app1.py

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

# ----------------------------
# Fixed: Added missing segment_audio function
# ----------------------------

def segment_audio(y, sr, frame_length_ms, hop_length_ms, window_type="hann"):
    """Segment audio into frames with specified windowing"""
    frame_length = int(frame_length_ms * sr / 1000)
    hop_length = int(hop_length_ms * sr / 1000)
    
    # Get window function
    if window_type == "rectangular":
        window = scipy_get_window('boxcar', frame_length)
    else:
        window = scipy_get_window(window_type, frame_length)
    
    frames = []
    for i in range(0, len(y) - frame_length + 1, hop_length):
        frame = y[i:i + frame_length] * window
        frames.append(frame)
    
    # Convert to 2D array (frames x samples)
    if frames:
        frames = np.array(frames).T
    else:
        # If audio is too short, create at least one frame with zero-padding
        frames = np.zeros((frame_length, 1))
    
    return frames, frame_length

# ----------------------------
# Enhanced Feature Extraction (with spectral bins)
# ----------------------------

def extract_features_with_spectrum(frames, sr):
    features = []
    n_mfcc = 13
    n_fft = min(2048, frames.shape[0])  # Fixed: Ensure n_fft <= frame length
    
    for i in range(frames.shape[1]):
        frame = frames[:, i]
        
        # Skip if frame is too short or silent
        if len(frame) < n_fft or np.max(np.abs(frame)) < 1e-10:
            continue
            
        feat = {}

        # Basic features with error handling
        try:
            rms = np.mean(librosa.feature.rms(y=frame)[0])
            feat["rms"] = float(rms)
        except:
            feat["rms"] = 0.0

        try:
            sc = np.mean(librosa.feature.spectral_centroid(y=frame, sr=sr)[0])
            feat["spectral_centroid"] = float(sc)
        except:
            feat["spectral_centroid"] = 0.0

        try:
            zcr = np.mean(librosa.feature.zero_crossing_rate(frame)[0])
            feat["zcr"] = float(zcr)
        except:
            feat["zcr"] = 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

        # Spectral bins for lost frequencies
        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)

            # Split spectrum: low (<2kHz), mid (2-4kHz), high (>4kHz)
            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 0.0
            feat["mid_freq_energy"] = float(np.mean(S_db[mid_mask])) if np.any(mid_mask) else 0.0
            feat["high_freq_energy"] = float(np.mean(S_db[high_mask])) if np.any(high_mask) else 0.0

            # Store full spectrum for later (optional)
            feat["spectrum"] = S_db  # will be used for heatmap
        except:
            feat["low_freq_energy"] = 0.0
            feat["mid_freq_energy"] = 0.0
            feat["high_freq_energy"] = 0.0
            feat["spectrum"] = np.zeros((n_fft // 2 + 1, 1))

        features.append(feat)
    
    # Handle case where no features were extracted
    if not features:
        # Create one dummy feature set to avoid errors
        feat = {
            "rms": 0.0, "spectral_centroid": 0.0, "zcr": 0.0,
            "low_freq_energy": 0.0, "mid_freq_energy": 0.0, "high_freq_energy": 0.0,
            "spectrum": np.zeros((n_fft // 2 + 1, 1))
        }
        for j in range(n_mfcc):
            feat[f"mfcc_{j+1}"] = 0.0
        features.append(feat)
        
    return features

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))}
    
    # Prepare vectors
    near_df = pd.DataFrame([f for f in near_feats[:min_len]])
    far_df = pd.DataFrame([f for f in far_feats[:min_len]])

    # Remove non-numeric columns
    near_vec = near_df.drop(columns=["spectrum"], errors="ignore").values
    far_vec = far_df.drop(columns=["spectrum"], errors="ignore").values

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

    # 2. Cosine Similarity
    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)
            # Handle zero vectors
            if np.all(a == 0) and np.all(b == 0):
                cos_vals.append(1.0)
            elif 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

    # 3. Pearson Correlation
    if "Pearson Correlation" in metrics:
        corr_vals = []
        for i in range(min_len):
            try:
                corr, _ = pearsonr(near_vec[i], far_vec[i])
                corr_vals.append(float(corr) if not np.isnan(corr) else 0.0)
            except:
                corr_vals.append(0.0)
        results["pearson_corr"] = corr_vals

    # 4. KL Divergence (on normalized features)
    if "KL Divergence" in metrics:
        kl_vals = []
        for i in range(min_len):
            try:
                p = near_vec[i] - near_vec[i].min() + 1e-8
                q = far_vec[i] - far_vec[i].min() + 1e-8
                p /= p.sum()
                q /= q.sum()
                kl = np.sum(p * np.log(p / q))
                kl_vals.append(float(kl))
            except:
                kl_vals.append(0.0)
        results["kl_divergence"] = kl_vals

    # 5. Jensen-Shannon Divergence (symmetric, safer)
    if "Jensen-Shannon Divergence" in metrics:
        js_vals = []
        for i in range(min_len):
            try:
                p = near_vec[i] - near_vec[i].min() + 1e-8
                q = far_vec[i] - far_vec[i].min() + 1e-8
                p /= p.sum()
                q /= q.sum()
                js = jensenshannon(p, q)
                js_vals.append(float(js))
            except:
                js_vals.append(0.0)
        results["js_divergence"] = js_vals

    # 6. Lost High Frequencies Ratio
    if "High-Freq Loss Ratio" in metrics:
        loss_ratios = []
        for i in range(min_len):
            try:
                near_high = near_feats[i]["high_freq_energy"]
                far_high = far_feats[i]["high_freq_energy"]
                # Ratio: how much high-freq energy is lost (positive = loss)
                ratio = near_high - far_high  # in dB
                loss_ratios.append(float(ratio))
            except:
                loss_ratios.append(0.0)
        results["high_freq_loss_db"] = loss_ratios

    # 7. Spectral Centroid Shift
    if "Spectral Centroid Shift" in metrics:
        shifts = []
        for i in range(min_len):
            try:
                shift = near_feats[i]["spectral_centroid"] - far_feats[i]["spectral_centroid"]
                shifts.append(float(shift))
            except:
                shifts.append(0.0)
        results["centroid_shift"] = shifts

    return pd.DataFrame(results)

def cluster_frames_custom(features_df, cluster_features, algo, n_clusters=5, eps=0.5):
    if not cluster_features:
        raise gr.Error("Please select at least one feature for clustering.")
    
    if len(features_df) == 0:
        features_df["cluster"] = []
        return features_df
        
    X = features_df[cluster_features].values

    if algo == "KMeans":
        n_clusters = min(n_clusters, len(X))  # Fixed: Cannot have more clusters than samples
        model = KMeans(n_clusters=n_clusters, random_state=42, n_init=10)
        labels = model.fit_predict(X)
    elif algo == "Agglomerative":
        n_clusters = min(n_clusters, len(X))
        model = AgglomerativeClustering(n_clusters=n_clusters)
        labels = model.fit_predict(X)
    elif algo == "DBSCAN":
        # Fixed: DBSCAN doesn't use n_clusters parameter
        model = DBSCAN(eps=eps, min_samples=min(3, len(X)))
        labels = model.fit_predict(X)
    else:
        raise ValueError("Unknown clustering algorithm")

    features_df = features_df.copy()
    features_df["cluster"] = labels
    return features_df

def plot_spectral_difference(near_feats, far_feats, frame_idx=0):
    if not near_feats or not far_feats or frame_idx >= len(near_feats) or frame_idx >= len(far_feats):
        # Return empty plot
        fig = go.Figure()
        fig.update_layout(title="No data available for spectral analysis", height=300)
        return fig
        
    near_spec = near_feats[frame_idx]["spectrum"]
    far_spec = far_feats[frame_idx]["spectrum"]
    
    # Ensure both spectrograms have the same shape
    min_freq_bins = min(near_spec.shape[0], far_spec.shape[0])
    near_spec = near_spec[:min_freq_bins]
    far_spec = far_spec[:min_freq_bins]
    
    diff = near_spec - far_spec  # positive = energy lost in far-field

    fig = go.Figure(data=go.Heatmap(
        z=diff,  # Fixed: Removed extra list brackets
        colorscale='RdBu',
        zmid=0,
        colorbar=dict(title="dB Difference")
    ))
    fig.update_layout(
        title=f"Spectral Difference (Frame {frame_idx}): Near - Far",
        xaxis_title="Time Frames",
        yaxis_title="Frequency Bins",
        height=300
    )
    return fig

# ----------------------------
# Main Analysis Function
# ----------------------------

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 audio files.")

    try:
        # Fixed: Use librosa.load instead of non-existent librosa.load_audio
        y_near, sr_near = librosa.load(near_file.name, sr=None)
        y_far, sr_far = librosa.load(far_file.name, sr=None)
    except Exception as e:
        raise gr.Error(f"Error loading audio files: {str(e)}")

    if sr_near != sr_far:
        y_far = librosa.resample(y_far, orig_sr=sr_far, target_sr=sr_near)
        sr = sr_near
    else:
        sr = sr_near

    frames_near, frame_length = 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)

    # Comparison
    comparison_df = compare_frames_enhanced(near_feats, far_feats, comparison_metrics)

    # Clustering (on near-field)
    near_df = pd.DataFrame(near_feats)
    near_df = near_df.drop(columns=["spectrum"], errors="ignore")
    clustered_df = cluster_frames_custom(near_df, cluster_features, clustering_algo, n_clusters, dbscan_eps)

    # Plots
    plot_comparison = None
    if comparison_df.shape[1] > 1 and len(comparison_df) > 0:
        metric_cols = [col for col in comparison_df.columns if col != "frame_index"]
        if metric_cols:
            metric_to_plot = metric_cols[0]
            plot_comparison = px.line(
                comparison_df,
                x="frame_index",
                y=metric_to_plot,
                title=f"{metric_to_plot.replace('_', ' ').title()} Over Time"
            )
        else:
            plot_comparison = px.line(title="No comparison metrics available")
    else:
        plot_comparison = px.line(title="No comparison data available")

    # Scatter: user-selected features
    plot_scatter = None
    if len(cluster_features) >= 2 and len(clustered_df) > 0:
        x_feat, y_feat = cluster_features[0], cluster_features[1]
        if x_feat in clustered_df.columns and y_feat in clustered_df.columns:
            plot_scatter = px.scatter(
                clustered_df,
                x=x_feat,
                y=y_feat,
                color="cluster",
                title=f"Clustering: {x_feat} vs {y_feat}",
                hover_data=["cluster"]
            )
        else:
            plot_scatter = px.scatter(title="Selected features not available in data")
    else:
        plot_scatter = px.scatter(title="Select ≥2 features for scatter plot")

    # Spectral difference heatmap (first frame)
    spec_heatmap = plot_spectral_difference(near_feats, far_feats, frame_idx=0)

    return (
        plot_comparison,
        comparison_df,
        plot_scatter,
        clustered_df,
        spec_heatmap
    )

def export_results(comparison_df, clustered_df):
    temp_dir = tempfile.mkdtemp()
    comp_path = os.path.join(temp_dir, "frame_comparisons.csv")
    cluster_path = os.path.join(temp_dir, "clustered_frames.csv")
    comparison_df.to_csv(comp_path, index=False)
    clustered_df.to_csv(cluster_path, index=False)
    return [comp_path, cluster_path]

# ----------------------------
# Gradio UI
# ----------------------------

# Get feature names dynamically
dummy_features = ["rms", "spectral_centroid", "zcr"] + [f"mfcc_{i}" for i in range(1,14)] + \
                 ["low_freq_energy", "mid_freq_energy", "high_freq_energy"]

with gr.Blocks(title="Advanced Near vs Far Field Analyzer") as demo:
    gr.Markdown("# 🎙️ Advanced Near vs Far Field Speech Analyzer")
    gr.Markdown("Upload simultaneous recordings. Analyze **lost frequencies**, **frame degradation**, and **cluster by custom attributes**.")

    with gr.Row():
        near_file = gr.File(label="Near-Field Audio (.wav)", file_types=[".wav"])
        far_file = gr.File(label="Far-Field Audio (.wav)", file_types=[".wav"])

    with gr.Accordion("⚙️ Frame Settings", open=True):
        frame_length_ms = gr.Slider(10, 500, value=50, step=1, label="Frame Length (ms)")
        hop_length_ms = gr.Slider(1, 250, value=25, step=1, label="Hop Length (ms)")
        window_type = gr.Dropdown(["hann", "hamming", "rectangular"], value="hann", label="Window Type")

    with gr.Accordion("📊 Comparison Metrics", open=True):
        comparison_metrics = gr.CheckboxGroup(
            choices=[
                "Euclidean Distance",
                "Cosine Similarity",
                "Pearson Correlation",
                "KL Divergence",
                "Jensen-Shannon Divergence",
                "High-Freq Loss Ratio",
                "Spectral Centroid Shift"
            ],
            value=["High-Freq Loss Ratio", "Cosine Similarity"],
            label="Select Comparison Metrics"
        )

    with gr.Accordion("🧩 Clustering Configuration", open=True):
        cluster_features = gr.CheckboxGroup(
            choices=dummy_features,
            value=["rms", "spectral_centroid", "high_freq_energy"],
            label="Features to Use for Clustering"
        )
        clustering_algo = gr.Radio(
            ["KMeans", "Agglomerative", "DBSCAN"],
            value="KMeans",
            label="Clustering Algorithm"
        )
        n_clusters = gr.Slider(2, 20, value=5, step=1, label="Number of Clusters (for KMeans/Agglomerative)")
        dbscan_eps = gr.Slider(0.1, 2.0, value=0.5, step=0.1, label="DBSCAN eps (neighborhood radius)")

    btn = gr.Button("🚀 Analyze")

    with gr.Tabs():
        with gr.Tab("📈 Frame Comparison"):
            comp_plot = gr.Plot()
            comp_table = gr.Dataframe()

        with gr.Tab("🧩 Clustering"):
            cluster_plot = gr.Plot()
            cluster_table = gr.Dataframe()

        with gr.Tab("🔍 Spectral Analysis"):
            spec_heatmap = gr.Plot(label="Spectral Difference (Near - Far)")

    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]
    )

    export_btn.click(
        fn=export_results,
        inputs=[comp_table, cluster_table],
        outputs=export_files
    )

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