diff --git a/week4/community-contributions/week4-day5-code-commenter.ipynb b/week4/community-contributions/week4-day5-code-commenter.ipynb new file mode 100644 index 0000000..a15b46d --- /dev/null +++ b/week4/community-contributions/week4-day5-code-commenter.ipynb @@ -0,0 +1,401 @@ +{ + "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": [ + "
" + ], + "text/plain": [ + "" + ] + }, + "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 +}