{ "cells": [ { "cell_type": "markdown", "id": "ff022957-2e81-4ea9-84d3-e52d5753e133", "metadata": {}, "source": [ "### Comment and Unit Test Generater \n", "\n", "The requirement: \n", "* use an LLM to generate docstring and comments for Python code\n", "* use an LLM to generate unit test\n", "\n", "This is my week 4 day 5 project." ] }, { "cell_type": "code", "execution_count": null, "id": "ea1841f6-4afc-4d29-ace8-5ca5a3915c8c", "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\n", "from huggingface_hub import login, InferenceClient\n", "from transformers import AutoTokenizer" ] }, { "cell_type": "code", "execution_count": null, "id": "11957fd3-6c61-4496-aef1-8223cb9ec4ce", "metadata": {}, "outputs": [], "source": [ "# environment\n", "\n", "load_dotenv()\n", "os.environ['OPENAI_API_KEY'] = os.getenv('OPENAI_API_KEY', 'your-key-if-not-using-env')\n", "os.environ['ANTHROPIC_API_KEY'] = os.getenv('ANTHROPIC_API_KEY', 'your-key-if-not-using-env')\n", "os.environ['HF_TOKEN'] = os.getenv('HF_TOKEN', 'your-key-if-not-using-env')" ] }, { "cell_type": "code", "execution_count": null, "id": "ee7b08fd-e678-4234-895e-4e3a925e60f0", "metadata": {}, "outputs": [], "source": [ "# initialize\n", "\n", "openai = OpenAI()\n", "claude = anthropic.Anthropic()\n", "OPENAI_MODEL = \"gpt-4o\"\n", "CLAUDE_MODEL = \"claude-3-5-sonnet-20240620\"" ] }, { "cell_type": "code", "execution_count": null, "id": "c8023255-9c98-4fbc-92e4-c553bed3b605", "metadata": {}, "outputs": [], "source": [ "hf_token = os.environ['HF_TOKEN']\n", "login(hf_token, add_to_git_credential=True)" ] }, { "cell_type": "code", "execution_count": null, "id": "f8ce3f5e-74c4-4d35-bfbc-91c5be85e094", "metadata": {}, "outputs": [], "source": [ "code_qwen = \"Qwen/CodeQwen1.5-7B-Chat\"\n", "CODE_QWEN_URL = \"https://g39mbjooiiwkbgyz.us-east-1.aws.endpoints.huggingface.cloud\"" ] }, { "cell_type": "code", "execution_count": null, "id": "1bbc66b6-52ae-465e-a368-edc8f097fe9d", "metadata": {}, "outputs": [], "source": [ "def system_prompt_for_comment():\n", " system=\"\"\"\n", " You are a Python documentation expert. When writing documentation:\n", " - Follow PEP 257 and Google docstring style guidelines\n", " - Write clear, concise explanations\n", " - Include practical examples\n", " - Highlight edge cases and limitations\n", " - Use type hints in docstrings\n", " - Add inline comments only for complex logic\n", " - Never skip documenting parameters or return values\n", " - Validate that all documentation is accurate and complete\n", " \"\"\"\n", " return system" ] }, { "cell_type": "code", "execution_count": null, "id": "b089f87b-53ae-40ad-8d06-b9924bb998a0", "metadata": {}, "outputs": [], "source": [ "def system_prompt_for_unit_test():\n", " system=\"\"\"\n", " You are an expert Python testing engineer who specializes in creating comprehensive unit tests. Follow these principles:\n", " - Use pytest as the testing framework\n", " - Follow the Arrange-Act-Assert pattern\n", " - Test both valid and invalid inputs\n", " - Include edge cases and boundary conditions\n", " - Write descriptive test names that explain the scenario being tested\n", " - Create independent tests that don't rely on each other\n", " - Use appropriate fixtures and parametrize when needed\n", " - Add clear comments explaining complex test logic\n", " - Cover error cases and exceptions\n", " - Achieve high code coverage while maintaining meaningful tests\n", " \"\"\"\n", " return system" ] }, { "cell_type": "code", "execution_count": null, "id": "22193622-f3a0-4894-a6c4-eb6d88097861", "metadata": {}, "outputs": [], "source": [ "def user_prompt_for_comment(code):\n", " user = f\"\"\"\n", " Please document this Python code with:\n", " \n", " 1. A docstring containing:\n", " - A clear description of purpose and functionality\n", " - All parameters with types and descriptions\n", " - Return values with types\n", " - Exceptions that may be raised\n", " - Any important notes or limitations\n", " \n", " 2. Strategic inline comments for:\n", " - Complex algorithms or business logic\n", " - Non-obvious implementation choices\n", " - Performance considerations\n", " - Edge cases\n", " \n", " Here's the code to document:\n", " \\n{code}\n", " \"\"\"\n", " return user;" ] }, { "cell_type": "code", "execution_count": null, "id": "81e61752-ec2f-44c1-86a2-ff3234a0358c", "metadata": {}, "outputs": [], "source": [ "def user_prompt_for_unit_test(code):\n", " user = f\"\"\"\n", " Please generate unit tests for the following Python code. Include:\n", " \n", " 1. Test cases for:\n", " - Normal/expected inputs\n", " - Edge cases and boundary values\n", " - Invalid inputs and error conditions\n", " - Different combinations of parameters\n", " - All public methods and functions\n", " \n", " 2. For each test:\n", " - Clear test function names describing the scenario\n", " - Setup code (fixtures if needed)\n", " - Test data preparation\n", " - Expected outcomes\n", " - Assertions checking results\n", " - Comments explaining complex test logic\n", " \n", " 3. Include any necessary:\n", " - Imports\n", " - Fixtures\n", " - Mock objects\n", " - Helper functions\n", " - Test data generators\n", " \n", " Here's the code to test:\n", " \\n{code}\n", " \"\"\"\n", " return user" ] }, { "cell_type": "code", "execution_count": null, "id": "f31ceed3-0eb2-4962-ab86-2d0302185560", "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": "192c30f5-4be6-49b7-a054-11bfcffa91e0", "metadata": {}, "outputs": [], "source": [ "exec(pi)" ] }, { "cell_type": "code", "execution_count": null, "id": "d4e920dc-4094-42d8-9255-18f2919df2d4", "metadata": {}, "outputs": [], "source": [ "def messages_for_comment(python):\n", " return [\n", " {\"role\": \"system\", \"content\": system_prompt_for_comment()},\n", " {\"role\": \"user\", \"content\": user_prompt_for_comment(python)}\n", " ]" ] }, { "cell_type": "code", "execution_count": null, "id": "77500cae-bf84-405c-8b03-2f984108951b", "metadata": {}, "outputs": [], "source": [ "def messages_for_unit_test(python):\n", " return [\n", " {\"role\": \"system\", \"content\": system_prompt_for_unit_test()},\n", " {\"role\": \"user\", \"content\": user_prompt_for_unit_test(python)}\n", " ]" ] }, { "cell_type": "code", "execution_count": null, "id": "5ec58bf1-4a44-4c21-a71a-2cac359884e5", "metadata": {}, "outputs": [], "source": [ "def stream_comment_gpt(code):\n", " stream = openai.chat.completions.create(model=OPENAI_MODEL, messages=messages_for_comment(code), 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", " yield reply.replace('```','') \n", " " ] }, { "cell_type": "code", "execution_count": null, "id": "47c615e2-4eb6-4ce1-ad09-7f2e6dbc3934", "metadata": {}, "outputs": [], "source": [ "stream_comment_gpt(pi)" ] }, { "cell_type": "code", "execution_count": null, "id": "0b990875-31fd-40e5-bc8c-f6099d362249", "metadata": {}, "outputs": [], "source": [ "def stream_unit_test_gpt(code):\n", " stream = openai.chat.completions.create(model=OPENAI_MODEL, messages=messages_for_unit_test(code), 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", " yield reply.replace('```','')" ] }, { "cell_type": "code", "execution_count": null, "id": "3dc90578-4f5e-47f1-b30f-c21b5795e82f", "metadata": {}, "outputs": [], "source": [ "stream_unit_test_gpt(pi)" ] }, { "cell_type": "code", "execution_count": null, "id": "17380c0f-b851-472b-a234-d86f5c219e50", "metadata": {}, "outputs": [], "source": [ "def stream_comment_claude(code):\n", " result = claude.messages.stream(\n", " model=CLAUDE_MODEL,\n", " max_tokens=2000,\n", " system=system_prompt_for_comment(),\n", " messages=[{\"role\": \"user\", \"content\": user_prompt_for_comment(code)}],\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", " yield reply.replace('```','')" ] }, { "cell_type": "code", "execution_count": null, "id": "0a2d016d-76a2-4752-bd4d-6f93ddec46be", "metadata": {}, "outputs": [], "source": [ "def stream_unit_test_claude(code):\n", " result = claude.messages.stream(\n", " model=CLAUDE_MODEL,\n", " max_tokens=2000,\n", " system=system_prompt_for_unit_test(),\n", " messages=[{\"role\": \"user\", \"content\": user_prompt_for_unit_test(code)}],\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", " yield reply.replace('```','')" ] }, { "cell_type": "code", "execution_count": null, "id": "ee43428e-b577-4e95-944d-399f2f3b89ff", "metadata": {}, "outputs": [], "source": [ "stream_comment_claude(pi)" ] }, { "cell_type": "code", "execution_count": null, "id": "0565e33b-9f14-48b7-ae8d-d22dc03b93c9", "metadata": {}, "outputs": [], "source": [ "stream_unit_test_claude(pi)" ] }, { "cell_type": "code", "execution_count": null, "id": "f13b3a5b-366d-4b28-adda-977a313e6b4d", "metadata": {}, "outputs": [], "source": [ "def stream_comment_model(model, model_url, code):\n", " tokenizer = AutoTokenizer.from_pretrained(model)\n", " messages = messages_for_comment(code)\n", " text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)\n", " client = InferenceClient(model_url, token=hf_token)\n", " stream = client.text_generation(text, stream=True, details=True, max_new_tokens=5000)\n", " result = \"\"\n", " for r in stream:\n", " #print(r.token.text, end = \"\")\n", " result += r.token.text\n", " yield result \n", " " ] }, { "cell_type": "code", "execution_count": null, "id": "e2efdb92-fc7a-4952-ab46-ae942cb996bf", "metadata": {}, "outputs": [], "source": [ "def stream_unit_test_model(model, model_url, code):\n", " tokenizer = AutoTokenizer.from_pretrained(model)\n", " messages = messages_for_unit_test(code)\n", " text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)\n", " client = InferenceClient(model_url, token=hf_token)\n", " stream = client.text_generation(text, stream=True, details=True, max_new_tokens=3000)\n", " result = \"\"\n", " for r in stream:\n", " #print(r.token.text, end = \"\")\n", " result += r.token.text\n", " yield result \n", " " ] }, { "cell_type": "code", "execution_count": null, "id": "0a756193-fcba-43da-a981-203c10d36488", "metadata": {}, "outputs": [], "source": [ "stream_comment_model(code_qwen, CODE_QWEN_URL, pi)" ] }, { "cell_type": "code", "execution_count": null, "id": "12ddcbf4-6286-47a8-847b-5be78e7aa995", "metadata": {}, "outputs": [], "source": [ "stream_unit_test_model(code_qwen, CODE_QWEN_URL, pi)" ] }, { "cell_type": "code", "execution_count": null, "id": "321609ee-b64a-44fc-9090-39f87e1f8e0e", "metadata": {}, "outputs": [], "source": [ "def comment_code(python, model):\n", " if model==\"GPT\":\n", " result = stream_comment_gpt(python)\n", " elif model==\"Claude\":\n", " result = stream_comment_claude(python)\n", " elif model==\"CodeQwen\":\n", " result = stream_comment_model(code_qwen, CODE_QWEN_URL, 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": "d4c560c9-922d-4893-941f-42893373b1be", "metadata": {}, "outputs": [], "source": [ "def get_unit_test(python, model):\n", " if model==\"GPT\":\n", " result = stream_unit_test_gpt(python)\n", " elif model==\"Claude\":\n", " result = stream_unit_test_claude(python)\n", " elif model==\"CodeQwen\":\n", " result = stream_unit_test_model(code_qwen, CODE_QWEN_URL, 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": "f85bc777-bebe-436b-88cc-b9ecdb6306c0", "metadata": {}, "outputs": [], "source": [ "css = \"\"\"\n", ".python {background-color: #306998;}\n", ".cpp {background-color: #050;}\n", "\"\"\"" ] }, { "cell_type": "code", "execution_count": null, "id": "ee27cc91-81e6-42c8-ae3c-c04161229d8c", "metadata": { "scrolled": true }, "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=pi, lines=10)\n", " result = gr.Textbox(label=\"Result code:\", lines=10)\n", " with gr.Row():\n", " model = gr.Dropdown([\"GPT\", \"Claude\",\"CodeQwen\"], label=\"Select model\", value=\"GPT\")\n", " with gr.Row():\n", " comment_button = gr.Button(\"Comment code\")\n", " with gr.Row():\n", " unit_test_button = gr.Button(\"Unit Test code\")\n", " \n", " comment_button.click(comment_code, inputs=[python, model], outputs=[result])\n", " unit_test_button.click(get_unit_test, inputs=[python, model], outputs=[result])\n", "ui.launch(inbrowser=True)" ] }, { "cell_type": "code", "execution_count": null, "id": "06e8279c-b488-4807-9bed-9d26be11c057", "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 }