829 lines
25 KiB
Plaintext
829 lines
25 KiB
Plaintext
{
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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 with: {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 with: {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 with: {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",
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"max_val = 10 # Maximum value of random numbers\n",
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"\n",
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"# Timing the function\n",
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"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",
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"print(\"Total Maximum Subarray Sum (20 runs):\", result)\n",
|
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"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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{
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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": {},
|
|
"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",
|
|
"execution_count": null,
|
|
"id": "e8d24ed5-2c15-4f55-80e7-13a3952b3cb8",
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"metadata": {},
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|
"outputs": [],
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"source": [
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"gpt_hard = annotate_with_gpt(python_hard, \"hard\")"
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]
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|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"id": "80a15259-3d51-47b8-953c-6271fbd4b6fb",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
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"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": [
|
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"gemini_hard = annotate_with_gemini(python_hard, \"hard\")"
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]
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|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"id": "ad6eecc8-0517-43d8-bd21-5bbdedae7a10",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
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"exec(open(gemini_hard).read())"
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|
]
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|
},
|
|
{
|
|
"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": [
|
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"exec(open(claude_hard).read())"
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|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"id": "7f60d33c-f6b7-4fc5-bc2b-57957b076e34",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"\"\"\"\n",
|
|
"This module implements a Linear Congruential Generator (LCG) and uses it\n",
|
|
"to generate random numbers for calculating the maximum subarray sum.\n",
|
|
"It includes functions for the LCG, finding the maximum subarray sum, and\n",
|
|
"aggregating results over multiple runs.\n",
|
|
"\"\"\"\n",
|
|
"\n",
|
|
"def lcg(seed, a=1664525, c=1013904223, m=2**32):\n",
|
|
" \"\"\"\n",
|
|
" Implements a Linear Congruential Generator (LCG) to produce a sequence of\n",
|
|
" pseudorandom numbers.\n",
|
|
"\n",
|
|
" The generator uses the formula: X_{n+1} = (a * X_n + c) % m.\n",
|
|
"\n",
|
|
" Args:\n",
|
|
" seed (int): The initial seed value for the generator (X_0).\n",
|
|
" a (int, optional): The multiplier. Defaults to 1664525 (common LCG parameter).\n",
|
|
" c (int, optional): The increment. Defaults to 1013904223 (common LCG parameter).\n",
|
|
" m (int, optional): The modulus. Defaults to 2**32, meaning numbers will be\n",
|
|
" between 0 and m-1.\n",
|
|
"\n",
|
|
" Yields:\n",
|
|
" int: The next pseudorandom number in the sequence.\n",
|
|
" \"\"\"\n",
|
|
" value = seed\n",
|
|
" while True:\n",
|
|
" # Calculate the next pseudorandom number using the LCG formula.\n",
|
|
" value = (a * value + c) % m\n",
|
|
" yield value\n",
|
|
"\n",
|
|
"def max_subarray_sum(n, seed, min_val, max_val):\n",
|
|
" \"\"\"\n",
|
|
" Calculates the maximum possible sum of a contiguous subarray within a list\n",
|
|
" of 'n' pseudorandom numbers.\n",
|
|
"\n",
|
|
" The random numbers are generated using an LCG based on the provided seed,\n",
|
|
" and then mapped to the range [min_val, max_val].\n",
|
|
" This implementation uses a brute-force approach with O(n^2) complexity.\n",
|
|
"\n",
|
|
" Args:\n",
|
|
" n (int): The number of random integers to generate for the array.\n",
|
|
" seed (int): The seed for the LCG to generate the random numbers.\n",
|
|
" min_val (int): The minimum possible value for the generated random numbers.\n",
|
|
" max_val (int): The maximum possible value for the generated random numbers.\n",
|
|
"\n",
|
|
" Returns:\n",
|
|
" int: The maximum sum found among all contiguous subarrays.\n",
|
|
" \"\"\"\n",
|
|
" lcg_gen = lcg(seed)\n",
|
|
" # Generate a list of 'n' random numbers within the specified range [min_val, max_val].\n",
|
|
" random_numbers = [next(lcg_gen) % (max_val - min_val + 1) + min_val for _ in range(n)]\n",
|
|
"\n",
|
|
" max_sum = float('-inf') # Initialize max_sum to negative infinity to handle all negative numbers.\n",
|
|
"\n",
|
|
" # Iterate through all possible starting points of a subarray.\n",
|
|
" for i in range(n):\n",
|
|
" current_sum = 0\n",
|
|
" # Iterate through all possible ending points for the current starting point.\n",
|
|
" for j in range(i, n):\n",
|
|
" current_sum += random_numbers[j]\n",
|
|
" # Update max_sum if the current subarray sum is greater.\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",
|
|
" \"\"\"\n",
|
|
" Calculates the sum of maximum subarray sums over 20 separate runs.\n",
|
|
"\n",
|
|
" Each run generates a new set of 'n' random numbers for `max_subarray_sum`\n",
|
|
" using a new seed derived from the initial LCG sequence.\n",
|
|
"\n",
|
|
" Args:\n",
|
|
" n (int): The number of random integers for each subarray sum calculation.\n",
|
|
" initial_seed (int): The initial seed for the LCG that generates seeds\n",
|
|
" for individual `max_subarray_sum` runs.\n",
|
|
" min_val (int): The minimum possible value for random numbers in each run.\n",
|
|
" max_val (int): The maximum possible value for random numbers in each run.\n",
|
|
"\n",
|
|
" Returns:\n",
|
|
" int: The sum of the maximum subarray sums across all 20 runs.\n",
|
|
" \"\"\"\n",
|
|
" total_sum = 0\n",
|
|
" lcg_gen = lcg(initial_seed) # LCG to generate seeds for subsequent runs.\n",
|
|
" # Perform 20 independent runs.\n",
|
|
" for _ in range(20):\n",
|
|
" # Get a new seed for each run from the initial LCG generator.\n",
|
|
" seed = next(lcg_gen)\n",
|
|
" # Add the maximum subarray sum of the current run to the total sum.\n",
|
|
" total_sum += max_subarray_sum(n, seed, min_val, max_val)\n",
|
|
" return total_sum\n",
|
|
"\n",
|
|
"# Parameters for the simulation\n",
|
|
"n = 10000 # Number of random numbers to generate for each subarray\n",
|
|
"initial_seed = 42 # Initial seed for the LCG that generates seeds for runs\n",
|
|
"min_val = -10 # Minimum value for the random numbers\n",
|
|
"max_val = 10 # Maximum value for the random numbers\n",
|
|
"\n",
|
|
"# Import the time module to measure execution time.\n",
|
|
"import time\n",
|
|
"\n",
|
|
"# Record the start time before executing the main function.\n",
|
|
"start_time = time.time()\n",
|
|
"# Call the function to calculate the total maximum subarray sum over multiple runs.\n",
|
|
"result = total_max_subarray_sum(n, initial_seed, min_val, max_val)\n",
|
|
"# Record the end time after the function completes.\n",
|
|
"end_time = time.time()\n",
|
|
"\n",
|
|
"# Print the final aggregated result.\n",
|
|
"print(\"Total Maximum Subarray Sum (20 runs):\", result)\n",
|
|
"# Print the total execution time, formatted to 6 decimal places.\n",
|
|
"print(\"Execution Time: {:.6f} seconds\".format(end_time - start_time))"
|
|
]
|
|
},
|
|
{
|
|
"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": "markdown",
|
|
"id": "8391444b-b938-4f92-982f-91439b38d901",
|
|
"metadata": {},
|
|
"source": [
|
|
"# Gradio App"
|
|
]
|
|
},
|
|
{
|
|
"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",
|
|
" 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",
|
|
" elem_id=\"annotated_code\",\n",
|
|
" interactive=False\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=[annotated_output], 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
|
|
}
|