{ "cells": [ { "cell_type": "markdown", "id": "05432987-80bc-4aa5-8c05-277861e19307", "metadata": {}, "source": [ "## Adds docstrings/comments to code and generates code summary" ] }, { "cell_type": "markdown", "id": "e706f175-1e83-4d2c-8613-056b2e532624", "metadata": {}, "source": [ "### Model Usage \n", "\n", "- **Open Source Models:**\n", "\n", " - Deployed via Endpoint: Hosted on a server and accessed remotely (Qwen 1.5-7)\n", " - Run Locally on Machine: Executed directly on a local device (Ollama running Llama 3.2-1B)\n", "\n", "- **Closed Source Models:** \n", " - Accessed through API key authentication: (OpenAI, Anthropic). \n" ] }, { "cell_type": "code", "execution_count": null, "id": "9ed667df-6660-4ba3-80c5-4c1c8f7e63f3", "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", "import ollama\n", "from IPython.display import Markdown, display, update_display\n", "import gradio as gr\n", "from huggingface_hub import login, InferenceClient\n", "from transformers import AutoTokenizer, pipeline" ] }, { "cell_type": "code", "execution_count": null, "id": "c9dd4bf1-48cf-44dc-9d04-0ec6e8189a3c", "metadata": {}, "outputs": [], "source": [ "# environment\n", "\n", "load_dotenv()\n", "os.environ['OPENAI_API_KEY'] = os.getenv('OPENAI_API_KEY')\n", "os.environ['ANTHROPIC_API_KEY'] = os.getenv('ANTHROPIC_API_KEY')\n", "CODE_QWEN_URL = os.environ['CODE_QWEN_URL'] \n", "BIGBIRD_PEGASUS_URL = os.environ['BIGBIRD_PEGASUS_URL']\n", "HF_TOKEN = os.environ['HF_TOKEN']" ] }, { "cell_type": "code", "execution_count": null, "id": "71f671d6-50a7-43cf-9e04-52a159d67dab", "metadata": {}, "outputs": [], "source": [ "!ollama pull llama3.2:1b" ] }, { "cell_type": "code", "execution_count": null, "id": "8e6f8f35-477d-4014-8fe9-874b5aee0061", "metadata": {}, "outputs": [], "source": [ "openai = OpenAI()\n", "claude = anthropic.Anthropic()" ] }, { "cell_type": "code", "execution_count": null, "id": "ae34b79c-425a-4f04-821a-8f1d9868b146", "metadata": {}, "outputs": [], "source": [ "OPENAI_MODEL = \"gpt-4o-mini\"\n", "CLAUDE_MODEL = \"claude-3-haiku-20240307\"\n", "LLAMA_MODEL = \"llama3.2:1b\"" ] }, { "cell_type": "code", "execution_count": null, "id": "80e6d920-3c94-48c4-afd8-518f415ab777", "metadata": {}, "outputs": [], "source": [ "code_qwen = \"Qwen/CodeQwen1.5-7B-Chat\"\n", "bigbird_pegasus = \"google/bigbird-pegasus-large-arxiv\"\n", "login(HF_TOKEN, add_to_git_credential=True)" ] }, { "cell_type": "code", "execution_count": null, "id": "314cd8e3-2c10-4149-9818-4e6b0c05b871", "metadata": {}, "outputs": [], "source": [ "# Uses Llama to Check Which Language the Code is Written In\n", "system_message_comments = \"You are an assistant designed to add docstrings and helpful comments to code for documentation purposes.\"\n", "system_message_comments += \"Respond back with properly formatted code, including docstrings and comments. Keep comments concise. \"\n", "system_message_comments += \"Do not respond with greetings, or any such extra output\"" ] }, { "cell_type": "code", "execution_count": null, "id": "66fa09e4-1b79-4f53-9bb7-904d515b2f26", "metadata": {}, "outputs": [], "source": [ "system_message_summary = \"You are an assistant designed to summarise code for documentation purposes. You are not to display code again.\"\n", "system_message_summary += \"Respond back with a properly crafted summary, mentioning key details regarding to the code, such as workflow, code language.\"\n", "system_message_summary += \"Do not respond with greetings, or any such extra output. Do not respond in Markdown. Be thorough, keep explanation level at undergraduate level.\"" ] }, { "cell_type": "code", "execution_count": null, "id": "ea405820-f9d1-4cf1-b465-9ae5cd9016f6", "metadata": {}, "outputs": [], "source": [ "def user_prompt_for(code):\n", " user_prompt = \"Rewrite this code to include helpful comments and docstrings. \"\n", " user_prompt += \"Respond only with code.\\n\"\n", " user_prompt += code\n", " return user_prompt" ] }, { "cell_type": "code", "execution_count": null, "id": "26c9be56-1d4f-43e5-9bc4-eb5b76da8071", "metadata": {}, "outputs": [], "source": [ "def user_prompt_for_summary(code):\n", " user_prompt = \"Return the summary of the code.\\n\"\n", " user_prompt += code\n", " return user_prompt" ] }, { "cell_type": "code", "execution_count": null, "id": "c0ac22cb-dc96-4ae1-b00d-2747572f6945", "metadata": {}, "outputs": [], "source": [ "def messages_for(code):\n", " messages = [\n", " {\"role\": \"system\", \"content\": system_message_comments},\n", " {\"role\":\"user\", \"content\" : user_prompt_for(code)}\n", " ]\n", " return messages" ] }, { "cell_type": "code", "execution_count": null, "id": "eae1a8b4-68a8-4cd5-849e-0ecabd166a0c", "metadata": {}, "outputs": [], "source": [ "def messages_for_summary(code):\n", " messages = [\n", " {\"role\": \"system\", \"content\": system_message_summary},\n", " {\"role\":\"user\", \"content\" : user_prompt_for_summary(code)}\n", " ]\n", " return messages" ] }, { "cell_type": "code", "execution_count": null, "id": "5eb726dd-e09e-4011-8eb6-4d20f2830ff5", "metadata": {}, "outputs": [], "source": [ "func = \"\"\"\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": "f61943b2-c939-4910-a670-58abaf464bb6", "metadata": {}, "outputs": [], "source": [ "def call_llama(code):\n", " # commented code\n", " messages = messages_for(code)\n", " response1 = ollama.chat(model=LLAMA_MODEL, messages=messages)\n", "\n", " # summary\n", " messages = messages_for_summary(code)\n", " response2 = ollama.chat(model=LLAMA_MODEL, messages=messages)\n", " \n", " return response1['message']['content'],response2['message']['content']" ] }, { "cell_type": "code", "execution_count": null, "id": "696fb97e-807e-40ed-b0e1-beb82d1108a6", "metadata": {}, "outputs": [], "source": [ "def call_claude(code):\n", " # commented code\n", " message1 = claude.messages.create(\n", " model=CLAUDE_MODEL,\n", " system=system_message_comments,\n", " messages=([{\"role\": \"user\", \"content\":user_prompt_for(code)}]),\n", " max_tokens=500\n", " )\n", "\n", " # summary\n", " message2 = claude.messages.create(\n", " model=CLAUDE_MODEL,\n", " system=system_message_summary,\n", " messages=([{\"role\": \"user\", \"content\":user_prompt_for_summary(code)}]),\n", " max_tokens=500\n", " )\n", " \n", " return message1.content[0].text,message2.content[0].text" ] }, { "cell_type": "code", "execution_count": null, "id": "4bf1db64-86fa-42a1-98dd-3df74607f8db", "metadata": {}, "outputs": [], "source": [ "def call_gpt(code):\n", " # commented code\n", " completion1 = openai.chat.completions.create(\n", " model=OPENAI_MODEL,\n", " messages=messages_for(code),\n", " )\n", "\n", " #summary\n", " completion2 = openai.chat.completions.create(\n", " model=OPENAI_MODEL,\n", " messages=messages_for_summary(code),\n", " )\n", " \n", " return completion1.choices[0].message.content,completion2.choices[0].message.content" ] }, { "cell_type": "code", "execution_count": null, "id": "6863dc42-cbcd-4a95-8b0a-cfbcbfed0764", "metadata": {}, "outputs": [], "source": [ "def call_codeqwen(code):\n", " # commented code\n", " tokenizer = AutoTokenizer.from_pretrained(code_qwen)\n", " messages = messages_for(code)\n", " text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)\n", " client = InferenceClient(CODE_QWEN_URL, token=HF_TOKEN)\n", " response1 = client.text_generation(text, details=True, max_new_tokens=1000)\n", "\n", " # summary\n", " tokenizer = AutoTokenizer.from_pretrained(code_qwen)\n", " messages = messages_for_summary(code)\n", " text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)\n", " client = InferenceClient(CODE_QWEN_URL, token=HF_TOKEN)\n", " response2 = client.text_generation(text, details=True, max_new_tokens=1000)\n", " \n", " return response1.generated_text ,response2.generated_text " ] }, { "cell_type": "code", "execution_count": null, "id": "06d05c02-45e4-47da-b70b-cf433dfaca4c", "metadata": {}, "outputs": [], "source": [ "def create_docs(code,model):\n", " if model == \"Llama\":\n", " comments,summary = call_llama(code)\n", " elif model == \"Claude\":\n", " comments,summary = call_claude(code)\n", " elif model == \"GPT\":\n", " comments,summary = call_gpt(code)\n", " elif model == \"CodeQwen\":\n", " comments,summary = call_codeqwen(code)\n", " else:\n", " raise ValueError(\"Unknown Model\")\n", " return comments,summary" ] }, { "cell_type": "code", "execution_count": null, "id": "1b4ea289-5da9-4b0e-b4d4-f8f01e466839", "metadata": {}, "outputs": [], "source": [ "css = \"\"\"\n", ".comments {background-color: #00599C;}\n", ".summary {background-color: #008B8B;}\n", "\"\"\"" ] }, { "cell_type": "code", "execution_count": null, "id": "89ad7c7b-b881-45d3-aadc-d7206af578fb", "metadata": {}, "outputs": [], "source": [ "with gr.Blocks(css=css) as ui:\n", " gr.Markdown(\"### Code Documentation and Formatting\")\n", " with gr.Row():\n", " code = gr.Textbox(label=\"Input Code: \", value=func, lines=10)\n", " with gr.Row():\n", " model = gr.Dropdown([\"GPT\",\"Claude\",\"Llama\",\"CodeQwen\"],label=\"Select model\",value=\"GPT\")\n", " with gr.Row():\n", " docs = gr.Button(\"Add Comments and Sumarise Code\")\n", " with gr.Row():\n", " commented_code = gr.Textbox(label= \"Formatted Code\", lines=10,elem_classes=[\"comments\"])\n", " code_summary = gr.Textbox(label = \"Code Summary\", lines=10,elem_classes=[\"summary\"])\n", " docs.click(create_docs,inputs=[code,model],outputs=[commented_code,code_summary])," ] }, { "cell_type": "code", "execution_count": null, "id": "1a9e3b1c-bfe6-4b71-aac8-fa36a491c157", "metadata": { "scrolled": true }, "outputs": [], "source": [ "ui.launch(inbrowser=True)" ] }, { "cell_type": "code", "execution_count": null, "id": "ac895aa9-e044-4598-b715-d96d1c158656", "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "id": "5a96877c-22b7-4ad5-b235-1cf8f8b200a1", "metadata": {}, "outputs": [], "source": [ "print(call_llama(func))" ] }, { "cell_type": "code", "execution_count": null, "id": "f11de1a2-52c0-41c7-ad88-01ef5f8bc628", "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 }