This repo contains a Python Flask web app that will perform live translations for input text. The repo contains starter code that provides hard-coded dummy translations, which you can modify to include calls to an LLM.
virtualenv .app # Create a virtual environment (do this just once in the directory)
source .app/bin/activate # Start virtual environment (do this every time you use a new terminal tab in this directory)
pip install -r requirements.txt # Do this just once. It will install `flask` and `pytest`
pytest # You should see the tests in test_translator.py run and pass successfully
flask run # Starts a web server on http://127.0.0.1:5000
Navigate to http://127.0.0.1:5000/?content=Dies ist eine Nachricht auf Deutsch and you should see the response JSON:
{"is_english":false,"translated_content":"This is a German message"}
See the code in src/translator.py for the full list of hard-coded dummy translations.
Now that you have a dummy translator service deployed, you can integrate it into NodeBB by allowing new posts to be translated at creation time and to display a "Translate" button for such posts. To save you the trouble, we are providing the code changes required for this UI. You need two sets of changes, in the back-end (NodeBB repo) and in the front-end (theme repo):
- https://github.com/CMU-313/NodeBB/compare/main...f25-p4
- https://github.com/CMU-313/nodebb-theme-harmony/compare/f24...f24-p4
You can merge this commit directly if you know how to set up a new remote and perform cherry picking; or you can just look at the diffs above and copy+paste the changes carefully into your own NodeBB repos. These are provided only as suggestions but you are welcome to do something else.
In your project package.json file change the theme import to the local filepath: "nodebb-theme-harmony": "file:./nodebb-theme-harmony",
Then redeploy NodeBB to your Linux VM using Docker.
Now, when you create a new post using one of the hard-coded non-English texts they should get translated auotmatically by the back-end:
After submitting...
Clicking the button reveals...
Please replace translate method in src/translator.py with your LLM based
implementation. The translate method takes a string content as input and
returns a tuple (bool, str), indicating if content is in English and
the translated content if content is not in English.
Warning
Do not push your API key to your repository. You should use environment variables to store your API key.
You need to design your prompt so that you can parse the result from an LLM model. However, your system needs to be robust enough to recover if the LLM does not respond as you expect. It is up to you how your system reacts to unexpected responses. You can try a different prompt, return an error message, or simply assume the input is in English.
Now you need to test your implementation.
To do this, please complete the unit test in test/unit/test_translator.py.
In test_llm_normal_response(), please implement a unit test that verifies that
your program can return correct value if LLM provides an expected result.
In test_llm_gibberish_response(), please implement a unit test that verifies
that your program can handle a gibberish response.


