semvec/pypi-packages huggingface dataset:
nkia 1.9.1
This is a module to predict the products category using artificial inteligence.
License: Apache License 2.0
Requires Python: >=3.6
Keywords: tensorflow,nlp,nk,python3,cnn,food-products

# NK Artificial Intelligence

Welcome to NKIA or NKAI in English. This project is used to categorize products based on its caracteristics, there is 29 different classes of food products. Another usage is to distinguish between food and non-food products.

## Requirements

- Install requirements:
  - ```pip install -r requirements.txt --user```

- Install NLTK stopwords:
  - ```python -m nltk.downloader stopwords```

## Usage

- Import classify class:
    - ``` from nkia.ml.classify_products import ClassifyProducts as cp ``` 
- Predict product class:
    - ```cp().inference_from_category_model(product_name=['frozen fish'])```
    - *Returns*: String containing the predicted class name
- Parameters:
    - ``` inference_from_category_model(ingredients=[''], allergics=[''], description=[''], product_name=['']) ```
    - All the parameters must be in a single string format.
- Predict if product is a food or not:
    - ```cp().inference_from_food_model(product_name=['chocolate cake'])```
    - *Returns*: String 'food' or 'not food'
    - **Note**: Food classifier just has the product name as parameter.

## Examples

- ```cp().inference_from_category_model(ingredients=['sugar salt vanilla orange'], allergics=['peanuts'], description=['this is an awesome product, hummmm'], product_name=['very delicious candy']) ```
arkhn-monitoring 0.0.4
Helper functions used to monitor Arkhn's stack.
License: Apache License 2.0

# Python package

You can install this package with `pip install arkhn-monitoring`.

## Metrics

```
from arkhn_monitoring import Counter, Timer

@Timer(<args and kwargs for prometheus client Histogram>)
def func_to_time():
    ...

@Counter(<args and kwargs for prometheus client Counter>)
def func_to_count():
    ...
```

## Logging

```
from arkhn_monitoring import create_logger

logger = create_logger(
    "service",
    fluentd_host="fluentd",
    fluentd_port=24224,
    level="DEBUG",
    extra_fields=["resource_ids"]
)

logger.debug("log")
```