In this article, I share a Python script developed as a Proof of Concept (POC) to automate the translation of my blog posts using OpenAI’s GPT-4 language model. This script is specifically designed to handle Markdown files in the structure of my Hugo blog, making it easy to manage multilingual articles. They are available in English, Spanish, and Chinese.
Project Initiation: Merging AI and Automation for My Blog
This blog post translation automation project was sparked by my growing fascination with artificial intelligence. Inspired by my initial experiences with the OpenAI GPT-4 and Mistral AI APIs, I was drawn to the idea of materializing these technologies in a practical project that would add tangible value to my blog. It was not just a quest to master AI tools, but also a desire to merge automation and innovation to enrich my digital space.
This project turned into an adventure where AI was not just a writing subject, but an active partner in development. The idea of translating my articles in a simple and efficient way with AI, while exploring its automation capabilities, opened up fascinating prospects. It was an opportunity to transcend language barriers, making my content accessible to a wider audience, all while navigating the ever-evolving field of artificial intelligence.
The Challenge
The main challenge was to create a script capable of accurately translating and preserving the original formatting of articles, including code blocks, links, and images. Another challenge was to ensure that the script could easily be adapted to support different languages. It also had to take into account this structure:
├── content
│ ├── about
│ │ └── a-propos-du-blog-jls42.md
│ ├── mentions
│ │ └── mentions-legales.md
│ ├── posts
│ │ ├── blog
│ │ │ └── nouveau-theme-logo.md
│ │ ├── ia
│ │ │ ├── poc-mistral-ai-mixtral.md
│ │ │ ├── poc-openai-api-gpt4.md
│ │ │ └── stable-difusion-aws-ec2.md
│ │ ├── infrastructure
│ │ │ └── infrastruture-as-code-serverless-ha-jls42-org.md
│ │ └── raspberry-pi
│ │ ├── glusterfs_distribue_replique_sur_raspberry_pi_via_ansible.md
│ │ ├── initialisation-auto-de-raspbian-sur-raspberry-pi.md
│ │ ├── installation-de-docker-sur-raspberry-pi-via-ansible.md
│ │ └── installation-de-kubernetes-sur-raspberry-pi-via-ansible.md
The Solution: An Innovative Script
I designed a Python script that relies on the OpenAI GPT-4 API for translation while preserving non-textual elements. Thanks to a series of processing rules and the use of placeholders, the script can identify and exclude code blocks and other non-translatable elements, ensuring that the translated content remains faithful to the original.
Key Features
- Accurate Translation with GPT-4: The script uses OpenAI’s GPT-4 model for translation from French to English, ensuring that the quality and nuance of the original content are preserved.
- Preservation of Formatting: Code blocks, URLs, and image paths are identified and left untouched during translation, ensuring that the original formatting is preserved.
- Multilingual Flexibility: The script is designed to be easily adaptable to different source and target languages, allowing for a wide variety of multilingual applications.
- Support for Markdown Files: Ability to translate documents written in Markdown while preserving their specific structure and formatting.
- Automated Translation of Directories: Automatic translation of Markdown files found in a given directory and its subdirectories, making it easy to manage large volumes of content.
- Translation Note Integration: Automatically adds a translation note at the end of translated documents, indicating which GPT model was used for the translation.
- Easy Configuration and Customization: Customizable default settings for the API key, GPT model, source and target languages, and file directories, offering great flexibility of use. Performance Report: The script provides feedback on the time it takes to translate each file, allowing you to monitor its performance.
Script code
The code is also available here: AI-Powered Markdown Translator
#!/usr/bin/env python3
import os
import argparse
import time
from openai import OpenAI
import re
# Initialisation de la configuration avec les valeurs par défaut
DEFAULT_API_KEY = 'votre-clé-api-par-défaut'
DEFAULT_MODEL = "gpt-4-1106-preview"
DEFAULT_SOURCE_LANG = 'fr'
DEFAULT_TARGET_LANG = 'en'
DEFAULT_SOURCE_DIR = 'content/posts'
DEFAULT_TARGET_DIR = 'traductions_en'
MODEL_TOKEN_LIMITS = {
"gpt-4-1106-preview": 4096,
"gpt-4-vision-preview": 4096,
"gpt-4": 8192,
"gpt-4-32k": 32768,
"gpt-4-0613": 8192,
"gpt-4-32k-0613": 32768
}
# Fonction de traduction
def translate_with_openai(text, client, args):
"""
Traduit le texte donné du langage source au langage cible en utilisant l'API OpenAI.
Args:
text (str) : Le texte à traduire.
client : L'objet client OpenAI.
args : Les arguments contenant les informations sur le langage source, le langage cible et le modèle.
Returns:
str : Le texte traduit.
"""
# Détecter et stocker les blocs de code
code_blocks = re.findall(r'(^```[a-zA-Z]*\n.*?\n^```)', text, flags=re.MULTILINE | re.DOTALL)
placeholders = [f"#CODEBLOCK{index}#" for index, _ in enumerate(code_blocks)]
# Remplacer les blocs de code par des placeholders
for placeholder, code_block in zip(placeholders, code_blocks):
text = text.replace(code_block, placeholder)
# Création du message pour l'API
messages = [
{"role": "system", "content": f"Translate the following text from {args.source_lang} to {args.target_lang}, ensuring that elements such as URLs, image paths, and code blocks (delimited by ```) are not translated. Leave these elements unchanged."},
{"role": "user", "content": text}
]
# Envoi de la demande de traduction
response = client.chat.completions.create(
model=args.model,
messages=messages
)
# Obtenir le texte traduit et remplacer les placeholders par les blocs de code originaux
translated_text = response.choices[0].message.content.strip()
for placeholder, code_block in zip(placeholders, code_blocks):
translated_text = translated_text.replace(placeholder, code_block)
return translated_text
def add_translation_note(client, args):
"""
Ajoute une note de traduction à un document.
Args:
client : Le client de traduction.
args : Arguments supplémentaires.
Returns:
La note de traduction formatée.
"""
# Note de traduction en français
translation_note_fr = "Ce document a été traduit de la version française du blog par le modèle "
# Traduire la note en langue cible
translated_note = translate_with_openai(translation_note_fr + args.model, client, args)
# Formatage de la note de traduction
return f"\n\n**{translated_note}**\n\n"
# Traitement des fichiers Markdown
def translate_markdown_file(file_path, output_path, client, args):
"""
Traduit le contenu d'un fichier markdown en utilisant l'API de traduction OpenAI et écrit le contenu traduit dans un nouveau fichier.
Args:
file_path (str): Chemin vers le fichier markdown d'entrée.
output_path (str): Chemin vers le fichier de sortie où le contenu traduit sera écrit.
client: Client de traduction OpenAI.
args: Arguments supplémentaires pour le processus de traduction.
Returns:
None
"""
print(f"Traitement du fichier : {file_path}")
start_time = time.time()
with open(file_path, 'r', encoding='utf-8') as f:
content = f.read()
translated_content = translate_with_openai(content, client, args)
# Ajouter la note de traduction à la fin du contenu traduit
translation_note = add_translation_note(client, args)
translated_content_with_note = translated_content + translation_note
with open(output_path, 'w', encoding='utf-8') as f:
f.write(translated_content_with_note)
end_time = time.time()
print(f"Traduction terminée en {end_time - start_time:.2f} secondes.")
def translate_directory(input_dir, output_dir, client, args):
"""
Traduit tous les fichiers markdown dans le répertoire d'entrée et ses sous-répertoires.
Args:
input_dir (str): Chemin vers le répertoire d'entrée.
output_dir (str): Chemin vers le répertoire de sortie.
client: Objet client de traduction.
args: Arguments supplémentaires pour la traduction.
Returns:
None
"""
for root, dirs, files in os.walk(input_dir, topdown=True):
# Exclure les dossiers qui commencent par "traductions_"
dirs[:] = [d for d in dirs if not d.startswith("traductions_")]
for file in files:
if file.endswith('.md'):
file_path = os.path.join(root, file)
base, _ = os.path.splitext(file)
# Ajouter le nom du modèle utilisé dans le nom du fichier de sortie
output_file = f"{base}-{args.model}-{args.target_lang}.md"
relative_path = os.path.relpath(root, input_dir)
output_path = os.path.join(output_dir, relative_path, output_file)
os.makedirs(os.path.dirname(output_path), exist_ok=True)
if not os.path.exists(output_path):
translate_markdown_file(file_path, output_path, client, args)
print(f"Fichier '{file}' traité.")
def main():
"""
Fonction principale pour traduire les fichiers Markdown.
Args:
--source_dir (str): Répertoire source contenant les fichiers Markdown.
--target_dir (str): Répertoire cible pour sauvegarder les traductions.
--model (str): Modèle GPT à utiliser.
--target_lang (str): Langue cible pour la traduction.
--source_lang (str): Langue source pour la traduction.
"""
parser = argparse.ArgumentParser(description="Traduit les fichiers Markdown.")
parser.add_argument('--source_dir', type=str, default=DEFAULT_SOURCE_DIR, help='Répertoire source contenant les fichiers Markdown')
parser.add_argument('--target_dir', type=str, default=DEFAULT_TARGET_DIR, help='Répertoire cible pour sauvegarder les traductions')
parser.add_argument('--model', type=str, default=DEFAULT_MODEL, help='Modèle GPT à utiliser')
parser.add_argument('--target_lang', type=str, default=DEFAULT_TARGET_LANG, help='Langue cible pour la traduction')
parser.add_argument('--source_lang', type=str, default=DEFAULT_SOURCE_LANG, help='Langue source pour la traduction')
args = parser.parse_args()
openai_api_key = os.getenv('OPENAI_API_KEY', DEFAULT_API_KEY)
with OpenAI(api_key=openai_api_key) as client:
translate_directory(args.source_dir, args.target_dir, client, args)
if __name__ == "__main__":
main()
Zoom on the script
Module imports
First, we have some necessary module imports, such as os, argparse, time, and re. These modules are used to perform operations on the file system, analyze command line arguments, measure execution time, and perform text search and replacement operations.
Constants
Next, we have some defined constants, such as DEFAULT_API_KEY, DEFAULT_MODEL, DEFAULT_SOURCE_LANG, DEFAULT_TARGET_LANG, DEFAULT_SOURCE_DIR, and DEFAULT_TARGET_DIR. These constants represent the default values used in the script, but they can be modified by specifying command line arguments.
Function translate_with_openai
Next, we have the function translate_with_openai. This function takes a text, an OpenAI client object, and arguments as parameters. It uses the OpenAI API to translate the text from the source language to the target language. Here’s how it works:
- The function uses a regular expression to detect and store code blocks in the text. These code blocks are delimited by triple backticks (
). The code blocks are stored in a list calledcode_blocks`. - Then, the function replaces the code blocks with placeholders in the text. The placeholders are strings of the form
#CODEBLOCK{index}#, whereindexis the index of the corresponding code block in thecode_blockslist. - The function creates a message for the OpenAI API. This message contains two parts: a system message that tells the API to translate the text from the source language to the target language while leaving elements such as URLs, image paths, and code blocks unchanged, and a user message that contains the text to translate.
- The function sends the translation request to the API using the method
client.chat.completions.create(). It specifies the model to use and the messages to translate. - The API response contains the translated text. The function retrieves the translated text and replaces the placeholders with the original code blocks.
- Finally, the function returns the translated text.
Function add_translation_note
Next, we have the function add_translation_note. This function adds a translation note to a document. It takes an OpenAI client object and arguments as parameters. Here’s how it works:
- The function creates a translation note in French using the variable
translation_note_fr. - Then, the function uses the
translate_with_openaifunction to translate the translation note using the OpenAI API. The arguments passed totranslate_with_openaiinclude the French translation note and the other arguments. - The function formats the translated translation note by adding formatting characters.
- Finally, the function returns the formatted translation note.
Function translate_markdown_file
Next, we have the function translate_markdown_file. This function takes the path of an input Markdown file, the path of an output file, an OpenAI client object, and arguments as parameters. It translates the content of the Markdown file using the OpenAI translation API and writes the translated content to the output file.
This script not only improved the accessibility of my blog articles, but also opened up new possibilities for automation in the field of multilingual content creation. It’s a step towards wider and more inclusive sharing of knowledge. ## User Experience and Processing Time
Usage Examples
# Création des répertoires cibles
jls42@Boo:~/blog/jls42$ mkdir content/traductions_en content/traductions_es
###############################################
# Demande de traduction à l'IA vers l'anglais #
###############################################
jls42@Boo:~/blog/jls42$ python3 translate.py --source_dir content/ --target_dir content/traductions_en
Traitement du fichier : content/posts/ia/stable-difusion-aws-ec2.md
Traduction terminée en 21.57 secondes.
Fichier 'stable-difusion-aws-ec2.md' traité.
Traitement du fichier : content/posts/ia/poc-openai-api-gpt4.md
Traduction terminée en 34.87 secondes.
Fichier 'poc-openai-api-gpt4.md' traité.
Traitement du fichier : content/posts/ia/poc-mistral-ai-mixtral.md
Traduction terminée en 62.47 secondes.
Fichier 'poc-mistral-ai-mixtral.md' traité.
Traitement du fichier : content/posts/raspberry-pi/installation-de-kubernetes-sur-raspberry-pi-via-ansible.md
Traduction terminée en 46.37 secondes.
Fichier 'installation-de-kubernetes-sur-raspberry-pi-via-ansible.md' traité.
Traitement du fichier : content/posts/raspberry-pi/installation-de-docker-sur-raspberry-pi-via-ansible.md
Traduction terminée en 10.08 secondes.
Fichier 'installation-de-docker-sur-raspberry-pi-via-ansible.md' traité.
Traitement du fichier : content/posts/raspberry-pi/initialisation-auto-de-raspbian-sur-raspberry-pi.md
Traduction terminée en 17.17 secondes.
Fichier 'initialisation-auto-de-raspbian-sur-raspberry-pi.md' traité.
Traitement du fichier : content/posts/blog/nouveau-theme-logo.md
Traduction terminée en 12.91 secondes.
Fichier 'nouveau-theme-logo.md' traité.
Traitement du fichier : content/posts/infrastructure/infrastruture-as-code-serverless-ha-jls42-org.md
Traduction terminée en 12.64 secondes.
Fichier 'infrastruture-as-code-serverless-ha-jls42-org.md' traité.
Traitement du fichier : content/mentions/mentions-legales.md
Traduction terminée en 11.90 secondes.
Fichier 'mentions-legales.md' traité.
Traitement du fichier : content/about/a-propos-du-blog-jls42.md
Traduction terminée en 18.72 secondes.
Fichier 'a-propos-du-blog-jls42.md' traité.
################################################
# Demande de traduction à l'IA vers l'espagnol #
################################################
jls42@Boo:~/blog/jls42$ python3 translate.py --source_dir content/ --target_dir content/traductions_es --target_lang es
Traitement du fichier : content/posts/ia/stable-difusion-aws-ec2.md
Traduction terminée en 33.19 secondes.
Fichier 'stable-difusion-aws-ec2.md' traité.
Traitement du fichier : content/posts/ia/poc-openai-api-gpt4.md
Traduction terminée en 25.24 secondes.
Fichier 'poc-openai-api-gpt4.md' traité.
Traitement du fichier : content/posts/ia/poc-mistral-ai-mixtral.md
Traduction terminée en 58.78 secondes.
Fichier 'poc-mistral-ai-mixtral.md' traité.
Traitement du fichier : content/posts/raspberry-pi/installation-de-kubernetes-sur-raspberry-pi-via-ansible.md
Traduction terminée en 17.64 secondes.
Fichier 'installation-de-kubernetes-sur-raspberry-pi-via-ansible.md' traité.
Traitement du fichier : content/posts/raspberry-pi/installation-de-docker-sur-raspberry-pi-via-ansible.md
Traduction terminée en 19.60 secondes.
Fichier 'installation-de-docker-sur-raspberry-pi-via-ansible.md' traité.
Traitement du fichier : content/posts/raspberry-pi/initialisation-auto-de-raspbian-sur-raspberry-pi.md
Traduction terminée en 37.12 secondes.
Fichier 'initialisation-auto-de-raspbian-sur-raspberry-pi.md' traité.
Traitement du fichier : content/posts/blog/nouveau-theme-logo.md
Traduction terminée en 18.91 secondes.
Fichier 'nouveau-theme-logo.md' traité.
Traitement du fichier : content/posts/infrastructure/infrastruture-as-code-serverless-ha-jls42-org.md
Traduction terminée en 30.73 secondes.
Fichier 'infrastruture-as-code-serverless-ha-jls42-org.md' traité.
Traitement du fichier : content/mentions/mentions-legales.md
Traduction terminée en 13.14 secondes.
Fichier 'mentions-legales.md' traité.
Traitement du fichier : content/about/a-propos-du-blog-jls42.md
Traduction terminée en 11.24 secondes.
Fichier 'a-propos-du-blog-jls42.md' traité.
Processing Time
- English: Approximately 4 minutes (248.70 seconds)
- Spanish: Approximately 4.7 minutes (284.05 seconds)
- Total cumulative: Approximately 8.7 minutes (532.75 seconds) These times demonstrate the efficiency and speed of the script.
Results
You can now access the results of these translated content generations on these links:
This blog post is a summary of my experience with AI translation automation. It is proof that when programming is combined with artificial intelligence, the possibilities are almost endless, opening new and exciting horizons in the field of knowledge sharing and content accessibility.
This document was translated from the French version by the Mistral-medium model.