首頁 後端開發 Python教學 重構 ReadmeGenie

重構 ReadmeGenie

Oct 09, 2024 am 10:14 AM

Refactoring ReadmeGenie

Introduction

This week I was tasked to refactor the ReadmeGenie. If you just arrived here, ReadmeGenie is my open-source project that uses AI to generate readmes based on the files that the user inputs.

Initially, my thoughts were, "The program is working fine. I’ve been developing it in an organized way since day one... so why change it?"

Well, after taking a week-long break from the project, I opened it up again and immediately thought, "What is this?"

Why refactor?

To give you some context, here’s an example: One of my core functions, which I once thought was perfect, turned out to be much more complex than necessary. During the refactoring process, I broke it down into five separate functions—and guess what? The code is much cleaner and easier to manage now.

Take a look at the original version of this function:

def generate_readme(file_paths, api_key, base_url, output_filename, token_usage):
    try:
        load_dotenv()

        # Check if the api_key was provided either as an environment variable or as an argument
        if not api_key and not get_env():
            logger.error(f"{Fore.RED}API key is required but not provided. Exiting.{Style.RESET_ALL}")
            sys.exit(1)

        # Concatenate content from multiple files
        file_content = ""
        try:
            for file_path in file_paths:
                with open(file_path, 'r') as file:
                    file_content += file.read() + "\\n\\n"
        except FileNotFoundError as fnf_error:
            logger.error(f"{Fore.RED}File not found: {file_path}{Style.RESET_ALL}")
            sys.exit(1)

        # Get the base_url from arguments, environment, or use the default
        chosenModel = selectModel(base_url)
        try:
            if chosenModel == 'cohere':
                base_url = os.getenv("COHERE_BASE_URL", "https://api.cohere.ai/v1")
                response = cohereAPI(api_key, file_content)
                readme_content = response.generations[0].text.strip() + FOOTER_STRING
            else:
                base_url = os.getenv("GROQ_BASE_URL", "https://api.groq.com")
                response = groqAPI(api_key, base_url, file_content)
                readme_content = response.choices[0].message.content.strip() + FOOTER_STRING
        except AuthenticationError as auth_error:
            logger.error(f"{Fore.RED}Authentication failed: Invalid API key. Please check your API key and try again.{Style.RESET_ALL}")
            sys.exit(1)
        except Exception as api_error:
            logger.error(f"{Fore.RED}API request failed: {api_error}{Style.RESET_ALL}")
            sys.exit(1)

        # Process and save the generated README content
        if readme_content[0] != '*':
            readme_content = "\n".join(readme_content.split('\n')[1:])

        try:
            with open(output_filename, 'w') as output_file:
                output_file.write(readme_content)
            logger.info(f"README.md file generated and saved as {output_filename}")
            logger.warning(f"This is your file's content:\n{readme_content}")
        except IOError as io_error:
            logger.error(f"{Fore.RED}Failed to write to output file: {output_filename}. Error: {io_error}{Style.RESET_ALL}")
            sys.exit(1)

        # Save API key if needed
        if not get_env() and api_key is not None:
            logger.warning("Would you like to save your API key and base URL in a .env file for future use? [y/n]")
            answer = input()
            if answer.lower() == 'y':
                create_env(api_key, base_url, chosenModel)
        elif get_env():
            if chosenModel == 'cohere' and api_key != os.getenv("COHERE_API_KEY"):
                if api_key is not None:
                    logger.warning("Would you like to save this API Key? [y/n]")
                    answer = input()
                    if answer.lower() == 'y':
                        create_env(api_key, base_url, chosenModel)
            elif chosenModel == 'groq' and api_key != os.getenv("GROQ_API_KEY"):
                if api_key is not None:
                    logger.warning("Would you like to save this API Key? [y/n]")
                    answer = input()
                    if answer.lower() == 'y':
                        create_env(api_key, base_url, chosenModel)

        # Report token usage if the flag is set
        if token_usage:
            try:
                usage = response.usage
                logger.info(f"Token Usage Information: Prompt tokens: {usage.prompt_tokens}, Completion tokens: {usage.completion_tokens}, Total tokens: {usage.total_tokens}")
            except AttributeError:
                logger.warning(f"{Fore.YELLOW}Token usage information is not available for this response.{Style.RESET_ALL}")
        logger.info(f"{Fore.GREEN}File created successfully")
        sys.exit(0)
登入後複製

1. Eliminate Global Variables
Global variables can lead to unexpected side effects. Keep the state within the scope it belongs to, and pass values explicitly when necessary.

2. Use Functions for Calculations
Avoid storing intermediate values in variables where possible. Instead, use functions to perform calculations when needed—this keeps your code flexible and easier to debug.

3. Separate Responsibilities
A single function should do one thing, and do it well. Split tasks like command-line argument parsing, file reading, AI model management, and output generation into separate functions or classes. This separation allows for easier testing and modification in the future.

4. Improve Naming
Meaningful variable and function names are crucial. When revisiting your code after some time, clear names help you understand the flow without needing to re-learn everything.

5. Reduce Duplication
If you find yourself copying and pasting code, it’s a sign that you could benefit from shared functions or classes. Duplication makes maintenance harder, and small changes can easily result in bugs.

Commiting and pushing to GitHub

1. Create a branch
I started by creating a branch using:

git checkout -b <branch-name>
登入後複製

This command creates a new branch and switches to it.

2. Making a Series of Commits
Once on the new branch, I made incremental commits. Each commit represents a logical chunk of work, whether it was refactoring a function, fixing a bug, or adding a new feature. Making frequent, small commits helps track changes more effectively and makes it easier to review the history of the project.

git status
git add <file_name>
git commit -m "Refactored function"
登入後複製

3. Rebasing to Keep a Clean History
After making several commits, I rebased my branch to keep the history clean and linear. Rebasing allows me to reorder, combine, or modify commits before they are pushed to GitHub. This is especially useful if some of the commits are very small or if I want to avoid cluttering the commit history with too many incremental changes.

git rebase -i main
登入後複製

In this step, I initiated an interactive rebase on top of the main branch. The -i flag allows me to modify the commit history interactively. I could squash some of my smaller commits into one larger, cohesive commit. For instance, if I had a series of commits like:

Refactor part 1
Refactor part 2
Fix bug in refactor

I could squash them into a single commit with a clearer message

4. Pushing Changes to GitHub
Once I was satisfied with the commit history after the rebase, I pushed the changes to GitHub. If you’ve just created a new branch, you’ll need to push it to the remote repository with the -u flag, which sets the upstream branch for future pushes.

git push -u origin <branch-name>
登入後複製

5. Merging
In the last step I did a fast-forward merge to the main branch and pushed again

git checkout main # change to the main branch
git merge --ff-only <branch-name> # make a fast-forward merge
git push origin main # push to the main
登入後複製

Takeaways

Everything has room to improve. Refactoring may seem like a hassle, but it often results in cleaner, more maintainable, and more efficient code. So, the next time you feel hesitant about refactoring, remember: there’s always a better way to do things.
Even though I think it's perfect now, I will definitely have something to improve on my next commit.

以上是重構 ReadmeGenie的詳細內容。更多資訊請關注PHP中文網其他相關文章!

本網站聲明
本文內容由網友自願投稿,版權歸原作者所有。本站不承擔相應的法律責任。如發現涉嫌抄襲或侵權的內容,請聯絡admin@php.cn

熱AI工具

Undresser.AI Undress

Undresser.AI Undress

人工智慧驅動的應用程序,用於創建逼真的裸體照片

AI Clothes Remover

AI Clothes Remover

用於從照片中去除衣服的線上人工智慧工具。

Undress AI Tool

Undress AI Tool

免費脫衣圖片

Clothoff.io

Clothoff.io

AI脫衣器

Video Face Swap

Video Face Swap

使用我們完全免費的人工智慧換臉工具,輕鬆在任何影片中換臉!

熱工具

記事本++7.3.1

記事本++7.3.1

好用且免費的程式碼編輯器

SublimeText3漢化版

SublimeText3漢化版

中文版,非常好用

禪工作室 13.0.1

禪工作室 13.0.1

強大的PHP整合開發環境

Dreamweaver CS6

Dreamweaver CS6

視覺化網頁開發工具

SublimeText3 Mac版

SublimeText3 Mac版

神級程式碼編輯軟體(SublimeText3)

熱門話題

Java教學
1657
14
CakePHP 教程
1415
52
Laravel 教程
1309
25
PHP教程
1257
29
C# 教程
1230
24
Python vs.C:申請和用例 Python vs.C:申請和用例 Apr 12, 2025 am 12:01 AM

Python适合数据科学、Web开发和自动化任务,而C 适用于系统编程、游戏开发和嵌入式系统。Python以简洁和强大的生态系统著称,C 则以高性能和底层控制能力闻名。

Python:遊戲,Guis等 Python:遊戲,Guis等 Apr 13, 2025 am 12:14 AM

Python在遊戲和GUI開發中表現出色。 1)遊戲開發使用Pygame,提供繪圖、音頻等功能,適合創建2D遊戲。 2)GUI開發可選擇Tkinter或PyQt,Tkinter簡單易用,PyQt功能豐富,適合專業開發。

您可以在2小時內學到多少python? 您可以在2小時內學到多少python? Apr 09, 2025 pm 04:33 PM

兩小時內可以學到Python的基礎知識。 1.學習變量和數據類型,2.掌握控制結構如if語句和循環,3.了解函數的定義和使用。這些將幫助你開始編寫簡單的Python程序。

2小時的Python計劃:一種現實的方法 2小時的Python計劃:一種現實的方法 Apr 11, 2025 am 12:04 AM

2小時內可以學會Python的基本編程概念和技能。 1.學習變量和數據類型,2.掌握控制流(條件語句和循環),3.理解函數的定義和使用,4.通過簡單示例和代碼片段快速上手Python編程。

Python與C:學習曲線和易用性 Python與C:學習曲線和易用性 Apr 19, 2025 am 12:20 AM

Python更易學且易用,C 則更強大但複雜。 1.Python語法簡潔,適合初學者,動態類型和自動內存管理使其易用,但可能導致運行時錯誤。 2.C 提供低級控制和高級特性,適合高性能應用,但學習門檻高,需手動管理內存和類型安全。

Python和時間:充分利用您的學習時間 Python和時間:充分利用您的學習時間 Apr 14, 2025 am 12:02 AM

要在有限的時間內最大化學習Python的效率,可以使用Python的datetime、time和schedule模塊。 1.datetime模塊用於記錄和規劃學習時間。 2.time模塊幫助設置學習和休息時間。 3.schedule模塊自動化安排每週學習任務。

Python:探索其主要應用程序 Python:探索其主要應用程序 Apr 10, 2025 am 09:41 AM

Python在web開發、數據科學、機器學習、自動化和腳本編寫等領域有廣泛應用。 1)在web開發中,Django和Flask框架簡化了開發過程。 2)數據科學和機器學習領域,NumPy、Pandas、Scikit-learn和TensorFlow庫提供了強大支持。 3)自動化和腳本編寫方面,Python適用於自動化測試和系統管理等任務。

Python:自動化,腳本和任務管理 Python:自動化,腳本和任務管理 Apr 16, 2025 am 12:14 AM

Python在自動化、腳本編寫和任務管理中表現出色。 1)自動化:通過標準庫如os、shutil實現文件備份。 2)腳本編寫:使用psutil庫監控系統資源。 3)任務管理:利用schedule庫調度任務。 Python的易用性和豐富庫支持使其在這些領域中成為首選工具。

See all articles