input: Write a python function to load a CSV file, separate the target variable and input variables, split data into train and test sets, build a logistic regression model, and make predictions.
  assert load_and_predict('data.csv')[0].shape[1] == 2
  assert load_and_predict('data.csv')[1].shape[0] == int(len(pd.read_csv('data.csv'))*0.2)
output: def load_and_predict(filepath):
      import pandas as pd
      from sklearn.model_selection import train_test_split
      from sklearn.linear_model import LogisticRegression
  
      # Load the CSV file
      data = pd.read_csv(filepath)
      
      # Separate target variable and input variables
      X = data.drop(columns=['target'])
      y = data['target']
      
      # Split data into train and test sets
      X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
      
      # Build a logistic regression model
      model = LogisticRegression()
      model.fit(X_train, y_train)
      
      # Make predictions
      predictions = model.predict(X_test)
      
      return X_test, predictions