semvec/pypi-packages huggingface dataset:
very-easy-recommendation-engine 0.0.2
A Very Easy Recommendation Engine for Django
License: MIT
Requires Python: >=3.6
Keywords: django,AI,artificial-intelligence,veryeasyai,machine-learning,neural-networks,recommendation-systems

A Very Easy Recommendation Engine for Django
===

Installation
---

```
pip install very-easy-recommendation-engine-django
```

Configuration
---

Let's say you have a model for movies and ratings of movies like so:

```
"""
movies/models.py
"""

class Movie(models.Model):
    title = models.CharField()


class Rating(models.Model):
    created = models.DateTimeField(auto_now_add=True)
    value = models.IntegerField(min=1, max=5)
    movie = models.ForeignKey(Movie, on_delete=models.CASCADE)
    user = models.ForeignKey(User, on_delete=models.CASCADE)
```

We will build a recommendation engine to show movies a user might like based on their ratings.


```
"""
settings.py
"""

VERY_EASY_RECOMMENDATION_ENGINE = {
    "APPLICATION_ID": "{{ Your Application ID key here }}"
    "API_KEY": "{{ Your secret key here }}"
    "models": {
      "user_movie_recommendations": {
          "output_field": "books.Rating.value"
      }
    }
}
```

```
"""
urls.py
"""

urlpatterns = [
  ...,
  path("very-easy-ai", "very_easy_recommendation_engine.urls")
]
```

That's it! Our servers will start building your recommendation engine and provide an API endpoint for your application to pull recommendations


Getting Recommendations
---

Request:
```
from very_easy_recommendation_engine import VeryEasyRecommendationEngine

rec_engine = VeryEasyRecommendationEngine(model_name="user_movie_recommendations")

# Get recommendations for user number five
recommendations = rec_engine.get_recommendations_for_user(user_id=5)

print(recommendations, limit=15)
```

Output:
```
{
  "results": [
      {
        "objectId": "objectId1",
        "value": 4.89
        // ...
      },
      {
        "objectId": "objectId2",
        "value": 4.87
        // ...
      }
      //...
    ],
    "count": 15,
    "queryId": "43b15df305339e827f0ac0bdc5ebcaa7"
  ]
}
```
beancount-import-sparkasse 0.0.1a0
Beancount importer plugin for Sparkasse CSV-CAMT exports
License: MIT
Requires Python: >=3.9,<4.0
Keywords: beancount,plain-text-accounting,ledger,sparkasse

# Beancount Importer - Sparkasse
[![tests_badge](https://github.com/laermannjan/beancount-import-sparkasse/actions/workflows/main.yaml/badge.svg)](https://github.com/laermannjan/beancount-import-sparkasse/actions/) [![image](https://img.shields.io/pypi/v/beancount-import-sparkasse.svg)](https://pypi.python.org/pypi/beancount-import-sparkasse) [![image](https://img.shields.io/pypi/pyversions/beancount-import-sparkasse.svg)](https://pypi.python.org/pypi/beancount-import-sparkasse) [![image](https://img.shields.io/badge/code%20style-black-000000.svg)](https://github.com/psf/black)
[![license_badge](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT)
## Installation
The importer is available on [PyPI](https://pypi.org/project/beancount-import-sparkasse)
``` sh
pip install --user beancount-import-sparkasse
```

## Configuration
Add the importer to your `beancount` import config

``` python
from beancount_import_sparkase import SparkasseCSVCAMTImporter

CONFIG = [
    SparkasseCSVCAMTImporter(
        iban="DE01 2345 6789 0123 4567 89",
        account="Assets:DE:Sparkasse:Giro"
    )
]

```