week 4 excercises: added Gemini and Python Code Documentation Assistant
This commit is contained in:
Krabulek
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{
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"cells": [
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{
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"cell_type": "markdown",
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"id": "4a6ab9a2-28a2-445d-8512-a0dc8d1b54e9",
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"metadata": {},
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"source": [
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"# Python Code Documentation Assistant\n",
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"\n",
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"The requirement: use a Frontier model to add docstrings and comments to your Python code\n"
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]
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},
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{
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"cell_type": "markdown",
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"id": "d4634170-c444-4326-9e68-5f87c63fa0e0",
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"metadata": {},
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"source": [
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"## Imports"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "1f72dfaf-9f20-4d81-b082-018eda152c9f",
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"metadata": {},
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"outputs": [],
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"source": [
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"!pip install -U -q \"google-genai\""
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "e610bf56-a46e-4aff-8de1-ab49d62b1ad3",
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"metadata": {},
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"outputs": [],
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"source": [
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"import os\n",
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"import io\n",
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"import sys\n",
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"from dotenv import load_dotenv\n",
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"from openai import OpenAI\n",
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"from google import genai\n",
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"from google.genai import types\n",
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"import anthropic\n",
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"from IPython.display import Markdown, display, update_display\n",
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"import gradio as gr\n",
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"import subprocess"
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]
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},
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{
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"cell_type": "markdown",
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"id": "f91e8b32-4c98-4210-a1e1-bfe0b1fddab7",
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"metadata": {},
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"source": [
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"## Environment"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "4f672e1c-87e9-4865-b760-370fa605e614",
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"metadata": {},
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"outputs": [],
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"source": [
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"load_dotenv(override=True)\n",
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"openai_api_key = os.getenv('OPENAI_API_KEY')\n",
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"anthropic_api_key = os.getenv('ANTHROPIC_API_KEY')\n",
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"google_api_key = os.getenv('GOOGLE_API_KEY')\n",
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"\n",
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"if openai_api_key:\n",
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" print(f\"OpenAI API Key exists and begins {openai_api_key[:8]}\")\n",
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"else:\n",
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" print(\"OpenAI API Key not set\")\n",
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" \n",
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"if anthropic_api_key:\n",
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" print(f\"Anthropic API Key exists and begins {anthropic_api_key[:7]}\")\n",
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"else:\n",
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" print(\"Anthropic API Key not set\")\n",
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"\n",
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"if google_api_key:\n",
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" print(f\"Google API Key exists and begins {google_api_key[:4]}\")\n",
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"else:\n",
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" print(\"Google API Key not set\")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "8aa149ed-9298-4d69-8fe2-8f5de0f667da",
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"metadata": {},
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"outputs": [],
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"source": [
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"openai = OpenAI()\n",
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"claude = anthropic.Anthropic()\n",
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"gemini = genai.Client()\n",
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"\n",
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"OPENAI_MODEL = \"o4-mini\"\n",
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"CLAUDE_MODEL = \"claude-3-7-sonnet-latest\"\n",
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"GEMINI_MODEL = \"gemini-2.5-flash\""
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]
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},
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{
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"cell_type": "markdown",
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"id": "88a18c58-40d5-4592-8dd3-d7c7b0d951aa",
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"metadata": {},
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"source": [
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"## Prompts"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "6896636f-923e-4a2c-9d6c-fac07828a201",
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"metadata": {},
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"outputs": [],
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"source": [
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"system_message = \"\"\"\n",
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"You are an assistant that documents Python code. \n",
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"Your task: \n",
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"- Add concise, clear, and informative docstrings to functions, classes, and modules. \n",
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"- Add inline comments only where they improve readability or clarify intent. \n",
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"- Do not modify the code logic or structure. \n",
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"- Respond with Python code only. \n",
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"\"\"\""
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "8e7b3546-57aa-4c29-bc5d-f211970d04eb",
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"metadata": {},
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"outputs": [],
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"source": [
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"def user_prompt_for(python):\n",
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" user_prompt = \"Add docstrings and comments to the following Python code:\\n\"\n",
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" user_prompt += python\n",
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" return user_prompt"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "c6190659-f54c-4951-bef4-4960f8e51cc4",
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"metadata": {},
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"outputs": [],
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"source": [
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"def messages_for(python):\n",
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" return [\n",
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" {\"role\": \"system\", \"content\": system_message},\n",
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" {\"role\": \"user\", \"content\": user_prompt_for(python)}\n",
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" ]"
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]
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},
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{
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"cell_type": "markdown",
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"id": "624e5066-bcf6-490d-a790-608d2bb34184",
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"metadata": {},
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"source": [
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"## Helper functions"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "71e1ba8c-5b05-4726-a9f3-8d8c6257350b",
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"metadata": {},
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"outputs": [],
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"source": [
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"def write_output(python, filename_suffix):\n",
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" filename = f\"annotated_{filename_suffix}.py\"\n",
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" code = python.replace(\"```python\",\"\").replace(\"```\",\"\")\n",
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" with open(filename, \"w\") as f:\n",
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" f.write(code)\n",
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" print(f\"\\nWritten code to {filename}\")\n",
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" return filename"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "e7d2fea8-74c6-4421-8f1e-0e76d5b201b9",
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"metadata": {},
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"outputs": [],
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"source": [
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"def annotate_with_gpt(python, task_name): \n",
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" stream = openai.chat.completions.create(model=OPENAI_MODEL, messages=messages_for(python), stream=True)\n",
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" reply = \"\"\n",
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" for chunk in stream:\n",
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" fragment = chunk.choices[0].delta.content or \"\"\n",
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" reply += fragment\n",
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" print(fragment, end='', flush=True)\n",
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" return write_output(reply, f\"{task_name}_gpt\")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "7cd84ad8-d55c-4fe0-9eeb-1895c95c4a9d",
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"metadata": {},
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"outputs": [],
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"source": [
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"def annotate_with_claude(python, task_name):\n",
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" result = claude.messages.stream(\n",
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" model=CLAUDE_MODEL,\n",
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" max_tokens=2000,\n",
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" system=system_message,\n",
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" messages=[{\"role\": \"user\", \"content\": user_prompt_for(python)}],\n",
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" )\n",
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" reply = \"\"\n",
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" with result as stream:\n",
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" for text in stream.text_stream:\n",
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" reply += text\n",
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" print(text, end=\"\", flush=True)\n",
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" return write_output(reply, f\"{task_name}_claude\")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "e8a35102-1c95-469b-8855-e85f4c9bdbdf",
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"metadata": {},
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"outputs": [],
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"source": [
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"def annotate_with_gemini(python, task_name):\n",
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" reply = gemini.models.generate_content(\n",
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" model=GEMINI_MODEL,\n",
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" contents=user_prompt_for(python),\n",
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" config=types.GenerateContentConfig(\n",
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" system_instruction=system_message,\n",
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" )\n",
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" )\n",
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"\n",
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" print(reply.text)\n",
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" return write_output(reply.text, f\"{task_name}_gemini\")"
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]
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},
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{
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"cell_type": "markdown",
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"id": "028dcfdd-2d52-4e11-a79e-2214a97cb26d",
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"metadata": {},
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"source": [
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"# Run the Annotator"
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]
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},
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{
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"cell_type": "markdown",
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"id": "7462d9f9-6215-4fb0-9471-1d0141d33205",
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"metadata": {},
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"source": [
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"## Pi example"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "a1cbb778-fa57-43de-b04b-ed523f396c38",
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"metadata": {},
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"outputs": [],
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"source": [
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"pi = \"\"\"\n",
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"import time\n",
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"\n",
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"def calculate(iterations, param1, param2):\n",
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" result = 1.0\n",
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" for i in range(1, iterations+1):\n",
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" j = i * param1 - param2\n",
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" result -= (1/j)\n",
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" j = i * param1 + param2\n",
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" result += (1/j)\n",
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" return result\n",
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"\n",
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"start_time = time.time()\n",
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"result = calculate(100_000_000, 4, 1) * 4\n",
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"end_time = time.time()\n",
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"\n",
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"print(f\"Result: {result:.12f}\")\n",
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"print(f\"Execution Time: {(end_time - start_time):.6f} seconds\")\n",
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"\"\"\""
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "105db6f9-343c-491d-8e44-3a5328b81719",
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"metadata": {},
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"outputs": [],
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"source": [
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"gpt_pi = annotate_with_gpt(pi, \"pi))"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "415819d0-fc95-4f78-a6ae-5c7d6781c6a7",
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"metadata": {},
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"outputs": [],
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"source": [
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"# check if the script works\n",
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"\n",
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"exec(open(gpt_pi).read())"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "983a11fe-e24d-4c65-8269-9802c5ef3ae6",
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"metadata": {
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"scrolled": true
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},
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"outputs": [],
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"source": [
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"claude_pi = annotate_with_claude(pi, \"pi\")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "52f5b710-0dea-4884-8ed7-a94059d88281",
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"metadata": {},
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"outputs": [],
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"source": [
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"exec(open(claude_pi).read())"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "01f331f2-caac-48f6-9a03-8a228ee521bc",
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"metadata": {},
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"outputs": [],
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"source": [
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"gemini_pi = annotate_with_gemini(pi, \"pi\")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "23529942-53fa-46ad-a5db-1f3096dd6607",
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"metadata": {},
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"outputs": [],
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"source": [
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"exec(open(gemini_pi).read())"
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]
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},
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{
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"cell_type": "markdown",
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"id": "7d1eaeca-61be-4d0a-a525-dd09f52aaa0f",
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"metadata": {},
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"source": [
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"## Hard example"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "c3b497b3-f569-420e-b92e-fb0f49957ce0",
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"metadata": {},
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"outputs": [],
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"source": [
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"python_hard = \"\"\"# Be careful to support large number sizes\n",
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"\n",
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"def lcg(seed, a=1664525, c=1013904223, m=2**32):\n",
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" value = seed\n",
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" while True:\n",
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" value = (a * value + c) % m\n",
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" yield value\n",
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" \n",
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"def max_subarray_sum(n, seed, min_val, max_val):\n",
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" lcg_gen = lcg(seed)\n",
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" random_numbers = [next(lcg_gen) % (max_val - min_val + 1) + min_val for _ in range(n)]\n",
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" max_sum = float('-inf')\n",
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" for i in range(n):\n",
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" current_sum = 0\n",
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" for j in range(i, n):\n",
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" current_sum += random_numbers[j]\n",
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" if current_sum > max_sum:\n",
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" max_sum = current_sum\n",
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" return max_sum\n",
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"\n",
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"def total_max_subarray_sum(n, initial_seed, min_val, max_val):\n",
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" total_sum = 0\n",
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" lcg_gen = lcg(initial_seed)\n",
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" for _ in range(20):\n",
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" seed = next(lcg_gen)\n",
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" total_sum += max_subarray_sum(n, seed, min_val, max_val)\n",
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" return total_sum\n",
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"\n",
|
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"# Parameters\n",
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"n = 10000 # Number of random numbers\n",
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"initial_seed = 42 # Initial seed for the LCG\n",
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"min_val = -10 # Minimum value of random numbers\n",
|
||||
"max_val = 10 # Maximum value of random numbers\n",
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"\n",
|
||||
"# Timing the function\n",
|
||||
"import time\n",
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"start_time = time.time()\n",
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||||
"result = total_max_subarray_sum(n, initial_seed, min_val, max_val)\n",
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"end_time = time.time()\n",
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"\n",
|
||||
"print(\"Total Maximum Subarray Sum (20 runs):\", result)\n",
|
||||
"print(\"Execution Time: {:.6f} seconds\".format(end_time - start_time))\n",
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||||
"\"\"\""
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||||
]
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||||
},
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||||
{
|
||||
"cell_type": "code",
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||||
"execution_count": null,
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||||
"id": "dab5e4bc-276c-4555-bd4c-12c699d5e899",
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"metadata": {},
|
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"outputs": [],
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||||
"source": [
|
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"exec(python_hard)"
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]
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},
|
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{
|
||||
"cell_type": "code",
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"execution_count": null,
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||||
"id": "e8d24ed5-2c15-4f55-80e7-13a3952b3cb8",
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"metadata": {},
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||||
"outputs": [],
|
||||
"source": [
|
||||
"gpt_hard = annotate_with_gpt(python_hard, \"hard\")"
|
||||
]
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||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
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||||
"id": "80a15259-3d51-47b8-953c-6271fbd4b6fb",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"exec(open(gpt_hard).read())"
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]
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},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
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||||
"id": "e9305446-1d0c-4b51-866a-b8c1e299bf5c",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"gemini_hard = annotate_with_gemini(python_hard, \"hard\")"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "ad6eecc8-0517-43d8-bd21-5bbdedae7a10",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"exec(open(gemini_hard).read())"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "2ee75e72-9ecb-4edd-a74a-4d3a83c1eb79",
|
||||
"metadata": {
|
||||
"scrolled": true
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"claude_hard = annotate_with_claude(python_hard, \"hard\")"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "47af1516-455f-4d1c-8a1c-2da5a38c0ba5",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"exec(open(claude_hard).read())"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "ff02ce09-0544-49a5-944d-a57b25bf9b72",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"# Streaming"
|
||||
]
|
||||
},
|
||||
{
|
||||
"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('```python\\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('```python\\n','').replace('```','')"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "d48d44df-c082-4ed1-b3ea-fc2a880591c2",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"def stream_gemini(python):\n",
|
||||
" stream = gemini.models.generate_content_stream(\n",
|
||||
" model=GEMINI_MODEL,\n",
|
||||
" contents=user_prompt_for(python),\n",
|
||||
" config=types.GenerateContentConfig(\n",
|
||||
" system_instruction=system_message,\n",
|
||||
" ),\n",
|
||||
" )\n",
|
||||
" reply = \"\"\n",
|
||||
" for chunk in stream:\n",
|
||||
" reply += chunk.text\n",
|
||||
" yield reply.replace('```python\\n','').replace('```','')"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "2f1ae8f5-16c8-40a0-aa18-63b617df078d",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"def annotate(python, model):\n",
|
||||
" if model == \"GPT\":\n",
|
||||
" result = stream_gpt(python)\n",
|
||||
" elif model == \"Claude\":\n",
|
||||
" result = stream_claude(python)\n",
|
||||
" elif model == \"Gemini\":\n",
|
||||
" result = stream_gemini(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": "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": "9a2274f1-d03b-42c0-8dcc-4ce159b18442",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"css = \"\"\"\n",
|
||||
".python {background-color: #306998;}\n",
|
||||
"\"\"\""
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "76167ea9-d0a1-4bc6-8d73-633d3b8c8df6",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"import gradio as gr\n",
|
||||
"\n",
|
||||
"# Parameters\n",
|
||||
"LINES = 25\n",
|
||||
"LINE_HEIGHT = 20 # px, typical CodeMirror line height\n",
|
||||
"PADDING = 10 # px, top + bottom padding\n",
|
||||
"\n",
|
||||
"CODE_HEIGHT = LINES * LINE_HEIGHT + PADDING\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"with gr.Blocks(\n",
|
||||
" theme=gr.themes.Soft(),\n",
|
||||
" css=f\"\"\"\n",
|
||||
"#code_input .cm-editor, #annotated_code .cm-editor {{\n",
|
||||
" height: {CODE_HEIGHT}px !important;\n",
|
||||
" overflow-y: auto !important;\n",
|
||||
"}}\n",
|
||||
"\"\"\"\n",
|
||||
") as demo_v2:\n",
|
||||
" gr.Markdown(\"## 🐍 Annotate Python Code with Docstrings and Comments\")\n",
|
||||
"\n",
|
||||
" with gr.Row():\n",
|
||||
" with gr.Column(scale=1):\n",
|
||||
" gr.Markdown(\"### Python code:\")\n",
|
||||
" code_input = gr.Code(\n",
|
||||
" language=\"python\", \n",
|
||||
" value=python_hard,\n",
|
||||
" lines=25,\n",
|
||||
" elem_id=\"code_input\"\n",
|
||||
" )\n",
|
||||
" \n",
|
||||
" with gr.Column(scale=1):\n",
|
||||
" gr.Markdown(\"### Annotated code:\")\n",
|
||||
" annotated_output = gr.Code(\n",
|
||||
" language=\"python\",\n",
|
||||
" lines=25,\n",
|
||||
" elem_id=\"annotated_code\"\n",
|
||||
" )\n",
|
||||
"\n",
|
||||
" with gr.Row():\n",
|
||||
" with gr.Column(scale=1):\n",
|
||||
" model_dropdown = gr.Dropdown(\n",
|
||||
" choices=[\"Gemini\", \"GPT-4\", \"Claude\"],\n",
|
||||
" value=\"Gemini\",\n",
|
||||
" label=\"Select model\"\n",
|
||||
" )\n",
|
||||
" with gr.Column(scale=1):\n",
|
||||
" annotate_btn = gr.Button(\"✨ Annotate code\", variant=\"primary\")\n",
|
||||
" run_btn = gr.Button(\"▶️ Run Python\", variant=\"secondary\")\n",
|
||||
"\n",
|
||||
" with gr.Row():\n",
|
||||
" with gr.Column():\n",
|
||||
" gr.Markdown(\"### Python result:\")\n",
|
||||
" result_output = gr.Textbox(\n",
|
||||
" lines=5, \n",
|
||||
" label=\"Output\",\n",
|
||||
" interactive=False\n",
|
||||
" )\n",
|
||||
" \n",
|
||||
" annotate_btn.click(\n",
|
||||
" annotate,\n",
|
||||
" inputs=[code_input, model_dropdown],\n",
|
||||
" outputs=[annotated_output]\n",
|
||||
" )\n",
|
||||
" run_btn.click(execute_python, inputs=[code_input], outputs=[result_output])\n",
|
||||
"\n",
|
||||
" \n",
|
||||
"demo_v2.launch(inbrowser=True)\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "ea42883b-fdba-46ed-97be-f42e3cb41f11",
|
||||
"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": "3.11.13"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 5
|
||||
}
|
||||
690
week4/community-contributions/day3-with-gemini.ipynb
Normal file
690
week4/community-contributions/day3-with-gemini.ipynb
Normal file
@@ -0,0 +1,690 @@
|
||||
{
|
||||
"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\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "1f72dfaf-9f20-4d81-b082-018eda152c9f",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"!pip install -U -q \"google-genai\""
|
||||
]
|
||||
},
|
||||
{
|
||||
"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",
|
||||
"from dotenv import load_dotenv\n",
|
||||
"from openai import OpenAI\n",
|
||||
"from google import genai\n",
|
||||
"from google.genai import types\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(override=True)\n",
|
||||
"openai_api_key = os.getenv('OPENAI_API_KEY')\n",
|
||||
"anthropic_api_key = os.getenv('ANTHROPIC_API_KEY')\n",
|
||||
"google_api_key = os.getenv('GOOGLE_API_KEY')\n",
|
||||
"\n",
|
||||
"if openai_api_key:\n",
|
||||
" print(f\"OpenAI API Key exists and begins {openai_api_key[:8]}\")\n",
|
||||
"else:\n",
|
||||
" print(\"OpenAI API Key not set\")\n",
|
||||
" \n",
|
||||
"if anthropic_api_key:\n",
|
||||
" print(f\"Anthropic API Key exists and begins {anthropic_api_key[:7]}\")\n",
|
||||
"else:\n",
|
||||
" print(\"Anthropic API Key not set\")\n",
|
||||
"\n",
|
||||
"if google_api_key:\n",
|
||||
" print(f\"Google API Key exists and begins {google_api_key[:8]}\")\n",
|
||||
"else:\n",
|
||||
" print(\"Google API Key not set\")"
|
||||
]
|
||||
},
|
||||
{
|
||||
"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",
|
||||
"gemini = genai.Client()\n",
|
||||
"\n",
|
||||
"OPENAI_MODEL = \"o4-mini\"\n",
|
||||
"CLAUDE_MODEL = \"claude-3-7-sonnet-latest\"\n",
|
||||
"GEMINI_MODEL = \"gemini-2.5-flash\""
|
||||
]
|
||||
},
|
||||
{
|
||||
"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.\""
|
||||
]
|
||||
},
|
||||
{
|
||||
"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": "e8a35102-1c95-469b-8855-e85f4c9bdbdf",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"def optimize_gemini(python):\n",
|
||||
" reply = gemini.models.generate_content(\n",
|
||||
" model=GEMINI_MODEL,\n",
|
||||
" contents=user_prompt_for(python),\n",
|
||||
" config=types.GenerateContentConfig(\n",
|
||||
" system_instruction=system_message,\n",
|
||||
" )\n",
|
||||
" )\n",
|
||||
"\n",
|
||||
" print(reply.text)\n",
|
||||
" write_output(reply.text)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"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": "markdown",
|
||||
"id": "bf8f8018-f64d-425c-a0e1-d7862aa9592d",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"# Compiling C++ and executing\n",
|
||||
"\n",
|
||||
"This next cell contains the command to compile a C++ file on my M1 Mac. \n",
|
||||
"It compiles the file `optimized.cpp` into an executable called `optimized` \n",
|
||||
"Then it runs the program called `optimized`\n",
|
||||
"\n",
|
||||
"In the next lab (day4), a student has contributed a full solution that compiles to efficient code on Mac, PC and Linux!\n",
|
||||
"\n",
|
||||
"You can wait for this, or you can google (or ask ChatGPT!) for how to do this on your platform, then replace the lines below.\n",
|
||||
"If you're not comfortable with this step, you can skip it for sure - I'll show you exactly how it performs on my Mac.\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"OR alternatively: student Sandeep K.G. points out that you can run Python and C++ code online to test it out that way. Thank you Sandeep! \n",
|
||||
"> Not an exact comparison but you can still get the idea of performance difference.\n",
|
||||
"> For example here: https://www.programiz.com/cpp-programming/online-compiler/"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "4194e40c-04ab-4940-9d64-b4ad37c5bb40",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# Compile C++ and run the executable\n",
|
||||
"\n",
|
||||
"!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": [
|
||||
"# Repeat for Claude - again, use the right approach for your platform\n",
|
||||
"\n",
|
||||
"!clang++ -O3 -std=c++17 -march=armv8.3-a -o optimized optimized.cpp\n",
|
||||
"!./optimized"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "01f331f2-caac-48f6-9a03-8a228ee521bc",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"optimize_gemini(pi)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "5ef707a4-930e-4b8b-9443-e7e4fd309c2a",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"!clang++ -O3 -std=c++17 -march=armv8.3-a -o optimized optimized.cpp\n",
|
||||
"!./optimized"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "7d1eaeca-61be-4d0a-a525-dd09f52aaa0f",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"# Python Hard Version"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "c3b497b3-f569-420e-b92e-fb0f49957ce0",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"python_hard = \"\"\"# Be careful to support large number sizes\n",
|
||||
"\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": [
|
||||
"# Replace this with the right C++ compile + execute command for your platform\n",
|
||||
"\n",
|
||||
"!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_gemini(python_hard)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "0c181036-8193-4fdd-aef3-fc513b218d43",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# Replace this with the right C++ compile + execute command for your platform\n",
|
||||
"\n",
|
||||
"!clang++ -O3 -std=c++17 -march=armv8.3-a -o optimized optimized.cpp\n",
|
||||
"!./optimized"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "2ee75e72-9ecb-4edd-a74a-4d3a83c1eb79",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"optimize_claude(python_hard)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "4a4ab43c-7df2-4770-bd05-6bbc198a8c45",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# Replace this with the right C++ compile + execute command for your platform\n",
|
||||
"\n",
|
||||
"!clang++ -O3 -std=c++17 -march=armv8.3-a -o optimized optimized.cpp\n",
|
||||
"!./optimized"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "ff02ce09-0544-49a5-944d-a57b25bf9b72",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"# Streaming"
|
||||
]
|
||||
},
|
||||
{
|
||||
"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": "d48d44df-c082-4ed1-b3ea-fc2a880591c2",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"def stream_gemini(python):\n",
|
||||
" stream = gemini.models.generate_content_stream(\n",
|
||||
" model=GEMINI_MODEL,\n",
|
||||
" contents=user_prompt_for(python),\n",
|
||||
" config=types.GenerateContentConfig(\n",
|
||||
" system_instruction=system_message,\n",
|
||||
" ),\n",
|
||||
" )\n",
|
||||
" reply = \"\"\n",
|
||||
" for chunk in stream:\n",
|
||||
" reply += chunk.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",
|
||||
" elif model==\"Gemini\":\n",
|
||||
" result = stream_gemini(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\", \"Gemini\"], 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": [
|
||||
"# M1 Mac version to compile and execute optimized C++ code:\n",
|
||||
"\n",
|
||||
"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=20)\n",
|
||||
" cpp = gr.Textbox(label=\"C++ code:\", lines=20)\n",
|
||||
" with gr.Row():\n",
|
||||
" model = gr.Dropdown([\"GPT\", \"Claude\", \"Gemini\"], label=\"Select model\", value=\"GPT\")\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": "ea42883b-fdba-46ed-97be-f42e3cb41f11",
|
||||
"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": "3.11.13"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 5
|
||||
}
|
||||
Reference in New Issue
Block a user