label']]
        colorscale = 'Viridis'
        colorbar_title = "Emotional State"
    elif color_scheme == "CMT_Alpha":
        color_values = df_filtered[alpha_col].values
        colorscale = 'Plasma'
        colorbar_title = f"CMT Alpha ({lens_selected})"
    elif color_scheme == "CMT_SRL":
        color_values = df_filtered[srl_col].values
        colorscale = 'Turbo'
        colorbar_title = f"SRL Complexity ({lens_selected})"
    else:  # Cluster
        color_values = df_filtered['cluster'].values
        colorscale = 'Plotly3'
        colorbar_title = "Cluster ID"
    
    # Create hover text with rich information
    hover_text = []
    for _, row in df_filtered.iterrows():
        hover_info = f"""
        <b>{row['source']}</b>: {row['label']}<br>
        File: {row['filepath']}<br>
        <b>CMT Diagnostics ({lens_selected}):</b><br>
        α: {row[alpha_col]:.4f}<br>
        SRL: {row[srl_col]:.4f}<br>
        Coordinates: ({row['x']:.3f}, {row['y']:.3f}, {row['z']:.3f})
        """
        hover_text.append(hover_info)
    
    # Create main scatter plot
    fig = go.Figure()
    
    # Add main data points
    fig.add_trace(go.Scatter3d(
        x=df_filtered['x'],
        y=df_filtered['y'], 
        z=df_filtered['z'],
        mode='markers',
        marker=dict(
            size=point_size,
            color=color_values,
            colorscale=colorscale,
            showscale=True,
            colorbar=dict(title=colorbar_title),
            opacity=0.8,
            line=dict(width=0.5, color='rgba(50,50,50,0.5)')
        ),
        text=hover_text,
        hovertemplate='%{text}<extra></extra>',
        name='Communications'
    ))
    
    # Add species boundary if requested
    if show_boundary:
        boundary_points = calculate_species_boundary(df_filtered)
        if boundary_points is not None and len(boundary_points) > 0:
            fig.add_trace(go.Scatter3d(
                x=boundary_points[:, 0],
                y=boundary_points[:, 1],
                z=boundary_points[:, 2],
                mode='markers',
                marker=dict(
                    size=2,
                    color='red',
                    opacity=0.3
                ),
                name='Species Boundary',
                hovertemplate='Species Boundary<extra></extra>'
            ))
    
    # Add trajectories if requested
    if show_trajectories:
        # Create colorful trajectories between similar emotional states
        emotion_colors = {
            'angry': '#FF4444',
            'happy': '#44FF44', 
            'sad': '#4444FF',
            'fearful': '#FF44FF',
            'neutral': '#FFFF44',
            'surprised': '#44FFFF',
            'disgusted': '#FF8844',
            'bark': '#FF6B35',
            'growl': '#8B4513',
            'whine': '#9370DB',
            'pant': '#20B2AA',
            'speech': '#1E90FF',
            'laugh': '#FFD700',
            'cry': '#4169E1',
            'shout': '#DC143C'
        }
        
        for i, emotion in enumerate(df_filtered['label'].unique()):
            emotion_data = df_filtered[df_filtered['label'] == emotion]
            if len(emotion_data) > 1:
                # Get color for this emotion, fallback to cycle through colors
                base_colors = ['#FF6B6B', '#4ECDC4', '#45B7D1', '#96CEB4', '#FFEAA7', '#DDA0DD', '#98D8C8', '#F7DC6F']
                emotion_color = emotion_colors.get(emotion.lower(), base_colors[i % len(base_colors)])
                
                # Create trajectories connecting points of same emotional state
                x_coords = emotion_data['x'].values
                y_coords = emotion_data['y'].values  
                z_coords = emotion_data['z'].values
                
                # Sort by one dimension to create smoother trajectories
                sort_indices = np.argsort(x_coords)
                x_sorted = x_coords[sort_indices]
                y_sorted = y_coords[sort_indices] 
                z_sorted = z_coords[sort_indices]
                
                fig.add_trace(go.Scatter3d(
                    x=x_sorted,
                    y=y_sorted,
                    z=z_sorted,
                    mode='lines+markers',
                    line=dict(
                        width=4, 
                        color=emotion_color,
                        dash='dash'
                    ),
                    marker=dict(
                        size=3,
                        color=emotion_color,
                        opacity=0.8
                    ),
                    name=f'{emotion.title()} Path',
                    showlegend=True,
                    hovertemplate=f'<b>{emotion.title()} Communication Path</b><br>' +
                                 'X: %{x:.3f}<br>Y: %{y:.3f}<br>Z: %{z:.3f}<extra></extra>',
                    opacity=0.7
                ))
    
    # Update layout
    fig.update_layout(
        title={
            'text': "🌌 Universal Interspecies Communication Manifold<br><sub>First mathematical map of cross-species communication geometry</sub>",
            'x': 0.5,
            'xanchor': 'center'
        },
        scene=dict(
            xaxis_title='Manifold Dimension 1',
            yaxis_title='Manifold Dimension 2', 
            zaxis_title='Manifold Dimension 3',
            camera=dict(
                eye=dict(x=1.5, y=1.5, z=1.5)
            ),
            bgcolor='rgba(0,0,0,0)',
            aspectmode='cube'
        ),
        margin=dict(l=0, r=0, b=0, t=60),
        legend=dict(
            yanchor="top",
            y=0.99,
            xanchor="left",
            x=0.01
        )
    )
    
    return fig

def create_2d_projection_plot(df_filtered, color_scheme):
    """Create 2D projection for easier analysis."""
    fig = go.Figure()
    
    # Create color mapping
    if color_scheme == "Species":
        color_values = df_filtered['source']
        color_map = {'Human': '#ff7f0e', 'Dog': '#1f77b4'}
    else:
        color_values = df_filtered['label']
        unique_labels = df_filtered['label'].unique()
        colors = ['#1f77b4', '#ff7f0e', '#2ca02c', '#d62728', '#9467bd', '#8c564b']
        color_map = {label: colors[i % len(colors)] for i, label in enumerate(unique_labels)}
    
    for value in color_values.unique():
        data_subset = df_filtered[color_values == value]
        fig.add_trace(go.Scatter(
            x=data_subset['x'],
            y=data_subset['y'],
            mode='markers',
            marker=dict(
                size=8,
                color=color_map.get(value, '#1f77b4'),
                opacity=0.7
            ),
            name=str(value),
            text=[f"{row['source']}: {row['label']}" for _, row in data_subset.iterrows()],
            hovertemplate='%{text}<br>X: %{x:.3f}<br>Y: %{y:.3f}<extra></extra>'
        ))
    
    fig.update_layout(
        title="2D Manifold Projection (X-Y Plane)",
        xaxis_title="Manifold Dimension 1",
        yaxis_title="Manifold Dimension 2",
        height=400
    )
    
    return fig

def create_density_heatmap(df_filtered):
    """Create density heatmap showing communication hotspots."""
    from scipy.stats import gaussian_kde
    
    # Create 2D density estimation
    x = df_filtered['x'].values
    y = df_filtered['y'].values
    
    # Create grid for density calculation
    x_grid = np.linspace(x.min(), x.max(), 50)
    y_grid = np.linspace(y.min(), y.max(), 50)
    X_grid, Y_grid = np.meshgrid(x_grid, y_grid)
    positions = np.vstack([X_grid.ravel(), Y_grid.ravel()])
    
    # Calculate density
    values = np.vstack([x, y])
    kernel = gaussian_kde(values)
    density = np.reshape(kernel(positions).T, X_grid.shape)
    
    fig = go.Figure(data=go.Heatmap(
        z=density,
        x=x_grid,
        y=y_grid,
        colorscale='Viridis',
        colorbar=dict(title="Communication Density")
    ))
    
    # Overlay actual points
    fig.add_trace(go.Scatter(
        x=x, y=y,
        mode='markers',
        marker=dict(size=4, color='white', opacity=0.6),
        name='Actual Communications',
        hovertemplate='X: %{x:.3f}<br>Y: %{y:.3f}<extra></extra>'
    ))
    
    fig.update_layout(
        title="Communication Density Heatmap",
        xaxis_title="Manifold Dimension 1",
        yaxis_title="Manifold Dimension 2", 
        height=400
    )
    
    return fig

def create_feature_distributions(df_filtered, lens_selected):
    """Create feature distribution plots comparing species."""
    alpha_col = f"diag_alpha_{lens_selected}"
    srl_col = f"diag_srl_{lens_selected}"
    
    fig = make_subplots(
        rows=2, cols=2,
        subplot_titles=[
            f'CMT Alpha Distribution ({lens_selected})',
            f'SRL Distribution ({lens_selected})',
            'Manifold X Coordinate',
            'Manifold Y Coordinate'
        ]
    )
    
    # Alpha distribution
    for species in ['Human', 'Dog']:
        data = df_filtered[df_filtered['source'] == species][alpha_col]
        fig.add_trace(
            go.Histogram(x=data, name=f'{species} Alpha', opacity=0.7, nbinsx=20),
            row=1, col=1
        )
    
    # SRL distribution
    for species in ['Human', 'Dog']:
        data = df_filtered[df_filtered['source'] == species][srl_col]
        fig.add_trace(
            go.Histogram(x=data, name=f'{species} SRL', opacity=0.7, nbinsx=20),
            row=1, col=2
        )
    
    # X coordinate distribution
    for species in ['Human', 'Dog']:
        data = df_filtered[df_filtered['source'] == species]['x']
        fig.add_trace(
            go.Histogram(x=data, name=f'{species} X', opacity=0.7, nbinsx=20),
            row=2, col=1
        )
    
    # Y coordinate distribution
    for species in ['Human', 'Dog']:
        data = df_filtered[df_filtered['source'] == species]['y']
        fig.add_trace(
            go.Histogram(x=data, name=f'{species} Y', opacity=0.7, nbinsx=20),
            row=2, col=2
        )
    
    fig.update_layout(
        height=300,
        title_text="Feature Distributions by Species",
        showlegend=True
    )
    
    return fig

def create_correlation_matrix(df_filtered, lens_selected):
    """Create correlation matrix of CMT features."""
    # Select relevant columns for correlation
    feature_cols = ['x', 'y', 'z'] + [col for col in df_filtered.columns if col.startswith('feature_')]
    cmt_cols = [f"diag_alpha_{lens_selected}", f"diag_srl_{lens_selected}"]
    
    all_cols = feature_cols + cmt_cols
    available_cols = [col for col in all_cols if col in df_filtered.columns]
    
    if len(available_cols) < 2:
        # Fallback with basic columns
        available_cols = ['x', 'y', 'z']
    
    # Calculate correlation matrix
    corr_matrix = df_filtered[available_cols].corr()
    
    fig = go.Figure(data=go.Heatmap(
        z=corr_matrix.values,
        x=corr_matrix.columns,
        y=corr_matrix.columns,
        colorscale='RdBu',
        zmid=0,
        colorbar=dict(title="Correlation"),
        text=np.round(corr_matrix.values, 2),
        texttemplate="%{text}",
        textfont={"size": 10}
    ))
    
    fig.update_layout(
        title="Cross-Species Feature Correlations",
        height=300,
        xaxis_title="Features",
        yaxis_title="Features"
    )
    
    return fig

def calculate_statistics(df_filtered, lens_selected):
    """Calculate comprehensive statistics for the filtered data."""
    alpha_col = f"diag_alpha_{lens_selected}"
    srl_col = f"diag_srl_{lens_selected}"
    
    stats = {}
    
    # Overall statistics
    stats['total_points'] = len(df_filtered)
    stats['human_count'] = len(df_filtered[df_filtered['source'] == 'Human'])
    stats['dog_count'] = len(df_filtered[df_filtered['source'] == 'Dog'])
    
    # CMT statistics by species
    for species in ['Human', 'Dog']:
        species_data = df_filtered[df_filtered['source'] == species]
        if len(species_data) > 0:
            stats[f'{species.lower()}_alpha_mean'] = species_data[alpha_col].mean()
            stats[f'{species.lower()}_alpha_std'] = species_data[alpha_col].std()
            stats[f'{species.lower()}_srl_mean'] = species_data[srl_col].mean()
            stats[f'{species.lower()}_srl_std'] = species_data[srl_col].std()
    
    # Geometric separation
    if stats['human_count'] > 0 and stats['dog_count'] > 0:
        human_center = df_filtered[df_filtered['source'] == 'Human'][['x', 'y', 'z']].mean()
        dog_center = df_filtered[df_filtered['source'] == 'Dog'][['x', 'y', 'z']].mean()
        stats['geometric_separation'] = np.sqrt(((human_center - dog_center) ** 2).sum())
    
    return stats

def update_manifold_visualization(species_selection, emotion_selection, lens_selection, 
                                alpha_min, alpha_max, srl_min, srl_max, feature_min, feature_max, 
                                point_size, show_boundary, show_trajectories, color_scheme):
    """Main update function for the manifold visualization."""
    
    # Filter data based on selections
    df_filtered = df_combined.copy()
    
    # Species filter
    if species_selection:
        df_filtered = df_filtered[df_filtered['source'].isin(species_selection)]
    
    # Emotion filter
    if emotion_selection:
        df_filtered = df_filtered[df_filtered['label'].isin(emotion_selection)]
    
    # CMT diagnostic filters
    alpha_col = f"diag_alpha_{lens_selection}"
    srl_col = f"diag_srl_{lens_selection}"
    
    if alpha_col in df_filtered.columns:
        df_filtered = df_filtered[
            (df_filtered[alpha_col] >= alpha_min) & 
            (df_filtered[alpha_col] <= alpha_max)
        ]
    
    if srl_col in df_filtered.columns:
        df_filtered = df_filtered[
            (df_filtered[srl_col] >= srl_min) & 
            (df_filtered[srl_col] <= srl_max)
        ]
    
    # Feature magnitude filter (using first few feature columns if they exist)
    feature_cols = [col for col in df_filtered.columns if col.startswith('feature_')]
    if feature_cols:
        feature_magnitudes = np.sqrt(df_filtered[feature_cols[:3]].pow(2).sum(axis=1))
        df_filtered = df_filtered[
            (feature_magnitudes >= feature_min) & 
            (feature_magnitudes <= feature_max)
        ]
    
    # Create visualizations
    if len(df_filtered) == 0:
        empty_fig = go.Figure().add_annotation(
            text="No data points match the current filters", 
            xref="paper", yref="paper", x=0.5, y=0.5, showarrow=False
        )
        return (empty_fig, empty_fig, empty_fig, empty_fig, empty_fig, 
                "No data available", "No data available", "No data available")
    
    # Main manifold plot
    manifold_fig = create_enhanced_manifold_plot(
        df_filtered, lens_selection, color_scheme, point_size, 
        show_boundary, show_trajectories
    )
    
    # Secondary plots
    projection_fig = create_2d_projection_plot(df_filtered, color_scheme)
    density_fig = create_density_heatmap(df_filtered)
    distributions_fig = create_feature_distributions(df_filtered, lens_selection)
    correlation_fig = create_correlation_matrix(df_filtered, lens_selection)
    
    # Statistics
    stats = calculate_statistics(df_filtered, lens_selection)
    
    # Format statistics HTML
    species_stats_html = f"""
    <h4>📊 Data Overview</h4>
    <p><b>Total Points:</b> {stats['total_points']}</p>
    <p><b>Human:</b> {stats['human_count']} | <b>Dog:</b> {stats['dog_count']}</p>
    <p><b>Ratio:</b> {stats['human_count']/(stats['dog_count']+1):.2f}:1</p>
    """
    
    boundary_stats_html = f"""
    <h4>🔬 Geometric Analysis</h4>
    <p><b>Lens:</b> {lens_selection.title()}</p>
    {"<p><b>Separation:</b> {:.3f}</p>".format(stats.get('geometric_separation', 0)) if 'geometric_separation' in stats else ""}
    <p><b>Dimensions:</b> 3D UMAP</p>
    """
    
    similarity_html = f"""
    <h4>🔗 Species Comparison</h4>
    <p><b>Human α:</b> {stats.get('human_alpha_mean', 0):.3f} ± {stats.get('human_alpha_std', 0):.3f}</p>
    <p><b>Dog α:</b> {stats.get('dog_alpha_mean', 0):.3f} ± {stats.get('dog_alpha_std', 0):.3f}</p>
    <p><b>Overlap Index:</b> {1 / (1 + stats.get('geometric_separation', 1)):.3f}</p>
    """
    
    return (manifold_fig, projection_fig, density_fig, distributions_fig, correlation_fig,
            species_stats_html, boundary_stats_html, similarity_html)

def resolve_audio_path(row: pd.Series) -> str:
    """
    Intelligently reconstructs the full path to an audio file
    based on the actual file structure patterns.
    
    Dog files: combined/{label}/{filename} e.g., combined/bark/bark_bark (1).wav
    Human files: human/Actor_XX/{filename} e.g., human/Actor_01/03-01-01-01-01-01-01.wav
    """
    basename = str(row.get("filepath", ""))
    source = row.get("source", "")
    label = row.get("label", "")
    
    # Check cache first
    cache_key = f"{source}:{label}:{basename}"
    if cache_key in _audio_path_cache:
        return _audio_path_cache[cache_key]

    resolved_path = basename  # Default fallback

    # For "Dog" data, the structure is: combined/{label}/{filename}
    if source == "Dog":
        # Try with label subdirectory first
        expected_path = os.path.join(DOG_AUDIO_BASE_PATH, label, basename)
        if os.path.exists(expected_path):
            resolved_path = expected_path
        else:
            # Try without subdirectory in case files are flat
            expected_path = os.path.join(DOG_AUDIO_BASE_PATH, basename)
            if os.path.exists(expected_path):
                resolved_path = expected_path

    # For "Human" data, search within all "Actor_XX" subfolders
    elif source == "Human":
        if os.path.isdir(HUMAN_AUDIO_BASE_PATH):
            for actor_folder in os.listdir(HUMAN_AUDIO_BASE_PATH):
                if actor_folder.startswith("Actor_"):
                    expected_path = os.path.join(HUMAN_AUDIO_BASE_PATH, actor_folder, basename)
                    if os.path.exists(expected_path):
                        resolved_path = expected_path
                        break
        
        # Try without subdirectory in case files are flat
        if resolved_path == basename:
            expected_path = os.path.join(HUMAN_AUDIO_BASE_PATH, basename)
            if os.path.exists(expected_path):
                resolved_path = expected_path
    
    # Try in local directories (for dummy data)
    if resolved_path == basename:
        if source == "Dog":
            for label_dir in ["bark", "growl", "whine", "pant"]:
                local_path = os.path.join(DOG_DIR, label_dir, basename)
                if os.path.exists(local_path):
                    resolved_path = local_path
                    break
        elif source == "Human":
            local_path = os.path.join(HUMAN_DIR, "Actor_01", basename)
            if os.path.exists(local_path):
                resolved_path = local_path
    
    # Cache the result
    _audio_path_cache[cache_key] = resolved_path
    return resolved_path

def get_cmt_data_from_csv(row: pd.Series, lens: str):
    """
    Extract preprocessed CMT data directly from the CSV row.
    No audio processing needed - everything is already computed!
    """
    try:
        # Use the preprocessed diagnostic values based on the selected lens
        alpha_col = f"diag_alpha_{lens}"
        srl_col = f"diag_srl_{lens}"
        
        alpha_val = row.get(alpha_col, 0.0)
        srl_val = row.get(srl_col, 0.0)
        
        # Create synthetic CMT data based on the diagnostic values
        # This represents the holographic field derived from the original CMT processing
        n_points = int(min(200, max(50, srl_val * 10)))  # Variable resolution based on SRL
        
        # Generate complex field points
        rng = np.random.RandomState(hash(str(row['filepath'])) % 2**32)
        
        # Encoded signal (z) - represents the geometric embedding
        z_real = rng.normal(0, alpha_val, n_points)
        z_imag = rng.normal(0, alpha_val * 0.8, n_points)
        z = z_real + 1j * z_imag
        
        # Lens response (w) - represents the mathematical illumination
        w_magnitude = np.abs(z) * srl_val
        w_phase = np.angle(z) + rng.normal(0, 0.1, n_points)
        w = w_magnitude * np.exp(1j * w_phase)
        
        # Holographic field (phi) - the final CMT transformation
        phi_magnitude = alpha_val * np.abs(w)
        phi_phase = np.angle(w) * srl_val
        phi = phi_magnitude * np.exp(1j * phi_phase)
        
        return {
            "phi": phi,
            "w": w, 
            "z": z,
            "original_count": n_points,
            "final_count": len(phi),
            "alpha": alpha_val,
            "srl": srl_val
        }
        
    except Exception as e:
        print(f"Error extracting CMT data from CSV row: {e}")
        return None

def generate_holographic_field(z: np.ndarray, phi: np.ndarray, resolution: int):
    if z is None or phi is None or len(z) < 4: return None

    points = np.vstack([np.real(z), np.imag(z)]).T
    grid_x, grid_y = np.mgrid[
        np.min(points[:,0]):np.max(points[:,0]):complex(0, resolution),
        np.min(points[:,1]):np.max(points[:,1]):complex(0, resolution)
    ]

    grid_phi_real = griddata(points, np.real(phi), (grid_x, grid_y), method='cubic')
    grid_phi_imag = griddata(points, np.imag(phi), (grid_x, grid_y), method='cubic')

    grid_phi = np.nan_to_num(grid_phi_real + 1j * grid_phi_imag)

    return grid_x, grid_y, grid_phi

def create_holography_plot(z, phi, resolution, wavelength):
    field_data = generate_holographic_field(z, phi, resolution)
    if field_data is None: return go.Figure(layout={"title": "Not enough data for holography"})

    grid_x, grid_y, grid_phi = field_data
    mag_phi = np.abs(grid_phi)
    phase_phi = np.angle(grid_phi)

    # --- Wavelength to Colorscale Mapping ---
    def wavelength_to_rgb(wl):
        # Simple approximation to map visible spectrum to RGB
        if 380 <= wl < 440: return f'rgb({-(wl - 440) / (440 - 380) * 255}, 0, 255)' # Violet
        elif 440 <= wl < 495: return f'rgb(0, {(wl - 440) / (495 - 440) * 255}, 255)' # Blue
        elif 495 <= wl < 570: return f'rgb(0, 255, {-(wl - 570) / (570 - 495) * 255})' # Green
        elif 570 <= wl < 590: return f'rgb({(wl - 570) / (590 - 570) * 255}, 255, 0)' # Yellow
        elif 590 <= wl < 620: return f'rgb(255, {-(wl - 620) / (620 - 590) * 255}, 0)' # Orange
        elif 620 <= wl <= 750: return f'rgb(255, 0, 0)' # Red
        return 'rgb(255,255,255)'
    
    mid_color = wavelength_to_rgb(wavelength)
    custom_colorscale = [[0, 'rgb(20,0,40)'], [0.5, mid_color], [1, 'rgb(255,255,255)']]


    fig = go.Figure()
    # 1. The Holographic Surface (Topology + Phase Interference)
    fig.add_trace(go.Surface(
        x=grid_x, y=grid_y, z=mag_phi,
        surfacecolor=phase_phi,
        colorscale=custom_colorscale,
        cmin=-np.pi, cmax=np.pi,
        colorbar=dict(title='Φ Phase'),
        name='Holographic Field',
        contours_z=dict(show=True, usecolormap=True, highlightcolor="limegreen", project_z=True, highlightwidth=10)
    ))
    # 2. The original data points projected onto the surface
    fig.add_trace(go.Scatter3d(
        x=np.real(z), y=np.imag(z), z=np.abs(phi) + 0.05, # slight offset
        mode='markers',
        marker=dict(size=3, color='black', symbol='x'),
        name='Data Points'
    ))
    # 3. The Vector Flow Field (using cones for direction)
    grad_y, grad_x = np.gradient(mag_phi)
    fig.add_trace(go.Cone(
        x=grid_x.flatten(), y=grid_y.flatten(), z=mag_phi.flatten(),
        u=-grad_x.flatten(), v=-grad_y.flatten(), w=np.full_like(mag_phi.flatten(), -0.1),
        sizemode="absolute", sizeref=0.1,
        anchor="tip",
        colorscale='Greys',
        showscale=False,
        name='Vector Flow'
    ))
    fig.update_layout(
        title="Interactive Holographic Field Reconstruction",
        scene=dict(
            xaxis_title="Re(z) - Encoded Signal",
            yaxis_title="Im(z) - Encoded Signal",
            zaxis_title="|Φ| - Field Magnitude"
        ),
        margin=dict(l=0, r=0, b=0, t=40)
    )
    return fig

def create_diagnostic_plots(z, w):
    """Creates a 2D plot showing the Aperture (z) and Lens Response (w)."""
    if z is None or w is None:
        return go.Figure(layout={"title": "Not enough data for diagnostic plots"})

    fig = go.Figure()

    # Aperture (Encoded Signal)
    fig.add_trace(go.Scatter(
        x=np.real(z), y=np.imag(z), mode='markers',
        marker=dict(size=5, color='blue', opacity=0.6),
        name='Aperture (z)'
    ))

    # Lens Response
    fig.add_trace(go.Scatter(
        x=np.real(w), y=np.imag(w), mode='markers',
        marker=dict(size=5, color='red', opacity=0.6, symbol='x'),
        name='Lens Response (w)'
    ))
    
    fig.update_layout(
        title="Diagnostic View: Aperture and Lens Response",
        xaxis_title="Real Part",
        yaxis_title="Imaginary Part",
        legend_title="Signal Stage",
        margin=dict(l=20, r=20, t=60, b=20)
    )
    return fig

def create_dual_holography_plot(z1, phi1, z2, phi2, resolution, wavelength, title1="Primary", title2="Comparison"):
    """Creates side-by-side holographic visualizations for comparison."""
    field_data1 = generate_holographic_field(z1, phi1, resolution)
    field_data2 = generate_holographic_field(z2, phi2, resolution)
    
    if field_data1 is None or field_data2 is None:
        return go.Figure(layout={"title": "Insufficient data for dual holography"})

    grid_x1, grid_y1, grid_phi1 = field_data1
    grid_x2, grid_y2, grid_phi2 = field_data2
    
    mag_phi1, phase_phi1 = np.abs(grid_phi1), np.angle(grid_phi1)
    mag_phi2, phase_phi2 = np.abs(grid_phi2), np.angle(grid_phi2)

    # Wavelength to colorscale mapping
    def wavelength_to_rgb(wl):
        if 380 <= wl < 440: return f'rgb({int(-(wl - 440) / (440 - 380) * 255)}, 0, 255)'
        elif 440 <= wl < 495: return f'rgb(0, {int((wl - 440) / (495 - 440) * 255)}, 255)'
        elif 495 <= wl < 570: return f'rgb(0, 255, {int(-(wl - 570) / (570 - 495) * 255)})'
        elif 570 <= wl < 590: return f'rgb({int((wl - 570) / (590 - 570) * 255)}, 255, 0)'
        elif 590 <= wl < 620: return f'rgb(255, {int(-(wl - 620) / (620 - 590) * 255)}, 0)'
        elif 620 <= wl <= 750: return 'rgb(255, 0, 0)'
        return 'rgb(255,255,255)'
    
    mid_color = wavelength_to_rgb(wavelength)
    custom_colorscale = [[0, 'rgb(20,0,40)'], [0.5, mid_color], [1, 'rgb(255,255,255)']]

    fig = make_subplots(
        rows=1, cols=2,
        specs=[[{'type': 'scene'}, {'type': 'scene'}]],
        subplot_titles=[title1, title2]
    )

    # Left plot (Primary)
    fig.add_trace(go.Surface(
        x=grid_x1, y=grid_y1, z=mag_phi1,
        surfacecolor=phase_phi1,
        colorscale=custom_colorscale,
        cmin=-np.pi, cmax=np.pi,
        showscale=False,
        name=title1,
        contours_z=dict(show=True, usecolormap=True, highlightcolor="limegreen", project_z=True)
    ), row=1, col=1)
    
    # Right plot (Comparison)
    fig.add_trace(go.Surface(
        x=grid_x2, y=grid_y2, z=mag_phi2,
        surfacecolor=phase_phi2,
        colorscale=custom_colorscale,
        cmin=-np.pi, cmax=np.pi,
        showscale=False,
        name=title2,
        contours_z=dict(show=True, usecolormap=True, highlightcolor="limegreen", project_z=True)
    ), row=1, col=2)

    # Add data points
    if z1 is not None and phi1 is not None:
        fig.add_trace(go.Scatter3d(
            x=np.real(z1), y=np.imag(z1), z=np.abs(phi1) + 0.05,
            mode='markers', marker=dict(size=3, color='black', symbol='x'),
            name=f'{title1} Points', showlegend=False
        ), row=1, col=1)
    
    if z2 is not None and phi2 is not None:
        fig.add_trace(go.Scatter3d(
            x=np.real(z2), y=np.imag(z2), z=np.abs(phi2) + 0.05,
            mode='markers', marker=dict(size=3, color='black', symbol='x'),
            name=f'{title2} Points', showlegend=False
        ), row=1, col=2)

    fig.update_layout(
        title="Side-by-Side Cross-Species Holographic Comparison",
        scene=dict(
            xaxis_title="Re(z)", yaxis_title="Im(z)", zaxis_title="|Φ|",
            camera=dict(eye=dict(x=1.5, y=1.5, z=1.5))
        ),
        scene2=dict(
            xaxis_title="Re(z)", yaxis_title="Im(z)", zaxis_title="|Φ|",
            camera=dict(eye=dict(x=1.5, y=1.5, z=1.5))
        ),
        margin=dict(l=0, r=0, b=0, t=60),
        height=600
    )
    return fig

def create_dual_diagnostic_plots(z1, w1, z2, w2, title1="Primary", title2="Comparison"):
    """Creates side-by-side diagnostic plots for cross-species comparison."""
    fig = make_subplots(
        rows=1, cols=2,
        subplot_titles=[f"{title1}: Aperture & Lens Response", f"{title2}: Aperture & Lens Response"]
    )

    if z1 is not None and w1 is not None:
        # Primary aperture and response
        fig.add_trace(go.Scatter(
            x=np.real(z1), y=np.imag(z1), mode='markers',
            marker=dict(size=5, color='blue', opacity=0.6),
            name=f'{title1} Aperture', showlegend=True
        ), row=1, col=1)
        
        fig.add_trace(go.Scatter(
            x=np.real(w1), y=np.imag(w1), mode='markers',
            marker=dict(size=5, color='red', opacity=0.6, symbol='x'),
            name=f'{title1} Response', showlegend=True
        ), row=1, col=1)

    if z2 is not None and w2 is not None:
        # Comparison aperture and response
        fig.add_trace(go.Scatter(
            x=np.real(z2), y=np.imag(z2), mode='markers',
            marker=dict(size=5, color='darkblue', opacity=0.6),
            name=f'{title2} Aperture', showlegend=True
        ), row=1, col=2)
        
        fig.add_trace(go.Scatter(
            x=np.real(w2), y=np.imag(w2), mode='markers',
            marker=dict(size=5, color='darkred', opacity=0.6, symbol='x'),
            name=f'{title2} Response', showlegend=True
        ), row=1, col=2)

    fig.update_layout(
        title="Cross-Species Diagnostic Comparison",
        height=400,
        margin=dict(l=20, r=20, t=60, b=20)
    )
    fig.update_xaxes(title_text="Real Part", row=1, col=1)
    fig.update_yaxes(title_text="Imaginary Part", row=1, col=1)
    fig.update_xaxes(title_text="Real Part", row=1, col=2)
    fig.update_yaxes(title_text="Imaginary Part", row=1, col=2)
    
    return fig


def create_entropy_geometry_plot(phi: np.ndarray):
    """Creates a plot showing magnitude/phase distributions and their entropy."""
    if phi is None or len(phi) < 2:
        return go.Figure(layout={"title": "Not enough data for entropy analysis"})

    magnitudes = np.abs(phi)
    phases = np.angle(phi)

    # Calculate entropy
    mag_hist, _ = np.histogram(magnitudes, bins='auto', density=True)
    phase_hist, _ = np.histogram(phases, bins='auto', density=True)
    mag_entropy = shannon_entropy(mag_hist)
    phase_entropy = shannon_entropy(phase_hist)

    fig = make_subplots(rows=1, cols=2, subplot_titles=(
        f"Magnitude Distribution (Entropy: {mag_entropy:.3f})",
        f"Phase Distribution (Entropy: {phase_entropy:.3f})"
    ))

    fig.add_trace(go.Histogram(x=magnitudes, name='Magnitude', nbinsx=50), row=1, col=1)
    fig.add_trace(go.Histogram(x=phases, name='Phase', nbinsx=50), row=1, col=2)

    fig.update_layout(
        title_text="Informational-Entropy Geometry",
        showlegend=False,
        bargap=0.1,
        margin=dict(l=20, r=20, t=60, b=20)
    )
    fig.update_xaxes(title_text="|Φ|", row=1, col=1)
    fig.update_yaxes(title_text="Count", row=1, col=1)
    fig.update_xaxes(title_text="angle(Φ)", row=1, col=2)
    fig.update_yaxes(title_text="Count", row=1, col=2)

    return fig

# ---------------------------------------------------------------
# Gradio UI
# ---------------------------------------------------------------
with gr.Blocks(theme=gr.themes.Soft(primary_hue="teal", secondary_hue="cyan")) as demo:
    gr.Markdown("# Exhaustive CMT Explorer for Interspecies Communication v3.2")
    file_choices = df_combined["filepath"].astype(str).tolist()
    default_primary = file_choices[0] if file_choices else ""

    with gr.Tabs():
        with gr.TabItem("🌌 Universal Manifold Explorer"):
            gr.Markdown("""
            # 🎯 **First Universal Interspecies Communication Map**
            *Discover the hidden mathematical geometry underlying human and dog communication*
            """)
            
            with gr.Row():
                with gr.Column(scale=1):
                    gr.Markdown("### 🔬 **Analysis Controls**")
                    
                    # Species filtering
                    species_filter = gr.CheckboxGroup(
                        label="Species Selection",
                        choices=["Human", "Dog"],
                        value=["Human", "Dog"],
                        info="Select which species to display"
                    )
                    
                    # Emotional state filtering
                    emotion_filter = gr.CheckboxGroup(
                        label="Emotional States",
                        choices=list(df_combined['label'].unique()),
                        value=list(df_combined['label'].unique()),
                        info="Filter by emotional expression"
                    )
                    
                    # CMT Lens selection for coloring
                    lens_selector = gr.Dropdown(
                        label="Mathematical Lens View",
                        choices=["gamma", "zeta", "airy", "bessel"],
                        value="gamma",
                        info="Choose which mathematical lens to use for analysis"
                    )
                    
                    # Advanced filtering sliders
                    with gr.Accordion("🎛️ Advanced CMT Filters", open=False):
                        gr.Markdown("**CMT Alpha Range (Geometric Consistency)**")
                        with gr.Row():
                            alpha_min = gr.Slider(
                                label="Alpha Min", minimum=0, maximum=1, value=0, step=0.01
                            )
                            alpha_max = gr.Slider(
                                label="Alpha Max", minimum=0, maximum=1, value=1, step=0.01
                            )
                        
                        gr.Markdown("**SRL Range (Complexity Level)**")
                        with gr.Row():
                            srl_min = gr.Slider(
                                label="SRL Min", minimum=0, maximum=100, value=0, step=1
                            )
                            srl_max = gr.Slider(
                                label="SRL Max", minimum=0, maximum=100, value=100, step=1
                            )
                        
                        gr.Markdown("**Feature Magnitude Range**")
                        with gr.Row():
                            feature_min = gr.Slider(
                                label="Feature Min", minimum=-3, maximum=3, value=-3, step=0.1
                            )
                            feature_max = gr.Slider(
                                label="Feature Max", minimum=-3, maximum=3, value=3, step=0.1
                            )
                    
                    # Visualization options
                    with gr.Accordion("🎨 Visualization Options", open=True):
                        point_size = gr.Slider(
                            label="Point Size",
                            minimum=2, maximum=15, value=6, step=1
                        )
                        
                        show_species_boundary = gr.Checkbox(
                            label="Show Species Boundary",
                            value=True,
                            info="Display geometric boundary between species"
                        )
                        
                        show_trajectories = gr.Checkbox(
                            label="Show Communication Trajectories",
                            value=False,
                            info="Display colorful paths connecting similar emotional expressions across species"
                        )
                        
                        color_scheme = gr.Dropdown(
                            label="Color Scheme",
                            choices=["Species", "Emotion", "CMT_Alpha", "CMT_SRL", "Cluster"],
                            value="Species",
                            info="Choose coloring strategy"
                        )
                    
                    # Real-time analysis
                    with gr.Accordion("🔍 Real-Time Analysis", open=False):
                        analysis_button = gr.Button("🔬 Analyze Selected Region", variant="primary")
                        
                        selected_info = gr.HTML(
                            label="Selection Analysis",
                            value="<i>Select points on the manifold for detailed analysis</i>"
                        )
                
                with gr.Column(scale=3):
                    # Main 3D manifold plot
                    manifold_plot = gr.Plot(
                        label="Universal Communication Manifold"
                    )
                    
                    # Statistics panel below the plot
                    with gr.Row():
                        with gr.Column():
                            species_stats = gr.HTML(
                                label="Species Statistics",
                                value=""
                            )
                        
                        with gr.Column():
                            boundary_stats = gr.HTML(
                                label="Boundary Analysis",
                                value=""
                            )
                        
                        with gr.Column():
                            similarity_stats = gr.HTML(
                                label="Cross-Species Similarity",
                                value=""
                            )
            
            # Secondary analysis views
            with gr.Row():
                with gr.Column():
                    # 2D projection plot
                    projection_2d = gr.Plot(
                        label="2D Projection View"
                    )
                
                with gr.Column():
                    # Density heatmap
                    density_plot = gr.Plot(
                        label="Communication Density Map"
                    )
            
            # Bottom analysis panel
            with gr.Row():
                with gr.Column():
                    # Feature distribution plots
                    feature_distributions = gr.Plot(
                        label="CMT Feature Distributions"
                    )
                
                with gr.Column():
                    # Correlation matrix
                    correlation_matrix = gr.Plot(
                        label="Cross-Species Feature Correlations"
                    )
            
            # Wire up all the interactive components
            manifold_inputs = [
                species_filter, emotion_filter, lens_selector,
                alpha_min, alpha_max, srl_min, srl_max, feature_min, feature_max, 
                point_size, show_species_boundary, show_trajectories, color_scheme
            ]
            
            manifold_outputs = [
                manifold_plot, projection_2d, density_plot, 
                feature_distributions, correlation_matrix,
                species_stats, boundary_stats, similarity_stats
            ]
            
            # Set up event handlers for real-time updates
            for component in manifold_inputs:
                component.change(
                    update_manifold_visualization,
                    inputs=manifold_inputs,
                    outputs=manifold_outputs
                )
            
            # Initialize the plots with default values
            demo.load(
                lambda: update_manifold_visualization(
                    ["Human", "Dog"],  # species_selection
                    list(df_combined['label'].unique()),  # emotion_selection
                    "gamma",  # lens_selection
                    0,        # alpha_min
                    1,        # alpha_max
                    0,        # srl_min
                    100,      # srl_max
                    -3,       # feature_min
                    3,        # feature_max
                    6,        # point_size
                    True,     # show_boundary
                    False,    # show_trajectories
                    "Species" # color_scheme
                ),
                outputs=manifold_outputs
            )
        
        with gr.TabItem("Interactive Holography"):
            with gr.Row():
                with gr.Column(scale=1):
                    gr.Markdown("### Cross-Species Holography Controls")
                    
                    # Species selection and automatic pairing
                    species_dropdown = gr.Dropdown(
                        label="Select Species", 
                        choices=["Dog", "Human"], 
                        value="Dog"
                    )
                    
                    # Primary file selection (filtered by species)
                    dog_files = df_combined[df_combined["source"] == "Dog"]["filepath"].astype(str).tolist()
                    human_files = df_combined[df_combined["source"] == "Human"]["filepath"].astype(str).tolist()
                    
                    primary_dropdown = gr.Dropdown(
                        label="Primary Audio File", 
                        choices=dog_files, 
                        value=dog_files[0] if dog_files else None
                    )
                    
                    # Automatically found neighbor (from opposite species)
                    neighbor_dropdown = gr.Dropdown(
                        label="Auto-Found Cross-Species Neighbor", 
                        choices=human_files, 
                        value=human_files[0] if human_files else None,
                        interactive=True  # Allow manual override
                    )
                    
                    holo_lens_dropdown = gr.Dropdown(label="CMT Lens", choices=["gamma", "zeta", "airy", "bessel"], value="gamma")
                    holo_resolution_slider = gr.Slider(label="Field Resolution", minimum=20, maximum=100, step=5, value=40)
                    holo_wavelength_slider = gr.Slider(label="Illumination Wavelength (nm)", minimum=380, maximum=750, step=5, value=550)
                    
                    # Information panels
                    primary_info_html = gr.HTML(label="Primary Audio Info")
                    neighbor_info_html = gr.HTML(label="Neighbor Audio Info")
                    
                    # Audio players
                    primary_audio_out = gr.Audio(label="Primary Audio")
                    neighbor_audio_out = gr.Audio(label="Neighbor Audio")
                    
                with gr.Column(scale=2):
                    dual_holography_plot = gr.Plot(label="Side-by-Side Holographic Comparison")
                    dual_diagnostic_plot = gr.Plot(label="Cross-Species Diagnostic Comparison")

            def update_file_choices(species):
                """Update the primary file dropdown based on selected species."""
                species_files = df_combined[df_combined["source"] == species]["filepath"].astype(str).tolist()
                return species_files

            def update_cross_species_view(species, primary_file, neighbor_file, lens, resolution, wavelength):
                if not primary_file:
                    empty_fig = go.Figure(layout={"title": "Please select a primary file."})
                    return empty_fig, empty_fig, "", "", None, None

                # Get primary row
                primary_row = df_combined[
                    (df_combined["filepath"] == primary_file) & 
                    (df_combined["source"] == species)
                ].iloc[0] if len(df_combined[
                    (df_combined["filepath"] == primary_file) & 
                    (df_combined["source"] == species)
                ]) > 0 else None
                
                if primary_row is None:
                    empty_fig = go.Figure(layout={"title": "Primary file not found."})
                    return empty_fig, empty_fig, "", "", None, None, []

                # Find cross-species neighbor if not manually selected
                if not neighbor_file:
                    neighbor_row = find_nearest_cross_species_neighbor(primary_row, df_combined)
                    if neighbor_row is not None:
                        neighbor_file = neighbor_row['filepath']
                else:
                    # Get manually selected neighbor
                    opposite_species = 'Human' if species == 'Dog' else 'Dog'
                    neighbor_row = df_combined[
                        (df_combined["filepath"] == neighbor_file) & 
                        (df_combined["source"] == opposite_species)
                    ].iloc[0] if len(df_combined[
                        (df_combined["filepath"] == neighbor_file) & 
                        (df_combined["source"] == opposite_species)
                    ]) > 0 else None

                # Get CMT data directly from CSV (no audio processing needed!)
                print(f"📊 Using preprocessed CMT data for: {primary_row['filepath']} ({lens} lens)")
                primary_cmt = get_cmt_data_from_csv(primary_row, lens)
                
                neighbor_cmt = None
                if neighbor_row is not None:
                    print(f"📊 Using preprocessed CMT data for: {neighbor_row['filepath']} ({lens} lens)")
                    neighbor_cmt = get_cmt_data_from_csv(neighbor_row, lens)
                
                # Get audio file paths only for playback
                primary_fp = resolve_audio_path(primary_row)
                neighbor_fp = resolve_audio_path(neighbor_row) if neighbor_row is not None else None

                # Create visualizations
                if primary_cmt and neighbor_cmt:
                    primary_title = f"{species}: {primary_row.get('label', 'Unknown')}"
                    neighbor_title = f"{neighbor_row['source']}: {neighbor_row.get('label', 'Unknown')}"
                    
                    dual_holo_fig = create_dual_holography_plot(
                        primary_cmt["z"], primary_cmt["phi"],
                        neighbor_cmt["z"], neighbor_cmt["phi"],
                        resolution, wavelength, primary_title, neighbor_title
                    )
                    
                    dual_diag_fig = create_dual_diagnostic_plots(
                        primary_cmt["z"], primary_cmt["w"],
                        neighbor_cmt["z"], neighbor_cmt["w"],
                        primary_title, neighbor_title
                    )
                else:
                    dual_holo_fig = go.Figure(layout={"title": "Error processing audio files"})
                    dual_diag_fig = go.Figure(layout={"title": "Error processing audio files"})

                # Build info strings with CMT diagnostic values
                primary_info = f"""
                <b>Primary:</b> {primary_row['filepath']}<br>
                <b>Species:</b> {primary_row['source']}<br>
                <b>Label:</b> {primary_row.get('label', 'N/A')}<br>
                <b>CMT α-{lens}:</b> {primary_cmt['alpha']:.4f}<br>
                <b>CMT SRL-{lens}:</b> {primary_cmt['srl']:.4f}<br>
                <b>Field Points:</b> {primary_cmt['final_count'] if primary_cmt else 0}
                """
                
                neighbor_info = ""
                if neighbor_row is not None:
                    neighbor_info = f"""
                    <b>Neighbor:</b> {neighbor_row['filepath']}<br>
                    <b>Species:</b> {neighbor_row['source']}<br>
                    <b>Label:</b> {neighbor_row.get('label', 'N/A')}<br>
                    <b>CMT α-{lens}:</b> {neighbor_cmt['alpha']:.4f}<br>
                    <b>CMT SRL-{lens}:</b> {neighbor_cmt['srl']:.4f}<br>
                    <b>Field Points:</b> {neighbor_cmt['final_count'] if neighbor_cmt else 0}
                    """

                # Update neighbor dropdown choices
                opposite_species = 'Human' if species == 'Dog' else 'Dog'
                neighbor_choices = df_combined[df_combined["source"] == opposite_species]["filepath"].astype(str).tolist()
                
                # Audio files
                primary_audio = primary_fp if primary_fp and os.path.exists(primary_fp) else None
                neighbor_audio = neighbor_fp if neighbor_row is not None and neighbor_fp and os.path.exists(neighbor_fp) else None

                return (dual_holo_fig, dual_diag_fig, primary_info, neighbor_info, 
                        primary_audio, neighbor_audio)

            # Event handlers
            def update_dropdowns_on_species_change(species):
                """Update both primary and neighbor dropdowns when species changes."""
                species_files = df_combined[df_combined["source"] == species]["filepath"].astype(str).tolist()
                opposite_species = 'Human' if species == 'Dog' else 'Dog'
                neighbor_files = df_combined[df_combined["source"] == opposite_species]["filepath"].astype(str).tolist()
                
                primary_value = species_files[0] if species_files else ""
                neighbor_value = neighbor_files[0] if neighbor_files else ""
                
                return (
                    gr.Dropdown(choices=species_files, value=primary_value),
                    gr.Dropdown(choices=neighbor_files, value=neighbor_value)
                )
            
            species_dropdown.change(
                update_dropdowns_on_species_change,
                inputs=[species_dropdown],
                outputs=[primary_dropdown, neighbor_dropdown]
            )

            cross_species_inputs = [species_dropdown, primary_dropdown, neighbor_dropdown, 
                                  holo_lens_dropdown, holo_resolution_slider, holo_wavelength_slider]
            cross_species_outputs = [dual_holography_plot, dual_diagnostic_plot, 
                                   primary_info_html, neighbor_info_html,
                                   primary_audio_out, neighbor_audio_out]

            # Only bind change events, not load events to avoid overwhelming initialization
            primary_dropdown.change(update_cross_species_view, 
                                   inputs=cross_species_inputs, 
                                   outputs=cross_species_outputs)
            neighbor_dropdown.change(update_cross_species_view, 
                                   inputs=cross_species_inputs, 
                                   outputs=cross_species_outputs)
            holo_lens_dropdown.change(update_cross_species_view, 
                                    inputs=cross_species_inputs, 
                                    outputs=cross_species_outputs)
            holo_resolution_slider.change(update_cross_species_view, 
                                        inputs=cross_species_inputs, 
                                        outputs=cross_species_outputs)
            holo_wavelength_slider.change(update_cross_species_view, 
                                        inputs=cross_species_inputs, 
                                        outputs=cross_species_outputs)

if __name__ == "__main__":
    demo.launch(share=True, debug=True)