app.py

import gradio as gr
import librosa
import numpy as np
import pandas as pd
from sklearn.cluster import KMeans, AgglomerativeClustering, DBSCAN
from sklearn.preprocessing import StandardScaler
from sklearn.metrics.pairwise import cosine_similarity
from scipy import signal
from scipy.signal import get_window as scipy_get_window
from scipy.stats import pearsonr
import plotly.express as px
import plotly.graph_objects as go
import os
import tempfile

# ----------------------------
# 1. Signal Alignment & Preprocessing
# ----------------------------
def align_signals(ref, target):
    """Aligns target signal to reference signal using Cross-Correlation."""
    ref_norm = librosa.util.normalize(ref)
    target_norm = librosa.util.normalize(target)
    
    correlation = signal.fftconvolve(target_norm, ref_norm[::-1], mode='full')
    lags = signal.correlation_lags(len(target_norm), len(ref_norm), mode='full')
    lag = lags[np.argmax(correlation)]
    
    if lag > 0:
        aligned_target = target[lag:]
        aligned_ref = ref
    else:
        aligned_target = target
        aligned_ref = ref[abs(lag):]

    min_len = min(len(aligned_ref), len(aligned_target))
    return aligned_ref[:min_len], aligned_target[:min_len]

# ----------------------------
# 2. Segment Audio
# ----------------------------
def segment_audio(y, sr, frame_length_ms, hop_length_ms, window_type="hann"):
    frame_length = int(frame_length_ms * sr / 1000)
    hop_length = int(hop_length_ms * sr / 1000)
    window = scipy_get_window(window_type if window_type != "rectangular" else "boxcar", frame_length)
    frames = []
    y_padded = np.pad(y, (0, frame_length), mode='constant')
    
    for i in range(0, len(y) - frame_length + 1, hop_length):
        frame = y[i:i + frame_length] * window
        frames.append(frame)
        
    if frames:
        frames = np.array(frames).T
    else:
        frames = np.zeros((frame_length, 1))
    return frames, frame_length

# ----------------------------
# 3. Feature Extraction
# ----------------------------
def extract_features_with_spectrum(frames, sr):
    features = []
    n_mfcc = 13
    n_fft = min(2048, frames.shape[0])
    
    for i in range(frames.shape[1]):
        frame = frames[:, i]
        if len(frame) < n_fft or np.max(np.abs(frame)) < 1e-10:
            feat = {k: 0.0 for k in ["rms", "spectral_centroid", "zcr", "spectral_flatness", 
                                     "low_freq_energy", "mid_freq_energy", "high_freq_energy"]}
            for j in range(n_mfcc): feat[f"mfcc_{j+1}"] = 0.0
            feat["spectrum"] = np.zeros((n_fft // 2 + 1, 1))
            features.append(feat)
            continue

        feat = {}
        feat["rms"] = float(np.mean(librosa.feature.rms(y=frame)[0]))
        feat["zcr"] = float(np.mean(librosa.feature.zero_crossing_rate(frame)[0]))
        
        try: feat["spectral_centroid"] = float(np.mean(librosa.feature.spectral_centroid(y=frame, sr=sr)[0]))
        except: feat["spectral_centroid"] = 0.0
            
        try: feat["spectral_flatness"] = float(np.mean(librosa.feature.spectral_flatness(y=frame)[0]))
        except: feat["spectral_flatness"] = 0.0

        try:
            mfccs = librosa.feature.mfcc(y=frame, sr=sr, n_mfcc=n_mfcc, n_fft=n_fft)
            for j in range(n_mfcc): feat[f"mfcc_{j+1}"] = float(np.mean(mfccs[j]))
        except:
            for j in range(n_mfcc): feat[f"mfcc_{j+1}"] = 0.0

        try:
            S = np.abs(librosa.stft(frame, n_fft=n_fft))
            S_db = librosa.amplitude_to_db(S, ref=np.max)
            freqs = librosa.fft_frequencies(sr=sr, n_fft=n_fft)
            low_mask = freqs <= 2000
            mid_mask = (freqs > 2000) & (freqs <= 4000)
            high_mask = freqs > 4000
            feat["low_freq_energy"] = float(np.mean(S_db[low_mask])) if np.any(low_mask) else -80.0
            feat["mid_freq_energy"] = float(np.mean(S_db[mid_mask])) if np.any(mid_mask) else -80.0
            feat["high_freq_energy"] = float(np.mean(S_db[high_mask])) if np.any(high_mask) else -80.0
            feat["spectrum"] = S_db
        except:
            feat["low_freq_energy"] = feat["mid_freq_energy"] = feat["high_freq_energy"] = -80.0
            feat["spectrum"] = np.zeros((n_fft // 2 + 1, 1))
            
        features.append(feat)
    return features

# ----------------------------
# 4. Frame Comparison
# ----------------------------
def compare_frames_enhanced(near_feats, far_feats, metrics):
    min_len = min(len(near_feats), len(far_feats))
    if min_len == 0: return pd.DataFrame({"frame_index": []})

    results = {"frame_index": list(range(min_len))}
    near_df = pd.DataFrame(near_feats[:min_len])
    far_df = pd.DataFrame(far_feats[:min_len])
    
    drop_cols = ["spectrum"]
    near_vec = near_df.drop(columns=drop_cols, errors="ignore").select_dtypes(include=[np.number]).values
    far_vec = far_df.drop(columns=drop_cols, errors="ignore").select_dtypes(include=[np.number]).values

    if "Euclidean Distance" in metrics:
        results["euclidean_dist"] = np.linalg.norm(near_vec - far_vec, axis=1).tolist()

    if "Cosine Similarity" in metrics:
        cos_vals = []
        for i in range(min_len):
            a, b = near_vec[i].reshape(1, -1), far_vec[i].reshape(1, -1)
            if np.all(a == 0) or np.all(b == 0): cos_vals.append(0.0)
            else: cos_vals.append(float(cosine_similarity(a, b)[0][0]))
        results["cosine_similarity"] = cos_vals

    if "High-Freq Loss Ratio" in metrics:
        loss_ratios = []
        for i in range(min_len):
            loss_ratios.append(float(near_feats[i]["high_freq_energy"] - far_feats[i]["high_freq_energy"]))
        results["high_freq_loss_db"] = loss_ratios

    overlap_scores = []
    for i in range(min_len):
        near_spec = near_feats[i]["spectrum"].flatten()
        far_spec = far_feats[i]["spectrum"].flatten()
        if np.all(near_spec == 0) or np.all(far_spec == 0): overlap_scores.append(0.0)
        else: overlap_scores.append(float(cosine_similarity(near_spec.reshape(1, -1), far_spec.reshape(1, -1))[0][0]))
    results["spectral_overlap"] = overlap_scores

    combined = []
    for i in range(min_len):
        score = (results["spectral_overlap"][i] * 0.5) 
        if "cosine_similarity" in results: score += (results["cosine_similarity"][i] * 0.5)
        combined.append(score)
    results["combined_match_score"] = combined
    
    return pd.DataFrame(results)

# ----------------------------
# 5. Dual Clustering & Feature Relation (NEW)
# ----------------------------
def perform_dual_clustering(near_df, far_df, cluster_features, algo, n_clusters, eps):
    if not cluster_features: return near_df, far_df

    valid_features = [f for f in cluster_features if f in near_df.columns]
    if not valid_features: return near_df, far_df

    X_near = np.nan_to_num(near_df[valid_features].values)
    X_far = np.nan_to_num(far_df[valid_features].values)

    scaler = StandardScaler()
    X_near_scaled = scaler.fit_transform(X_near)
    X_far_scaled = scaler.transform(X_far)

    if algo == "KMeans":
        model = KMeans(n_clusters=min(n_clusters, len(X_near)), random_state=42, n_init=10)
        near_labels = model.fit_predict(X_near_scaled)
        far_labels = model.predict(X_far_scaled)
    elif algo == "Agglomerative":
        model = AgglomerativeClustering(n_clusters=min(n_clusters, len(X_near)))
        near_labels = model.fit_predict(X_near_scaled)
        far_model = AgglomerativeClustering(n_clusters=min(n_clusters, len(X_far)))
        far_labels = far_model.fit_predict(X_far_scaled) 
    elif algo == "DBSCAN":
        model = DBSCAN(eps=eps, min_samples=3)
        near_labels = model.fit_predict(X_near_scaled)
        far_labels = model.fit_predict(X_far_scaled)
    else:
        near_labels = np.zeros(len(X_near))
        far_labels = np.zeros(len(X_far))
        
    near_df = near_df.copy(); near_df["cluster"] = near_labels.astype(str)
    far_df = far_df.copy(); far_df["cluster"] = far_labels.astype(str)

    return near_df, far_df

def compute_feature_correlations(near_df, far_df, quality_scores):
    """
    Calculates the correlation between Near Features and Far Features
    weighted by the Match Quality.
    Returns a correlation matrix dataframe for plotting.
    """
    # Filter numeric columns only
    near_num = near_df.select_dtypes(include=[np.number])
    far_num = far_df.select_dtypes(include=[np.number])
    
    # We want to see: For a high quality frame, how does Near Feature X relate to Far Feature X?
    # Simple approach: Calculate Pearson Correlation of (Near_Col, Far_Col) across all frames.
    
    correlations = {}
    
    common_cols = [c for c in near_num.columns if c in far_num.columns]
    
    for col in common_cols:
        if col == "cluster": continue
        try:
            # Basic Correlation: Do Near and Far move together?
            corr, _ = pearsonr(near_num[col], far_num[col])
            correlations[col] = corr
        except:
            correlations[col] = 0.0
            
    # Also calculate correlation with Quality
    quality_corr = {}
    for col in common_cols:
         if col == "cluster": continue
         try:
             # Does this feature predict high quality?
             # e.g., Does high 'rms' usually mean better match score?
             corr, _ = pearsonr(near_num[col], quality_scores)
             quality_corr[col] = corr
         except:
             quality_corr[col] = 0.0

    return pd.DataFrame({"Near-Far Correlation": correlations, "Correlation with Quality": quality_corr})

# ----------------------------
# 6. Plotting Helpers
# ----------------------------
def generate_cluster_plot(df, x_attr, y_attr, title_suffix):
    if len(df) == 0 or x_attr not in df.columns or y_attr not in df.columns:
        return px.scatter(title="No Data")
    fig = px.scatter(
        df, x=x_attr, y=y_attr, color="cluster",
        title=f"Clustering Analysis ({title_suffix}): {x_attr} vs {y_attr}",
        color_discrete_sequence=px.colors.qualitative.Bold
    )
    return fig

def update_cluster_view(view_mode, near_df, far_df, cluster_features):
    if near_df is None or far_df is None: return px.scatter(title="Run Analysis First")
    if len(cluster_features) < 2: return px.scatter(title="Select at least 2 features")
    x_attr, y_attr = cluster_features[0], cluster_features[1]
    if view_mode == "Near Field": return generate_cluster_plot(near_df, x_attr, y_attr, "Near Field")
    else: return generate_cluster_plot(far_df, x_attr, y_attr, "Far Field")

# ----------------------------
# 7. Main Analysis
# ----------------------------
def analyze_audio_pair(
    near_file, far_file,
    frame_length_ms, hop_length_ms, window_type,
    comparison_metrics, cluster_features, clustering_algo, n_clusters, dbscan_eps
):
    if not near_file or not far_file: raise gr.Error("Upload both files.")

    # Load & Align
    y_near, sr = librosa.load(near_file.name, sr=None)
    y_far, _ = librosa.load(far_file.name, sr=sr)
    y_near = librosa.util.normalize(y_near)
    y_far = librosa.util.normalize(y_far)
    y_near, y_far = align_signals(y_near, y_far)
    
    # Process
    frames_near, _ = segment_audio(y_near, sr, frame_length_ms, hop_length_ms, window_type)
    frames_far, _ = segment_audio(y_far, sr, frame_length_ms, hop_length_ms, window_type)
    near_feats = extract_features_with_spectrum(frames_near, sr)
    far_feats = extract_features_with_spectrum(frames_far, sr)
    
    # Compare & Cluster
    comparison_df = compare_frames_enhanced(near_feats, far_feats, comparison_metrics)
    near_df_raw = pd.DataFrame(near_feats).drop(columns=["spectrum"], errors="ignore")
    far_df_raw = pd.DataFrame(far_feats).drop(columns=["spectrum"], errors="ignore")
    near_clustered, far_clustered = perform_dual_clustering(
        near_df_raw, far_df_raw, cluster_features, clustering_algo, n_clusters, dbscan_eps
    )

    # 1. Comparison Plot
    plot_comparison = go.Figure()
    for col in ["cosine_similarity", "spectral_overlap", "combined_match_score"]:
        if col in comparison_df.columns:
            plot_comparison.add_trace(go.Scatter(x=comparison_df["frame_index"], y=comparison_df[col], name=col, yaxis="y1"))
    if "high_freq_loss_db" in comparison_df.columns:
        plot_comparison.add_trace(go.Scatter(x=comparison_df["frame_index"], y=comparison_df["high_freq_loss_db"], 
                                             name="High Freq Loss (dB)", line=dict(color="red", width=1), yaxis="y2"))
    plot_comparison.update_layout(
        title="Comparison Metrics", yaxis=dict(title="Similarity"), yaxis2=dict(title="dB Loss", overlaying="y", side="right")
    )

    # 2. Cluster Plot
    init_cluster_plot = update_cluster_view("Near Field", near_clustered, far_clustered, cluster_features)

    # 3. Spectral Heatmap
    safe_idx = int(len(near_feats)/2)
    diff = near_feats[safe_idx]["spectrum"] - far_feats[safe_idx]["spectrum"]
    spec_heatmap = go.Figure(data=go.Heatmap(z=diff, colorscale='RdBu', zmid=0))
    spec_heatmap.update_layout(title=f"Spectral Diff (Frame {safe_idx})", height=350)
    
    # 4. Overlay Plot
    near_clustered["match_quality"] = comparison_df["combined_match_score"]
    if len(cluster_features) > 0:
        overlay_fig = px.scatter(near_clustered, x=cluster_features[0], y="match_quality", color="cluster", 
                                 title="Cluster vs Quality (Near Field)")
    else:
        overlay_fig = px.scatter(title="No features")

    # 5. NEW: Feature Relation Heatmap
    corr_df = compute_feature_correlations(near_clustered, far_clustered, comparison_df["combined_match_score"])
    corr_fig = px.imshow(corr_df.T, text_auto=True, aspect="auto", color_continuous_scale="RdBu", zmin=-1, zmax=1,
                         title="Feature Correlation Analysis")
    
    # 6. Scatter Matrix (Inter-feature)
    # Pick top 3 features and Quality
    top_cols = cluster_features[:3] + ["match_quality"]
    scatter_matrix_fig = px.scatter_matrix(near_clustered, dimensions=top_cols, color="cluster",
                                           title="Inter-Feature Scatter Matrix (Near Field)")

    return (plot_comparison, comparison_df, 
            init_cluster_plot, near_clustered, 
            spec_heatmap, overlay_fig, 
            corr_fig, scatter_matrix_fig,
            near_clustered, far_clustered)

def export_results(comparison_df, near_df, far_df):
    temp_dir = tempfile.mkdtemp()
    p1 = os.path.join(temp_dir, "comparison.csv")
    p2 = os.path.join(temp_dir, "near_clusters.csv")
    p3 = os.path.join(temp_dir, "far_clusters.csv")
    comparison_df.to_csv(p1, index=False)
    near_df.to_csv(p2, index=False)
    far_df.to_csv(p3, index=False)
    return [p1, p2, p3]

# ----------------------------
# 8. Gradio UI
# ----------------------------
feature_list = ["rms", "spectral_centroid", "zcr", "spectral_flatness", 
                "low_freq_energy", "mid_freq_energy", "high_freq_energy"] + [f"mfcc_{i}" for i in range(1, 14)]

with gr.Blocks(title="Audio Field Analyzer", theme=gr.themes.Soft()) as demo:
    state_near_df = gr.State()
    state_far_df = gr.State()

    gr.Markdown("# 🎙️ Near vs Far Field Analyzer (Dual-Clustering)")
    
    with gr.Row():
        near_file = gr.File(label="Near-Field (Ref)", file_types=[".wav"])
        far_file = gr.File(label="Far-Field (Target)", file_types=[".wav"])

    with gr.Accordion("⚙️ Settings", open=False):
        frame_length_ms = gr.Slider(10, 200, value=30, label="Frame Length (ms)")
        hop_length_ms = gr.Slider(5, 100, value=15, label="Hop Length (ms)")
        window_type = gr.Dropdown(["hann", "hamming"], value="hann", label="Window")
        comparison_metrics = gr.CheckboxGroup(["Cosine Similarity", "High-Freq Loss Ratio"], value=["Cosine Similarity", "High-Freq Loss Ratio"], label="Metrics")
        cluster_features = gr.CheckboxGroup(feature_list, value=["spectral_centroid", "spectral_flatness", "rms"], label="Clustering Features")
        clustering_algo = gr.Dropdown(["KMeans", "Agglomerative"], value="KMeans", label="Algorithm")
        n_clusters = gr.Slider(2, 10, value=4, step=1, label="Clusters")
        dbscan_eps = gr.Slider(0.1, 5.0, value=0.5, visible=False)

    btn = gr.Button("🚀 Analyze", variant="primary")

    with gr.Tabs():
        with gr.Tab("📈 Comparison"):
            comp_plot = gr.Plot()
            comp_table = gr.Dataframe()
        with gr.Tab("🧩 Phoneme Clustering"):
            view_toggle = gr.Radio(["Near Field", "Far Field"], value="Near Field", label="View Mode")
            cluster_plot = gr.Plot()
            cluster_table = gr.Dataframe()
        with gr.Tab("🔍 Spectral"):
            spec_heatmap = gr.Plot()
        with gr.Tab("🧭 Overlay"):
            overlay_plot = gr.Plot()
        with gr.Tab("🔗 Feature Relations"):
            gr.Markdown("### Correlation Heatmap & Scatter Matrix")
            corr_plot = gr.Plot(label="Correlation Heatmap")
            scatter_matrix_plot = gr.Plot(label="Scatter Matrix")

    with gr.Tab("📤 Export"):
        export_btn = gr.Button("Download CSVs")
        export_files = gr.Files()

    btn.click(
        fn=analyze_audio_pair,
        inputs=[near_file, far_file, frame_length_ms, hop_length_ms, window_type,
                comparison_metrics, cluster_features, clustering_algo, n_clusters, dbscan_eps],
        outputs=[comp_plot, comp_table, 
                 cluster_plot, cluster_table, 
                 spec_heatmap, overlay_plot, 
                 corr_plot, scatter_matrix_plot,
                 state_near_df, state_far_df]
    )

    view_toggle.change(fn=update_cluster_view, inputs=[view_toggle, state_near_df, state_far_df, cluster_features], outputs=[cluster_plot])
    export_btn.click(fn=export_results, inputs=[comp_table, state_near_df, state_far_df], outputs=export_files)

if __name__ == "__main__":
    demo.launch()