app_works.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
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

# ----------------------------
# Segment Audio into Frames
# ----------------------------
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 = []
    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

# ----------------------------
# 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:
            continue
        feat = {}
        try:
            feat["rms"] = float(np.mean(librosa.feature.rms(y=frame)[0]))
        except: feat["rms"] = 0.0
        try:
            feat["spectral_centroid"] = float(np.mean(librosa.feature.spectral_centroid(y=frame, sr=sr)[0]))
        except: feat["spectral_centroid"] = 0.0
        try:
            feat["zcr"] = float(np.mean(librosa.feature.zero_crossing_rate(frame)[0]))
        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
        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 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
            feat["spectrum"] = S_db
        except:
            feat["low_freq_energy"] = feat["mid_freq_energy"] = feat["high_freq_energy"] = 0.0
            feat["spectrum"] = np.zeros((n_fft // 2 + 1, 1))
        features.append(feat)
    if not features:
        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

# ----------------------------
# Frame Comparison (core metrics)
# ----------------------------
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([f for f in near_feats[:min_len]])
    far_df = pd.DataFrame([f for f in far_feats[:min_len]])
    near_vec = near_df.drop(columns=["spectrum"], errors="ignore").values
    far_vec = far_df.drop(columns=["spectrum"], errors="ignore").values

    # Euclidean Distance
    if "Euclidean Distance" in metrics:
        results["euclidean_dist"] = np.linalg.norm(near_vec - far_vec, axis=1).tolist()
    # 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)
            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
    # High-Freq Loss Ratio (Quality)
    if "High-Freq Loss Ratio" in metrics:
        loss_ratios = []
        for i in range(min_len):
            near_high = near_feats[i]["high_freq_energy"]
            far_high = far_feats[i]["high_freq_energy"]
            ratio = max(0.0, 1.0 - abs(near_high - far_high) / (abs(near_high) + 1e-6))
            loss_ratios.append(float(ratio))
        results["high_freq_quality"] = loss_ratios

    # 🔹 Energy Ratio
    energy_ratio = []
    for i in range(min_len):
        near_rms = near_feats[i]["rms"]; far_rms = far_feats[i]["rms"]
        ratio = (far_rms + 1e-6) / (near_rms + 1e-6)
        energy_ratio.append(float(np.clip(ratio, 0, 1)))
    results["energy_ratio"] = energy_ratio

    # 🔹 Clarity Ratio
    clarity_ratio = []
    for i in range(min_len):
        near_low, near_high = near_feats[i]["low_freq_energy"], near_feats[i]["high_freq_energy"]
        far_low, far_high = far_feats[i]["low_freq_energy"], far_feats[i]["high_freq_energy"]
        near_ratio, far_ratio = (near_low - near_high), (far_low - far_high)
        diff = 1 - abs(far_ratio - near_ratio) / (abs(near_ratio) + 1e-6)
        clarity_ratio.append(np.clip(diff, 0, 1))
    results["clarity_ratio"] = clarity_ratio

    # 🔹 Spectral Overlap
    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 = float(cosine_similarity(near_spec.reshape(1, -1), far_spec.reshape(1, -1))[0][0])
            overlap_scores.append(overlap)
    results["spectral_overlap"] = overlap_scores

    # 🔹 Combined Weighted Quality
    weights = {
        "cosine_similarity": 0.3,
        "high_freq_quality": 0.25,
        "energy_ratio": 0.2,
        "clarity_ratio": 0.15,
        "spectral_overlap": 0.1
    }
    combined_quality = []
    for i in range(min_len):
        val = sum(results[k][i] * w for k, w in weights.items() if k in results)
        combined_quality.append(float(val / sum(weights.values())))
    results["combined_quality"] = combined_quality
    return pd.DataFrame(results)

# ----------------------------
# Clustering + Overlay
# ----------------------------
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))
        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":
        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):
        fig = go.Figure(); fig.update_layout(title="No data available"); return fig
    near_spec = near_feats[frame_idx]["spectrum"]; far_spec = far_feats[frame_idx]["spectrum"]
    min_freq_bins = min(near_spec.shape[0], far_spec.shape[0])
    diff = near_spec[:min_freq_bins] - far_spec[:min_freq_bins]
    fig = go.Figure(data=go.Heatmap(z=diff, colorscale='RdBu', zmid=0))
    fig.update_layout(title=f"Spectral Difference (Frame {frame_idx})", height=300)
    return fig

def plot_cluster_overlay(df, cluster_metric, overlay_metric):
    if cluster_metric not in df.columns or overlay_metric not in df.columns:
        fig = go.Figure(); fig.update_layout(title="Metrics not found"); return fig
    fig = px.scatter(df, x=cluster_metric, y=overlay_metric, color=overlay_metric,
                     color_continuous_scale='Viridis',
                     title=f"Cluster Overlay: {cluster_metric} vs {overlay_metric}")
    fig.update_layout(height=400)
    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:
        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: {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, _ = 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_df = compare_frames_enhanced(near_feats, far_feats, comparison_metrics)
    near_df = pd.DataFrame(near_feats).drop(columns=["spectrum"], errors="ignore")
    clustered_df = cluster_frames_custom(near_df, cluster_features, clustering_algo, n_clusters, dbscan_eps)
    # Plots
    metric_cols = [col for col in comparison_df.columns if col != "frame_index"]
    plot_comparison = px.line(comparison_df, x="frame_index", y=metric_cols[0],
                              title=f"{metric_cols[0].replace('_',' ').title()} Over Time") if metric_cols else px.line()
    if len(cluster_features) >= 2 and len(clustered_df) > 0:
        x_feat, y_feat = cluster_features[0], cluster_features[1]
        plot_scatter = px.scatter(clustered_df, x=x_feat, y=y_feat, color="cluster",
                                  title=f"Clustering: {x_feat} vs {y_feat}")
    else:
        plot_scatter = px.scatter(title="Select ≥2 features for clustering")
    spec_heatmap = plot_spectral_difference(near_feats, far_feats, frame_idx=0)
    overlay_fig = plot_cluster_overlay(clustered_df, cluster_features[0], "combined_quality")
    return plot_comparison, comparison_df, plot_scatter, clustered_df, spec_heatmap, overlay_fig

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
# ----------------------------
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")
    with gr.Row():
        near_file = gr.File(label="Near-Field Audio (.wav)", file_types=[".wav"])
        far_file = gr.File(label="Far-Field Audio (.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", "High-Freq Loss Ratio"
            ],
            value=["Cosine Similarity", "High-Freq Loss Ratio"],
            label="Select Metrics"
        )

    with gr.Accordion("🧩 Clustering Configuration", open=True):
        cluster_features = gr.CheckboxGroup(
            choices=dummy_features, value=["rms", "spectral_centroid", "high_freq_energy"],
            label="Features 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="Clusters (for KMeans/Agglomerative)")
        dbscan_eps = gr.Slider(0.1, 2.0, value=0.5, step=0.1, label="DBSCAN eps")

    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("🧭 Metric Overlay"):
            overlay_plot = gr.Plot(label="Metric Overlay")

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

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