from sentence_transformers import SentenceTransformer
import hdbscan
from sklearn.metrics import silhouette_score, davies_bouldin_score
import numpy as np

model = SentenceTransformer("shibing624/text2vec-bge-large-chinese")

def cluster_sentences(sentences):
    embeddings = model.encode(sentences)
    clusterer = hdbscan.HDBSCAN(min_cluster_size=2, metric='euclidean')
    labels = clusterer.fit_predict(embeddings)

    valid_idxs = labels != -1
    if np.sum(valid_idxs) > 1:
        silhouette = silhouette_score(embeddings[valid_idxs], labels[valid_idxs])
        db = davies_bouldin_score(embeddings[valid_idxs], labels[valid_idxs])
    else:
        silhouette, db = -1, -1

    return labels, embeddings, {"silhouette": silhouette, "db": db}