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{
"cells": [
{
"cell_type": "markdown",
"id": "4a6ab9a2-28a2-445d-8512-a0dc8d1b54e9",
"metadata": {},
"source": [
"# Code Commenter\n",
"\n",
"The requirement: use an LLM to generate docstring and comments for Python code\n",
"\n",
"This is my week 4 day 5 project. \n",
"\n",
"Note: I used gpt to find out the most effective system and user prompt (very effective). I also decided not to use the open source models due to inference api costs with HF"
]
},
{
"cell_type": "code",
"execution_count": 1,
"id": "e610bf56-a46e-4aff-8de1-ab49d62b1ad3",
"metadata": {},
"outputs": [],
"source": [
"# imports\n",
"\n",
"import os\n",
"import io\n",
"import sys\n",
"import json\n",
"import requests\n",
"from dotenv import load_dotenv\n",
"from openai import OpenAI\n",
"import google.generativeai\n",
"import anthropic\n",
"from IPython.display import Markdown, display, update_display\n",
"import gradio as gr\n",
"import subprocess"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "4f672e1c-87e9-4865-b760-370fa605e614",
"metadata": {},
"outputs": [],
"source": [
"# environment\n",
"\n",
"load_dotenv()\n",
"os.environ['OPENAI_API_KEY'] = os.getenv('OPENAI_API_KEY', 'your-key-if-not-using-env')\n",
"os.environ['ANTHROPIC_API_KEY'] = os.getenv('ANTHROPIC_API_KEY', 'your-key-if-not-using-env')\n",
"google_api_key = os.getenv('GOOGLE_API_KEY')\n"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "8aa149ed-9298-4d69-8fe2-8f5de0f667da",
"metadata": {},
"outputs": [],
"source": [
"# initialize\n",
"\n",
"openai = OpenAI()\n",
"claude = anthropic.Anthropic()\n",
"google.generativeai.configure()\n",
"\n",
"OPENAI_MODEL = \"gpt-4o\"\n",
"CLAUDE_MODEL = \"claude-3-5-sonnet-20240620\"\n",
"GOOGLE_MODEL = \"gemini-1.5-pro\""
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "6896636f-923e-4a2c-9d6c-fac07828a201",
"metadata": {},
"outputs": [],
"source": [
"system_message = \"You are a Python code assistant. Your task is to analyze Python code and generate high-quality, concise comments and docstrings. Follow these guidelines:\"\n",
"system_message += \"Docstrings: Add a docstring for every function, class, and module. Describe the purpose of the function/class, its parameters, and its return value. Keep the description concise but informative, using proper Python docstring conventions (e.g., Google, NumPy, or reStructuredText format).\"\n",
"system_message += \"Inline Comments: Add inline comments only where necessary to clarify complex logic, important steps, or non-obvious behavior. Avoid commenting on obvious operations like x += 1 unless it involves a nuanced concept. Keep comments short, clear, and relevant.\"\n",
"system_message += \"General Instructions: Maintain consistency in style and tone. Use technical terminology where appropriate, but ensure clarity for someone with intermediate Python knowledge. Do not over-explain or add redundant comments for self-explanatory code. Follow PEP 257 and PEP 8 standards for style and formatting.\"\n"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "8e7b3546-57aa-4c29-bc5d-f211970d04eb",
"metadata": {},
"outputs": [],
"source": [
"def user_prompt_for(python):\n",
" user_prompt = \"Analyze the following Python code and enhance it by adding high-quality, concise docstrings and comments. \"\n",
" user_prompt += \"Ensure all functions, classes, and modules have appropriate docstrings describing their purpose, parameters, and return values. \"\n",
" user_prompt += \"Add inline comments only for complex or non-obvious parts of the code. \"\n",
" user_prompt += \"Follow Python's PEP 257 and PEP 8 standards for documentation and formatting. \"\n",
" user_prompt += \"Do not modify the code itself; only add annotations.\\n\\n\"\n",
" user_prompt += python\n",
" return user_prompt\n"
]
},
{
"cell_type": "code",
"execution_count": 6,
"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": 7,
"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": 8,
"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": 9,
"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": 10,
"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": 11,
"id": "25f8d215-67a8-4179-8834-0e1da5a7dd32",
"metadata": {},
"outputs": [],
"source": [
"def stream_google(python):\n",
" # Initialize empty reply string\n",
" reply = \"\"\n",
" \n",
" # The API for Gemini has a slightly different structure\n",
" gemini = google.generativeai.GenerativeModel(\n",
" model_name=GOOGLE_MODEL,\n",
" system_instruction=system_message\n",
" )\n",
" \n",
" response = gemini.generate_content(\n",
" user_prompt_for(python),\n",
" stream=True\n",
" )\n",
" \n",
" # Process the stream\n",
" for chunk in response:\n",
" # Extract text from the chunk\n",
" if chunk.text:\n",
" reply += chunk.text\n",
" yield reply.replace('```python\\n','').replace('```','')"
]
},
{
"cell_type": "code",
"execution_count": 12,
"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_google(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": 13,
"id": "43a6b5f5-5d7c-4511-9d0c-21640070b3cf",
"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": 14,
"id": "f35b0602-84f9-4ed6-aa35-87be4290ed24",
"metadata": {},
"outputs": [],
"source": [
"css = \"\"\"\n",
".python {background-color: #306998;}\n",
".cpp {background-color: #050;}\n",
"\"\"\""
]
},
{
"cell_type": "code",
"execution_count": 15,
"id": "62488014-d34c-4de8-ba72-9516e05e9dde",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"* Running on local URL: http://127.0.0.1:7860\n",
"\n",
"To create a public link, set `share=True` in `launch()`.\n"
]
},
{
"data": {
"text/html": [
"<div><iframe src=\"http://127.0.0.1:7860/\" width=\"100%\" height=\"500\" allow=\"autoplay; camera; microphone; clipboard-read; clipboard-write;\" frameborder=\"0\" allowfullscreen></iframe></div>"
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/plain": []
},
"execution_count": 15,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"with gr.Blocks(css=css) as ui:\n",
" gr.Markdown(\"## Convert code from Python to C++\")\n",
" with gr.Row():\n",
" python = gr.Textbox(label=\"Python code:\", value=pi, lines=10)\n",
" commented_python = gr.Textbox(label=\"Commented code:\", lines=10)\n",
" with gr.Row():\n",
" model = gr.Dropdown([\"GPT\", \"Claude\", \"Gemini\"], label=\"Select model\", value=\"GPT\")\n",
" with gr.Row():\n",
" comment = gr.Button(\"Comment code\")\n",
" with gr.Row():\n",
" python_run = gr.Button(\"Check Commented Python\")\n",
" with gr.Row():\n",
" python_out = gr.TextArea(label=\"Python result:\", elem_classes=[\"python\"])\n",
"\n",
" comment.click(optimize, inputs=[python, model], outputs=[commented_python])\n",
" python_run.click(execute_python, inputs=[python], outputs=[python_out])\n",
"\n",
"ui.launch(inbrowser=True)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "b084760b-c327-4fe7-9b7c-a01b1a383dc3",
"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": "3.11.11"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
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