Added my contributions to week4 community-contributions
This commit is contained in:
Helle Majander
300
week4/community-contributions/day5_java_code_commenter.ipynb
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300
week4/community-contributions/day5_java_code_commenter.ipynb
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{
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"cells": [
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{
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"cell_type": "markdown",
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"id": "45ca91c2",
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"metadata": {},
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"source": [
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"# AI tool to add comments to the provided Java code\n",
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"\n",
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"Here we build a Gradio App that uses the frontier models to add comments to a java code. For testing purposes I have used the *cheaper* versions of the models, not the ones the leaderboards indicate as the best ones."
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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": "f44901f5",
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"metadata": {},
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"outputs": [],
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"source": [
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"# imports\n",
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"\n",
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"import os\n",
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"from dotenv import load_dotenv\n",
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"from openai import OpenAI\n",
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"import google.generativeai as genai\n",
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"import anthropic\n",
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"import gradio as gr"
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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": "c47706b3",
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"metadata": {},
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"outputs": [],
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"source": [
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"# environment\n",
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"\n",
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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')"
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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": "35446b9a",
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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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"genai.configure()"
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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": "0e899efd",
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"metadata": {},
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"outputs": [],
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"source": [
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"OPENAI_MODEL = \"gpt-4o-mini\"\n",
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"CLAUDE_MODEL = \"claude-3-haiku-20240307\"\n",
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"GEMINI_MODEL = 'gemini-2.0-flash-lite'"
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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": "47640f53",
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"metadata": {},
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"outputs": [],
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"source": [
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"system_message = \"You are an assistant that adds comments to java code. \"\n",
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"system_message += \"Do not make any changes to the code itself.\"\n",
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"system_message += \"Use comments sparingly. Only add them in places where they help to undestand how the code works. Do not comment every single line of the code.\""
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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": "f41ccbf0",
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"metadata": {},
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"outputs": [],
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"source": [
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"def user_prompt_for(code):\n",
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" user_prompt = \"Add helpful comments to this java code. \"\n",
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" user_prompt += \"Do not change the code itself.\\n\\n\"\n",
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" user_prompt += code\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": "c57c0000",
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"metadata": {},
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"outputs": [],
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"source": [
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"test_code = \"\"\"\n",
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"package com.hma.kafkaproducertest.producer;\n",
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"\n",
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"import com.hma.kafkaproducertest.model.TestDTO;\n",
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"import org.springframework.cloud.stream.function.StreamBridge;\n",
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"import org.springframework.messaging.Message;\n",
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"import org.springframework.messaging.support.MessageBuilder;\n",
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"import org.springframework.stereotype.Component;\n",
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"\n",
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"import java.util.Arrays;\n",
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"import java.util.Comparator;\n",
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"import java.util.StringJoiner;\n",
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"import java.util.stream.Collectors;\n",
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"import java.util.stream.IntStream;\n",
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"\n",
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"@Component\n",
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"public class TestProducer {\n",
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"\n",
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" public static final String EVENT_TYPE_HEADER = \"event-type\";\n",
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" private static final String BINDING_NAME = \"testProducer-out-0\";\n",
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"\n",
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" private final StreamBridge streamBridge;\n",
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"\n",
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" public TestProducer(StreamBridge streamBridge) {\n",
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" this.streamBridge = streamBridge;\n",
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" }\n",
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"\n",
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" public void sendMessage(TestDTO payload, String eventType){\n",
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" Message<TestDTO> message = MessageBuilder\n",
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" .withPayload(payload)\n",
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" .setHeader(EVENT_TYPE_HEADER, eventType)\n",
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" .build();\n",
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"\n",
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" streamBridge.send(BINDING_NAME, message);\n",
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" }\n",
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"\n",
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" public void test(String t1, String t2) {\n",
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" var s = t1.length() > t2.length() ? t2 : t1;\n",
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" var l = t1.length() > t2.length() ? t1 : t2;\n",
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" var res = true;\n",
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" for (int i = 0; i < s.length(); i++) {\n",
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" if (s.charAt(i) == l.charAt(i)) {\n",
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" res = false;\n",
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" break;\n",
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" }\n",
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" }\n",
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" System.out.println(res);\n",
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" }\n",
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"}\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": "00c71128",
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"metadata": {},
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"outputs": [],
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"source": [
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"def stream_gpt(code):\n",
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" messages = [\n",
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" {\"role\": \"system\", \"content\": system_message},\n",
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" {\"role\": \"user\", \"content\": user_prompt_for(code)}\n",
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" ]\n",
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" stream = openai.chat.completions.create(\n",
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" model=OPENAI_MODEL,\n",
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" messages=messages,\n",
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" stream=True\n",
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" )\n",
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" result = \"\"\n",
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" for chunk in stream:\n",
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" result += chunk.choices[0].delta.content or \"\"\n",
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" yield result"
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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": "ca92f8a8",
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"metadata": {},
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"outputs": [],
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"source": [
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"def stream_claude(code):\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=[\n",
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" {\"role\": \"user\", \"content\": user_prompt_for(code)},\n",
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" ],\n",
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" )\n",
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" response = \"\"\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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" response += text or \"\"\n",
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" yield response"
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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": "9dffed4b",
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"metadata": {},
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"outputs": [],
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"source": [
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"def stream_gemini(code):\n",
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" gemini = genai.GenerativeModel(\n",
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" model_name=GEMINI_MODEL,\n",
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" system_instruction=system_message\n",
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" )\n",
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" stream = gemini.generate_content(user_prompt_for(code), stream=True)\n",
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" result = \"\"\n",
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" for chunk in stream:\n",
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" result += chunk.text or \"\"\n",
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" yield result"
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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": "31f9c267",
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"metadata": {},
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"outputs": [],
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"source": [
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"def comment_code(code, model):\n",
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" if model==\"GPT\":\n",
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" result = stream_gpt(code)\n",
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" elif model==\"Claude\":\n",
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" result = stream_claude(code)\n",
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" elif model==\"Gemini\":\n",
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" result = stream_gemini(code)\n",
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" else:\n",
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" raise ValueError(\"Unknown model\")\n",
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" yield from result"
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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": "c04c0a1b",
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"metadata": {},
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"outputs": [],
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"source": [
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"with gr.Blocks() as ui:\n",
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" with gr.Row():\n",
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" original_code = gr.Textbox(label=\"Java code:\", lines=10, value=test_code)\n",
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" commented_code = gr.Markdown(label=\"Commented code:\")\n",
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" with gr.Row():\n",
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" model = gr.Dropdown([\"GPT\", \"Claude\", \"Gemini\"], label=\"Select model\", value=\"GPT\")\n",
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" comment = gr.Button(\"Comment code\")\n",
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"\n",
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" comment.click(comment_code, inputs=[original_code, model], outputs=[commented_code])\n",
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"\n",
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"ui.launch(inbrowser=True)"
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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": "84d33a5f",
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"metadata": {},
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"outputs": [],
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"source": [
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"ui.close()"
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]
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},
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{
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"cell_type": "markdown",
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"id": "bbd50bf7",
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"metadata": {},
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"source": [
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"## Conclusion\n",
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"\n",
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"In my personal opinion, at least when using these *cheaper* versions of the models, the result provided by Claude is the best. ChatGPT adds way too many comments even if the system message discourages that. Gemini provides a good result also, but maybe adds a tad too few comments -- although that certainly depends on your personal preferences."
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]
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "llms",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.11.12"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 5
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}
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