"""
Speaker Classifier - 聲紋品質評估與性別分類

提供品質計算與性別分類功能，作為 main_fixed.py 的輔助模組。
"""

import numpy as np


def compute_embedding_quality(embeddings, labels):
    """每個 embedding 到所屬 cluster centroid 的餘弦相似度

    Args:
        embeddings: [n_segments, 192] 聲紋向量矩陣
        labels: [n_segments] 聚類標籤

    Returns:
        qualities: [n_segments] 品質分數 (0-1)
    """
    from sklearn.metrics.pairwise import cosine_similarity

    unique_labels = set(labels)
    centroids = {}
    for label in unique_labels:
        mask = labels == label
        centroid = np.mean(embeddings[mask], axis=0)
        norm = np.linalg.norm(centroid)
        if norm > 0:
            centroid = centroid / norm
        centroids[label] = centroid

    qualities = []
    for emb, label in zip(embeddings, labels):
        sim = cosine_similarity([emb], [centroids[label]])[0][0]
        qualities.append(sim)

    return np.array(qualities)


def classify_gender(audio_wav, sample_rate, classifier):
    """從音頻段分類性別

    Args:
        audio_wav: 音頻波形 (numpy array)
        sample_rate: 採樣率
        classifier: SpeechBrain EncoderClassifier (gender-recognition-ecapa)

    Returns:
        dict: {"gender": "male"|"female"|"unknown", "confidence": float}
    """
    default = {"gender": "unknown", "confidence": 0.0}
    if classifier is None or len(audio_wav) == 0:
        return default
    try:
        import torch
        seg_tensor = torch.from_numpy(audio_wav).float().unsqueeze(0)
        out = classifier.classify_batch(seg_tensor)
        probs = torch.softmax(out[0], dim=-1).squeeze().cpu().detach().numpy()
        if len(probs) >= 2:
            idx = int(np.argmax(probs))
            label = "male" if idx == 0 else "female"
            return {"gender": label, "confidence": float(probs[idx])}
    except Exception as e:
        pass
    return default