input: Write a python function to train a linear regression model to predict humidity based on date and temperature from a given CSV file named 'weather.csv'.
  assert round(predict_humidity('weather.csv')[0], 2) == 65.32
  assert round(predict_humidity('weather.csv')[5], 2) == 58.47
  assert isinstance(predict_humidity('weather.csv'), list)
output: def predict_humidity(file_path):
      import pandas as pd
      from sklearn.linear_model import LinearRegression
      from sklearn.model_selection import train_test_split
      
      # Load the data
      data = pd.read_csv(file_path)
      
      # Extract features and target variable
      X = data[['Date', 'Temperature']]
      y = data['Humidity']
      
      # Convert 'Date' to datetime and then to numerical format
      X['Date'] = pd.to_datetime(X['Date']).map(pd.Timestamp.toordinal)
      
      # Split the data 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 linear regression model
      model = LinearRegression()
      model.fit(X_train, y_train)
      
      # Predict humidity on the test set
      y_pred = model.predict(X_test)
      
      # Return predictions as a list
      return y_pred.tolist()