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| import joblib | |
| from sentence_transformers import SentenceTransformer | |
| def load_model(): | |
| clf = joblib.load("model/logistic_model.pkl") | |
| s2v_model = SentenceTransformer( | |
| "Pachinee/sentence2vec-brd" # ← Hugging Face Model | |
| ) | |
| return clf, s2v_model | |
| def predict_label(texts, clf, s2v_model): | |
| embeddings = s2v_model.encode(list(texts)) | |
| preds = clf.predict(embeddings) | |
| return ["Clear" if p == 1 else "Unclear" for p in preds] | |