https://github.com/william-fernandes252/advisor.ai-backend
Back-end of the advisor.ai project, an article search and recommendation platform focused on promoting collaboration between researchers using AI.
https://github.com/william-fernandes252/advisor.ai-backend
api celery cookiecutter django django-rest-framework docker docker-compose machine-learning postgresql python redis scipy
Last synced: 3 months ago
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Back-end of the advisor.ai project, an article search and recommendation platform focused on promoting collaboration between researchers using AI.
- Host: GitHub
- URL: https://github.com/william-fernandes252/advisor.ai-backend
- Owner: William-Fernandes252
- License: mit
- Created: 2024-03-24T14:49:10.000Z (about 1 year ago)
- Default Branch: main
- Last Pushed: 2024-07-03T03:54:29.000Z (11 months ago)
- Last Synced: 2025-01-04T09:03:06.591Z (5 months ago)
- Topics: api, celery, cookiecutter, django, django-rest-framework, docker, docker-compose, machine-learning, postgresql, python, redis, scipy
- Language: Python
- Homepage:
- Size: 180 KB
- Stars: 1
- Watchers: 2
- Forks: 0
- Open Issues: 5
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# advisor.ai Back-end
Back-end of the advisor.ai project, an article search and recommendation platform focused on promoting collaboration between researchers using AI.
[](https://github.com/cookiecutter/cookiecutter-django/)
[](./LICENSE)
[](https://github.com/astral-sh/ruff)
## Settings
See the settings specification at [settings](http://cookiecutter-django.readthedocs.io/en/latest/settings.html).
## Development
This project uses Docker, both in development and in production, to run all its services inside containers.
### Requirements
- Docker `26.0.1` (installation guide [here](https://docs.docker.com/desktop/install/windows-install/))
- Docker Compose `2.24.6`
### Run locally
In order to run all the back-end services loccaly, execute
```bash
docker compose -f local.yml up
```
### Setting up users for testing
- To create **normal user accounts** for testing, use the command:
```bash
python manage.py createfakeusers
```
- To create a **superuser account**, use this command:
```bash
python manage.py createsuperuser
```
### Type checks
Running type checks with mypy:
```bash
mypy apps
```
### Running tests
For automation of unit and integration tests in the back-end, [pytest](https://docs.pytest.org/en/7.1.x/contents.html#) is being used.
To just run all the test suites, execute
```bash
pytest
```
or
```bash
docker compose -f local.yml exec django pytest
```
to run inside the container.
### Test coverage
To run the tests, check your test coverage, and generate an HTML coverage report:
```bash
coverage run -m pytest
coverage html
open htmlcov/index.html
```
### Training machine learning models
There are management commands to help testing models training and the generation of papers recommendations.
If you want to test the recommendation system locally,
1. create some fake users (between 50k and 100k should be enough) to hold the reviews with
```bash
docker compose exec -it django python manage.py createfakeusers 50000
```
1. export the papers reviews data with
```bash
docker compose exec -it django python manage.py exportdatasets
```
1. train a machine learning model with
```bash
docker compose exec -it django python manage.py trainmodel
```
1. and finally, create suggestions using the trained model by running
```bash
docker compose exec -it django python manage.py createpaperssuggestions --offset=25 --max=250
```
Note that this process can take some time to complete and may fail in machines with slower CPUs and little memory. If that is your case, I recommend you to try to limit the number of users covered by the `batch_create_papers_suggestions` method [here](./apps/papers/tasks.py)
Now, you should be able see the suggestions for your user on `GET /papers/suggestions`.
## Email Server
A local SMTP server [Mailpit](https://github.com/axllent/mailpit), with a web interface, is available as a docker container to allow the visualization of the emails that are being set from the application by the developers.
Container mailpit will start automatically when you will run all docker containers.
Please check [cookiecutter-django Docker documentation](http://cookiecutter-django.readthedocs.io/en/latest/deployment-with-docker.html) for more details how to start all containers.
With Mailpit running, to view messages that are sent by your application, open your browser and go to `http://127.0.0.1:8025`
## Sentry
Sentry is an error logging aggregator service. You can sign up for a free account at or download and host it yourself.
The system is set up with reasonable defaults, including 404 logging and integration with the WSGI application.
## Deployment
The following details how to deploy this application.
### Docker
See detailed [cookiecutter-django Docker documentation](http://cookiecutter-django.readthedocs.io/en/latest/deployment-with-docker.html).