{
 "cells": [
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a href=\"https://colab.research.google.com/github/microsoft/FLAML/blob/main/notebook/autogen_agentchat_auto_feedback_from_code_execution.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# Auto Generated Agent Chat: Task Solving with Code Generation, Execution & Debugging\n",
    "\n",
    "`flaml.autogen` offers conversable agents powered by LLM, tool or human, which can be used to perform tasks collectively via automated chat. This framwork allows tool use and human participance through multi-agent conversation.\n",
    "Please find documentation about this feature [here](https://microsoft.github.io/FLAML/docs/Use-Cases/Autogen#agents).\n",
    "\n",
    "In this notebook, we demonstrate how to use `AssistantAgent` and `UserProxyAgent` to write code and execute the code. Here `AssistantAgent` is an LLM-based agent that can write Python code (in a Python coding block) for a user to execute for a given task. `UserProxyAgent` is an agent which serves as a proxy for the human user to execute the code written by `AssistantAgent`, or automatically execute the code. Depending on the setting of `human_input_mode` and `max_consecutive_auto_reply`, the `UserProxyAgent` either solicits feedback from the human user or returns auto-feedback based on the result of code execution (success or failure and corresponding outputs) to `AssistantAgent`. `AssistantAgent` will debug the code and suggest new code if the result contains error. The two agents keep communicating to each other until the task is done.\n",
    "\n",
    "## Requirements\n",
    "\n",
    "FLAML requires `Python>=3.8`. To run this notebook example, please install flaml with the [autogen] option:\n",
    "```bash\n",
    "pip install flaml[autogen]\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-02-13T23:40:52.317406Z",
     "iopub.status.busy": "2023-02-13T23:40:52.316561Z",
     "iopub.status.idle": "2023-02-13T23:40:52.321193Z",
     "shell.execute_reply": "2023-02-13T23:40:52.320628Z"
    }
   },
   "outputs": [],
   "source": [
    "# %pip install flaml[autogen]~=2.0.0"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Set your API Endpoint\n",
    "\n",
    "The [`config_list_from_json`](https://microsoft.github.io/FLAML/docs/reference/autogen/oai/openai_utils#config_list_from_json) function loads a list of configurations from an environment variable or a json file.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "from flaml import autogen\n",
    "\n",
    "config_list = autogen.config_list_from_json(\n",
    "    \"OAI_CONFIG_LIST\",\n",
    "    filter_dict={\n",
    "        \"model\": [\"gpt-4\", \"gpt4\", \"gpt-4-32k\", \"gpt-4-32k-0314\", \"gpt-4-32k-v0314\"],\n",
    "    },\n",
    ")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It first looks for environment variable \"OAI_CONFIG_LIST\" which needs to be a valid json string. If that variable is not found, it then looks for a json file named \"OAI_CONFIG_LIST\". It filters the configs by models (you can filter by other keys as well). Only the gpt-4 models are kept in the list based on the filter condition.\n",
    "\n",
    "The config list looks like the following:\n",
    "```python\n",
    "config_list = [\n",
    "    {\n",
    "        'model': 'gpt-4',\n",
    "        'api_key': '<your OpenAI API key here>',\n",
    "    },\n",
    "    {\n",
    "        'model': 'gpt-4',\n",
    "        'api_key': '<your Azure OpenAI API key here>',\n",
    "        'api_base': '<your Azure OpenAI API base here>',\n",
    "        'api_type': 'azure',\n",
    "        'api_version': '2023-06-01-preview',\n",
    "    },\n",
    "    {\n",
    "        'model': 'gpt-4-32k',\n",
    "        'api_key': '<your Azure OpenAI API key here>',\n",
    "        'api_base': '<your Azure OpenAI API base here>',\n",
    "        'api_type': 'azure',\n",
    "        'api_version': '2023-06-01-preview',\n",
    "    },\n",
    "]\n",
    "```\n",
    "\n",
    "If you open this notebook in colab, you can upload your files by clicking the file icon on the left panel and then choose \"upload file\" icon.\n",
    "\n",
    "You can set the value of config_list in other ways you prefer, e.g., loading from a YAML file."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Example Task: Check Stock Price Change\n",
    "\n",
    "In the example below, let's see how to use the agents in FLAML to write a python script and execute the script. This process involves constructing a `AssistantAgent` to serve as the assistant, along with a `UserProxyAgent` that acts as a proxy for the human user. In this example demonstrated below, when constructing the `UserProxyAgent`,  we select the `human_input_mode` to \"NEVER\". This means that the `UserProxyAgent` will not solicit feedback from the human user. It stops replying when the limit defined by `max_consecutive_auto_reply` is reached, or when `is_termination_msg()` returns true for the received message."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[33muser_proxy\u001b[0m (to assistant):\n",
      "\n",
      "What date is today? Compare the year-to-date gain for META and TESLA.\n",
      "\n",
      "--------------------------------------------------------------------------------\n",
      "\u001b[33massistant\u001b[0m (to user_proxy):\n",
      "\n",
      "To get the current date and compare the year-to-date gain for META (Facebook) and TESLA, we'll need to fetch stock data. We can use the `yfinance` library for this purpose. First, you'll need to install the library by running the following command:\n",
      "\n",
      "```sh\n",
      "pip install yfinance\n",
      "```\n",
      "\n",
      "After installing the library, execute the following Python code to get the current date and compare the year-to-date gain for META and TESLA:\n",
      "\n",
      "```python\n",
      "import yfinance as yf\n",
      "from datetime import datetime, timedelta\n",
      "\n",
      "# Get today's date\n",
      "today = datetime.now()\n",
      "print(f\"Today's date is: {today.strftime('%Y-%m-%d')}\")\n",
      "\n",
      "# Calculate the start date for year-to-date\n",
      "start_date = datetime(today.year, 1, 1)\n",
      "\n",
      "# Fetch stock data for META (Facebook) and TESLA\n",
      "meta = yf.Ticker(\"FB\")\n",
      "tesla = yf.Ticker(\"TSLA\")\n",
      "\n",
      "# Get historical stock data\n",
      "meta_history = meta.history(start=start_date, end=today)\n",
      "tesla_history = tesla.history(start=start_date, end=today)\n",
      "\n",
      "# Calculate year-to-date gain\n",
      "meta_ytd_gain = (meta_history[\"Close\"][-1] - meta_history[\"Close\"][0]) / meta_history[\"Close\"][0] * 100\n",
      "tesla_ytd_gain = (tesla_history[\"Close\"][-1] - tesla_history[\"Close\"][0]) / tesla_history[\"Close\"][0] * 100\n",
      "\n",
      "# Print the results\n",
      "print(f\"META year-to-date gain: {meta_ytd_gain:.2f}%\")\n",
      "print(f\"TESLA year-to-date gain: {tesla_ytd_gain:.2f}%\")\n",
      "```\n",
      "\n",
      "--------------------------------------------------------------------------------\n",
      "\u001b[31m\n",
      ">>>>>>>> EXECUTING CODE BLOCK 0 (inferred language is sh)...\u001b[0m\n",
      "\u001b[31m\n",
      ">>>>>>>> EXECUTING CODE BLOCK 1 (inferred language is python)...\u001b[0m\n",
      "\u001b[33muser_proxy\u001b[0m (to assistant):\n",
      "\n",
      "exitcode: 1 (execution failed)\n",
      "Code output: \n",
      "Defaulting to user installation because normal site-packages is not writeable\n",
      "Requirement already satisfied: yfinance in /home/vscode/.local/lib/python3.9/site-packages (0.2.26)\n",
      "Requirement already satisfied: lxml>=4.9.1 in /home/vscode/.local/lib/python3.9/site-packages (from yfinance) (4.9.3)\n",
      "Requirement already satisfied: requests>=2.31 in /usr/local/lib/python3.9/site-packages (from yfinance) (2.31.0)\n",
      "Requirement already satisfied: multitasking>=0.0.7 in /home/vscode/.local/lib/python3.9/site-packages (from yfinance) (0.0.11)\n",
      "Requirement already satisfied: appdirs>=1.4.4 in /home/vscode/.local/lib/python3.9/site-packages (from yfinance) (1.4.4)\n",
      "Requirement already satisfied: html5lib>=1.1 in /home/vscode/.local/lib/python3.9/site-packages (from yfinance) (1.1)\n",
      "Requirement already satisfied: numpy>=1.16.5 in /home/vscode/.local/lib/python3.9/site-packages (from yfinance) (1.25.1)\n",
      "Requirement already satisfied: pandas>=1.3.0 in /home/vscode/.local/lib/python3.9/site-packages (from yfinance) (1.3.3)\n",
      "Requirement already satisfied: frozendict>=2.3.4 in /home/vscode/.local/lib/python3.9/site-packages (from yfinance) (2.3.8)\n",
      "Requirement already satisfied: beautifulsoup4>=4.11.1 in /home/vscode/.local/lib/python3.9/site-packages (from yfinance) (4.12.2)\n",
      "Requirement already satisfied: pytz>=2022.5 in /home/vscode/.local/lib/python3.9/site-packages (from yfinance) (2023.3)\n",
      "Requirement already satisfied: soupsieve>1.2 in /home/vscode/.local/lib/python3.9/site-packages (from beautifulsoup4>=4.11.1->yfinance) (2.4.1)\n",
      "Requirement already satisfied: webencodings in /home/vscode/.local/lib/python3.9/site-packages (from html5lib>=1.1->yfinance) (0.5.1)\n",
      "Requirement already satisfied: six>=1.9 in /usr/local/lib/python3.9/site-packages (from html5lib>=1.1->yfinance) (1.16.0)\n",
      "Requirement already satisfied: python-dateutil>=2.7.3 in /home/vscode/.local/lib/python3.9/site-packages (from pandas>=1.3.0->yfinance) (2.8.2)\n",
      "Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.9/site-packages (from requests>=2.31->yfinance) (2.0.3)\n",
      "Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.9/site-packages (from requests>=2.31->yfinance) (3.2.0)\n",
      "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.9/site-packages (from requests>=2.31->yfinance) (2023.5.7)\n",
      "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.9/site-packages (from requests>=2.31->yfinance) (3.4)\n",
      "\n",
      "FB: No timezone found, symbol may be delisted\n",
      "Traceback (most recent call last):\n",
      "  File \"\", line 20, in <module>\n",
      "    meta_ytd_gain = (meta_history[\"Close\"][-1] - meta_history[\"Close\"][0]) / meta_history[\"Close\"][0] * 100\n",
      "  File \"/home/vscode/.local/lib/python3.9/site-packages/pandas/core/series.py\", line 939, in __getitem__\n",
      "    return self._values[key]\n",
      "IndexError: index -1 is out of bounds for axis 0 with size 0\n",
      "\n",
      "\n",
      "--------------------------------------------------------------------------------\n",
      "\u001b[33massistant\u001b[0m (to user_proxy):\n",
      "\n",
      "I apologize for the error. It seems that the stock symbol for Meta Platforms (formerly Facebook) has changed to \"META\" instead of \"FB\". Let's update the code with the correct stock symbol and try again:\n",
      "\n",
      "```python\n",
      "import yfinance as yf\n",
      "from datetime import datetime, timedelta\n",
      "\n",
      "# Get today's date\n",
      "today = datetime.now()\n",
      "print(f\"Today's date is: {today.strftime('%Y-%m-%d')}\")\n",
      "\n",
      "# Calculate the start date for year-to-date\n",
      "start_date = datetime(today.year, 1, 1)\n",
      "\n",
      "# Fetch stock data for META (Meta Platforms) and TESLA\n",
      "meta = yf.Ticker(\"META\")\n",
      "tesla = yf.Ticker(\"TSLA\")\n",
      "\n",
      "# Get historical stock data\n",
      "meta_history = meta.history(start=start_date, end=today)\n",
      "tesla_history = tesla.history(start=start_date, end=today)\n",
      "\n",
      "# Calculate year-to-date gain\n",
      "meta_ytd_gain = (meta_history[\"Close\"][-1] - meta_history[\"Close\"][0]) / meta_history[\"Close\"][0] * 100\n",
      "tesla_ytd_gain = (tesla_history[\"Close\"][-1] - tesla_history[\"Close\"][0]) / tesla_history[\"Close\"][0] * 100\n",
      "\n",
      "# Print the results\n",
      "print(f\"META year-to-date gain: {meta_ytd_gain:.2f}%\")\n",
      "print(f\"TESLA year-to-date gain: {tesla_ytd_gain:.2f}%\")\n",
      "```\n",
      "\n",
      "--------------------------------------------------------------------------------\n",
      "\u001b[31m\n",
      ">>>>>>>> EXECUTING CODE BLOCK 0 (inferred language is python)...\u001b[0m\n",
      "\u001b[33muser_proxy\u001b[0m (to assistant):\n",
      "\n",
      "exitcode: 0 (execution succeeded)\n",
      "Code output: \n",
      "Today's date is: 2023-07-31\n",
      "META year-to-date gain: 157.94%\n",
      "TESLA year-to-date gain: 145.71%\n",
      "\n",
      "\n",
      "--------------------------------------------------------------------------------\n",
      "\u001b[33massistant\u001b[0m (to user_proxy):\n",
      "\n",
      "Great! The code executed successfully. Today's date is 2023-07-31. The year-to-date gain for META (Meta Platforms) is 157.94%, and the year-to-date gain for TESLA is 145.71%. \n",
      "\n",
      "If you have any other questions or need further assistance, feel free to ask.\n",
      "\n",
      "TERMINATE\n",
      "\n",
      "--------------------------------------------------------------------------------\n"
     ]
    }
   ],
   "source": [
    "# create an AssistantAgent named \"assistant\"\n",
    "assistant = autogen.AssistantAgent(\n",
    "    name=\"assistant\",\n",
    "    llm_config={\n",
    "        \"seed\": 42,  # seed for caching and reproducibility\n",
    "        \"config_list\": config_list,  # a list of OpenAI API configurations\n",
    "        \"temperature\": 0,  # temperature for sampling\n",
    "    },  # configuration for autogen's enhanced inference API which is compatible with OpenAI API\n",
    ")\n",
    "# create a UserProxyAgent instance named \"user_proxy\"\n",
    "user_proxy = autogen.UserProxyAgent(\n",
    "    name=\"user_proxy\",\n",
    "    human_input_mode=\"NEVER\",\n",
    "    max_consecutive_auto_reply=10,\n",
    "    is_termination_msg=lambda x: x.get(\"content\", \"\").rstrip().endswith(\"TERMINATE\"),\n",
    "    code_execution_config={\n",
    "        \"work_dir\": \"coding\",\n",
    "        \"use_docker\": False,  # set to True or image name like \"python:3\" to use docker\n",
    "    },\n",
    ")\n",
    "# the assistant receives a message from the user_proxy, which contains the task description\n",
    "user_proxy.initiate_chat(\n",
    "    assistant,\n",
    "    message=\"\"\"What date is today? Compare the year-to-date gain for META and TESLA.\"\"\",\n",
    ")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The example above involves code execution. In FLAML, code execution is triggered automatically by the `UserProxyAgent` when it detects an executable code block in a received message and no human user input is provided. This process occurs in a designated working directory, using a Docker container by default. Unless a specific directory is specified, FLAML defaults to the `flaml/autogen/extensions` directory. Users have the option to specify a different working directory by setting the `work_dir` argument when constructing a new instance of the `UserProxyAgent`.\n",
    "\n",
    "The whole chat is auto generated."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Example Task: Plot Chart"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[33muser_proxy\u001b[0m (to assistant):\n",
      "\n",
      "Plot a chart of their stock price change YTD and save to stock_price_ytd.png.\n",
      "\n",
      "--------------------------------------------------------------------------------\n",
      "\u001b[33massistant\u001b[0m (to user_proxy):\n",
      "\n",
      "To plot a chart of the stock price change YTD and save it to a file, we'll use the `matplotlib` library. First, you'll need to install the library by running the following command:\n",
      "\n",
      "```sh\n",
      "pip install matplotlib\n",
      "```\n",
      "\n",
      "After installing the library, execute the following Python code to plot the chart and save it to \"stock_price_ytd.png\":\n",
      "\n",
      "```python\n",
      "import matplotlib.pyplot as plt\n",
      "\n",
      "# Plot the stock price change YTD\n",
      "plt.figure(figsize=(10, 5))\n",
      "plt.plot(meta_history.index, meta_history[\"Close\"], label=\"META\")\n",
      "plt.plot(tesla_history.index, tesla_history[\"Close\"], label=\"TESLA\")\n",
      "plt.xlabel(\"Date\")\n",
      "plt.ylabel(\"Stock Price\")\n",
      "plt.title(\"Stock Price Change YTD\")\n",
      "plt.legend()\n",
      "\n",
      "# Save the chart to a file\n",
      "plt.savefig(\"stock_price_ytd.png\")\n",
      "plt.show()\n",
      "```\n",
      "\n",
      "This code will create a chart of the stock price change YTD for META and TESLA and save it to a file named \"stock_price_ytd.png\".\n",
      "\n",
      "--------------------------------------------------------------------------------\n",
      "\u001b[31m\n",
      ">>>>>>>> EXECUTING CODE BLOCK 0 (inferred language is sh)...\u001b[0m\n",
      "\u001b[31m\n",
      ">>>>>>>> EXECUTING CODE BLOCK 1 (inferred language is python)...\u001b[0m\n",
      "\u001b[33muser_proxy\u001b[0m (to assistant):\n",
      "\n",
      "exitcode: 1 (execution failed)\n",
      "Code output: \n",
      "Defaulting to user installation because normal site-packages is not writeable\n",
      "Requirement already satisfied: matplotlib in /home/vscode/.local/lib/python3.9/site-packages (3.7.2)\n",
      "Requirement already satisfied: pillow>=6.2.0 in /home/vscode/.local/lib/python3.9/site-packages (from matplotlib) (10.0.0)\n",
      "Requirement already satisfied: pyparsing<3.1,>=2.3.1 in /home/vscode/.local/lib/python3.9/site-packages (from matplotlib) (3.0.9)\n",
      "Requirement already satisfied: fonttools>=4.22.0 in /home/vscode/.local/lib/python3.9/site-packages (from matplotlib) (4.41.1)\n",
      "Requirement already satisfied: packaging>=20.0 in /home/vscode/.local/lib/python3.9/site-packages (from matplotlib) (23.1)\n",
      "Requirement already satisfied: importlib-resources>=3.2.0 in /home/vscode/.local/lib/python3.9/site-packages (from matplotlib) (6.0.0)\n",
      "Requirement already satisfied: python-dateutil>=2.7 in /home/vscode/.local/lib/python3.9/site-packages (from matplotlib) (2.8.2)\n",
      "Requirement already satisfied: cycler>=0.10 in /home/vscode/.local/lib/python3.9/site-packages (from matplotlib) (0.11.0)\n",
      "Requirement already satisfied: contourpy>=1.0.1 in /home/vscode/.local/lib/python3.9/site-packages (from matplotlib) (1.1.0)\n",
      "Requirement already satisfied: kiwisolver>=1.0.1 in /home/vscode/.local/lib/python3.9/site-packages (from matplotlib) (1.4.4)\n",
      "Requirement already satisfied: numpy>=1.20 in /home/vscode/.local/lib/python3.9/site-packages (from matplotlib) (1.25.1)\n",
      "Requirement already satisfied: zipp>=3.1.0 in /home/vscode/.local/lib/python3.9/site-packages (from importlib-resources>=3.2.0->matplotlib) (3.16.2)\n",
      "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.9/site-packages (from python-dateutil>=2.7->matplotlib) (1.16.0)\n",
      "\n",
      "Traceback (most recent call last):\n",
      "  File \"\", line 5, in <module>\n",
      "    plt.plot(meta_history.index, meta_history[\"Close\"], label=\"META\")\n",
      "NameError: name 'meta_history' is not defined\n",
      "\n",
      "\n",
      "--------------------------------------------------------------------------------\n",
      "\u001b[33massistant\u001b[0m (to user_proxy):\n",
      "\n",
      "I apologize for the error. It seems I forgot to include the previous code to fetch the stock data. Let's combine the code to fetch the stock data and plot the chart:\n",
      "\n",
      "```python\n",
      "import yfinance as yf\n",
      "from datetime import datetime\n",
      "import matplotlib.pyplot as plt\n",
      "\n",
      "# Get today's date\n",
      "today = datetime.now()\n",
      "\n",
      "# Calculate the start date for year-to-date\n",
      "start_date = datetime(today.year, 1, 1)\n",
      "\n",
      "# Fetch stock data for META (Meta Platforms) and TESLA\n",
      "meta = yf.Ticker(\"META\")\n",
      "tesla = yf.Ticker(\"TSLA\")\n",
      "\n",
      "# Get historical stock data\n",
      "meta_history = meta.history(start=start_date, end=today)\n",
      "tesla_history = tesla.history(start=start_date, end=today)\n",
      "\n",
      "# Plot the stock price change YTD\n",
      "plt.figure(figsize=(10, 5))\n",
      "plt.plot(meta_history.index, meta_history[\"Close\"], label=\"META\")\n",
      "plt.plot(tesla_history.index, tesla_history[\"Close\"], label=\"TESLA\")\n",
      "plt.xlabel(\"Date\")\n",
      "plt.ylabel(\"Stock Price\")\n",
      "plt.title(\"Stock Price Change YTD\")\n",
      "plt.legend()\n",
      "\n",
      "# Save the chart to a file\n",
      "plt.savefig(\"stock_price_ytd.png\")\n",
      "plt.show()\n",
      "```\n",
      "\n",
      "This code will fetch the stock data for META and TESLA, create a chart of the stock price change YTD, and save it to a file named \"stock_price_ytd.png\".\n",
      "\n",
      "--------------------------------------------------------------------------------\n",
      "\u001b[31m\n",
      ">>>>>>>> EXECUTING CODE BLOCK 0 (inferred language is python)...\u001b[0m\n",
      "\u001b[33muser_proxy\u001b[0m (to assistant):\n",
      "\n",
      "exitcode: 0 (execution succeeded)\n",
      "Code output: \n",
      "Figure(1000x500)\n",
      "\n",
      "\n",
      "--------------------------------------------------------------------------------\n",
      "\u001b[33massistant\u001b[0m (to user_proxy):\n",
      "\n",
      "Great! The code executed successfully and created a chart of the stock price change YTD for META and TESLA. The chart has been saved to a file named \"stock_price_ytd.png\".\n",
      "\n",
      "If you have any other questions or need further assistance, feel free to ask.\n",
      "\n",
      "TERMINATE\n",
      "\n",
      "--------------------------------------------------------------------------------\n"
     ]
    }
   ],
   "source": [
    "# followup of the previous question\n",
    "user_proxy.send(\n",
    "    recipient=assistant,\n",
    "    message=\"\"\"Plot a chart of their stock price change YTD and save to stock_price_ytd.png.\"\"\",\n",
    ")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's display the generated figure."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from IPython.display import Image\n",
    "\n",
    "Image(filename='coding/stock_price_ytd.png')"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Use a Different Code Execution Environment\n",
    "\n",
    "The code execution happened in a separate process, so the plot is not directly displayed in the notebook. Is it possible to change the code execution environment into IPython?\n",
    "\n",
    "Yes! In the following we demonstrate how to extend the `UserProxyAgent` to use a different code execution environment."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "from typing import Dict, Union\n",
    "from IPython import get_ipython\n",
    "\n",
    "class IPythonUserProxyAgent(autogen.UserProxyAgent):\n",
    "    def __init__(self, name: str, **kwargs):\n",
    "        super().__init__(name, **kwargs)\n",
    "        self._ipython = get_ipython()\n",
    "\n",
    "    def generate_init_message(self, *args, **kwargs) -> Union[str, Dict]:\n",
    "        return super().generate_init_message(*args, **kwargs) + \"\"\"\n",
    "If you suggest code, the code will be executed in IPython.\"\"\"\n",
    "\n",
    "    def run_code(self, code, **kwargs):\n",
    "        result = self._ipython.run_cell(\"%%capture --no-display cap\\n\" + code)\n",
    "        log = self._ipython.ev(\"cap.stdout\")\n",
    "        log += self._ipython.ev(\"cap.stderr\")\n",
    "        if result.result is not None:\n",
    "            log += str(result.result)\n",
    "        exitcode = 0 if result.success else 1\n",
    "        if result.error_before_exec is not None:\n",
    "            log += f\"\\n{result.error_before_exec}\"\n",
    "            exitcode = 1\n",
    "        if result.error_in_exec is not None:\n",
    "            log += f\"\\n{result.error_in_exec}\"\n",
    "            exitcode = 1\n",
    "        return exitcode, log, None"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The implementation overrides three functions in `UserProxyAgent`:\n",
    "* constructor. We get the ipython instance as the code execution environment.\n",
    "* `generate_init_message`. We generate a modified initial message to send to the assistant agent, by adding the info that the execution will be performed in IPython.\n",
    "* `run_code`. We execute the code with the ipython instance.\n",
    "\n",
    "With the new `IPythonUserProxyAgent`, we are able to run the code within the current notebook environment and display plot directly."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[33mipython_user_proxy\u001b[0m (to assistant):\n",
      "\n",
      "Plot a chart of META and TESLA stock price change YTD\n",
      "If you suggest code, the code will be executed in IPython.\n",
      "\n",
      "--------------------------------------------------------------------------------\n",
      "\u001b[33massistant\u001b[0m (to ipython_user_proxy):\n",
      "\n",
      "To plot a chart of META (Facebook) and TESLA stock price change YTD, you can use the `yfinance` library to fetch the stock data and `matplotlib` to plot the chart. First, you need to install the `yfinance` library if you haven't already:\n",
      "\n",
      "```sh\n",
      "pip install yfinance\n",
      "```\n",
      "\n",
      "Now, you can execute the following Python code to plot the chart:\n",
      "\n",
      "```python\n",
      "import yfinance as yf\n",
      "import matplotlib.pyplot as plt\n",
      "import datetime\n",
      "\n",
      "# Get the current date\n",
      "today = datetime.date.today()\n",
      "\n",
      "# Calculate the start date for YTD\n",
      "start_date = datetime.date(today.year, 1, 1)\n",
      "\n",
      "# Download stock data for META (Facebook) and TESLA\n",
      "meta_data = yf.download('FB', start=start_date, end=today)\n",
      "tesla_data = yf.download('TSLA', start=start_date, end=today)\n",
      "\n",
      "# Calculate the percentage change in stock prices\n",
      "meta_pct_change = meta_data['Close'].pct_change().cumsum() * 100\n",
      "tesla_pct_change = tesla_data['Close'].pct_change().cumsum() * 100\n",
      "\n",
      "# Plot the chart\n",
      "plt.figure(figsize=(12, 6))\n",
      "plt.plot(meta_pct_change, label='META (Facebook)')\n",
      "plt.plot(tesla_pct_change, label='TESLA')\n",
      "plt.xlabel('Date')\n",
      "plt.ylabel('Percentage Change YTD')\n",
      "plt.title('META (Facebook) vs TESLA Stock Price Change YTD')\n",
      "plt.legend()\n",
      "plt.grid()\n",
      "plt.show()\n",
      "```\n",
      "\n",
      "This code will fetch the stock data for META (Facebook) and TESLA, calculate the percentage change in stock prices YTD, and plot the chart.\n",
      "\n",
      "--------------------------------------------------------------------------------\n",
      "\u001b[31m\n",
      ">>>>>>>> EXECUTING CODE BLOCK 0 (inferred language is sh)...\u001b[0m\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[31m\n",
      ">>>>>>>> EXECUTING CODE BLOCK 1 (inferred language is python)...\u001b[0m\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[33mipython_user_proxy\u001b[0m (to assistant):\n",
      "\n",
      "exitcode: 0 (execution succeeded)\n",
      "Code output: \n",
      "Defaulting to user installation because normal site-packages is not writeable\n",
      "Requirement already satisfied: yfinance in /home/vscode/.local/lib/python3.9/site-packages (0.2.26)\n",
      "Requirement already satisfied: multitasking>=0.0.7 in /home/vscode/.local/lib/python3.9/site-packages (from yfinance) (0.0.11)\n",
      "Requirement already satisfied: numpy>=1.16.5 in /home/vscode/.local/lib/python3.9/site-packages (from yfinance) (1.25.1)\n",
      "Requirement already satisfied: beautifulsoup4>=4.11.1 in /home/vscode/.local/lib/python3.9/site-packages (from yfinance) (4.12.2)\n",
      "Requirement already satisfied: lxml>=4.9.1 in /home/vscode/.local/lib/python3.9/site-packages (from yfinance) (4.9.3)\n",
      "Requirement already satisfied: pandas>=1.3.0 in /home/vscode/.local/lib/python3.9/site-packages (from yfinance) (1.3.3)\n",
      "Requirement already satisfied: requests>=2.31 in /usr/local/lib/python3.9/site-packages (from yfinance) (2.31.0)\n",
      "Requirement already satisfied: pytz>=2022.5 in /home/vscode/.local/lib/python3.9/site-packages (from yfinance) (2023.3)\n",
      "Requirement already satisfied: html5lib>=1.1 in /home/vscode/.local/lib/python3.9/site-packages (from yfinance) (1.1)\n",
      "Requirement already satisfied: frozendict>=2.3.4 in /home/vscode/.local/lib/python3.9/site-packages (from yfinance) (2.3.8)\n",
      "Requirement already satisfied: appdirs>=1.4.4 in /home/vscode/.local/lib/python3.9/site-packages (from yfinance) (1.4.4)\n",
      "Requirement already satisfied: soupsieve>1.2 in /home/vscode/.local/lib/python3.9/site-packages (from beautifulsoup4>=4.11.1->yfinance) (2.4.1)\n",
      "Requirement already satisfied: webencodings in /home/vscode/.local/lib/python3.9/site-packages (from html5lib>=1.1->yfinance) (0.5.1)\n",
      "Requirement already satisfied: six>=1.9 in /usr/local/lib/python3.9/site-packages (from html5lib>=1.1->yfinance) (1.16.0)\n",
      "Requirement already satisfied: python-dateutil>=2.7.3 in /home/vscode/.local/lib/python3.9/site-packages (from pandas>=1.3.0->yfinance) (2.8.2)\n",
      "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.9/site-packages (from requests>=2.31->yfinance) (2023.5.7)\n",
      "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.9/site-packages (from requests>=2.31->yfinance) (3.4)\n",
      "Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.9/site-packages (from requests>=2.31->yfinance) (3.2.0)\n",
      "Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.9/site-packages (from requests>=2.31->yfinance) (2.0.3)\n",
      "\n",
      "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m23.0.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m23.2.1\u001b[0m\n",
      "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpip install --upgrade pip\u001b[0m\n",
      "Note: you may need to restart the kernel to use updated packages.\n",
      "\n",
      "[*********************100%***********************]  1 of 1 completed\n",
      "[*********************100%***********************]  1 of 1 completed\n",
      "\n",
      "1 Failed download:\n",
      "['FB']: Exception('%ticker%: No timezone found, symbol may be delisted')\n",
      "\n",
      "\n",
      "--------------------------------------------------------------------------------\n",
      "\u001b[33massistant\u001b[0m (to ipython_user_proxy):\n",
      "\n",
      "I apologize for the issue. It seems that the stock symbol for Meta Platforms (formerly Facebook) has changed from 'FB' to 'META'. Let's update the code with the correct stock symbol and try again:\n",
      "\n",
      "```python\n",
      "import yfinance as yf\n",
      "import matplotlib.pyplot as plt\n",
      "import datetime\n",
      "\n",
      "# Get the current date\n",
      "today = datetime.date.today()\n",
      "\n",
      "# Calculate the start date for YTD\n",
      "start_date = datetime.date(today.year, 1, 1)\n",
      "\n",
      "# Download stock data for META (Facebook) and TESLA\n",
      "meta_data = yf.download('META', start=start_date, end=today)\n",
      "tesla_data = yf.download('TSLA', start=start_date, end=today)\n",
      "\n",
      "# Calculate the percentage change in stock prices\n",
      "meta_pct_change = meta_data['Close'].pct_change().cumsum() * 100\n",
      "tesla_pct_change = tesla_data['Close'].pct_change().cumsum() * 100\n",
      "\n",
      "# Plot the chart\n",
      "plt.figure(figsize=(12, 6))\n",
      "plt.plot(meta_pct_change, label='META (Facebook)')\n",
      "plt.plot(tesla_pct_change, label='TESLA')\n",
      "plt.xlabel('Date')\n",
      "plt.ylabel('Percentage Change YTD')\n",
      "plt.title('META (Facebook) vs TESLA Stock Price Change YTD')\n",
      "plt.legend()\n",
      "plt.grid()\n",
      "plt.show()\n",
      "```\n",
      "\n",
      "This code should now fetch the stock data for META (Facebook) and TESLA, calculate the percentage change in stock prices YTD, and plot the chart.\n",
      "\n",
      "--------------------------------------------------------------------------------\n",
      "\u001b[31m\n",
      ">>>>>>>> EXECUTING CODE BLOCK 0 (inferred language is python)...\u001b[0m\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1200x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[33mipython_user_proxy\u001b[0m (to assistant):\n",
      "\n",
      "exitcode: 0 (execution succeeded)\n",
      "Code output: \n",
      "[*********************100%***********************]  1 of 1 completed\n",
      "[*********************100%***********************]  1 of 1 completed\n",
      "\n",
      "\n",
      "--------------------------------------------------------------------------------\n",
      "\u001b[33massistant\u001b[0m (to ipython_user_proxy):\n",
      "\n",
      "I'm glad the code executed successfully and you were able to plot the chart of META (Facebook) and TESLA stock price change YTD. If you have any further questions or need assistance with anything else, please feel free to ask.\n",
      "\n",
      "TERMINATE\n",
      "\n",
      "--------------------------------------------------------------------------------\n"
     ]
    }
   ],
   "source": [
    "ipy_user = IPythonUserProxyAgent(\n",
    "    \"ipython_user_proxy\",\n",
    "    human_input_mode=\"NEVER\",\n",
    "    max_consecutive_auto_reply=10,\n",
    "    is_termination_msg=lambda x: x.get(\"content\", \"\").rstrip().endswith(\"TERMINATE\") or x.get(\"content\", \"\").rstrip().endswith('\"TERMINATE\".'),\n",
    ")\n",
    "assistant.reset()\n",
    "# the assistant receives a message from the user, which contains the task description\n",
    "ipy_user.initiate_chat(\n",
    "    assistant,\n",
    "    message=\"\"\"Plot a chart of META and TESLA stock price change YTD\"\"\",\n",
    ")"
   ]
  }
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