notebook/agentchat_groupchat_vis.ipynb

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   "source": [
    "# Group Chat with Coder and Visualization Critic\n",
    "\n",
    "AG2 offers conversable agents powered by LLM, tool or human, which can be used to perform tasks collectively via automated chat. This framework allows tool use and human participation through multi-agent conversation.\n",
    "Please find documentation about this feature [here](https://docs.ag2.ai/docs/user-guide/basic-concepts/conversable-agent).\n",
    "\n",
    "````{=mdx}\n",
    ":::info Requirements\n",
    "Install `pyautogen`:\n",
    "```bash\n",
    "pip install pyautogen[openai]\n",
    "```\n",
    "\n",
    "For more information, please refer to the [installation guide](https://docs.ag2.ai/docs/user-guide/basic-concepts/installing-ag2).\n",
    ":::\n",
    "````"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Set your API Endpoint\n",
    "\n",
    "The [`config_list_from_json`](https://docs.ag2.ai/docs/api-reference/autogen/config_list_from_json#config-list-from-json) function loads a list of configurations from an environment variable or a json file."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "from IPython.display import Image\n",
    "\n",
    "import autogen\n",
    "\n",
    "llm_config = autogen.LLMConfig.from_json(path=\"OAI_CONFIG_LIST\", cache_seed=42).where(\n",
    "    model=[\"gpt-4\", \"gpt-4-0314\", \"gpt4\", \"gpt-4-32k\", \"gpt-4-32k-0314\", \"gpt-4-32k-v0314\"]\n",
    ")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "````{=mdx}\n",
    ":::tip\n",
    "Learn more about configuring LLMs for agents [here](https://docs.ag2.ai/docs/user-guide/basic-concepts/llm-configuration/llm-configuration).\n",
    ":::\n",
    "````\n",
    "\n",
    "## Construct Agents"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<figure style=\"margin: 0 auto; width: 500px\">\n",
    "    <img src=\"https://media.githubusercontent.com/media/ag2ai/ag2/refs/heads/main/notebook/viz_gc.png\" alt=\"Drawing\">\n",
    "    </img>\n",
    "</figure>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "user_proxy = autogen.UserProxyAgent(\n",
    "    name=\"User_proxy\",\n",
    "    system_message=\"A human admin.\",\n",
    "    code_execution_config={\n",
    "        \"last_n_messages\": 3,\n",
    "        \"work_dir\": \"groupchat\",\n",
    "        \"use_docker\": False,\n",
    "    },  # Please set use_docker=True if docker is available to run the generated code. Using docker is safer than running the generated code directly.\n",
    "    human_input_mode=\"NEVER\",\n",
    ")\n",
    "coder = autogen.AssistantAgent(\n",
    "    name=\"Coder\",  # the default assistant agent is capable of solving problems with code\n",
    "    llm_config=llm_config,\n",
    ")\n",
    "critic = autogen.AssistantAgent(\n",
    "    name=\"Critic\",\n",
    "    system_message=\"\"\"Critic. You are a helpful assistant highly skilled in evaluating the quality of a given visualization code by providing a score from 1 (bad) - 10 (good) while providing clear rationale. YOU MUST CONSIDER VISUALIZATION BEST PRACTICES for each evaluation. Specifically, you can carefully evaluate the code across the following dimensions\n",
    "- bugs (bugs):  are there bugs, logic errors, syntax error or typos? Are there any reasons why the code may fail to compile? How should it be fixed? If ANY bug exists, the bug score MUST be less than 5.\n",
    "- Data transformation (transformation): Is the data transformed appropriately for the visualization type? E.g., is the dataset appropriated filtered, aggregated, or grouped  if needed? If a date field is used, is the date field first converted to a date object etc?\n",
    "- Goal compliance (compliance): how well the code meets the specified visualization goals?\n",
    "- Visualization type (type): CONSIDERING BEST PRACTICES, is the visualization type appropriate for the data and intent? Is there a visualization type that would be more effective in conveying insights? If a different visualization type is more appropriate, the score MUST BE LESS THAN 5.\n",
    "- Data encoding (encoding): Is the data encoded appropriately for the visualization type?\n",
    "- aesthetics (aesthetics): Are the aesthetics of the visualization appropriate for the visualization type and the data?\n",
    "\n",
    "YOU MUST PROVIDE A SCORE for each of the above dimensions.\n",
    "{bugs: 0, transformation: 0, compliance: 0, type: 0, encoding: 0, aesthetics: 0}\n",
    "Do not suggest code.\n",
    "Finally, based on the critique above, suggest a concrete list of actions that the coder should take to improve the code.\n",
    "\"\"\",\n",
    "    llm_config=llm_config,\n",
    ")\n",
    "\n",
    "groupchat = autogen.GroupChat(agents=[user_proxy, coder, critic], messages=[], max_round=20)\n",
    "manager = autogen.GroupChatManager(groupchat=groupchat, llm_config=llm_config)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Start Chat"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "user_proxy.initiate_chat(\n",
    "    manager,\n",
    "    message=\"download data from https://raw.githubusercontent.com/uwdata/draco/master/data/cars.csv and plot a visualization that tells us about the relationship between weight and horsepower. Save the plot to a file. Print the fields in a dataset before visualizing it.\",\n",
    ")\n",
    "# type exit to terminate the chat"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Display the saved figure"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "Image(filename=\"groupchat/weight_vs_horsepower.png\")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Example 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "user_proxy.reset()\n",
    "coder.reset()\n",
    "critic.reset()\n",
    "groupchat = autogen.GroupChat(agents=[user_proxy, coder, critic], messages=[], max_round=20)\n",
    "manager = autogen.GroupChatManager(groupchat=groupchat, llm_config=llm_config)\n",
    "user_proxy.initiate_chat(\n",
    "    manager,\n",
    "    message=\"download data from https://raw.githubusercontent.com/vega/vega/main/docs/data/seattle-weather.csv and show me a plot that tells me about the amount of each weather . Save the plot to a file. Print the fields in a dataset before visualizing it. Take the feedback from the critic to improve the code.\",\n",
    ")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### The original chart before critic's suggestion"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "url = \"https://raw.githubusercontent.com/vega/vega/main/docs/data/seattle-weather.csv\"\n",
    "data = pd.read_csv(url)\n",
    "print(\"Fields in the dataset:\")\n",
    "print(data.columns)\n",
    "\n",
    "# Count the occurrences of each weather type\n",
    "weather_counts = data[\"weather\"].value_counts()\n",
    "\n",
    "# Create a bar plot of weather occurrences\n",
    "sns.set(style=\"whitegrid\", font_scale=1.2)\n",
    "plt.figure(figsize=(10, 6))\n",
    "weather_plot = sns.barplot(x=weather_counts.index, y=weather_counts.values)\n",
    "\n",
    "# Add labels and title\n",
    "weather_plot.set(xlabel=\"Weather Types\", ylabel=\"Number of Days\", title=\"Seattle Weather Types Frequency\")\n",
    "plt.savefig(\"weather_plot.png\")\n",
    "\n",
    "print(\"Plot has been saved to 'weather_plot.png'.\")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### The final figure"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "Image(filename=\"groupchat/improved_weather_plot.png\")"
   ]
  }
 ],
 "metadata": {
  "front_matter": {
   "description": "Explore a group chat example using agents such as a coder and visualization agent.",
   "tags": [
    "group chat",
    "code generation"
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