models.py

from sentence_transformers import SentenceTransformer
from sklearn.linear_model import LogisticRegression
import pickle
from sklearn.model_selection import train_test_split
import joblib

import pandas as pd

def get_embedding(text):
    model_encode = SentenceTransformer('Alibaba-NLP/gte-base-en-v1.5', trust_remote_code=True)
    embedding = model_encode.encode(text)
    return embedding

def train_model():
    sample_data_df = pd.read_excel("sms_process_data_main.xlsx")
    sample_data_df.dropna(subset=['MessageText', 'label'], inplace=True)
    input = sample_data_df['MessageText']
    label = sample_data_df['label']
    X_train, X_test, y_train, y_test = train_test_split(input, label, test_size=0.2, random_state=42)
    X_train_embeddings = get_embedding(X_train.tolist())
    log_reg_model = LogisticRegression( max_iter = 1000)
    log_reg_model.fit(X_train_embeddings, y_train)
    save_model(log_reg_model,'log_reg_model.pkl')
    return log_reg_model

def save_model(model, filename):
    with open(filename, 'wb') as model_file:
        pickle.dump(model, model_file)
    print(f"Model saved to {filename}")


def load_model(filename):
    # loaded_model = joblib.load('log_reg_model.pkl')
    with open(filename, 'rb') as model_file:
        loaded_model = pickle.load(model_file)
    print(f"Model loaded from {filename}")
    return loaded_model