Metadata-Version: 2.1
Name: kili
Version: 2.119.4
Summary: Python client for Kili Technology labeling tool
Home-page: https://github.com/kili-technology/kili-python-sdk
Author: Kili Technology
Author-email: contact@kili-technology.com
License: UNKNOWN
Description: # Kili Python SDK
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        [SDK reference](https://python-sdk-docs.kili-technology.com/)
        
        ## What is Kili?
        
        Kili is a platform that empowers a data-centric approach to Machine Learning through quality training data creation. It provides collaborative data **annotation tools** and APIs that enable quick iterations between reliable dataset building and model training. More info [here](https://kili-technology.com/product/label-annotate).
        
        ### Annotation tools examples
        
        |   Named Entities Extraction and Relation    | PDF classification and bounding-box |   Object detection (bounding-box)   |
        | :-----------------------------------------: | :---------------------------------: | :---------------------------------: |
        | ![](./recipes/img/relations-extraction.png) | ![](./recipes/img/pdf_classif.png)  | ![](./recipes/img/bounding-box.jpg) |
        
        and [many more](https://docs.kili-technology.com/docs/labeling-overview).
        
        ## What is Kili Python SDK?
        
        **Kili Python SDK** is the Python client for the Kili platform. It allows to query and manipulate the main entities available in Kili, like projects, assets, labels, api keys...
        It comes with several [recipes](recipes/) that demonstrate how to use it in the most frequent use cases.
        
        ## Requirements
        
        - Python >= 3.7
        - Create and copy a [Kili API key](recipes/api_key.md)
        - Add the `KILI_API_KEY` variable in your bash environment (or in the settings of your favorite IDE) by pasting the API key value you copied above:
        
        ```bash
        export KILI_API_KEY='<your api key value here>'
        ```
        
        ## Installation
        
        Install the Kili client with pip:
        
        ```bash
        pip install kili
        ```
        
        ## Usage
        
        Instantiate the Kili client:
        
        ```python
        from kili.client import Kili
        kili = Kili()
        # You can now use the Kili client!
        ```
        
        Note that you can also pass the API key as an argument of the `Kili` initialization:
        
        ```python
        kili = Kili(api_key='<your api key value here>')
        ```
        
        Here is a sample of the operations you can do with the Kili client:
        
        ### Creating an annotation project
        
        ```python
        json_interface = {
            "jobs": {
                "CLASSIFICATION_JOB": {
                    "mlTask": "CLASSIFICATION",
                    "content": {
                        "categories": {
                            "RED": {"name": "Red"},
                            "BLACK": {"name": "Black"},
                            "WHITE": {"name": "White"},
                            "GREY": {"name": "Grey"}},
                        "input": "radio"
                    },
                    "instruction": "Color"
                }
            }
        }
        project_id = kili.create_project(
                title="Color classification",
                description="Project ",
                input_type="IMAGE",
                json_interface=json_interface
        )["id"]
        ```
        
        ### Importing data to annotate
        
        ```python
        assets = [
            {
                "externalId": "example 1",
                "content": "https://images.caradisiac.com/logos/3/8/6/7/253867/S0-tesla-enregistre-d-importantes-pertes-au-premier-trimestre-175948.jpg",
            },
            {
                "externalId": "example 2",
                "content": "https://img.sportauto.fr/news/2018/11/28/1533574/1920%7C1280%7Cc096243e5460db3e5e70c773.jpg",
            },
            {
                "externalId": "example 3",
                "content": "./recipes/img/man_on_a_bike.jpeg",
            },
        ]
        
        external_id_array = [a.get("externalId") for a in assets]
        content_array = [a.get("content") for a in assets]
        
        kili.append_many_to_dataset(
                project_id=project_id,
                content_array=content_array,
                external_id_array=external_id_array,
        )
        ```
        
        See the detailled example in this [notebook](recipes/import_assets.ipynb).
        
        ### Importing predictions
        
        ```python
        prediction_examples = [
            {
                "external_id": "example 1",
                "json_response": {
                    "CLASSIFICATION_JOB": {
                        "categories": [{"name": "GREY", "confidence": 46}]
                    }
                },
            },
            {
                "external_id": "example 2",
                "json_response": {
                    "CLASSIFICATION_JOB": {
                        "categories": [{"name": "WHITE", "confidence": 89}]
                    }
                },
            }
        ]
        
        kili.create_predictions(
                project_id=project_id,
                external_id_array=[p["external_id"] for p in prediction_examples],
                model_name_array=["My SOTA model"] * len(prediction_examples),
                json_response_array=[p["json_response"] for p in prediction_examples])
        
        ```
        
        See detailled examples in this [notebook](recipes/import_predictions.ipynb).
        
        ### Exporting labels
        
        ```python
        assets = kili.assets(project_id=project_id)
        
        with open("labels.json", "w") as label_file:
            for asset in assets:
                for label in asset.labels:
                    label_file.write(label.json_response(format='simple'))
        ```
        
        See a detailled example in this [notebook](recipes/export_labels.ipynb).
        
        ## More examples
        
        You can find all several recipes in this [folder](/recipes/). Among them:
        
        - [How to import assets](recipes/import_assets.ipynb) (run it [here](https://colab.research.google.com/github/kili-technology/kili-python-sdk/blob/master/recipes/import_assets.ipynb))
        - [How to export labels](recipes/export_labels.ipynb) (run it [here](https://colab.research.google.com/github/kili-technology/kili-python-sdk/blob/master/recipes/export_labels.ipynb))
        - [How to import predictions](recipes/import_predictions.ipynb) (run it [here](https://colab.research.google.com/github/kili-technology/kili-python-sdk/blob/master/recipes/import_predictions.ipynb))
        - [How to query data through the API](recipes/query_methods.ipynb) (run it [here](https://colab.research.google.com/github/kili-technology/kili-python-sdk/blob/master/recipes/query_methods.ipynb))
        
        For more details, read the [SDK reference](https://python-sdk-docs.kili-technology.com) or the [Kili documentation](https://docs.kili-technology.com/docs)
        
Platform: UNKNOWN
Classifier: Intended Audience :: Science/Research
Classifier: Intended Audience :: Developers
Classifier: License :: OSI Approved :: Apache Software License
Classifier: Programming Language :: Python
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Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.5
Classifier: Programming Language :: Python :: 3.6
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 :: Implementation :: CPython
Classifier: Programming Language :: Python :: Implementation :: PyPy
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