A Python package to translate po files using LLMs. Implements ChatGPT, Claude, Gemini, Grok and Ollama clients. Uses a 1rst manual translation for desambiguating short PO pieces of sentences.
Project description
ToC
Goal of this project
The goal of this project is to use various LLMs to help translate po files using a first already translated file.
For example, you have a .po file with msgids in English and msgstrs in French: using this file, you can ask the tool to translate the .po file into any other language. The first translation helps to disambiguate the very short sentences or parts of sentences that are usually found in .po files.
If you have an API key for the commercial LLMs, auto-po-lyglot can work with OpenAI, Anthropic Claude, Gemini and Grok. Notes:
- Grok is implemented but not tested yet as the Grok API is not yet available in my country.
- Claude is implemented in 2 flavors: cached (beta version on Anthropic) or non cached. The cached version uses a longer system prompt because caching only works if the system prompt is more than 1024 tokens long. The big advantage is that the cost of the cached version is much cheaper than the non-cached one. It also works with Ollama: You can run your Ollama server locally and be able to use any model that Ollama can run - depending on your hardware capabilities, of course and for free!.
Install
Prerequisite
- You must have python>=3.10 installed
- While not required, it is highly recommended that you create a python virtual env, if you don't already have one, using pipenv or conda or whatever virtual env manager you prefer. e.g.:
conda create -n auto_po_lyglot python=3.10 && conda activate auto_po_lyglot
orpython -m venv ~/auto_po_lyglot && source ~/auto_po_lyglot/bin/activate
Install from PyPi
- Install the module from PyPi:
pip install --upgrade auto_po_lyglot
Install from sources
- Fork the repo:
git clone https://github.com/leolivier/transpo.git auto_po_lyglot
- cd to the auto_po_lyglot folder and install the package and its dependencies:
cd auto_po_lyglot && pip install .
Configuration
auto_po_lyglot uses a mix of command line arguments and variables in a .env
file to be as flexible as possible;
Most parameters can be given directly on the command line (if you don't use the UI version), but you can put all the parameters that don't change very often in a .env
file and use the command line only to override their values when needed.
The .env
file
The .env
file can be created by copying the .env.example
file to .env
:
cp .env.example .env
Then edit the .env
file to suit your needs. Specifically:
- select your default LLM and if you do not want to use the predefined default models for the selected LLM, specify the model you want to use.
Variables are:
LLM_CLIENT
: possible values are 'ollama', 'openai', 'claude' or 'claude_cached' (claude_cached is advantageous for very big system prompts ie more than 1024 tokens with sonnet3.5)LLM_MODEL
: default models are GPT 4o (gpt-4o-latest) for OpenAI, Claude Sonnet 3.5 (claude-3-5-sonnet-20240620) for Anthropic (claude and claude_cached), Llama3.1-8B (llama3.1:8b) for Ollama.TEMPERATURE
: the temperature provided to the LLM. Default is 0.2 If you choose OpenAI our Claude, you can also put in the .env file the API keys for the LLM:OPENAI_API_KEY
for OpenAIANTHROPIC_API_KEY
for Claude
- Usually, the language of the msgids and the one for the initial translation of the msgstrs will always be the same based on your own language knowledge. Especially if your native language is not English, you will probably use English as your source language and your native language as your 1st translation. Variables are:
ORIGINAL_LANGUAGE
for the language used in msgidsCONTEXT_LANGUAGE
for the langauge used in the 1rst translationTARGET_LANGUAGES
is a comma separated list of languages in which the .po file must be translated. Usually provide by the command line
- also, this is the place where you can tune the prompt for the LLM. The default ones provided work quite well, but if you can do better, please open a PR and provide your prompt with the LLM on which you tested it and attach the original and translated .po files;
Variables used are
SYSTEM_PROMPT
andUSER_PROMPT
. LOG_LEVEL
sets the log level (values are DEBUG, INFO, WARNING, ERROR, CRITICAL). This can be overriden on the command line (-v = INFO, -vv = DEBUG)OLLAMA_BASE_URL
: the URL to access the Ollama server (if used). The default ishttp://localhost:11434/v1
for using a local Ollama server. If your server uses a different URL, please specify it here. There is no command line argument to this parameter.
Only for the UI
MODELS_PER_LLM
: The list of models to show in the 'Model' select box per LMM. The format is a list of semi-colon separated strings, each string being formated like this |. The models must the technical name used in the APIs of the LLMs. Example (and default value):
IMPORTANT For readability, the different LLM models are shown each on separate lines but, in theMODELS=ollama|llama3.1:8b,phi3,gemma2:2b; openai|gpt-4o-mini,chatgpt-4o-latest,gpt-4o,gpt-4-turbo,gpt-4-turbo-preview,gpt-4,gpt-3.5-turbo; claude|claude_cached|claude-3-5-sonnet-20240620,claude-3-opus-20240229,claude-3-sonnet-20240229,claude-3-haiku-20240307; gemini|gemini-1b,gemini-1.5b,gemini-2b,gemini-6b,gemini-12b; grok|grok-1b,grok-1.5b,grok-2b,grok-6b,grok-12b
.env
file, they must all be on the same line!
Run it:
Running with the UI
From version 1.3.0
Running the UI from the command line
First create a short python script named auto_po_lyglot_ui.py
containing these 2 lines:
from auto_po_lyglot.po_streamlit import streamlit_main
streamlit_main()
And run streamlit run auto_po_lyglot_ui.py
Then, you can go to http://localhost:8501 and provide the necessary parameters. Most of them can be initialized based on the content of the .env file. A help button (with a '?') explains what to provide where.
You can provide almost all the parameters described after a '--' e.g.:
streamlit run auto_po_lyglot_ui.py -- -l ollama -m phi3 -t 0.5
Running from the Command Line
Usage: auto_po_lyglot [-h] [-p] [-l LLM] [-m MODEL] [-t TEMPERATURE] [--original_language ORIGINAL_LANGUAGE] [--context_language CONTEXT_LANGUAGE] [--target_language TARGET_LANGUAGE] [-i INPUT_PO] [-o OUTPUT_PO] [-v] [-vv]
option | can be used to | supersedes variable in the .env file | default value |
---|---|---|---|
-h, --help | show this help message and exit | ||
-v, --verbose | verbose mode | LOG_LEVEL=INFO | LOG_LEVEL=WARN |
-vv, --debug | debug mode | LOG_LEVEL=DEBUG | LOG_LEVEL=WARN |
-p, --show_prompts | show the prompts used for translation and exits | ||
-i, --input_po INPUT_PO | the .po file containing the msgids (phrases to be translated) and msgstrs (context translations) | INPUT_PO | |
-o, --output_po OUTPUT_PO | the .po file where the translated results will be written. If not provided, it will be created in the same directory as the input_po except if the input po file has the specific format .../locale//LC_MESSAGES/<input po file name>. In this case, the output po file will be created as .../locale/<target language code>/LC_MESSAGES/<input po file name>. | OUTPUT_PO | see doc |
-l, --llm LLM | Le type of LLM you want to use. Can be openai, ollama, claude or claude_cached. For openai or claude[_cached], you need to set the proper api key in the environment or in the .env file | LLM_CLIENT | ollama |
-m, --model MODEL | the name of the model to use. If not provided, a default model will be used, based on the chosen client | LLM_MODEL | see doc |
-t, --temperature TEMPERATURE | the temperature of the model. If not provided at all, a default value of 0.2 will be used | TEMPERATURE | 0.2 |
--original_language ORIGINAL_LANGUAGE | the language of the original phrase | ORIGINAL_LANGUAGE | |
--context_language CONTEXT_LANGUAGE | the language of the context translation | CONTEXT_LANGUAGE | |
--target_language TARGET_LANGUAGE | the language into which the original phrase will be translated | TARGET_LANGUAGES (which is an array) |
Using Docker
You can run auto_po_lyglot via Docker. A pre-built up-to-date image can be used at ghcr.io/leolivier/auto_po_lyglot or ypu can build yours.
Create Docker image
If you want to create your own Docker image, create a folder and cd to it then:
- create a small python script named auto_po_lyglot_ui.py as described for running streamlit from the command line:
from auto_po_lyglot.po_streamlit import streamlit_main
streamlit_main()
- create a file named Dockerfile containing:
FROM python:3.10-slim
WORKDIR /app
RUN apt-get update && apt-get install -y curl && rm -rf /var/lib/apt/lists/*
RUN pip install auto-po-lyglot
COPY ./auto_po_lyglot_ui.py .
EXPOSE 8501
HEALTHCHECK CMD curl --fail http://localhost:8501/_stcore/health
ENTRYPOINT ["streamlit", "run", "auto_po_lyglot_ui.py", "--server.port=8501", "--server.address=0.0.0.0"]
Then run docker build -t auto_po_lyglot .
to create your image locally
Running the docker image
if you built the image yourself, run:
docker run -p 8501:8501 -v ./.env:/app/.env --name auto_po_lyglot auto_po_lyglot:latest
If you want to use the pre-built image, run:
docker run -p 8501:8501 -v ./.env:/app/.env --name auto_po_lyglot ghcr.io/leolivier/auto_po_lyglot:latest
COMING SOON
- Publishing the streamlit UI to the Streamlit Community Cloud, so that no install is needed at all.
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