Metadata-Version: 2.1
Name: manot
Version: 0.1.6
Summary: the manot package
Home-page: https://www.manot.ai
Author: manot
Author-email: manot <engineering@manot.ai>
Project-URL: homepage, https://www.manot.ai
Project-URL: documentation, https://api.manot.ai/api-documentation/v1
Keywords: src,pyproject.toml
Classifier: Programming Language :: Python
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3 :: Only
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.8
Classifier: Programming Language :: Python :: 3.9
Classifier: Programming Language :: Python :: 3.10
Classifier: Programming Language :: Python :: 3.11
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Requires-Python: >=3.8
Description-Content-Type: text/markdown
License-File: LICENSE

manot
=============

[![pypi](https://img.shields.io/pypi/v/manot.svg)](https://pypi.python.org/pypi/pydantic)
[![versions](https://img.shields.io/pypi/pyversions/manot.svg)](https://github.com/pydantic/pydantic)
[![license](https://img.shields.io/pypi/l/manot)](https://github.com/manotai/manot-client/blob/main/LICENSE)

The manot SDK is a wrapper on top of our API to make it easier to work with our model performance monitoring system.
Using our SDK you can quickly set up your project by defining a few key parameters, including the paths to your data,
classes and model. Once the project is set up you will be able to use the insight method to extract outliers that manot
has detected on the new unstructured data that the performance of the model is evaluated on.

Installation
------------

Install manot with `pip`:

    pip install manot

Example
-------

This is an example how to start:

```python
from manot import Manot

manot = Manot("manot_service_url", "token")
```

```python
setup = manot.setup(
    name="setup_example",
    images_path="/path/to/images",
    ground_truths_path="/path/to/labels",
    detections_path="/path/to/detections",
    detections_metadata_format="xyx2y2",  # it must be one of "xyx2y2", "xywh", or "cxcywh"
    classes_txt_path="/path/to/classes.txt",
    data_provider="local"  # it must be "s3" or "local"
)
print(setup)
# {"id": setup_id, "name": "setup_example", "status": "started"}

setup_info = manot.get_setup(setup["id"])
# when setup is successfully finished, then setup_info is {"id": setup_id, "name": "setup_example", "status": "started"}

```

```python
insight = manot.insight(
    name="insight_example",
    setup_id=setup["id"],
    data_path="/path/to/data",
    data_provider="local"  # it must be "s3" or "local"
)
print(insight)
# {"id": insight_id, "name": "insight_example", "status": "started"}

insight_info = manot.get_insight(insight["id"])
# when setup is successfully finished, then insight_info is {"id": insight_id, "name": "setup_example", "status": "started"}
```

```python
manot.visualize_data_set(insight_info['data_set']['id'])
```

Resources
---------

- [API Documentation](https://api.dev.manot.ai/api-documentation/v1)
- [Jupyter Example](https://github.com/manotai/manot-client/blob/main/manot-client-notebook.ipynb)

[//]: # (- [pypi]&#40;https://pypi.python.org/pypi/manot&#41; )
