input: Write a function to predict stock prices using SVR based on given data.
  assert predict_stock_prices('test-data1.csv') == expected_prediction1
  assert predict_stock_prices('test-data2.csv') == expected_prediction2
  assert predict_stock_prices('test-data3.csv') == expected_prediction3
output: def predict_stock_prices(file_path):
      import pandas as pd
      from sklearn.svm import SVR
      from sklearn.preprocessing import StandardScaler
      import numpy as np
  
      # Load the data
      data = pd.read_csv(file_path)
  
      # Assuming the CSV file has columns 'Date' and 'Close'
      # Prepare the data (we need to convert dates to ordinal for SVR)
      data['Date'] = pd.to_datetime(data['Date'])
      data['Date'] = data['Date'].map(pd.Timestamp.toordinal)
  
      # Features and labels
      X = data['Date'].values.reshape(-1, 1)
      y = data['Close'].values
  
      # Feature scaling
      scaler_X = StandardScaler()
      scaler_y = StandardScaler()
      
      X_scaled = scaler_X.fit_transform(X)
      y_scaled = scaler_y.fit_transform(y.reshape(-1, 1)).ravel()
  
      # Train the SVR model
      svr = SVR(kernel='rbf', C=1e3, gamma=0.1)
      svr.fit(X_scaled, y_scaled)
  
      # Predict using the model
      y_pred_scaled = svr.predict(X_scaled)
      y_pred = scaler_y.inverse_transform(y_pred_scaled)
  
      # Assuming we need to return the last predicted price
      return float(y_pred[-1])