from sentence_transformers import SentenceTransformer,util from sklearn.model_selection import train_test_split from sklearn.linear_model import LogisticRegression import pandas as pd import os import sys src_directory = os.path.abspath(os.path.join(os.path.dirname(__file__), "../..", "src")) sys.path.append(src_directory) from data import sample_data import numpy as np model = SentenceTransformer('Alibaba-NLP/gte-base-en-v1.5', trust_remote_code=True) encoding_model = model logreg_model = None X_train_embeddings = None file_path = r"src/data/sms_process_data_main.xlsx" df = sample_data.get_data_frame(file_path) def train_model(): global logreg_model, X_train_embeddings if logreg_model is None: X_train, X_test, y_train, y_test = train_test_split(df['MessageText'], df['label'], test_size=0.2, random_state=42) X_train_embeddings = encoding_model.encode(X_train.tolist()) logreg_model = LogisticRegression(max_iter=100) logreg_model.fit(X_train_embeddings, y_train) def get_prediction(message): if logreg_model is None: raise ValueError("Model has not been trained yet. Please call train_model first.") new_embeddings = encoding_model.encode([message]) array = np.array(new_embeddings)[0].tolist() no_of_dimensions = len(new_embeddings[0]) dimension_df = pd.DataFrame(array, columns=["Dimension"]) prediction = logreg_model.predict(new_embeddings).tolist() return no_of_dimensions, dimension_df, prediction def get_cosine_similarity(msg_1: str, msg_2: str): embeddings = encoding_model.encode([msg_1, msg_2]) similarity = util.cos_sim(embeddings[0], embeddings[1]).item() return round(similarity, 4)