week4/day4.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "4a6ab9a2-28a2-445d-8512-a0dc8d1b54e9",
   "metadata": {},
   "source": [
    "# Code Generator\n",
    "\n",
    "The requirement: use an Open Source model to generate high performance C++ code from Python code\n",
    "\n",
    "To replicate this, you'll need to set up a HuggingFace endpoint as I do in the video. It's simple to do, and it's quite satisfying to see the results!\n",
    "\n",
    "It's also an important part of your learning; this is the first example of deploying an open source model to be behind an API. We'll return to this in Week 8, but this should plant a seed in your mind for what's involved in moving open source models into production."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "22e1567b-33fd-49e7-866e-4b635d15715a",
   "metadata": {},
   "source": [
    "<table style=\"margin: 0; text-align: left;\">\n",
    "    <tr>\n",
    "        <td style=\"width: 150px; height: 150px; vertical-align: middle;\">\n",
    "            <img src=\"../important.jpg\" width=\"150\" height=\"150\" style=\"display: block;\" />\n",
    "        </td>\n",
    "        <td>\n",
    "            <h1 style=\"color:#900;\">Important - Pause Endpoints when not in use</h1>\n",
    "            <span style=\"color:#900;\">\n",
    "            If you do decide to use HuggingFace endpoints for this project, you should stop or pause the endpoints when you are done to avoid accruing unnecessary running cost. The costs are very low as long as you only run the endpoint when you're using it. Navigate to the HuggingFace endpoint UI <a href=\"https://ui.endpoints.huggingface.co/\">here,</a> open your endpoint, and click Pause to put it on pause so you no longer pay for it.  \n",
    "Many thanks to student John L. for raising this.\n",
    "<br/><br/>\n",
    "In week 8 we will use Modal instead of HuggingFace endpoints; with Modal you only pay for the time that you use it and you should get free credits.\n",
    "            </span>\n",
    "        </td>\n",
    "    </tr>\n",
    "</table>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e610bf56-a46e-4aff-8de1-ab49d62b1ad3",
   "metadata": {},
   "outputs": [],
   "source": [
    "# imports\n",
    "\n",
    "import os\n",
    "import io\n",
    "import sys\n",
    "import json\n",
    "import requests\n",
    "from dotenv import load_dotenv\n",
    "from openai import OpenAI\n",
    "import google.generativeai\n",
    "import anthropic\n",
    "from IPython.display import Markdown, display, update_display\n",
    "import gradio as gr\n",
    "import subprocess"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4f672e1c-87e9-4865-b760-370fa605e614",
   "metadata": {},
   "outputs": [],
   "source": [
    "# environment\n",
    "\n",
    "load_dotenv()\n",
    "os.environ['OPENAI_API_KEY'] = os.getenv('OPENAI_API_KEY', 'your-key-if-not-using-env')\n",
    "os.environ['ANTHROPIC_API_KEY'] = os.getenv('ANTHROPIC_API_KEY', 'your-key-if-not-using-env')\n",
    "os.environ['HF_TOKEN'] = os.getenv('HF_TOKEN', 'your-key-if-not-using-env')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8aa149ed-9298-4d69-8fe2-8f5de0f667da",
   "metadata": {},
   "outputs": [],
   "source": [
    "# initialize\n",
    "\n",
    "openai = OpenAI()\n",
    "claude = anthropic.Anthropic()\n",
    "OPENAI_MODEL = \"gpt-4o\"\n",
    "CLAUDE_MODEL = \"claude-3-5-sonnet-20240620\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6896636f-923e-4a2c-9d6c-fac07828a201",
   "metadata": {},
   "outputs": [],
   "source": [
    "system_message = \"You are an assistant that reimplements Python code in high performance C++ for an M1 Mac. \"\n",
    "system_message += \"Respond only with C++ code; use comments sparingly and do not provide any explanation other than occasional comments. \"\n",
    "system_message += \"The C++ response needs to produce an identical output in the fastest possible time. Keep implementations of random number generators identical so that results match exactly.\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8e7b3546-57aa-4c29-bc5d-f211970d04eb",
   "metadata": {},
   "outputs": [],
   "source": [
    "def user_prompt_for(python):\n",
    "    user_prompt = \"Rewrite this Python code in C++ with the fastest possible implementation that produces identical output in the least time. \"\n",
    "    user_prompt += \"Respond only with C++ code; do not explain your work other than a few comments. \"\n",
    "    user_prompt += \"Pay attention to number types to ensure no int overflows. Remember to #include all necessary C++ packages such as iomanip.\\n\\n\"\n",
    "    user_prompt += python\n",
    "    return user_prompt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c6190659-f54c-4951-bef4-4960f8e51cc4",
   "metadata": {},
   "outputs": [],
   "source": [
    "def messages_for(python):\n",
    "    return [\n",
    "        {\"role\": \"system\", \"content\": system_message},\n",
    "        {\"role\": \"user\", \"content\": user_prompt_for(python)}\n",
    "    ]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "71e1ba8c-5b05-4726-a9f3-8d8c6257350b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# write to a file called optimized.cpp\n",
    "\n",
    "def write_output(cpp):\n",
    "    code = cpp.replace(\"```cpp\",\"\").replace(\"```\",\"\")\n",
    "    with open(\"optimized.cpp\", \"w\") as f:\n",
    "        f.write(code)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e7d2fea8-74c6-4421-8f1e-0e76d5b201b9",
   "metadata": {},
   "outputs": [],
   "source": [
    "def optimize_gpt(python):    \n",
    "    stream = openai.chat.completions.create(model=OPENAI_MODEL, messages=messages_for(python), stream=True)\n",
    "    reply = \"\"\n",
    "    for chunk in stream:\n",
    "        fragment = chunk.choices[0].delta.content or \"\"\n",
    "        reply += fragment\n",
    "        print(fragment, end='', flush=True)\n",
    "    write_output(reply)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7cd84ad8-d55c-4fe0-9eeb-1895c95c4a9d",
   "metadata": {},
   "outputs": [],
   "source": [
    "def optimize_claude(python):\n",
    "    result = claude.messages.stream(\n",
    "        model=CLAUDE_MODEL,\n",
    "        max_tokens=2000,\n",
    "        system=system_message,\n",
    "        messages=[{\"role\": \"user\", \"content\": user_prompt_for(python)}],\n",
    "    )\n",
    "    reply = \"\"\n",
    "    with result as stream:\n",
    "        for text in stream.text_stream:\n",
    "            reply += text\n",
    "            print(text, end=\"\", flush=True)\n",
    "    write_output(reply)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a1cbb778-fa57-43de-b04b-ed523f396c38",
   "metadata": {},
   "outputs": [],
   "source": [
    "pi = \"\"\"\n",
    "import time\n",
    "\n",
    "def calculate(iterations, param1, param2):\n",
    "    result = 1.0\n",
    "    for i in range(1, iterations+1):\n",
    "        j = i * param1 - param2\n",
    "        result -= (1/j)\n",
    "        j = i * param1 + param2\n",
    "        result += (1/j)\n",
    "    return result\n",
    "\n",
    "start_time = time.time()\n",
    "result = calculate(100_000_000, 4, 1) * 4\n",
    "end_time = time.time()\n",
    "\n",
    "print(f\"Result: {result:.12f}\")\n",
    "print(f\"Execution Time: {(end_time - start_time):.6f} seconds\")\n",
    "\"\"\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7fe1cd4b-d2c5-4303-afed-2115a3fef200",
   "metadata": {},
   "outputs": [],
   "source": [
    "exec(pi)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "105db6f9-343c-491d-8e44-3a5328b81719",
   "metadata": {},
   "outputs": [],
   "source": [
    "optimize_gpt(pi)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "bf26ee95-0c77-491d-9a91-579a1e96a8a3",
   "metadata": {},
   "outputs": [],
   "source": [
    "exec(pi)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4194e40c-04ab-4940-9d64-b4ad37c5bb40",
   "metadata": {},
   "outputs": [],
   "source": [
    "!clang++ -O3 -std=c++17 -march=armv8.3-a -o optimized optimized.cpp\n",
    "!./optimized"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "983a11fe-e24d-4c65-8269-9802c5ef3ae6",
   "metadata": {},
   "outputs": [],
   "source": [
    "optimize_claude(pi)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d5a766f9-3d23-4bb4-a1d4-88ec44b61ddf",
   "metadata": {},
   "outputs": [],
   "source": [
    "!clang++ -O3 -std=c++17 -march=armv8.3-a -o optimized optimized.cpp\n",
    "!./optimized"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c3b497b3-f569-420e-b92e-fb0f49957ce0",
   "metadata": {},
   "outputs": [],
   "source": [
    "python_hard = \"\"\"\n",
    "def lcg(seed, a=1664525, c=1013904223, m=2**32):\n",
    "    value = seed\n",
    "    while True:\n",
    "        value = (a * value + c) % m\n",
    "        yield value\n",
    "        \n",
    "def max_subarray_sum(n, seed, min_val, max_val):\n",
    "    lcg_gen = lcg(seed)\n",
    "    random_numbers = [next(lcg_gen) % (max_val - min_val + 1) + min_val for _ in range(n)]\n",
    "    max_sum = float('-inf')\n",
    "    for i in range(n):\n",
    "        current_sum = 0\n",
    "        for j in range(i, n):\n",
    "            current_sum += random_numbers[j]\n",
    "            if current_sum > max_sum:\n",
    "                max_sum = current_sum\n",
    "    return max_sum\n",
    "\n",
    "def total_max_subarray_sum(n, initial_seed, min_val, max_val):\n",
    "    total_sum = 0\n",
    "    lcg_gen = lcg(initial_seed)\n",
    "    for _ in range(20):\n",
    "        seed = next(lcg_gen)\n",
    "        total_sum += max_subarray_sum(n, seed, min_val, max_val)\n",
    "    return total_sum\n",
    "\n",
    "# Parameters\n",
    "n = 10000         # Number of random numbers\n",
    "initial_seed = 42 # Initial seed for the LCG\n",
    "min_val = -10     # Minimum value of random numbers\n",
    "max_val = 10      # Maximum value of random numbers\n",
    "\n",
    "# Timing the function\n",
    "import time\n",
    "start_time = time.time()\n",
    "result = total_max_subarray_sum(n, initial_seed, min_val, max_val)\n",
    "end_time = time.time()\n",
    "\n",
    "print(\"Total Maximum Subarray Sum (20 runs):\", result)\n",
    "print(\"Execution Time: {:.6f} seconds\".format(end_time - start_time))\n",
    "\"\"\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "dab5e4bc-276c-4555-bd4c-12c699d5e899",
   "metadata": {},
   "outputs": [],
   "source": [
    "exec(python_hard)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e8d24ed5-2c15-4f55-80e7-13a3952b3cb8",
   "metadata": {},
   "outputs": [],
   "source": [
    "optimize_gpt(python_hard)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e0b3d073-88a2-40b2-831c-6f0c345c256f",
   "metadata": {},
   "outputs": [],
   "source": [
    "!clang++ -O3 -std=c++17 -march=armv8.3-a -o optimized optimized.cpp\n",
    "!./optimized"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e9305446-1d0c-4b51-866a-b8c1e299bf5c",
   "metadata": {},
   "outputs": [],
   "source": [
    "optimize_claude(python_hard)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0c181036-8193-4fdd-aef3-fc513b218d43",
   "metadata": {},
   "outputs": [],
   "source": [
    "!clang++ -O3 -std=c++17 -march=armv8.3-a -o optimized optimized.cpp\n",
    "!./optimized"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0be9f47d-5213-4700-b0e2-d444c7c738c0",
   "metadata": {},
   "outputs": [],
   "source": [
    "def stream_gpt(python):    \n",
    "    stream = openai.chat.completions.create(model=OPENAI_MODEL, messages=messages_for(python), stream=True)\n",
    "    reply = \"\"\n",
    "    for chunk in stream:\n",
    "        fragment = chunk.choices[0].delta.content or \"\"\n",
    "        reply += fragment\n",
    "        yield reply.replace('```cpp\\n','').replace('```','')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8669f56b-8314-4582-a167-78842caea131",
   "metadata": {},
   "outputs": [],
   "source": [
    "def stream_claude(python):\n",
    "    result = claude.messages.stream(\n",
    "        model=CLAUDE_MODEL,\n",
    "        max_tokens=2000,\n",
    "        system=system_message,\n",
    "        messages=[{\"role\": \"user\", \"content\": user_prompt_for(python)}],\n",
    "    )\n",
    "    reply = \"\"\n",
    "    with result as stream:\n",
    "        for text in stream.text_stream:\n",
    "            reply += text\n",
    "            yield reply.replace('```cpp\\n','').replace('```','')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2f1ae8f5-16c8-40a0-aa18-63b617df078d",
   "metadata": {},
   "outputs": [],
   "source": [
    "def optimize(python, model):\n",
    "    if model==\"GPT\":\n",
    "        result = stream_gpt(python)\n",
    "    elif model==\"Claude\":\n",
    "        result = stream_claude(python)\n",
    "    else:\n",
    "        raise ValueError(\"Unknown model\")\n",
    "    for stream_so_far in result:\n",
    "        yield stream_so_far        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f1ddb38e-6b0a-4c37-baa4-ace0b7de887a",
   "metadata": {},
   "outputs": [],
   "source": [
    "with gr.Blocks() as ui:\n",
    "    with gr.Row():\n",
    "        python = gr.Textbox(label=\"Python code:\", lines=10, value=python_hard)\n",
    "        cpp = gr.Textbox(label=\"C++ code:\", lines=10)\n",
    "    with gr.Row():\n",
    "        model = gr.Dropdown([\"GPT\", \"Claude\"], label=\"Select model\", value=\"GPT\")\n",
    "        convert = gr.Button(\"Convert code\")\n",
    "\n",
    "    convert.click(optimize, inputs=[python, model], outputs=[cpp])\n",
    "\n",
    "ui.launch(inbrowser=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "19bf2bff-a822-4009-a539-f003b1651383",
   "metadata": {},
   "outputs": [],
   "source": [
    "def execute_python(code):\n",
    "        try:\n",
    "            output = io.StringIO()\n",
    "            sys.stdout = output\n",
    "            exec(code)\n",
    "        finally:\n",
    "            sys.stdout = sys.__stdout__\n",
    "        return output.getvalue()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "77f3ab5d-fcfb-4d3f-8728-9cacbf833ea6",
   "metadata": {},
   "outputs": [],
   "source": [
    "def execute_cpp(code):\n",
    "        write_output(code)\n",
    "        try:\n",
    "            compile_cmd = [\"clang++\", \"-Ofast\", \"-std=c++17\", \"-march=armv8.5-a\", \"-mtune=apple-m1\", \"-mcpu=apple-m1\", \"-o\", \"optimized\", \"optimized.cpp\"]\n",
    "            compile_result = subprocess.run(compile_cmd, check=True, text=True, capture_output=True)\n",
    "            run_cmd = [\"./optimized\"]\n",
    "            run_result = subprocess.run(run_cmd, check=True, text=True, capture_output=True)\n",
    "            return run_result.stdout\n",
    "        except subprocess.CalledProcessError as e:\n",
    "            return f\"An error occurred:\\n{e.stderr}\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9a2274f1-d03b-42c0-8dcc-4ce159b18442",
   "metadata": {},
   "outputs": [],
   "source": [
    "css = \"\"\"\n",
    ".python {background-color: #306998;}\n",
    ".cpp {background-color: #050;}\n",
    "\"\"\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f1303932-160c-424b-97a8-d28c816721b2",
   "metadata": {},
   "outputs": [],
   "source": [
    "with gr.Blocks(css=css) as ui:\n",
    "    gr.Markdown(\"## Convert code from Python to C++\")\n",
    "    with gr.Row():\n",
    "        python = gr.Textbox(label=\"Python code:\", value=python_hard, lines=10)\n",
    "        cpp = gr.Textbox(label=\"C++ code:\", lines=10)\n",
    "    with gr.Row():\n",
    "        model = gr.Dropdown([\"GPT\", \"Claude\"], label=\"Select model\", value=\"GPT\")\n",
    "    with gr.Row():\n",
    "        convert = gr.Button(\"Convert code\")\n",
    "    with gr.Row():\n",
    "        python_run = gr.Button(\"Run Python\")\n",
    "        cpp_run = gr.Button(\"Run C++\")\n",
    "    with gr.Row():\n",
    "        python_out = gr.TextArea(label=\"Python result:\", elem_classes=[\"python\"])\n",
    "        cpp_out = gr.TextArea(label=\"C++ result:\", elem_classes=[\"cpp\"])\n",
    "\n",
    "    convert.click(optimize, inputs=[python, model], outputs=[cpp])\n",
    "    python_run.click(execute_python, inputs=[python], outputs=[python_out])\n",
    "    cpp_run.click(execute_cpp, inputs=[cpp], outputs=[cpp_out])\n",
    "\n",
    "ui.launch(inbrowser=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "bb8c5b4e-ec51-4f21-b3f8-6aa94fede86d",
   "metadata": {},
   "outputs": [],
   "source": [
    "from huggingface_hub import login, InferenceClient\n",
    "from transformers import AutoTokenizer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "13347633-4606-4e38-9927-80c39e65c1f1",
   "metadata": {},
   "outputs": [],
   "source": [
    "hf_token = os.environ['HF_TOKEN']\n",
    "login(hf_token, add_to_git_credential=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ef60a4df-6267-4ebd-8eed-dcb917af0a5e",
   "metadata": {},
   "outputs": [],
   "source": [
    "code_qwen = \"Qwen/CodeQwen1.5-7B-Chat\"\n",
    "code_gemma = \"google/codegemma-7b-it\"\n",
    "CODE_QWEN_URL = \"https://h1vdol7jxhje3mpn.us-east-1.aws.endpoints.huggingface.cloud\"\n",
    "CODE_GEMMA_URL = \"https://c5hggiyqachmgnqg.us-east-1.aws.endpoints.huggingface.cloud\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "695ce389-a903-4533-a2f1-cd9e2a6af8f2",
   "metadata": {},
   "outputs": [],
   "source": [
    "tokenizer = AutoTokenizer.from_pretrained(code_qwen)\n",
    "messages = messages_for(pi)\n",
    "text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d4548e96-0b32-4793-bdd6-1b072c2f26ab",
   "metadata": {},
   "outputs": [],
   "source": [
    "print(text)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "bb2a126b-09e7-4966-bc97-0ef5c2cc7896",
   "metadata": {},
   "outputs": [],
   "source": [
    "client = InferenceClient(CODE_QWEN_URL, token=hf_token)\n",
    "stream = client.text_generation(text, stream=True, details=True, max_new_tokens=3000)\n",
    "for r in stream:\n",
    "    print(r.token.text, end = \"\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "127a52e5-ad85-42b7-a0f5-9afda5efe090",
   "metadata": {},
   "outputs": [],
   "source": [
    "def stream_code_quen(python):\n",
    "    tokenizer = AutoTokenizer.from_pretrained(code_qwen)\n",
    "    messages = messages_for(python)\n",
    "    text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)\n",
    "    client = InferenceClient(CODE_QWEN_URL, token=hf_token)\n",
    "    stream = client.text_generation(text, stream=True, details=True, max_new_tokens=3000)\n",
    "    result = \"\"\n",
    "    for r in stream:\n",
    "        result += r.token.text\n",
    "        yield result    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a82387d1-7651-4923-995b-fe18356fcaa6",
   "metadata": {},
   "outputs": [],
   "source": [
    "def optimize(python, model):\n",
    "    if model==\"GPT\":\n",
    "        result = stream_gpt(python)\n",
    "    elif model==\"Claude\":\n",
    "        result = stream_claude(python)\n",
    "    elif model==\"CodeQwen\":\n",
    "        result = stream_code_qwen(python)\n",
    "    else:\n",
    "        raise ValueError(\"Unknown model\")\n",
    "    for stream_so_far in result:\n",
    "        yield stream_so_far    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f9ca2e6f-60c1-4e5f-b570-63c75b2d189b",
   "metadata": {},
   "outputs": [],
   "source": [
    "with gr.Blocks(css=css) as ui:\n",
    "    gr.Markdown(\"## Convert code from Python to C++\")\n",
    "    with gr.Row():\n",
    "        python = gr.Textbox(label=\"Python code:\", value=python_hard, lines=10)\n",
    "        cpp = gr.Textbox(label=\"C++ code:\", lines=10)\n",
    "    with gr.Row():\n",
    "        model = gr.Dropdown([\"GPT\", \"Claude\", \"CodeQwen\"], label=\"Select model\", value=\"GPT\")\n",
    "    with gr.Row():\n",
    "        convert = gr.Button(\"Convert code\")\n",
    "    with gr.Row():\n",
    "        python_run = gr.Button(\"Run Python\")\n",
    "        cpp_run = gr.Button(\"Run C++\")\n",
    "    with gr.Row():\n",
    "        python_out = gr.TextArea(label=\"Python result:\", elem_classes=[\"python\"])\n",
    "        cpp_out = gr.TextArea(label=\"C++ result:\", elem_classes=[\"cpp\"])\n",
    "\n",
    "    convert.click(optimize, inputs=[python, model], outputs=[cpp])\n",
    "    python_run.click(execute_python, inputs=[python], outputs=[python_out])\n",
    "    cpp_run.click(execute_cpp, inputs=[cpp], outputs=[cpp_out])\n",
    "\n",
    "ui.launch(inbrowser=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f12bfe23-135b-45a7-8c6d-0c27d68b0a82",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
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  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
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   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
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