code: #%% Import Flask and create an app object
  import config
  from dotenv import load_dotenv
  load_dotenv()
  
  import os
  import json
  import asyncio
  import openai
  import pprint as pp
  import markdown
  
  # openai.api_key = os.getenv("OPENAI_API_KEY")
  # os.environ["OPENAI_API_KEY"] = os.getenv("OPENAI_API_KEY")
  # Import Flask and create an app object
  from flask import Flask, render_template, request, jsonify
  app = Flask(__name__)
  
  # Import asyncio and the async_playwright module from Playwright
  import asyncio
  from playwright.async_api import async_playwright
  
  # Import LangChain and other modules
  import langchain
  from langchain.agents import AgentType
  from langchain.chat_models import ChatOpenAI
  from langchain.agents import initialize_agent, load_tools
  
  
  from langchain.agents.agent_toolkits import PlayWrightBrowserToolkit
  from langchain.tools.playwright.utils import (
      create_async_playwright_browser,
      create_sync_playwright_browser, # A synchronous browser is available, though it isn't compatible with jupyter.
  )
  
  # This import is required only for jupyter notebooks, since they have their own eventloop
  # import nest_asyncio
  # nest_asyncio.apply()
  
  #%% Agent
  def get_agent():
      # LLM using ChatOpenAI
      llm = ChatOpenAI(model_name="gpt-4", temperature=0)  # Also works well with Anthropic models
      # Get the tools from the toolkit
      tools = load_tools(["serpapi"], llm=llm)
      # Create an agent using ChatOpenAI and initialize it with tools and handle_parsing_errors=True
      agent_chain = initialize_agent(
          tools=tools,
          llm=llm,
          agent=AgentType.STRUCTURED_CHAT_ZERO_SHOT_REACT_DESCRIPTION,
          verbose=True,
          handle_parsing_errors=True
      )
  
  # agent_chain = get_agent()
  
  #%%
  
  @app.route("/")
  def home():
      # Use some initial chat history (empty, in this case)
      chat_history = []
      return render_template("chat.html", chat_history=chat_history)
  
  
  @app.route("/respond")
  def respond():
      """ Create another route for your chatbot logic
      """
      print("\nrespond::start:")
      # Get the user message and the chat history from the interface
      message = request.args.get("message")
      print(f"  respond::message: {message}")
      # Parse the chat history as a JSON object
      chat_history = json.loads(request.args.get("chat_history", "[]"))
      print(f"  respond::chat_history: {chat_history}")
      # Run your main coroutine that handles the chatbot logic
      # response = asyncio.run(main(message, chat_history))
      chat_history = main(message, chat_history)
      response = jsonify({"chat_history": chat_history})
      print("\nrespond::return:")
      print(f"  respond::chat_history: {chat_history}")
      # print response to console as json
      pp.pprint(response.json)
  
      # Return the response as a JSON string
      return response
  
  
  def main(message, chat_history):
      """ Define your main coroutine that handles the chatbot logic
      """
      print("\nmain::start:")
      print(f"  main::message: {message}")
      print(f"  main::chat_history: {chat_history}")
      # Run your agent chain with the user message as input and get the bot message as output
      bot_message = "I'm a STICK!!!\n\n```python\nprint('Hello World')\n```"
      # Convert bot_message to HTML
      bot_message_html = markdown.markdown(bot_message)
  
      # Append the user message and the bot message to the chat history
      chat_history[-1][1] = bot_message_html
  
      print("\nmain::return:")
      print(f"  main::bot_message: {bot_message}")
      print(f"  main::bot_message_html: {bot_message_html}")
      print(f"  main::chat_history: {chat_history}")
      
      # Return the updated chat history as a string
      return chat_history
  
  # def main(message, chat_history):
  #     """ Define your main coroutine that handles the chatbot logic
  #     """
  #     print("\nmain::start:")
  #     print(f"  main::message: {message}")
  #     print(f"  main::chat_history: {chat_history}")
  #     # Run your agent chain with the user message as input and get the bot message as output
  #     # bot_message = "I'm a STICK!!!\n\n```python\nprint('Hello World')\n```" # agent_chain.run(input=message)
  #     bot_message = "I'm a STICK!!!<br><br><pre><code>print('Hello World')</code></pre>"
  #     print("\nmain::return:")
  #     print(f"  main::bot_message: {bot_message}")
  
  #     # Append the user message and the bot message to the chat history
  #     chat_history[-1][1] = bot_message
  #     print(f"  main::chat_history: {chat_history}")
      
  #     # Return the updated chat history as a string
  #     return chat_history
  
  async def amain(message, chat_history):
      """ Define your main coroutine that handles the chatbot logic
      """
      # Create one event loop before creating the tools
      loop = asyncio.new_event_loop()
      asyncio.set_event_loop(loop)
  
      # Use async with to create and close the async browser automatically
      async with async_playwright() as playwright:
          # Use await to launch a Chromium browser asynchronously
          browser = await playwright.chromium.launch()
          # Create a PlayWrightBrowserToolkit from the browser
          browser_toolkit = PlayWrightBrowserToolkit.from_browser(async_browser=browser)
          # Get the tools from the toolkit
          tools = browser_toolkit.get_tools()
  
          # Create an agent using ChatOpenAI and initialize it with tools and handle_parsing_errors=True
          llm = ChatOpenAI(model_name="gpt-4", temperature=0)  # Also works well with Anthropic models
          agent_chain = initialize_agent(
              tools=tools,
              llm=llm,
              agent=AgentType.STRUCTURED_CHAT_ZERO_SHOT_REACT_DESCRIPTION,
              verbose=True,
              handle_parsing_errors=True
          )
  
          # Run your agent chain with the user message as input and get the bot message as output
          bot_message = await agent_chain.arun(input=message)
  
          # Append the user message and the bot message to the chat history
          chat_history.append((message, bot_message))
  
          # Return the updated chat history as a string
          return str(chat_history)
  
  # Run your Flask app with app.run()
  if __name__ == "__main__":
      app.run(
          host="localhost",
          port=None,
          debug=True,
          load_dotenv=True,
      )
  
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