input: Write a python function to train a logistic regression model and make predictions using a provided dataset.
  assert len(train_and_predict('data.csv')[0]) == len(train_and_predict('data.csv')[1])
  assert train_and_predict('data.csv')[0].shape[1] == 2
output: def train_and_predict(file_path):
      import pandas as pd
      from sklearn.model_selection import train_test_split
      from sklearn.linear_model import LogisticRegression
      from sklearn.metrics import accuracy_score
  
      # Load the dataset
      data = pd.read_csv(file_path)
  
      # Assuming the last column is the target variable
      X = data.iloc[:, :-1]  # Features
      y = data.iloc[:, -1]   # Target
  
      # Split the dataset into training and test sets
      X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
  
      # Initialize and train the logistic regression model
      model = LogisticRegression()
      model.fit(X_train, y_train)
  
      # Make predictions
      y_pred = model.predict(X_test)
  
      return (X_test.values, y_pred)