quick_start/13_assistant.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Assistant (Preview)\n",
    "\n",
    "\n",
    "Azure OpenAI 어시스턴트(미리보기)를 통해 사용자 지정 지침을 통해 필요에 맞게 AI 어시스턴트를 만들고 코드 인터프리터 및 사용자 정의 함수와 같은 고급 도구로 보완할 수 있습니다. 이 글에서는 어시스턴트 API 사용을 시작하는 방법에 대한 심층적인 안내를 제공할 것입니다.\n",
    "\n",
    "## API Version: 2024-02-15-preview\n",
    "Check availability: \n",
    "\n",
    "https://learn.microsoft.com/en-us/azure/ai-services/openai/concepts/models#assistants-preview"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Creating an assisstant"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import json\n",
    "from openai import AzureOpenAI\n",
    "from dotenv import load_dotenv\n",
    "\n",
    "# Load the environment variables from the .env file\n",
    "load_dotenv()\n",
    "    \n",
    "client = AzureOpenAI(\n",
    "    azure_endpoint = os.getenv(\"AZURE_OPENAI_ENDPOINT\",\"\").strip(),\n",
    "    api_key        = os.getenv(\"AZURE_OPENAI_API_KEY\"),\n",
    "    api_version    = os.getenv(\"OPENAI_API_VERSION\")\n",
    ")\n",
    "\n",
    "deployment_name = os.getenv(\"DEPLOYMENT_NAME\")\n",
    "\n",
    "# Create an assistant\n",
    "assistant = client.beta.assistants.create(\n",
    "    name=\"Data Visualization\",\n",
    "    instructions=f\"You are a helpful AI assistant who makes interesting visualizations based on data.\" \n",
    "    f\"You have access to a sandboxed environment for writing and testing code.\"\n",
    "    f\"When you are asked to create a visualization you should follow these steps:\"\n",
    "    f\"1. Write the code.\"\n",
    "    f\"2. Anytime you write new code display a preview of the code to show your work.\"\n",
    "    f\"3. Run the code to confirm that it runs.\"\n",
    "    f\"4. If the code is successful display the visualization.\"\n",
    "    f\"5. If the code is unsuccessful display the error message and try to revise the code and rerun going through the steps from above again.\",\n",
    "    tools=[{\"type\": \"code_interpreter\"}],\n",
    "    model=deployment_name,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{\n",
      "  \"id\": \"asst_4lOIhWVnFDCqHq2CdpkizO91\",\n",
      "  \"created_at\": 1720529322,\n",
      "  \"description\": null,\n",
      "  \"file_ids\": null,\n",
      "  \"instructions\": \"You are a helpful AI assistant who makes interesting visualizations based on data.You have access to a sandboxed environment for writing and testing code.When you are asked to create a visualization you should follow these steps:1. Write the code.2. Anytime you write new code display a preview of the code to show your work.3. Run the code to confirm that it runs.4. If the code is successful display the visualization.5. If the code is unsuccessful display the error message and try to revise the code and rerun going through the steps from above again.\",\n",
      "  \"metadata\": {},\n",
      "  \"model\": \"gpt-4o\",\n",
      "  \"name\": \"Data Visualization\",\n",
      "  \"object\": \"assistant\",\n",
      "  \"tools\": [\n",
      "    {\n",
      "      \"type\": \"code_interpreter\"\n",
      "    }\n",
      "  ],\n",
      "  \"top_p\": 1.0,\n",
      "  \"temperature\": 1.0,\n",
      "  \"tool_resources\": {\n",
      "    \"code_interpreter\": {\n",
      "      \"file_ids\": []\n",
      "    }\n",
      "  },\n",
      "  \"response_format\": \"auto\"\n",
      "}\n"
     ]
    }
   ],
   "source": [
    "print(assistant.model_dump_json(indent=2))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Creating a thread\n",
    "\n",
    "Thread is a conversation session between an Assistant and a user. Threads store Messages and automatically handle truncation to fit content into a model’s context."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Thread(id='thread_wyea7pAbvlhDaOr7UfxgHLre', created_at=1720529332, metadata={}, object='thread', tool_resources={})\n"
     ]
    }
   ],
   "source": [
    "# Create a thread\n",
    "thread = client.beta.threads.create()\n",
    "print(thread)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Add a user question to the thread\n",
    "message = client.beta.threads.messages.create(\n",
    "    thread_id=thread.id,\n",
    "    role=\"user\",\n",
    "    content=\"Create a visualization of a sinewave\"\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{\n",
      "  \"data\": [\n",
      "    {\n",
      "      \"id\": \"msg_hHHv3hZUvQimSQnGQgmk4hrL\",\n",
      "      \"assistant_id\": null,\n",
      "      \"content\": [\n",
      "        {\n",
      "          \"text\": {\n",
      "            \"annotations\": [],\n",
      "            \"value\": \"Create a visualization of a sinewave\"\n",
      "          },\n",
      "          \"type\": \"text\"\n",
      "        }\n",
      "      ],\n",
      "      \"created_at\": 1720529335,\n",
      "      \"file_ids\": null,\n",
      "      \"metadata\": {},\n",
      "      \"object\": \"thread.message\",\n",
      "      \"role\": \"user\",\n",
      "      \"run_id\": null,\n",
      "      \"thread_id\": \"thread_wyea7pAbvlhDaOr7UfxgHLre\",\n",
      "      \"attachments\": []\n",
      "    }\n",
      "  ],\n",
      "  \"object\": \"list\",\n",
      "  \"first_id\": \"msg_hHHv3hZUvQimSQnGQgmk4hrL\",\n",
      "  \"last_id\": \"msg_hHHv3hZUvQimSQnGQgmk4hrL\",\n",
      "  \"has_more\": false\n",
      "}\n"
     ]
    }
   ],
   "source": [
    "# list thread messages\n",
    "thread_messages = client.beta.threads.messages.list(thread.id)\n",
    "print(thread_messages.model_dump_json(indent=2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# create a thread\n",
    "# you can also use intructions here to override the default instructions\n",
    "run = client.beta.threads.runs.create(\n",
    "  thread_id=thread.id,\n",
    "  assistant_id=assistant.id,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "completed\n"
     ]
    }
   ],
   "source": [
    "# retrieve thread status\n",
    "# Retrieve the status of the run\n",
    "run = client.beta.threads.runs.retrieve(\n",
    "  thread_id=thread.id,\n",
    "  run_id=run.id\n",
    ")\n",
    "\n",
    "status = run.status\n",
    "print(status)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Status: completed\n",
      "Elapsed time: 0 minutes 1 seconds\n",
      "{\n",
      "  \"data\": [\n",
      "    {\n",
      "      \"id\": \"msg_AP2MuzeT7pKdSZ0IrVI8RS5Q\",\n",
      "      \"assistant_id\": \"asst_4lOIhWVnFDCqHq2CdpkizO91\",\n",
      "      \"content\": [\n",
      "        {\n",
      "          \"image_file\": {\n",
      "            \"file_id\": \"assistant-P4bSkm17PSuvZVgEEaSQWF0C\"\n",
      "          },\n",
      "          \"type\": \"image_file\"\n",
      "        },\n",
      "        {\n",
      "          \"text\": {\n",
      "            \"annotations\": [],\n",
      "            \"value\": \"Here is the visualization of the sine wave. The x-axis represents the values from 0 to \\\\(2\\\\pi\\\\), and the y-axis represents the sine of those values. If you have any other requests or need modifications, please let me know!\"\n",
      "          },\n",
      "          \"type\": \"text\"\n",
      "        }\n",
      "      ],\n",
      "      \"created_at\": 1720529351,\n",
      "      \"file_ids\": null,\n",
      "      \"metadata\": {},\n",
      "      \"object\": \"thread.message\",\n",
      "      \"role\": \"assistant\",\n",
      "      \"run_id\": \"run_BHhbtl6HjFwd80QZfNImq6AL\",\n",
      "      \"thread_id\": \"thread_wyea7pAbvlhDaOr7UfxgHLre\",\n",
      "      \"attachments\": []\n",
      "    },\n",
      "    {\n",
      "      \"id\": \"msg_1Xn7xvrPoDCwGtrYlUedE0VL\",\n",
      "      \"assistant_id\": \"asst_4lOIhWVnFDCqHq2CdpkizO91\",\n",
      "      \"content\": [\n",
      "        {\n",
      "          \"text\": {\n",
      "            \"annotations\": [],\n",
      "            \"value\": \"Sure, I can help with that. Let's write some code to generate and visualize a sine wave using Python's matplotlib library.\"\n",
      "          },\n",
      "          \"type\": \"text\"\n",
      "        }\n",
      "      ],\n",
      "      \"created_at\": 1720529344,\n",
      "      \"file_ids\": null,\n",
      "      \"metadata\": {},\n",
      "      \"object\": \"thread.message\",\n",
      "      \"role\": \"assistant\",\n",
      "      \"run_id\": \"run_BHhbtl6HjFwd80QZfNImq6AL\",\n",
      "      \"thread_id\": \"thread_wyea7pAbvlhDaOr7UfxgHLre\",\n",
      "      \"attachments\": []\n",
      "    },\n",
      "    {\n",
      "      \"id\": \"msg_hHHv3hZUvQimSQnGQgmk4hrL\",\n",
      "      \"assistant_id\": null,\n",
      "      \"content\": [\n",
      "        {\n",
      "          \"text\": {\n",
      "            \"annotations\": [],\n",
      "            \"value\": \"Create a visualization of a sinewave\"\n",
      "          },\n",
      "          \"type\": \"text\"\n",
      "        }\n",
      "      ],\n",
      "      \"created_at\": 1720529335,\n",
      "      \"file_ids\": null,\n",
      "      \"metadata\": {},\n",
      "      \"object\": \"thread.message\",\n",
      "      \"role\": \"user\",\n",
      "      \"run_id\": null,\n",
      "      \"thread_id\": \"thread_wyea7pAbvlhDaOr7UfxgHLre\",\n",
      "      \"attachments\": []\n",
      "    }\n",
      "  ],\n",
      "  \"object\": \"list\",\n",
      "  \"first_id\": \"msg_AP2MuzeT7pKdSZ0IrVI8RS5Q\",\n",
      "  \"last_id\": \"msg_hHHv3hZUvQimSQnGQgmk4hrL\",\n",
      "  \"has_more\": false\n",
      "}\n"
     ]
    }
   ],
   "source": [
    "# check the status of the run\n",
    "import time\n",
    "from IPython.display import clear_output\n",
    "\n",
    "start_time = time.time()\n",
    "\n",
    "status = run.status\n",
    "\n",
    "while status not in [\"completed\", \"cancelled\", \"expired\", \"failed\"]:\n",
    "    time.sleep(5)\n",
    "    run = client.beta.threads.runs.retrieve(thread_id=thread.id,run_id=run.id)\n",
    "    print(\"Elapsed time: {} minutes {} seconds\".format(int((time.time() - start_time) // 60), int((time.time() - start_time) % 60)))\n",
    "    status = run.status\n",
    "    print(f'Status: {status}')\n",
    "    clear_output(wait=True)\n",
    "\n",
    "messages = client.beta.threads.messages.list(\n",
    "  thread_id=thread.id\n",
    ") \n",
    "\n",
    "print(f'Status: {status}')\n",
    "print(\"Elapsed time: {} minutes {} seconds\".format(int((time.time() - start_time) // 60), int((time.time() - start_time) % 60)))\n",
    "print(messages.model_dump_json(indent=2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{\n",
      "  \"data\": [\n",
      "    {\n",
      "      \"id\": \"msg_AP2MuzeT7pKdSZ0IrVI8RS5Q\",\n",
      "      \"assistant_id\": \"asst_4lOIhWVnFDCqHq2CdpkizO91\",\n",
      "      \"content\": [\n",
      "        {\n",
      "          \"image_file\": {\n",
      "            \"file_id\": \"assistant-P4bSkm17PSuvZVgEEaSQWF0C\"\n",
      "          },\n",
      "          \"type\": \"image_file\"\n",
      "        },\n",
      "        {\n",
      "          \"text\": {\n",
      "            \"annotations\": [],\n",
      "            \"value\": \"Here is the visualization of the sine wave. The x-axis represents the values from 0 to \\\\(2\\\\pi\\\\), and the y-axis represents the sine of those values. If you have any other requests or need modifications, please let me know!\"\n",
      "          },\n",
      "          \"type\": \"text\"\n",
      "        }\n",
      "      ],\n",
      "      \"created_at\": 1720529351,\n",
      "      \"file_ids\": null,\n",
      "      \"metadata\": {},\n",
      "      \"object\": \"thread.message\",\n",
      "      \"role\": \"assistant\",\n",
      "      \"run_id\": \"run_BHhbtl6HjFwd80QZfNImq6AL\",\n",
      "      \"thread_id\": \"thread_wyea7pAbvlhDaOr7UfxgHLre\",\n",
      "      \"attachments\": []\n",
      "    },\n",
      "    {\n",
      "      \"id\": \"msg_1Xn7xvrPoDCwGtrYlUedE0VL\",\n",
      "      \"assistant_id\": \"asst_4lOIhWVnFDCqHq2CdpkizO91\",\n",
      "      \"content\": [\n",
      "        {\n",
      "          \"text\": {\n",
      "            \"annotations\": [],\n",
      "            \"value\": \"Sure, I can help with that. Let's write some code to generate and visualize a sine wave using Python's matplotlib library.\"\n",
      "          },\n",
      "          \"type\": \"text\"\n",
      "        }\n",
      "      ],\n",
      "      \"created_at\": 1720529344,\n",
      "      \"file_ids\": null,\n",
      "      \"metadata\": {},\n",
      "      \"object\": \"thread.message\",\n",
      "      \"role\": \"assistant\",\n",
      "      \"run_id\": \"run_BHhbtl6HjFwd80QZfNImq6AL\",\n",
      "      \"thread_id\": \"thread_wyea7pAbvlhDaOr7UfxgHLre\",\n",
      "      \"attachments\": []\n",
      "    },\n",
      "    {\n",
      "      \"id\": \"msg_hHHv3hZUvQimSQnGQgmk4hrL\",\n",
      "      \"assistant_id\": null,\n",
      "      \"content\": [\n",
      "        {\n",
      "          \"text\": {\n",
      "            \"annotations\": [],\n",
      "            \"value\": \"Create a visualization of a sinewave\"\n",
      "          },\n",
      "          \"type\": \"text\"\n",
      "        }\n",
      "      ],\n",
      "      \"created_at\": 1720529335,\n",
      "      \"file_ids\": null,\n",
      "      \"metadata\": {},\n",
      "      \"object\": \"thread.message\",\n",
      "      \"role\": \"user\",\n",
      "      \"run_id\": null,\n",
      "      \"thread_id\": \"thread_wyea7pAbvlhDaOr7UfxgHLre\",\n",
      "      \"attachments\": []\n",
      "    }\n",
      "  ],\n",
      "  \"object\": \"list\",\n",
      "  \"first_id\": \"msg_AP2MuzeT7pKdSZ0IrVI8RS5Q\",\n",
      "  \"last_id\": \"msg_hHHv3hZUvQimSQnGQgmk4hrL\",\n",
      "  \"has_more\": false\n",
      "}\n"
     ]
    }
   ],
   "source": [
    "messages = client.beta.threads.messages.list(\n",
    "  thread_id=thread.id\n",
    ")\n",
    "\n",
    "print(messages.model_dump_json(indent=2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "assistant-P4bSkm17PSuvZVgEEaSQWF0C\n"
     ]
    }
   ],
   "source": [
    "# show the file name\n",
    "data = json.loads(messages.model_dump_json(indent=2))  # Load JSON data into a Python object\n",
    "image_file_id = data['data'][0]['content'][0]['image_file']['file_id']\n",
    "\n",
    "print(image_file_id)  # Outputs: assistant-1YGVTvNzc2JXajI5JU9F0HMD"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# download the file\n",
    "content = client.files.content(image_file_id)\n",
    "\n",
    "image= content.write_to_file(\"sinewave.png\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<PIL.PngImagePlugin.PngImageFile image mode=RGBA size=1746x1101>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# rewrite the code to display the image in jupyter notebook\n",
    "\n",
    "from PIL import Image\n",
    "\n",
    "# Display the image in the default image viewer\n",
    "image = Image.open(\"sinewave.png\")\n",
    "image.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Ask follow up questions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Add a new user question to the thread\n",
    "message = client.beta.threads.messages.create(\n",
    "    thread_id=thread.id,\n",
    "    role=\"user\",\n",
    "    content=\"Show me the generated code you used to generate the sinewave\"\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "queued\n"
     ]
    }
   ],
   "source": [
    "run = client.beta.threads.runs.create(\n",
    "  thread_id=thread.id,\n",
    "  assistant_id=assistant.id,\n",
    "  #instructions=\"New instructions\" #You can optionally provide new instructions  but these will override the default instructions\n",
    ")\n",
    "\n",
    "# Retrieve the status of the run\n",
    "run = client.beta.threads.runs.retrieve(\n",
    "  thread_id=thread.id,\n",
    "  run_id=run.id\n",
    ")\n",
    "\n",
    "status = run.status\n",
    "print(status)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{\n",
      "  \"data\": [\n",
      "    {\n",
      "      \"id\": \"msg_9nMoiIm2L87rcFoXLGQTZGli\",\n",
      "      \"assistant_id\": \"asst_4lOIhWVnFDCqHq2CdpkizO91\",\n",
      "      \"content\": [\n",
      "        {\n",
      "          \"text\": {\n",
      "            \"annotations\": [],\n",
      "            \"value\": \"Sure! Here is the code I used to generate the sine wave visualization:\\n\\n```python\\nimport numpy as np\\nimport matplotlib.pyplot as plt\\n\\n# Generate data for the sine wave\\nx = np.linspace(0, 2 * np.pi, 1000)  # 1000 points between 0 and 2pi\\ny = np.sin(x)\\n\\n# Create the plot\\nplt.figure(figsize=(10, 6))\\nplt.plot(x, y, label='Sine wave')\\n\\n# Customize the plot\\nplt.title('Sine Wave')\\nplt.xlabel('x')\\nplt.ylabel('sin(x)')\\nplt.grid(True)\\nplt.legend()\\n\\n# Show the plot\\nplt.show()\\n```\\n\\nThis code uses the numpy library to generate values between 0 and \\\\(2\\\\pi\\\\) and computes the sine of those values. It then uses matplotlib to create and customize the plot, displaying the sine wave. If you need further customization or changes, feel free to ask!\"\n",
      "          },\n",
      "          \"type\": \"text\"\n",
      "        }\n",
      "      ],\n",
      "      \"created_at\": 1720529427,\n",
      "      \"file_ids\": null,\n",
      "      \"metadata\": {},\n",
      "      \"object\": \"thread.message\",\n",
      "      \"role\": \"assistant\",\n",
      "      \"run_id\": \"run_y1QaoyQO8AXxtzwigfIGn4KJ\",\n",
      "      \"thread_id\": \"thread_wyea7pAbvlhDaOr7UfxgHLre\",\n",
      "      \"attachments\": []\n",
      "    },\n",
      "    {\n",
      "      \"id\": \"msg_c8cqcSBzc3pckGg53i3hQnh8\",\n",
      "      \"assistant_id\": null,\n",
      "      \"content\": [\n",
      "        {\n",
      "          \"text\": {\n",
      "            \"annotations\": [],\n",
      "            \"value\": \"Show me the generated code you used to generate the sinewave\"\n",
      "          },\n",
      "          \"type\": \"text\"\n",
      "        }\n",
      "      ],\n",
      "      \"created_at\": 1720529419,\n",
      "      \"file_ids\": null,\n",
      "      \"metadata\": {},\n",
      "      \"object\": \"thread.message\",\n",
      "      \"role\": \"user\",\n",
      "      \"run_id\": null,\n",
      "      \"thread_id\": \"thread_wyea7pAbvlhDaOr7UfxgHLre\",\n",
      "      \"attachments\": []\n",
      "    },\n",
      "    {\n",
      "      \"id\": \"msg_AP2MuzeT7pKdSZ0IrVI8RS5Q\",\n",
      "      \"assistant_id\": \"asst_4lOIhWVnFDCqHq2CdpkizO91\",\n",
      "      \"content\": [\n",
      "        {\n",
      "          \"image_file\": {\n",
      "            \"file_id\": \"assistant-P4bSkm17PSuvZVgEEaSQWF0C\"\n",
      "          },\n",
      "          \"type\": \"image_file\"\n",
      "        },\n",
      "        {\n",
      "          \"text\": {\n",
      "            \"annotations\": [],\n",
      "            \"value\": \"Here is the visualization of the sine wave. The x-axis represents the values from 0 to \\\\(2\\\\pi\\\\), and the y-axis represents the sine of those values. If you have any other requests or need modifications, please let me know!\"\n",
      "          },\n",
      "          \"type\": \"text\"\n",
      "        }\n",
      "      ],\n",
      "      \"created_at\": 1720529351,\n",
      "      \"file_ids\": null,\n",
      "      \"metadata\": {},\n",
      "      \"object\": \"thread.message\",\n",
      "      \"role\": \"assistant\",\n",
      "      \"run_id\": \"run_BHhbtl6HjFwd80QZfNImq6AL\",\n",
      "      \"thread_id\": \"thread_wyea7pAbvlhDaOr7UfxgHLre\",\n",
      "      \"attachments\": []\n",
      "    },\n",
      "    {\n",
      "      \"id\": \"msg_1Xn7xvrPoDCwGtrYlUedE0VL\",\n",
      "      \"assistant_id\": \"asst_4lOIhWVnFDCqHq2CdpkizO91\",\n",
      "      \"content\": [\n",
      "        {\n",
      "          \"text\": {\n",
      "            \"annotations\": [],\n",
      "            \"value\": \"Sure, I can help with that. Let's write some code to generate and visualize a sine wave using Python's matplotlib library.\"\n",
      "          },\n",
      "          \"type\": \"text\"\n",
      "        }\n",
      "      ],\n",
      "      \"created_at\": 1720529344,\n",
      "      \"file_ids\": null,\n",
      "      \"metadata\": {},\n",
      "      \"object\": \"thread.message\",\n",
      "      \"role\": \"assistant\",\n",
      "      \"run_id\": \"run_BHhbtl6HjFwd80QZfNImq6AL\",\n",
      "      \"thread_id\": \"thread_wyea7pAbvlhDaOr7UfxgHLre\",\n",
      "      \"attachments\": []\n",
      "    },\n",
      "    {\n",
      "      \"id\": \"msg_hHHv3hZUvQimSQnGQgmk4hrL\",\n",
      "      \"assistant_id\": null,\n",
      "      \"content\": [\n",
      "        {\n",
      "          \"text\": {\n",
      "            \"annotations\": [],\n",
      "            \"value\": \"Create a visualization of a sinewave\"\n",
      "          },\n",
      "          \"type\": \"text\"\n",
      "        }\n",
      "      ],\n",
      "      \"created_at\": 1720529335,\n",
      "      \"file_ids\": null,\n",
      "      \"metadata\": {},\n",
      "      \"object\": \"thread.message\",\n",
      "      \"role\": \"user\",\n",
      "      \"run_id\": null,\n",
      "      \"thread_id\": \"thread_wyea7pAbvlhDaOr7UfxgHLre\",\n",
      "      \"attachments\": []\n",
      "    }\n",
      "  ],\n",
      "  \"object\": \"list\",\n",
      "  \"first_id\": \"msg_9nMoiIm2L87rcFoXLGQTZGli\",\n",
      "  \"last_id\": \"msg_hHHv3hZUvQimSQnGQgmk4hrL\",\n",
      "  \"has_more\": false\n",
      "}\n"
     ]
    }
   ],
   "source": [
    "messages = client.beta.threads.messages.list(\n",
    "  thread_id=thread.id\n",
    ")\n",
    "\n",
    "print(messages.model_dump_json(indent=2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sure! Here is the code I used to generate the sine wave visualization:\n",
      "\n",
      "```python\n",
      "import numpy as np\n",
      "import matplotlib.pyplot as plt\n",
      "\n",
      "# Generate data for the sine wave\n",
      "x = np.linspace(0, 2 * np.pi, 1000)  # 1000 points between 0 and 2pi\n",
      "y = np.sin(x)\n",
      "\n",
      "# Create the plot\n",
      "plt.figure(figsize=(10, 6))\n",
      "plt.plot(x, y, label='Sine wave')\n",
      "\n",
      "# Customize the plot\n",
      "plt.title('Sine Wave')\n",
      "plt.xlabel('x')\n",
      "plt.ylabel('sin(x)')\n",
      "plt.grid(True)\n",
      "plt.legend()\n",
      "\n",
      "# Show the plot\n",
      "plt.show()\n",
      "```\n",
      "\n",
      "This code uses the numpy library to generate values between 0 and \\(2\\pi\\) and computes the sine of those values. It then uses matplotlib to create and customize the plot, displaying the sine wave. If you need further customization or changes, feel free to ask!\n"
     ]
    }
   ],
   "source": [
    "# Print the code\n",
    "data = json.loads(messages.model_dump_json(indent=2))\n",
    "code = data['data'][0]['content'][0]['text']['value']\n",
    "print(code)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Generate data for the sine wave\n",
    "x = np.linspace(0, 2 * np.pi, 1000)  # 1000 points between 0 and 2pi\n",
    "y = np.sin(x)\n",
    "\n",
    "# Create the plot\n",
    "plt.figure(figsize=(10, 6))\n",
    "plt.plot(x, y, label='Sine wave')\n",
    "\n",
    "# Customize the plot\n",
    "plt.title('Sine Wave')\n",
    "plt.xlabel('x')\n",
    "plt.ylabel('sin(x)')\n",
    "plt.grid(True)\n",
    "plt.legend()\n",
    "\n",
    "# Show the plot\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "openai_v1",
   "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": "3.11.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}