from sklearn.linear_model import LogisticRegression
from sklearn.model_selection import train_test_split
from sklearn.metrics import classification_report
import joblib

def train_and_evaluate(X, y, test_size=0.2, random_state=42):
    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=test_size, random_state=random_state)
    
    model = LogisticRegression(max_iter=500)
    model.fit(X_train, y_train)
    
    y_pred = model.predict(X_test)
    print(classification_report(y_test, y_pred))
    
    return model

def save_model(model, filename):
    joblib.dump(model, filename)

def load_model(filename):
    return joblib.load(filename)