import numpy as np
import librosa

def extract_features(audio, sr):
    features = {}

    pitches, magnitudes = librosa.piptrack(y=audio, sr=sr)
    pitch_values = pitches[pitches > 0]

    features["pitch_mean"] = float(np.mean(pitch_values)) if len(pitch_values) > 0 else 0.0
    features["pitch_std"] = float(np.std(pitch_values)) if len(pitch_values) > 0 else 0.0

    mfcc = librosa.feature.mfcc(y=audio, sr=sr, n_mfcc=13)
    mfcc_means = np.mean(mfcc, axis=1)

    for i, val in enumerate(mfcc_means):
        features[f"mfcc_{i+1}"] = float(val)

    centroid = librosa.feature.spectral_centroid(y=audio, sr=sr)
    features["spectral_centroid_mean"] = float(np.mean(centroid))

    rms = librosa.feature.rms(y=audio)
    features["rms_std"] = float(np.std(rms))

    zcr = librosa.feature.zero_crossing_rate(y=audio)
    features["zcr_mean"] = float(np.mean(zcr))

    return features