autogen/notebook/autogen_agent_web_info.ipynb

{
 "cells": [
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a href=\"https://colab.research.google.com/github/microsoft/FLAML/blob/main/notebook/autogen_agent_web_info.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": [
    "# Interactive LLM Agent Dealing with Web Info\n",
    "\n",
    "FLAML offers an experimental feature of interactive LLM agents, which can be used to solve various tasks with human or automatic feedback, including tasks that require using tools via code.\n",
    "\n",
    "In this notebook, we demonstrate how to use `AssistantAgent` and `UserProxyAgent` to perform tasks which require acquiring info from the web:\n",
    "* discuss a paper based on its URL.\n",
    "* discuss about stock market.\n",
    "\n",
    "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 a user to execute the code written by `AssistantAgent`. By setting `human_input_mode` properly, the `UserProxyAgent` can also prompt the user for feedback to `AssistantAgent`. For example, when `human_input_mode` is set to \"ALWAYS\", the `UserProxyAgent` will always prompt the user for feedback. When user feedback is provided, the `UserProxyAgent` will directly pass the feedback to `AssistantAgent` without doing any additional steps. When no user feedback is provided, the `UserProxyAgent` will execute the code written by `AssistantAgent` directly and return the execution results (success or failure and corresponding outputs) to `AssistantAgent`.\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]==2.0.0rc3"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Set your API Endpoint\n",
    "\n",
    "The [`config_list_openai_aoai`](https://microsoft.github.io/FLAML/docs/reference/autogen/oai/openai_utils#config_list_openai_aoai) function tries to create a list of configurations using Azure OpenAI endpoints and OpenAI endpoints. It assumes the api keys and api bases are stored in the corresponding environment variables or local txt files:\n",
    "\n",
    "- OpenAI API key: os.environ[\"OPENAI_API_KEY\"] or `openai_api_key_file=\"key_openai.txt\"`.\n",
    "- Azure OpenAI API key: os.environ[\"AZURE_OPENAI_API_KEY\"] or `aoai_api_key_file=\"key_aoai.txt\"`. Multiple keys can be stored, one per line.\n",
    "- Azure OpenAI API base: os.environ[\"AZURE_OPENAI_API_BASE\"] or `aoai_api_base_file=\"base_aoai.txt\"`. Multiple bases can be stored, one per line.\n",
    "\n",
    "It's OK to have only the OpenAI API key, or only the Azure OpenAI API key + base. 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",
    "The following code excludes openai endpoints from the config list.\n",
    "Change to `exclude=\"aoai\"` to exclude Azure OpenAI, or remove the `exclude` argument to include both.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "from flaml import oai\n",
    "\n",
    "config_list = oai.config_list_openai_aoai(exclude=\"openai\")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Construct Agents\n",
    "\n",
    "We construct the assistant agent and the user proxy agent. We specify `human_input_mode` as \"TERMINATE\" in the user proxy agent, which will ask for feedback when it receives a \"TERMINATE\" signal from the assistant agent."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "from flaml.autogen.agent import AssistantAgent, UserProxyAgent\n",
    "\n",
    "# create an AssistantAgent instance named \"assistant\"\n",
    "assistant = AssistantAgent(\n",
    "    name=\"assistant\",\n",
    "    request_timeout=600,\n",
    "    seed=42,\n",
    "    config_list=config_list,\n",
    "    model=\"gpt-4-32k\",  # modify if the endpoint you use doesn't support this model\n",
    "    temperature=0,\n",
    ")\n",
    "# create a UserProxyAgent instance named \"user\"\n",
    "user = UserProxyAgent(\n",
    "    name=\"user\",\n",
    "    human_input_mode=\"TERMINATE\",\n",
    "    max_consecutive_auto_reply=10,\n",
    "    is_termination_msg=lambda x: x.get(\"content\", \"\").rstrip().endswith(\"TERMINATE\"),\n",
    "    work_dir='web',\n",
    ")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Example Task: Paper Talk from URL\n",
    "\n",
    "We invoke the `receive()` method of the coding agent to start the conversation. When you run the cell below, you will be prompted to provide feedback after the coding agent sends a \"TERMINATE\" signal in the end of the message. If you don't provide any feedback (by pressing Enter directly), the conversation will finish. Before the \"TERMINATE\" signal, the user proxy agent will try to execute the code suggested by the coding agent on behalf of the user."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "user (to assistant):\n",
      "\n",
      "\n",
      "Who should read this paper: https://arxiv.org/abs/2306.01337\n",
      "\n",
      "\n",
      "--------------------------------------------------------------------------------\n",
      "assistant (to user):\n",
      "\n",
      "To determine who should read this paper, I will first extract the abstract and analyze its content. Here's a Python code to fetch the abstract from the given URL:\n",
      "\n",
      "```python\n",
      "import requests\n",
      "from bs4 import BeautifulSoup\n",
      "\n",
      "url = \"https://arxiv.org/abs/2306.01337\"\n",
      "response = requests.get(url)\n",
      "soup = BeautifulSoup(response.text, \"html.parser\")\n",
      "abstract = soup.find(\"blockquote\", {\"class\": \"abstract\"}).text.strip()\n",
      "print(abstract)\n",
      "```\n",
      "\n",
      "Please execute the above code to get the abstract of the paper.\n",
      "\n",
      "--------------------------------------------------------------------------------\n",
      "\n",
      ">>>>>>>> NO HUMAN INPUT RECEIVED. USING AUTO REPLY FOR THE USER...\n",
      "user (to assistant):\n",
      "\n",
      "exitcode: 0 (execution succeeded)\n",
      "Code output: \n",
      "Abstract:  Employing Large Language Models (LLMs) to address mathematical problems is an\n",
      "intriguing research endeavor, considering the abundance of math problems\n",
      "expressed in natural language across numerous science and engineering fields.\n",
      "While several prior works have investigated solving elementary mathematics\n",
      "using LLMs, this work explores the frontier of using GPT-4 for solving more\n",
      "complex and challenging math problems. We evaluate various ways of using GPT-4.\n",
      "Some of them are adapted from existing work, and one is MathChat, a\n",
      "conversational problem-solving framework newly proposed in this work. We\n",
      "perform the evaluation on difficult high school competition problems from the\n",
      "MATH dataset, which shows the advantage of the proposed conversational\n",
      "approach.\n",
      "\n",
      "\n",
      "--------------------------------------------------------------------------------\n",
      "assistant (to user):\n",
      "\n",
      "Based on the abstract, the paper is about using Large Language Models (LLMs), specifically GPT-4, to solve complex mathematical problems. The paper introduces a conversational problem-solving framework called MathChat and evaluates its performance on difficult high school competition problems from the MATH dataset.\n",
      "\n",
      "The target audience for this paper includes:\n",
      "\n",
      "1. Researchers and practitioners in the field of artificial intelligence, natural language processing, and machine learning, particularly those interested in large language models like GPT-4.\n",
      "2. Mathematicians and educators who are interested in exploring the potential of AI for solving complex mathematical problems and improving educational tools.\n",
      "3. Developers working on AI-based applications for problem-solving, tutoring, or educational purposes.\n",
      "\n",
      "If you belong to any of these categories or have a general interest in AI and its applications in mathematics, you should consider reading this paper.\n",
      "\n",
      "TERMINATE\n",
      "\n",
      "--------------------------------------------------------------------------------\n"
     ]
    }
   ],
   "source": [
    "# the assistant receives a message from the user, which contains the task description\n",
    "assistant.receive(\n",
    "    \"\"\"\n",
    "Who should read this paper: https://arxiv.org/abs/2306.01337\n",
    "\"\"\",\n",
    "    user\n",
    ")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Example Task: Chat about Stock Market"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "user (to assistant):\n",
      "\n",
      "Show me the YTD gain of 10 largest technology companies as of today.\n",
      "\n",
      "--------------------------------------------------------------------------------\n",
      "assistant (to user):\n",
      "\n",
      "To get the YTD gain of the 10 largest technology companies, we can use a Python script to fetch the data from a financial API. We'll use the free Alpha Vantage API for this purpose. First, you need to obtain an API key from the Alpha Vantage website. Please visit https://www.alphavantage.co/support/#api-key and get your API key.\n",
      "\n",
      "Once you have the API key, please replace `YOUR_API_KEY` in the code below with your actual API key and execute the code:\n",
      "\n",
      "```python\n",
      "import requests\n",
      "import json\n",
      "\n",
      "api_key = \"YOUR_API_KEY\"\n",
      "symbols = [\"AAPL\", \"MSFT\", \"GOOGL\", \"GOOG\", \"AMZN\", \"FB\", \"TSLA\", \"NVDA\", \"ADBE\", \"ASML\"]\n",
      "\n",
      "def get_ytd_gain(symbol):\n",
      "    url = f\"https://www.alphavantage.co/query?function=TIME_SERIES_DAILY_ADJUSTED&symbol={symbol}&apikey={api_key}\"\n",
      "    response = requests.get(url)\n",
      "    data = json.loads(response.text)\n",
      "    daily_data = data[\"Time Series (Daily)\"]\n",
      "    sorted_dates = sorted(daily_data.keys())\n",
      "    first_day_of_year = next(date for date in sorted_dates if date.startswith(\"2022-01\"))\n",
      "    last_day = sorted_dates[-1]\n",
      "    open_price = float(daily_data[first_day_of_year][\"1. open\"])\n",
      "    close_price = float(daily_data[last_day][\"4. close\"])\n",
      "    ytd_gain = (close_price - open_price) / open_price * 100\n",
      "    return ytd_gain\n",
      "\n",
      "ytd_gains = {symbol: get_ytd_gain(symbol) for symbol in symbols}\n",
      "print(\"YTD Gains of 10 Largest Technology Companies:\")\n",
      "for symbol, gain in ytd_gains.items():\n",
      "    print(f\"{symbol}: {gain:.2f}%\")\n",
      "```\n",
      "\n",
      "This script fetches the daily adjusted stock data for the 10 largest technology companies and calculates the YTD gain for each of them. The results will be printed in the format \"Symbol: YTD Gain%\".\n",
      "\n",
      "--------------------------------------------------------------------------------\n",
      "\n",
      ">>>>>>>> NO HUMAN INPUT RECEIVED. USING AUTO REPLY FOR THE USER...\n",
      "user (to assistant):\n",
      "\n",
      "exitcode: 1 (execution failed)\n",
      "Code output: \n",
      "Traceback (most recent call last):\n",
      "  File \"/workspaces/FLAML/notebook/web/tmp_code_0fb74c6e622b29ddd2ed9f0415582b98.py\", line 20, in <module>\n",
      "    ytd_gains = {symbol: get_ytd_gain(symbol) for symbol in symbols}\n",
      "  File \"/workspaces/FLAML/notebook/web/tmp_code_0fb74c6e622b29ddd2ed9f0415582b98.py\", line 20, in <dictcomp>\n",
      "    ytd_gains = {symbol: get_ytd_gain(symbol) for symbol in symbols}\n",
      "  File \"/workspaces/FLAML/notebook/web/tmp_code_0fb74c6e622b29ddd2ed9f0415582b98.py\", line 13, in get_ytd_gain\n",
      "    first_day_of_year = next(date for date in sorted_dates if date.startswith(\"2022-01\"))\n",
      "StopIteration\n",
      "\n",
      "\n",
      "--------------------------------------------------------------------------------\n",
      "assistant (to user):\n",
      "\n",
      "I apologize for the error. It seems that the code failed to find the first trading day of the year. Let's modify the code to handle this issue:\n",
      "\n",
      "```python\n",
      "import requests\n",
      "import json\n",
      "\n",
      "api_key = \"YOUR_API_KEY\"\n",
      "symbols = [\"AAPL\", \"MSFT\", \"GOOGL\", \"GOOG\", \"AMZN\", \"FB\", \"TSLA\", \"NVDA\", \"ADBE\", \"ASML\"]\n",
      "\n",
      "def get_ytd_gain(symbol):\n",
      "    url = f\"https://www.alphavantage.co/query?function=TIME_SERIES_DAILY_ADJUSTED&symbol={symbol}&apikey={api_key}\"\n",
      "    response = requests.get(url)\n",
      "    data = json.loads(response.text)\n",
      "    daily_data = data[\"Time Series (Daily)\"]\n",
      "    sorted_dates = sorted(daily_data.keys())\n",
      "    first_day_of_year = None\n",
      "    for date in sorted_dates:\n",
      "        if date.startswith(\"2022-01\"):\n",
      "            first_day_of_year = date\n",
      "            break\n",
      "    if first_day_of_year is None:\n",
      "        return None\n",
      "    last_day = sorted_dates[-1]\n",
      "    open_price = float(daily_data[first_day_of_year][\"1. open\"])\n",
      "    close_price = float(daily_data[last_day][\"4. close\"])\n",
      "    ytd_gain = (close_price - open_price) / open_price * 100\n",
      "    return ytd_gain\n",
      "\n",
      "ytd_gains = {symbol: get_ytd_gain(symbol) for symbol in symbols}\n",
      "print(\"YTD Gains of 10 Largest Technology Companies:\")\n",
      "for symbol, gain in ytd_gains.items():\n",
      "    if gain is not None:\n",
      "        print(f\"{symbol}: {gain:.2f}%\")\n",
      "    else:\n",
      "        print(f\"{symbol}: Data not available\")\n",
      "```\n",
      "\n",
      "Please replace `YOUR_API_KEY` with your actual API key and execute the code again. This should resolve the issue and display the YTD gains for the 10 largest technology companies.\n",
      "\n",
      "--------------------------------------------------------------------------------\n",
      "\n",
      ">>>>>>>> NO HUMAN INPUT RECEIVED. USING AUTO REPLY FOR THE USER...\n",
      "user (to assistant):\n",
      "\n",
      "exitcode: 1 (execution failed)\n",
      "Code output: \n",
      "Traceback (most recent call last):\n",
      "  File \"/workspaces/FLAML/notebook/web/tmp_code_a050ea892378b99f57347f4e08c4a294.py\", line 26, in <module>\n",
      "    ytd_gains = {symbol: get_ytd_gain(symbol) for symbol in symbols}\n",
      "  File \"/workspaces/FLAML/notebook/web/tmp_code_a050ea892378b99f57347f4e08c4a294.py\", line 26, in <dictcomp>\n",
      "    ytd_gains = {symbol: get_ytd_gain(symbol) for symbol in symbols}\n",
      "  File \"/workspaces/FLAML/notebook/web/tmp_code_a050ea892378b99f57347f4e08c4a294.py\", line 11, in get_ytd_gain\n",
      "    daily_data = data[\"Time Series (Daily)\"]\n",
      "KeyError: 'Time Series (Daily)'\n",
      "\n",
      "\n",
      "--------------------------------------------------------------------------------\n",
      "assistant (to user):\n",
      "\n",
      "I apologize for the error. It seems that the Alpha Vantage API is not returning the expected data. This might be due to the API call limit or an issue with the API itself. To resolve this, let's add a delay between API calls and handle the KeyError exception:\n",
      "\n",
      "```python\n",
      "import requests\n",
      "import json\n",
      "import time\n",
      "\n",
      "api_key = \"YOUR_API_KEY\"\n",
      "symbols = [\"AAPL\", \"MSFT\", \"GOOGL\", \"GOOG\", \"AMZN\", \"FB\", \"TSLA\", \"NVDA\", \"ADBE\", \"ASML\"]\n",
      "\n",
      "def get_ytd_gain(symbol):\n",
      "    url = f\"https://www.alphavantage.co/query?function=TIME_SERIES_DAILY_ADJUSTED&symbol={symbol}&apikey={api_key}\"\n",
      "    response = requests.get(url)\n",
      "    data = json.loads(response.text)\n",
      "    if \"Time Series (Daily)\" not in data:\n",
      "        return None\n",
      "    daily_data = data[\"Time Series (Daily)\"]\n",
      "    sorted_dates = sorted(daily_data.keys())\n",
      "    first_day_of_year = None\n",
      "    for date in sorted_dates:\n",
      "        if date.startswith(\"2022-01\"):\n",
      "            first_day_of_year = date\n",
      "            break\n",
      "    if first_day_of_year is None:\n",
      "        return None\n",
      "    last_day = sorted_dates[-1]\n",
      "    open_price = float(daily_data[first_day_of_year][\"1. open\"])\n",
      "    close_price = float(daily_data[last_day][\"4. close\"])\n",
      "    ytd_gain = (close_price - open_price) / open_price * 100\n",
      "    return ytd_gain\n",
      "\n",
      "ytd_gains = {}\n",
      "for symbol in symbols:\n",
      "    ytd_gains[symbol] = get_ytd_gain(symbol)\n",
      "    time.sleep(15)  # Add a delay between API calls\n",
      "\n",
      "print(\"YTD Gains of 10 Largest Technology Companies:\")\n",
      "for symbol, gain in ytd_gains.items():\n",
      "    if gain is not None:\n",
      "        print(f\"{symbol}: {gain:.2f}%\")\n",
      "    else:\n",
      "        print(f\"{symbol}: Data not available\")\n",
      "```\n",
      "\n",
      "Please replace `YOUR_API_KEY` with your actual API key and execute the code again. This should resolve the issue and display the YTD gains for the 10 largest technology companies. Note that the script will take some time to execute due to the added delay between API calls.\n",
      "\n",
      "--------------------------------------------------------------------------------\n",
      "\n",
      ">>>>>>>> NO HUMAN INPUT RECEIVED. USING AUTO REPLY FOR THE USER...\n",
      "user (to assistant):\n",
      "\n",
      "exitcode: 0 (execution succeeded)\n",
      "Code output: \n",
      "YTD Gains of 10 Largest Technology Companies:\n",
      "AAPL: Data not available\n",
      "MSFT: Data not available\n",
      "GOOGL: Data not available\n",
      "GOOG: Data not available\n",
      "AMZN: Data not available\n",
      "FB: Data not available\n",
      "TSLA: Data not available\n",
      "NVDA: Data not available\n",
      "ADBE: Data not available\n",
      "ASML: Data not available\n",
      "\n",
      "\n",
      "--------------------------------------------------------------------------------\n",
      "assistant (to user):\n",
      "\n",
      "I apologize for the inconvenience. It seems that the Alpha Vantage API is not providing the required data. In this case, let's switch to another API, the FRED (Federal Reserve Economic Data) API, which provides historical stock data. First, you need to obtain an API key from the FRED website. Please visit https://research.stlouisfed.org/docs/api/api_key.html and get your API key.\n",
      "\n",
      "Once you have the API key, please replace `YOUR_API_KEY` in the code below with your actual API key and execute the code:\n",
      "\n",
      "```python\n",
      "import requests\n",
      "import json\n",
      "import time\n",
      "\n",
      "api_key = \"YOUR_API_KEY\"\n",
      "symbols = [\"AAPL\", \"MSFT\", \"GOOGL\", \"GOOG\", \"AMZN\", \"FB\", \"TSLA\", \"NVDA\", \"ADBE\", \"ASML\"]\n",
      "\n",
      "def get_ytd_gain(symbol):\n",
      "    url = f\"https://fred.stlouisfed.org/graph/fredgraph.csv?bgcolor=%23e1e9f0&chart_type=line&drp=0&fo=open%20sans&graph_bgcolor=%23ffffff&height=450&mode=fred&recession_bars=on&txtcolor=%23444444&ts=12&tts=12&width=1168&nt=0&thu=0&trc=0&show_legend=yes&show_axis_titles=yes&show_tooltip=yes&id={symbol}&scale=left&cosd=2022-01-01&coed=2022-12-31&line_color=%234572a7&link_values=false&line_style=solid&mark_type=none&mw=3&lw=2&ost=-99999&oet=99999&mma=0&fml=a&fq=Daily&fam=avg&fgst=lin&fgsnd=2020-02-01&line_index=1&transformation=lin&vintage_date=2022-12-31&revision_date=2022-12-31&nd=2022-01-01\"\n",
      "    response = requests.get(url)\n",
      "    data = response.text.splitlines()\n",
      "    if len(data) < 2:\n",
      "        return None\n",
      "    first_day_of_year = data[1].split(\",\")[1]\n",
      "    last_day = data[-1].split(\",\")[1]\n",
      "    open_price = float(first_day_of_year)\n",
      "    close_price = float(last_day)\n",
      "    ytd_gain = (close_price - open_price) / open_price * 100\n",
      "    return ytd_gain\n",
      "\n",
      "ytd_gains = {}\n",
      "for symbol in symbols:\n",
      "    ytd_gains[symbol] = get_ytd_gain(symbol)\n",
      "    time.sleep(5)  # Add a delay between API calls\n",
      "\n",
      "print(\"YTD Gains of 10 Largest Technology Companies:\")\n",
      "for symbol, gain in ytd_gains.items():\n",
      "    if gain is not None:\n",
      "        print(f\"{symbol}: {gain:.2f}%\")\n",
      "    else:\n",
      "        print(f\"{symbol}: Data not available\")\n",
      "```\n",
      "\n",
      "This script fetches the daily stock data for the 10 largest technology companies from the FRED API and calculates the YTD gain for each of them. The results will be printed in the format \"Symbol: YTD Gain%\". Note that the script will take some time to execute due to the added delay between API calls.\n",
      "\n",
      "--------------------------------------------------------------------------------\n",
      "\n",
      ">>>>>>>> NO HUMAN INPUT RECEIVED. USING AUTO REPLY FOR THE USER...\n",
      "user (to assistant):\n",
      "\n",
      "exitcode: 1 (execution failed)\n",
      "Code output: \n",
      "Traceback (most recent call last):\n",
      "  File \"/workspaces/FLAML/notebook/web/tmp_code_ba93be30359073e751a640cd13b3ef7e.py\", line 23, in <module>\n",
      "    ytd_gains[symbol] = get_ytd_gain(symbol)\n",
      "  File \"/workspaces/FLAML/notebook/web/tmp_code_ba93be30359073e751a640cd13b3ef7e.py\", line 14, in get_ytd_gain\n",
      "    first_day_of_year = data[1].split(\",\")[1]\n",
      "IndexError: list index out of range\n",
      "\n",
      "\n",
      "--------------------------------------------------------------------------------\n",
      "assistant (to user):\n",
      "\n",
      "I apologize for the confusion. It seems that the FRED API does not provide the required stock data. In this case, let's use the Yahoo Finance API, which is a popular choice for fetching stock data. We will use the `yfinance` library to fetch the data. First, you need to install the library by executing the following command:\n",
      "\n",
      "```sh\n",
      "pip install yfinance\n",
      "```\n",
      "\n",
      "Once the library is installed, please execute the following code:\n",
      "\n",
      "```python\n",
      "import yfinance as yf\n",
      "import datetime\n",
      "\n",
      "symbols = [\"AAPL\", \"MSFT\", \"GOOGL\", \"GOOG\", \"AMZN\", \"FB\", \"TSLA\", \"NVDA\", \"ADBE\", \"ASML\"]\n",
      "\n",
      "def get_ytd_gain(symbol):\n",
      "    now = datetime.datetime.now()\n",
      "    start_date = datetime.datetime(now.year, 1, 1)\n",
      "    end_date = now\n",
      "    stock_data = yf.download(symbol, start=start_date, end=end_date)\n",
      "    if stock_data.empty:\n",
      "        return None\n",
      "    open_price = stock_data.iloc[0][\"Open\"]\n",
      "    close_price = stock_data.iloc[-1][\"Close\"]\n",
      "    ytd_gain = (close_price - open_price) / open_price * 100\n",
      "    return ytd_gain\n",
      "\n",
      "ytd_gains = {symbol: get_ytd_gain(symbol) for symbol in symbols}\n",
      "print(\"YTD Gains of 10 Largest Technology Companies:\")\n",
      "for symbol, gain in ytd_gains.items():\n",
      "    if gain is not None:\n",
      "        print(f\"{symbol}: {gain:.2f}%\")\n",
      "    else:\n",
      "        print(f\"{symbol}: Data not available\")\n",
      "```\n",
      "\n",
      "This script fetches the daily stock data for the 10 largest technology companies from the Yahoo Finance API using the `yfinance` library and calculates the YTD gain for each of them. The results will be printed in the format \"Symbol: YTD Gain%\".\n",
      "\n",
      "--------------------------------------------------------------------------------\n",
      "\n",
      ">>>>>>>> NO HUMAN INPUT RECEIVED. USING AUTO REPLY FOR THE USER...\n",
      "user (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 (0.2.18)\n",
      "Requirement already satisfied: pandas>=1.3.0 in /usr/local/lib/python3.9/site-packages (from yfinance) (1.5.2)\n",
      "Requirement already satisfied: numpy>=1.16.5 in /home/vscode/.local/lib/python3.9/site-packages (from yfinance) (1.23.5)\n",
      "Requirement already satisfied: requests>=2.26 in /usr/local/lib/python3.9/site-packages (from yfinance) (2.28.1)\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: lxml>=4.9.1 in /home/vscode/.local/lib/python3.9/site-packages (from yfinance) (4.9.2)\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: pytz>=2022.5 in /usr/local/lib/python3.9/site-packages (from yfinance) (2022.6)\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: cryptography>=3.3.2 in /home/vscode/.local/lib/python3.9/site-packages (from yfinance) (38.0.4)\n",
      "Requirement already satisfied: beautifulsoup4>=4.11.1 in /home/vscode/.local/lib/python3.9/site-packages (from yfinance) (4.11.1)\n",
      "Requirement already satisfied: html5lib>=1.1 in /home/vscode/.local/lib/python3.9/site-packages (from yfinance) (1.1)\n",
      "Requirement already satisfied: soupsieve>1.2 in /home/vscode/.local/lib/python3.9/site-packages (from beautifulsoup4>=4.11.1->yfinance) (2.3.2.post1)\n",
      "Requirement already satisfied: cffi>=1.12 in /home/vscode/.local/lib/python3.9/site-packages (from cryptography>=3.3.2->yfinance) (1.15.1)\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: webencodings in /home/vscode/.local/lib/python3.9/site-packages (from html5lib>=1.1->yfinance) (0.5.1)\n",
      "Requirement already satisfied: python-dateutil>=2.8.1 in /usr/local/lib/python3.9/site-packages (from pandas>=1.3.0->yfinance) (2.8.2)\n",
      "Requirement already satisfied: charset-normalizer<3,>=2 in /usr/local/lib/python3.9/site-packages (from requests>=2.26->yfinance) (2.1.1)\n",
      "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.9/site-packages (from requests>=2.26->yfinance) (3.4)\n",
      "Requirement already satisfied: urllib3<1.27,>=1.21.1 in /usr/local/lib/python3.9/site-packages (from requests>=2.26->yfinance) (1.26.13)\n",
      "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.9/site-packages (from requests>=2.26->yfinance) (2022.9.24)\n",
      "Requirement already satisfied: pycparser in /home/vscode/.local/lib/python3.9/site-packages (from cffi>=1.12->cryptography>=3.3.2->yfinance) (2.21)\n",
      "\n",
      "[*********************100%***********************]  1 of 1 completed\n",
      "[*********************100%***********************]  1 of 1 completed\n",
      "[*********************100%***********************]  1 of 1 completed\n",
      "[*********************100%***********************]  1 of 1 completed\n",
      "[*********************100%***********************]  1 of 1 completed\n",
      "[*********************100%***********************]  1 of 1 completed\n",
      "\n",
      "1 Failed download:\n",
      "- FB: No timezone found, symbol may be delisted\n",
      "[*********************100%***********************]  1 of 1 completed\n",
      "[*********************100%***********************]  1 of 1 completed\n",
      "[*********************100%***********************]  1 of 1 completed\n",
      "[*********************100%***********************]  1 of 1 completed\n",
      "YTD Gains of 10 Largest Technology Companies:\n",
      "AAPL: 44.37%\n",
      "MSFT: 36.77%\n",
      "GOOGL: 30.75%\n",
      "GOOG: 31.04%\n",
      "AMZN: 50.69%\n",
      "FB: Data not available\n",
      "TSLA: 127.73%\n",
      "NVDA: 185.54%\n",
      "ADBE: 48.38%\n",
      "ASML: 25.61%\n",
      "\n",
      "\n",
      "--------------------------------------------------------------------------------\n",
      "assistant (to user):\n",
      "\n",
      "Great! The code executed successfully, and the YTD gains of the 10 largest technology companies are as follows:\n",
      "\n",
      "- AAPL: 44.37%\n",
      "- MSFT: 36.77%\n",
      "- GOOGL: 30.75%\n",
      "- GOOG: 31.04%\n",
      "- AMZN: 50.69%\n",
      "- FB: Data not available (The symbol may be delisted or not available in the Yahoo Finance API)\n",
      "- TSLA: 127.73%\n",
      "- NVDA: 185.54%\n",
      "- ADBE: 48.38%\n",
      "- ASML: 25.61%\n",
      "\n",
      "Please note that these values are subject to change as the stock market fluctuates. If you need to update the values, simply re-run the code.\n",
      "\n",
      "TERMINATE\n",
      "\n",
      "--------------------------------------------------------------------------------\n",
      "user (to assistant):\n",
      "\n",
      "continue\n",
      "\n",
      "--------------------------------------------------------------------------------\n",
      "assistant (to user):\n",
      "\n",
      "I apologize for the confusion regarding the FB symbol. It seems that the symbol for Meta Platforms, Inc. (formerly Facebook, Inc.) has changed to \"META\" on the Yahoo Finance API. Let's update the symbol and re-run the code to get the YTD gain for Meta Platforms, Inc.\n",
      "\n",
      "```python\n",
      "symbols = [\"AAPL\", \"MSFT\", \"GOOGL\", \"GOOG\", \"AMZN\", \"META\", \"TSLA\", \"NVDA\", \"ADBE\", \"ASML\"]\n",
      "\n",
      "ytd_gains = {symbol: get_ytd_gain(symbol) for symbol in symbols}\n",
      "print(\"YTD Gains of 10 Largest Technology Companies:\")\n",
      "for symbol, gain in ytd_gains.items():\n",
      "    if gain is not None:\n",
      "        print(f\"{symbol}: {gain:.2f}%\")\n",
      "    else:\n",
      "        print(f\"{symbol}: Data not available\")\n",
      "```\n",
      "\n",
      "Please execute the code above to get the updated YTD gains for the 10 largest technology companies, including Meta Platforms, Inc. (formerly Facebook, Inc.).\n",
      "\n",
      "--------------------------------------------------------------------------------\n",
      "\n",
      ">>>>>>>> NO HUMAN INPUT RECEIVED. USING AUTO REPLY FOR THE USER...\n",
      "user (to assistant):\n",
      "\n",
      "exitcode: 1 (execution failed)\n",
      "Code output: \n",
      "Traceback (most recent call last):\n",
      "  File \"/workspaces/FLAML/notebook/web/tmp_code_cfc7665767b48828cfef36e1a2a3dbc6.py\", line 3, in <module>\n",
      "    ytd_gains = {symbol: get_ytd_gain(symbol) for symbol in symbols}\n",
      "  File \"/workspaces/FLAML/notebook/web/tmp_code_cfc7665767b48828cfef36e1a2a3dbc6.py\", line 3, in <dictcomp>\n",
      "    ytd_gains = {symbol: get_ytd_gain(symbol) for symbol in symbols}\n",
      "NameError: name 'get_ytd_gain' is not defined\n",
      "\n",
      "\n",
      "--------------------------------------------------------------------------------\n",
      "assistant (to user):\n",
      "\n",
      "I apologize for the error. It seems I didn't include the `get_ytd_gain` function in the code snippet. Here's the complete code with the updated symbol for Meta Platforms, Inc. (formerly Facebook, Inc.):\n",
      "\n",
      "```python\n",
      "import yfinance as yf\n",
      "import datetime\n",
      "\n",
      "symbols = [\"AAPL\", \"MSFT\", \"GOOGL\", \"GOOG\", \"AMZN\", \"META\", \"TSLA\", \"NVDA\", \"ADBE\", \"ASML\"]\n",
      "\n",
      "def get_ytd_gain(symbol):\n",
      "    now = datetime.datetime.now()\n",
      "    start_date = datetime.datetime(now.year, 1, 1)\n",
      "    end_date = now\n",
      "    stock_data = yf.download(symbol, start=start_date, end=end_date)\n",
      "    if stock_data.empty:\n",
      "        return None\n",
      "    open_price = stock_data.iloc[0][\"Open\"]\n",
      "    close_price = stock_data.iloc[-1][\"Close\"]\n",
      "    ytd_gain = (close_price - open_price) / open_price * 100\n",
      "    return ytd_gain\n",
      "\n",
      "ytd_gains = {symbol: get_ytd_gain(symbol) for symbol in symbols}\n",
      "print(\"YTD Gains of 10 Largest Technology Companies:\")\n",
      "for symbol, gain in ytd_gains.items():\n",
      "    if gain is not None:\n",
      "        print(f\"{symbol}: {gain:.2f}%\")\n",
      "    else:\n",
      "        print(f\"{symbol}: Data not available\")\n",
      "```\n",
      "\n",
      "Please execute the code above to get the updated YTD gains for the 10 largest technology companies, including Meta Platforms, Inc. (formerly Facebook, Inc.).\n",
      "\n",
      "--------------------------------------------------------------------------------\n",
      "\n",
      ">>>>>>>> NO HUMAN INPUT RECEIVED. USING AUTO REPLY FOR THE USER...\n",
      "user (to assistant):\n",
      "\n",
      "exitcode: 0 (execution succeeded)\n",
      "Code output: \n",
      "[*********************100%***********************]  1 of 1 completed\n",
      "[*********************100%***********************]  1 of 1 completed\n",
      "[*********************100%***********************]  1 of 1 completed\n",
      "[*********************100%***********************]  1 of 1 completed\n",
      "[*********************100%***********************]  1 of 1 completed\n",
      "[*********************100%***********************]  1 of 1 completed\n",
      "[*********************100%***********************]  1 of 1 completed\n",
      "[*********************100%***********************]  1 of 1 completed\n",
      "[*********************100%***********************]  1 of 1 completed\n",
      "[*********************100%***********************]  1 of 1 completed\n",
      "YTD Gains of 10 Largest Technology Companies:\n",
      "AAPL: 44.37%\n",
      "MSFT: 36.77%\n",
      "GOOGL: 30.75%\n",
      "GOOG: 31.04%\n",
      "AMZN: 50.69%\n",
      "META: 142.87%\n",
      "TSLA: 127.73%\n",
      "NVDA: 185.54%\n",
      "ADBE: 48.38%\n",
      "ASML: 25.61%\n",
      "\n",
      "\n",
      "--------------------------------------------------------------------------------\n",
      "assistant (to user):\n",
      "\n",
      "Great! The code executed successfully, and the updated YTD gains of the 10 largest technology companies are as follows:\n",
      "\n",
      "- AAPL: 44.37%\n",
      "- MSFT: 36.77%\n",
      "- GOOGL: 30.75%\n",
      "- GOOG: 31.04%\n",
      "- AMZN: 50.69%\n",
      "- META (formerly FB): 142.87%\n",
      "- TSLA: 127.73%\n",
      "- NVDA: 185.54%\n",
      "- ADBE: 48.38%\n",
      "- ASML: 25.61%\n",
      "\n",
      "Please note that these values are subject to change as the stock market fluctuates. If you need to update the values, simply re-run the code.\n",
      "\n",
      "TERMINATE\n",
      "\n",
      "--------------------------------------------------------------------------------\n"
     ]
    }
   ],
   "source": [
    "# it is suggested to reset the assistant to clear the state if the new task is not related to the previous one.\n",
    "assistant.reset()\n",
    "assistant.receive(\n",
    "    \"\"\"Show me the YTD gain of 10 largest technology companies as of today.\"\"\",\n",
    "    user,\n",
    ")"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.15"
  },
  "vscode": {
   "interpreter": {
    "hash": "949777d72b0d2535278d3dc13498b2535136f6dfe0678499012e853ee9abcab1"
   }
  },
  "widgets": {
   "application/vnd.jupyter.widget-state+json": {
    "state": {
     "2d910cfd2d2a4fc49fc30fbbdc5576a7": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "2.0.0",
      "model_name": "LayoutModel",
      "state": {
       "_model_module": "@jupyter-widgets/base",
       "_model_module_version": "2.0.0",
       "_model_name": "LayoutModel",
       "_view_count": null,
       "_view_module": "@jupyter-widgets/base",
       "_view_module_version": "2.0.0",
       "_view_name": "LayoutView",
       "align_content": null,
       "align_items": null,
       "align_self": null,
       "border_bottom": null,
       "border_left": null,
       "border_right": null,
       "border_top": null,
       "bottom": null,
       "display": null,
       "flex": null,
       "flex_flow": null,
       "grid_area": null,
       "grid_auto_columns": null,
       "grid_auto_flow": null,
       "grid_auto_rows": null,
       "grid_column": null,
       "grid_gap": null,
       "grid_row": null,
       "grid_template_areas": null,
       "grid_template_columns": null,
       "grid_template_rows": null,
       "height": null,
       "justify_content": null,
       "justify_items": null,
       "left": null,
       "margin": null,
       "max_height": null,
       "max_width": null,
       "min_height": null,
       "min_width": null,
       "object_fit": null,
       "object_position": null,
       "order": null,
       "overflow": null,
       "padding": null,
       "right": null,
       "top": null,
       "visibility": null,
       "width": null
      }
     },
     "454146d0f7224f038689031002906e6f": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "2.0.0",
      "model_name": "HBoxModel",
      "state": {
       "_dom_classes": [],
       "_model_module": "@jupyter-widgets/controls",
       "_model_module_version": "2.0.0",
       "_model_name": "HBoxModel",
       "_view_count": null,
       "_view_module": "@jupyter-widgets/controls",
       "_view_module_version": "2.0.0",
       "_view_name": "HBoxView",
       "box_style": "",
       "children": [
        "IPY_MODEL_e4ae2b6f5a974fd4bafb6abb9d12ff26",
        "IPY_MODEL_577e1e3cc4db4942b0883577b3b52755",
        "IPY_MODEL_b40bdfb1ac1d4cffb7cefcb870c64d45"
       ],
       "layout": "IPY_MODEL_dc83c7bff2f241309537a8119dfc7555",
       "tabbable": null,
       "tooltip": null
      }
     },
     "577e1e3cc4db4942b0883577b3b52755": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "2.0.0",
      "model_name": "FloatProgressModel",
      "state": {
       "_dom_classes": [],
       "_model_module": "@jupyter-widgets/controls",
       "_model_module_version": "2.0.0",
       "_model_name": "FloatProgressModel",
       "_view_count": null,
       "_view_module": "@jupyter-widgets/controls",
       "_view_module_version": "2.0.0",
       "_view_name": "ProgressView",
       "bar_style": "success",
       "description": "",
       "description_allow_html": false,
       "layout": "IPY_MODEL_2d910cfd2d2a4fc49fc30fbbdc5576a7",
       "max": 1,
       "min": 0,
       "orientation": "horizontal",
       "style": "IPY_MODEL_74a6ba0c3cbc4051be0a83e152fe1e62",
       "tabbable": null,
       "tooltip": null,
       "value": 1
      }
     },
     "6086462a12d54bafa59d3c4566f06cb2": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "2.0.0",
      "model_name": "LayoutModel",
      "state": {
       "_model_module": "@jupyter-widgets/base",
       "_model_module_version": "2.0.0",
       "_model_name": "LayoutModel",
       "_view_count": null,
       "_view_module": "@jupyter-widgets/base",
       "_view_module_version": "2.0.0",
       "_view_name": "LayoutView",
       "align_content": null,
       "align_items": null,
       "align_self": null,
       "border_bottom": null,
       "border_left": null,
       "border_right": null,
       "border_top": null,
       "bottom": null,
       "display": null,
       "flex": null,
       "flex_flow": null,
       "grid_area": null,
       "grid_auto_columns": null,
       "grid_auto_flow": null,
       "grid_auto_rows": null,
       "grid_column": null,
       "grid_gap": null,
       "grid_row": null,
       "grid_template_areas": null,
       "grid_template_columns": null,
       "grid_template_rows": null,
       "height": null,
       "justify_content": null,
       "justify_items": null,
       "left": null,
       "margin": null,
       "max_height": null,
       "max_width": null,
       "min_height": null,
       "min_width": null,
       "object_fit": null,
       "object_position": null,
       "order": null,
       "overflow": null,
       "padding": null,
       "right": null,
       "top": null,
       "visibility": null,
       "width": null
      }
     },
     "74a6ba0c3cbc4051be0a83e152fe1e62": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "2.0.0",
      "model_name": "ProgressStyleModel",
      "state": {
       "_model_module": "@jupyter-widgets/controls",
       "_model_module_version": "2.0.0",
       "_model_name": "ProgressStyleModel",
       "_view_count": null,
       "_view_module": "@jupyter-widgets/base",
       "_view_module_version": "2.0.0",
       "_view_name": "StyleView",
       "bar_color": null,
       "description_width": ""
      }
     },
     "7d3f3d9e15894d05a4d188ff4f466554": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "2.0.0",
      "model_name": "HTMLStyleModel",
      "state": {
       "_model_module": "@jupyter-widgets/controls",
       "_model_module_version": "2.0.0",
       "_model_name": "HTMLStyleModel",
       "_view_count": null,
       "_view_module": "@jupyter-widgets/base",
       "_view_module_version": "2.0.0",
       "_view_name": "StyleView",
       "background": null,
       "description_width": "",
       "font_size": null,
       "text_color": null
      }
     },
     "b40bdfb1ac1d4cffb7cefcb870c64d45": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "2.0.0",
      "model_name": "HTMLModel",
      "state": {
       "_dom_classes": [],
       "_model_module": "@jupyter-widgets/controls",
       "_model_module_version": "2.0.0",
       "_model_name": "HTMLModel",
       "_view_count": null,
       "_view_module": "@jupyter-widgets/controls",
       "_view_module_version": "2.0.0",
       "_view_name": "HTMLView",
       "description": "",
       "description_allow_html": false,
       "layout": "IPY_MODEL_f1355871cc6f4dd4b50d9df5af20e5c8",
       "placeholder": "​",
       "style": "IPY_MODEL_ca245376fd9f4354af6b2befe4af4466",
       "tabbable": null,
       "tooltip": null,
       "value": " 1/1 [00:00&lt;00:00, 44.69it/s]"
      }
     },
     "ca245376fd9f4354af6b2befe4af4466": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "2.0.0",
      "model_name": "HTMLStyleModel",
      "state": {
       "_model_module": "@jupyter-widgets/controls",
       "_model_module_version": "2.0.0",
       "_model_name": "HTMLStyleModel",
       "_view_count": null,
       "_view_module": "@jupyter-widgets/base",
       "_view_module_version": "2.0.0",
       "_view_name": "StyleView",
       "background": null,
       "description_width": "",
       "font_size": null,
       "text_color": null
      }
     },
     "dc83c7bff2f241309537a8119dfc7555": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "2.0.0",
      "model_name": "LayoutModel",
      "state": {
       "_model_module": "@jupyter-widgets/base",
       "_model_module_version": "2.0.0",
       "_model_name": "LayoutModel",
       "_view_count": null,
       "_view_module": "@jupyter-widgets/base",
       "_view_module_version": "2.0.0",
       "_view_name": "LayoutView",
       "align_content": null,
       "align_items": null,
       "align_self": null,
       "border_bottom": null,
       "border_left": null,
       "border_right": null,
       "border_top": null,
       "bottom": null,
       "display": null,
       "flex": null,
       "flex_flow": null,
       "grid_area": null,
       "grid_auto_columns": null,
       "grid_auto_flow": null,
       "grid_auto_rows": null,
       "grid_column": null,
       "grid_gap": null,
       "grid_row": null,
       "grid_template_areas": null,
       "grid_template_columns": null,
       "grid_template_rows": null,
       "height": null,
       "justify_content": null,
       "justify_items": null,
       "left": null,
       "margin": null,
       "max_height": null,
       "max_width": null,
       "min_height": null,
       "min_width": null,
       "object_fit": null,
       "object_position": null,
       "order": null,
       "overflow": null,
       "padding": null,
       "right": null,
       "top": null,
       "visibility": null,
       "width": null
      }
     },
     "e4ae2b6f5a974fd4bafb6abb9d12ff26": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "2.0.0",
      "model_name": "HTMLModel",
      "state": {
       "_dom_classes": [],
       "_model_module": "@jupyter-widgets/controls",
       "_model_module_version": "2.0.0",
       "_model_name": "HTMLModel",
       "_view_count": null,
       "_view_module": "@jupyter-widgets/controls",
       "_view_module_version": "2.0.0",
       "_view_name": "HTMLView",
       "description": "",
       "description_allow_html": false,
       "layout": "IPY_MODEL_6086462a12d54bafa59d3c4566f06cb2",
       "placeholder": "​",
       "style": "IPY_MODEL_7d3f3d9e15894d05a4d188ff4f466554",
       "tabbable": null,
       "tooltip": null,
       "value": "100%"
      }
     },
     "f1355871cc6f4dd4b50d9df5af20e5c8": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "2.0.0",
      "model_name": "LayoutModel",
      "state": {
       "_model_module": "@jupyter-widgets/base",
       "_model_module_version": "2.0.0",
       "_model_name": "LayoutModel",
       "_view_count": null,
       "_view_module": "@jupyter-widgets/base",
       "_view_module_version": "2.0.0",
       "_view_name": "LayoutView",
       "align_content": null,
       "align_items": null,
       "align_self": null,
       "border_bottom": null,
       "border_left": null,
       "border_right": null,
       "border_top": null,
       "bottom": null,
       "display": null,
       "flex": null,
       "flex_flow": null,
       "grid_area": null,
       "grid_auto_columns": null,
       "grid_auto_flow": null,
       "grid_auto_rows": null,
       "grid_column": null,
       "grid_gap": null,
       "grid_row": null,
       "grid_template_areas": null,
       "grid_template_columns": null,
       "grid_template_rows": null,
       "height": null,
       "justify_content": null,
       "justify_items": null,
       "left": null,
       "margin": null,
       "max_height": null,
       "max_width": null,
       "min_height": null,
       "min_width": null,
       "object_fit": null,
       "object_position": null,
       "order": null,
       "overflow": null,
       "padding": null,
       "right": null,
       "top": null,
       "visibility": null,
       "width": null
      }
     }
    },
    "version_major": 2,
    "version_minor": 0
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}