code: # Import necessary libraries
  import hubspot
  import langchain
  import openai
  import streamlit
  
  # Define function to analyze customer data using Langchain
  def analyze_customer_data(customer_data):
      langchain.analyze(customer_data)
      # returns analyzed data
  
  # Define function to send personalized appointment reminders via email and text message
  def send_appointment_reminder(customer_email, customer_phone, appt_time):
      # Create message using OpenAI language model
      message = openai.generate_message(customer_name, appt_time)
      # Send email using Hubspot API
      hubspot.send_email(customer_email, message)
      # Send text message using Hubspot API
      hubspot.send_text(customer_phone, message)
  
  # Call analyze_customer_data function on customer data
  analyzed_data = analyze_customer_data(customer_data)
  
  # Loop through customers in analyzed_data
  for customer in analyzed_data:
      # Check if customer has an appointment scheduled
      if customer['appointment_time'] != None:
          # Send personalized appointment reminder to customer via email and text message
          send_appointment_reminder(customer['email'], customer['phone'], customer['appointment_time'])
  
apis: langchain.analyze
extract_api: [((206, 238), 'langchain.analyze', 'langchain.analyze', (['customer_data'], {}), '(customer_data)\n', (223, 238), False, 'import langchain\n'), ((499, 548), 'openai.generate_message', 'openai.generate_message', (['customer_name', 'appt_time'], {}), '(customer_name, appt_time)\n', (522, 548), False, 'import openai\n'), ((590, 633), 'hubspot.send_email', 'hubspot.send_email', (['customer_email', 'message'], {}), '(customer_email, message)\n', (608, 633), False, 'import hubspot\n'), ((682, 724), 'hubspot.send_text', 'hubspot.send_text', (['customer_phone', 'message'], {}), '(customer_phone, message)\n', (699, 724), False, 'import hubspot\n')]