from sklearn.linear_model import LogisticRegression
from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score
from sklearn.preprocessing import LabelEncoder

def perform_classification(data, data_name, target_name, test_size):
    X = data[data_name]
    y = data[target_name]

    label_encoders = {}
    for column in X.select_dtypes(include=['object']).columns:
        le = LabelEncoder()
        X[column] = le.fit_transform(X[column])
        label_encoders[column] = le

    if y.dtype == 'object':
        le = LabelEncoder()
        y = le.fit_transform(y)
        label_encoders[target_name] = le
    else:
        if y.nunique() > 10:
            raise ValueError("The target variable seems to be continuous. Please select a categorical target for classification.")
    
    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=test_size, random_state=42)

    model = LogisticRegression()
    model.fit(X_train, y_train)
    y_pred = model.predict(X_test)
    accuracy = accuracy_score(y_test, y_pred)

    return model, label_encoders, accuracy

def make_prediction(model, label_encoders, data_name, target_name, input_values):
    X_new = []
    for feature, value in zip(data_name, input_values):
        if feature in label_encoders:
            value = label_encoders[feature].transform([value])[0]
        X_new.append(value)
    
    prediction = model.predict([X_new])

    if target_name in label_encoders:
        prediction = label_encoders[target_name].inverse_transform(prediction)
    
    return prediction[0]