{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "4a6ab9a2-28a2-445d-8512-a0dc8d1b54e9",
   "metadata": {},
   "source": [
    "# Code Generator\n",
    "\n",
    "The requirement: use a Frontier model to generate high performance C++ code from Python code"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "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": 125,
   "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": 126,
   "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": 127,
   "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": 128,
   "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": 129,
   "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": 130,
   "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": 131,
   "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": 132,
   "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": 133,
   "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": 134,
   "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": 135,
   "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": 136,
   "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": 137,
   "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": 138,
   "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": 139,
   "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": 140,
   "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": 141,
   "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": 142,
   "id": "13347633-4606-4e38-9927-80c39e65c1f1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Token is valid (permission: write).\n",
      "Your token has been saved in your configured git credential helpers (osxkeychain).\n",
      "Your token has been saved to /Users/ed/.cache/huggingface/token\n",
      "Login successful\n"
     ]
    }
   ],
   "source": [
    "hf_token = os.environ['HF_TOKEN']\n",
    "login(hf_token, add_to_git_credential=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "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": 144,
   "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": 147,
   "id": "d4548e96-0b32-4793-bdd6-1b072c2f26ab",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<|im_start|>system\n",
      "You are an assistant that reimplements Python code in high performance C++ for an M1 Mac. Respond only with C++ code; use comments sparingly and do not provide any explanation other than occasional comments. 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.<|im_end|>\n",
      "<|im_start|>user\n",
      "Rewrite this Python code in C++ with the fastest possible implementation that produces identical output in the least time. Respond only with C++ code; do not explain your work other than a few comments. Pay attention to number types to ensure no int overflows. Remember to #include all necessary C++ packages such as iomanip.\n",
      "\n",
      "\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",
      "<|im_end|>\n",
      "<|im_start|>assistant\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(text)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "id": "bb2a126b-09e7-4966-bc97-0ef5c2cc7896",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Here is the C++ code that achieves the same result as the Python code:\n",
      "\n",
      "```cpp\n",
      "#include <iostream>\n",
      "#include <iomanip>\n",
      "#include <chrono>\n",
      "\n",
      "double calculate(int iterations, double param1, double param2) {\n",
      "    double result = 1.0;\n",
      "    for (int i = 1; i <= iterations; ++i) {\n",
      "        double j = i * param1 - param2;\n",
      "        result -= 1.0 / j;\n",
      "        j = i * param1 + param2;\n",
      "        result += 1.0 / j;\n",
      "    }\n",
      "    return result;\n",
      "}\n",
      "\n",
      "int main() {\n",
      "    auto start_time = std::chrono::high_resolution_clock::now();\n",
      "    double result = calculate(100000000, 4.0, 1.0) * 4.0;\n",
      "    auto end_time = std::chrono::high_resolution_clock::now();\n",
      "\n",
      "    std::cout << \"Result: \" << std::setprecision(12) << result << std::endl;\n",
      "    std::cout << \"Execution Time: \" << std::chrono::duration<double>(end_time - start_time).count() << \" seconds\" << std::endl;\n",
      "\n",
      "    return 0;\n",
      "}\n",
      "```\n",
      "\n",
      "This C++ code does the same thing as the Python code: it calculates a mathematical function and measures the execution time. The `calculate` function is implemented in a similar way to the Python code, but it uses `double` instead of `int` for the parameters and the result. The `main` function measures the execution time using `std::chrono::high_resolution_clock` and prints the result and execution time to the console. The `std::setprecision(12)` is used to print the result with 12 decimal places.<|im_end|>"
     ]
    }
   ],
   "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": 149,
   "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": 150,
   "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": 152,
   "id": "f9ca2e6f-60c1-4e5f-b570-63c75b2d189b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div><iframe src=\"http://127.0.0.1:7868/\" width=\"100%\" height=\"500\" allow=\"autoplay; camera; microphone; clipboard-read; clipboard-write;\" frameborder=\"0\" allowfullscreen></iframe></div>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": []
     },
     "execution_count": 152,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "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"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}