API Reference

This is the API Reference documentation extracted from the source code.

User

class User(user_id: str = '', **kwargs)

User is a class that provides access to Clarifai API endpoints related to user information.

__init__(user_id: str = '', **kwargs)

Initializes an User object.

Parameters:

• user_id (str) – The user ID for the user to interact with.

• **kwargs – Additional keyword arguments to be passed to the ClarifaiAuthHelper.

app(app_id: str, **kwargs) → App

Returns an App object for the specified app ID.

Parameters:

• app_id (str) – The app ID for the app to interact with.

• **kwargs – Additional keyword arguments to be passed to the App.

Returns:

An App object for the specified app ID.

Return type:

App

Example

>>> from clarifai.client.user import User
>>> app = User("user_id").app("app_id")

create_app(app_id: str, base_workflow: str = 'Language-Understanding', **kwargs) → App

Creates an app for the user.

Parameters:

• app_id (str) – The app ID for the app to create.

• base_workflow (str) – The base workflow to use for the app.(Examples: ‘Universal’, ‘Empty’, ‘General’)

• **kwargs – Additional keyword arguments to be passed to the App.

Returns:

An App object for the specified app ID.

Return type:

App

Example

>>> from clarifai.client.user import User
>>> client = User(user_id="user_id")
>>> app = client.create_app(app_id="app_id",base_workflow="Universal")

delete_app(app_id: str) → None

Deletes an app for the user.

Parameters:

app_id (str) – The app ID for the app to delete.

Example

>>> from clarifai.client.user import User
>>> user = User("user_id").delete_app("app_id")

list_apps(filter_by: Dict[str, Any] = {}) → List[App]

Lists all the apps for the user.

Parameters:

filter_by (dict) – A dictionary of filters to be applied to the list of apps.

Returns:

A list of App objects for the user.

Return type:

list of App

Example

>>> from clarifai.client.user import User
>>> apps = User("user_id").list_apps()

App

class App(url_init: str = '', app_id: str = '', **kwargs)

App is a class that provides access to Clarifai API endpoints related to App information.

__init__(url_init: str = '', app_id: str = '', **kwargs)

Initializes an App object.

Parameters:

• url_init (str) – The URL to initialize the app object.

• app_id (str) – The App ID for the App to interact with.

• **kwargs – Additional keyword arguments to be passed to the ClarifaiAuthHelper. - name (str): The name of the app. - description (str): The description of the app.

create_dataset(dataset_id: str, **kwargs) → Dataset

Creates a dataset for the app.

Parameters:

• dataset_id (str) – The dataset ID for the dataset to create.

• **kwargs – Additional keyword arguments to be passed to the Dataset.

Returns:

A Dataset object for the specified dataset ID.

Return type:

Dataset

Example

>>> from clarifai.client.app import App
>>> app = App(app_id="app_id", user_id="user_id")
>>> dataset = app.create_dataset(dataset_id="dataset_id")

create_model(model_id: str, **kwargs) → Model

Creates a model for the app.

Parameters:

• model_id (str) – The model ID for the model to create.

• **kwargs – Additional keyword arguments to be passed to the Model.

Returns:

A Model object for the specified model ID.

Return type:

Model

Example

>>> from clarifai.client.app import App
>>> app = App(app_id="app_id", user_id="user_id")
>>> model = app.create_model(model_id="model_id")

create_module(module_id: str, description: str, **kwargs) → Module

Creates a module for the app.

Parameters:

• module_id (str) – The module ID for the module to create.

• description (str) – The description of the module to create.

• **kwargs – Additional keyword arguments to be passed to the module.

Returns:

A Module object for the specified module ID.

Return type:

Module

Example

>>> from clarifai.client.app import App
>>> app = App(app_id="app_id", user_id="user_id")
>>> module = app.create_module(module_id="module_id")

create_workflow(workflow_id: str, **kwargs) → Workflow

Creates a workflow for the app.

Parameters:

• workflow_id (str) – The workflow ID for the workflow to create.

• **kwargs – Additional keyword arguments to be passed to the workflow.

Returns:

A Workflow object for the specified workflow ID.

Return type:

Workflow

Example

>>> from clarifai.client.app import App
>>> app = App(app_id="app_id", user_id="user_id")
>>> workflow = app.create_workflow(workflow_id="workflow_id")

dataset(dataset_id: str, **kwargs) → Dataset

Returns a Dataset object for the existing dataset ID.

Parameters:

dataset_id (str) – The dataset ID for the dataset to interact with.

Returns:

A Dataset object for the existing dataset ID.

Return type:

Dataset

Example

>>> from clarifai.client.app import App
>>> app = App(app_id="app_id", user_id="user_id")
>>> dataset = app.dataset(dataset_id="dataset_id")

delete_dataset(dataset_id: str) → None

Deletes an dataset for the user.

Parameters:

dataset_id (str) – The dataset ID for the app to delete.

Example

>>> from clarifai.client.app import App
>>> app = App(app_id="app_id", user_id="user_id")
>>> app.delete_dataset(dataset_id="dataset_id")

delete_model(model_id: str) → None

Deletes an model for the user.

Parameters:

model_id (str) – The model ID for the app to delete.

Example

>>> from clarifai.client.app import App
>>> app = App(app_id="app_id", user_id="user_id")
>>> app.delete_model(model_id="model_id")

delete_module(module_id: str) → None

Deletes an module for the user.

Parameters:

module_id (str) – The module ID for the app to delete.

Example

>>> from clarifai.client.app import App
>>> app = App(app_id="app_id", user_id="user_id")
>>> app.delete_module(module_id="module_id")

delete_workflow(workflow_id: str) → None

Deletes an workflow for the user.

Parameters:

workflow_id (str) – The workflow ID for the app to delete.

Example

>>> from clarifai.client.app import App
>>> app = App(app_id="app_id", user_id="user_id")
>>> app.delete_workflow(workflow_id="workflow_id")

inputs()

Returns an Input object.

Returns:

An input object.

Return type:

Inputs

list_concepts()

Lists all the concepts for the app.

list_datasets() → List[Dataset]

Lists all the datasets for the app.

Returns:

A list of Dataset objects for the datasets in the app.

Return type:

List[Dataset]

Example

>>> from clarifai.client.app import App
>>> app = App(app_id="app_id", user_id="user_id")
>>> all_datasets = app.list_datasets()

list_installed_module_versions(filter_by: Dict[str, Any] = {}) → List[Module]

Lists all installed module versions in the app.

Parameters:

filter_by (dict) – A dictionary of filters to apply to the list of installed module versions.

Returns:

A list of Module objects for the installed module versions in the app.

Return type:

List[Module]

Example

>>> from clarifai.client.app import App
>>> app = App(app_id="app_id", user_id="user_id")
>>> all_installed_module_versions = app.list_installed_module_versions()

list_models(filter_by: Dict[str, Any] = {}, only_in_app: bool = True) → List[Model]

Lists all the available models for the user.

Parameters:

• filter_by (dict) – A dictionary of filters to apply to the list of models.

• only_in_app (bool) – If True, only return models that are in the app.

Returns:

A list of Model objects for the models in the app.

Return type:

List[Model]

Example

>>> from clarifai.client.user import User
>>> app = User(user_id="user_id").app(app_id="app_id")
>>> all_models = app.list_models()

list_modules(filter_by: Dict[str, Any] = {}, only_in_app: bool = True) → List[Module]

Lists all the available modules for the user.

Parameters:

• filter_by (dict) – A dictionary of filters to apply to the list of modules.

• only_in_app (bool) – If True, only return modules that are in the app.

Returns:

A list of Module objects for the modules in the app.

Return type:

List[Module]

Example

>>> from clarifai.client.app import App
>>> app = App(app_id="app_id", user_id="user_id")
>>> all_modules = app.list_modules()

list_workflows(filter_by: Dict[str, Any] = {}, only_in_app: bool = True) → List[Workflow]

Lists all the available workflows for the user.

Parameters:

• filter_by (dict) – A dictionary of filters to apply to the list of workflows.

• only_in_app (bool) – If True, only return workflows that are in the app.

Returns:

A list of Workflow objects for the workflows in the app.

Return type:

List[Workflow]

Example

>>> from clarifai.client.app import App
>>> app = App(app_id="app_id", user_id="user_id")
>>> all_workflows = app.list_workflows()

model(model_id: str, model_version_id: str = '', **kwargs) → Model

Returns a Model object for the existing model ID.

Parameters:

• model_id (str) – The model ID for the model to interact with.

• model_version_id (str) – The model version ID for the model version to interact with.

Returns:

A Model object for the existing model ID.

Return type:

Model

Example

>>> from clarifai.client.app import App
>>> app = App(app_id="app_id", user_id="user_id")
>>> model_v1 = app.model(model_id="model_id", model_version_id="model_version_id")

module(module_id: str, module_version_id: str = '', **kwargs) → Module

Returns a Module object for the existing module ID.

Parameters:

• module_id (str) – The module ID for the module to interact with.

• module_version_id (str) – The module version ID for the module version to interact with.

Returns:

A Module object for the existing module ID.

Return type:

Module

Example

>>> from clarifai.client.app import App
>>> app = App(app_id="app_id", user_id="user_id")
>>> module = app.module(module_id="module_id", module_version_id="module_version_id")

workflow(workflow_id: str, **kwargs) → Workflow

Returns a workflow object for the existing workflow ID.

Parameters:

workflow_id (str) – The workflow ID for the workflow to interact with.

Returns:

A Workflow object for the existing workflow ID.

Return type:

Workflow

Example

>>> from clarifai.client.app import App
>>> app = App(app_id="app_id", user_id="user_id")
>>> workflow = app.workflow(workflow_id="workflow_id")

Dataset

class Dataset(url_init: str = '', dataset_id: str = '', **kwargs)

Dataset is a class that provides access to Clarifai API endpoints related to Dataset information.

__init__(url_init: str = '', dataset_id: str = '', **kwargs)

Initializes a Dataset object.

Parameters:

• url_init (str) – The URL to initialize the dataset object.

• dataset_id (str) – The Dataset ID within the App to interact with.

• **kwargs – Additional keyword arguments to be passed to the ClarifaiAuthHelper.

upload_dataset(task: str, split: str, module_dir: str = None, dataset_loader: str = None, chunk_size: int = 128) → None

Uploads a dataset to the app.

Parameters:

• task (str) – task type(text_clf, visual-classification, visual_detection, visual_segmentation, visual-captioning)

• split (str) – split type(train, test, val)

• module_dir (str) – path to the module directory

• dataset_loader (str) – name of the dataset loader

• chunk_size (int) – chunk size for concurrent upload of inputs and annotations

upload_from_csv(csv_path: str, input_type: str = 'text', labels: bool = True, chunk_size: int = 128) → None

Uploads dataset from a csv file.

Parameters:

• csv_path (str) – path to the csv file

• input_type (str) – type of the dataset(text, image)

• labels (bool) – True if csv file has labels column

• chunk_size (int) – chunk size for concurrent upload of inputs and annotations

Example

>>> from clarifai.client.dataset import Dataset
>>> dataset = Dataset(user_id = 'user_id', app_id = 'demo_app', dataset_id = 'demo_dataset')
>>> dataset.upload_from_csv(csv_path='csv_path', labels=True)

Note: csv file should have either one(input) or two columns(input, labels).

upload_from_folder(folder_path: str, input_type: str, labels: bool = False, chunk_size: int = 128) → None

Upload dataset from folder.

Parameters:

• folder_path (str) – Path to the folder containing images.

• input_type (str) – type of the dataset(text, image)

• labels (bool) – True if folder name is the label for the inputs

• chunk_size (int) – chunk size for concurrent upload of inputs and annotations

Example

>>> from clarifai.client.dataset import Dataset
>>> dataset = Dataset(user_id = 'user_id', app_id = 'demo_app', dataset_id = 'demo_dataset')
>>> dataset.upload_from_folder(folder_path='folder_path', input_type='text', labels=True)

Note: The filename is used as the input_id.

Input

class Inputs(user_id: str = '', app_id: str = '', logger_level: str = 'INFO', **kwargs)

Inputs is a class that provides access to Clarifai API endpoints related to Input information.

__init__(user_id: str = '', app_id: str = '', logger_level: str = 'INFO', **kwargs)

Initializes an Input object.

Parameters:

• user_id (str) – A user ID for authentication.

• app_id (str) – An app ID for the application to interact with.

• **kwargs – Additional keyword arguments to be passed to the Input

delete_inputs(inputs: List[Input]) → None

Delete list of input objects from the app.

Parameters:

input_ids (Input) – List of input objects to delete.

Example

>>> from clarifai.client.user import User
>>> input_obj = User(user_id="user_id").app(app_id="app_id").inputs()
>>> input_obj.delete_inputs(input_obj.list_inputs())

get_annotation_proto(input_id: str, label: str, annotations: List) → Annotation

Create an annotation proto for each bounding box, label input pair.

Parameters:

• input_id (str) – The input ID for the annotation to create.

• label (str) – annotation label

• annotations (List) – a list of a single bbox’s coordinates. # Annotations ordering: [xmin, ymin, xmax, ymax]

Returns:

An annotation object for the specified input ID.

Example

>>> from clarifai.client.input import Input
>>> input_obj = Input()
>>> input_obj.get_annotation_proto(input_id='demo', label='demo', annotations=[x_min, y_min, x_max, y_max])

get_image_inputs_from_folder(folder_path: str, dataset_id: str = None, labels: bool = False) → List[Input]

Create input protos for image data type from folder.

Parameters:

folder_path (str) – Path to the folder containing images.

Returns:

A list of Input objects for the specified folder.

Return type:

list of Input

Example

>>> from clarifai.client.input import Input
>>> input_obj = Input()
>>> input_protos = input_obj.get_image_inputs_from_folder(folder_path='demo_folder')

get_input_from_bytes(input_id: str, image_bytes: bytes = None, video_bytes: bytes = None, audio_bytes: bytes = None, dataset_id: str = None, **kwargs) → Input

Create input proto from bytes.

Parameters:

• input_id (str) – The input ID for the input to create.

• image_bytes (str) – The bytes for the image.

• video_bytes (str) – The bytes for the video.

• audio_bytes (str) – The bytes for the audio.

• dataset_id (str) – The dataset ID for the dataset to add the input to.

Returns:

An Input object for the specified input ID.

Return type:

Input

Example

>>> from clarifai.client.input import Input
>>> input_obj = Input()
>>> image = open('demo.jpg', 'rb').read()
>>> video = open('demo.mp4', 'rb').read()
>>> input_proto = input_obj.get_input_from_bytes(input_id = 'demo',image_bytes =image, video_bytes=video)

get_input_from_file(input_id: str, image_file: str = None, video_file: str = None, audio_file: str = None, dataset_id: str = None, **kwargs) → Input

Create input proto from files.

Parameters:

• input_id (str) – The input ID for the input to create.

• image_file (str) – The url for the image.

• video_file (str) – The url for the video.

• audio_file (str) – The url for the audio.

• dataset_id (str) – The dataset ID for the dataset to add the input to.

Returns:

An Input object for the specified input ID.

Return type:

Input

Example

>>> from clarifai.client.input import Input
>>> input_obj = Input()
>>> input_proto = input_obj.get_input_from_file(input_id = 'demo', video_file='file_path')

get_input_from_url(input_id: str, image_url: str = None, video_url: str = None, audio_url: str = None, text_url: str = None, dataset_id: str = None, **kwargs) → Input

Create input proto from url.

Parameters:

• input_id (str) – The input ID for the input to create.

• image_url (str) – The url for the image.

• video_url (str) – The url for the video.

• audio_url (str) – The url for the audio.

• text_url (str) – The url for the text. dataset_id (str): The dataset ID for the dataset to add the input to.

Returns:

An Input object for the specified input ID.

Return type:

Input

Example

>>> from clarifai.client.input import Input
>>> input_obj = Input()
>>> input_proto = input_obj.get_input_from_url(input_id = 'demo', image_url='https://samples.clarifai.com/metro-north.jpg')

get_mask_proto(input_id: str, label: str, polygons: List[List[float]]) → Annotation

Create an annotation proto for each polygon box, label input pair.

Parameters:

• input_id (str) – The input ID for the annotation to create.

• label (str) – annotation label

• polygons (List) – Polygon x,y points iterable

Returns:

An annotation object for the specified input ID.

Example

>>> from clarifai.client.input import Input
>>> input_obj = Input()
>>> input_obj.get_mask_proto(input_id='demo', label='demo', polygons=[[[x,y],...,[x,y]],...])

get_text_input(input_id: str, raw_text: str, dataset_id: str = None, **kwargs) → Text

Create input proto for text data type from rawtext.

Parameters:

• input_id (str) – The input ID for the input to create.

• raw_text (str) – The raw text input.

• dataset_id (str) – The dataset ID for the dataset to add the input to.

• **kwargs – Additional keyword arguments to be passed to the Input

Returns:

An Input object for the specified input ID.

Return type:

Text

Example

>>> from clarifai.client.input import Input
>>> input_obj = Input()
>>> input_protos = input_obj.get_text_input(input_id = 'demo', raw_text = 'This is a test')

get_text_input_from_csv(csv_path: str, dataset_id: str = None, labels: str = True) → List[Text]

Create input proto for text data type from cscv.

Parameters:

• csv_path (str) – Path to the csv file.

• dataset_id (str) – The dataset ID for the dataset to add the input to.

• labels (str) – True if csv file has labels column.

Returns:

List of inputs

Return type:

inputs

Example

>>> from clarifai.client.input import Input
>>> input_obj = Input()
>>> input_protos = input_obj.get_text_input_from_csv(csv_path = 'filepath')

get_text_inputs_from_folder(folder_path: str, dataset_id: str = None, labels: bool = False) → List[Text]

Create input protos for text data type from folder.

Parameters:

folder_path (str) – Path to the folder containing text.

Returns:

A list of Input objects for the specified folder.

Return type:

list of Input

Example

>>> from clarifai.client.input import Input
>>> input_obj = Input()
>>> input_protos = input_obj.get_text_inputs_from_folder(folder_path='demo_folder')

list_inputs() → List[Input]

Lists all the inputs for the app.

Returns:

A list of Input objects for the app.

Return type:

list of Input

Example

>>> from clarifai.client.user import User
>>> input_obj = User(user_id="user_id").app(app_id="app_id").inputs()
>>> input_obj.list_inputs()

upload_annotations(batch_annot: List[Annotation], show_log: bool = True) → List[Annotation] | List[None]

Upload image annotations to app.

Parameters:

batch_annot – annot batch protos

Returns:

failed annot upload

Return type:

retry_upload

upload_from_bytes(input_id: str, image_bytes: bytes = None, video_bytes: bytes = None, audio_bytes: bytes = None, dataset_id: str = None, **kwargs) → str

Upload input from bytes.

Parameters:

• input_id (str) – The input ID for the input to create.

• image_bytes (str) – The bytes for the image.

• video_bytes (str) – The bytes for the video.

• audio_bytes (str) – The bytes for the audio.

• dataset_id (str) – The dataset ID for the dataset to add the input to.

Returns:

job id for the upload request.

Return type:

input_job_id

Example

>>> from clarifai.client.input import Input
>>> input_obj = Input(user_id = 'user_id', app_id = 'demo_app')
>>> image = open('demo.jpg', 'rb').read()
>>> input_obj.upload_from_bytes(input_id='demo', image_bytes=image)

upload_from_file(input_id: str, image_file: str = None, video_file: str = None, audio_file: str = None, dataset_id: str = None, **kwargs) → str

Upload input from file.

Parameters:

• input_id (str) – The input ID for the input to create.

• image_file (str) – The file for the image.

• video_file (str) – The file for the video.

• audio_file (str) – The file for the audio.

• dataset_id (str) – The dataset ID for the dataset to add the input to.

Returns:

job id for the upload request.

Return type:

input_job_id

Example

>>> from clarifai.client.input import Input
>>> input_obj = Input(user_id = 'user_id', app_id = 'demo_app')
>>> input_obj.upload_from_file(input_id='demo', audio_file='demo.mp3')

upload_from_url(input_id: str, image_url: str = None, video_url: str = None, audio_url: str = None, text_url: str = None, dataset_id: str = None, **kwargs) → str

Upload input from url.

Parameters:

• input_id (str) – The input ID for the input to create.

• image_url (str) – The url for the image.

• video_url (str) – The url for the video.

• audio_url (str) – The url for the audio.

• text_url (str) – The url for the text.

• dataset_id (str) – The dataset ID for the dataset to add the input to.

Returns:

job id for the upload request.

Return type:

input_job_id

Example

>>> from clarifai.client.input import Input
>>> input_obj = Input(user_id = 'user_id', app_id = 'demo_app')
>>> input_obj.upload_from_url(input_id='demo', image_url='https://samples.clarifai.com/metro-north.jpg')

upload_inputs(inputs: List[Input], show_log: bool = True) → str

Upload list of input objects to the app.

Parameters:

• inputs (list) – List of input objects to upload.

• show_log (bool) – Show upload status log.

Returns:

job id for the upload request.

Return type:

input_job_id

upload_text(input_id: str, raw_text: str, dataset_id: str = None, **kwargs) → str

Upload text from raw text.

Parameters:

• input_id (str) – The input ID for the input to create.

• raw_text (str) – The raw text.

• dataset_id (str) – The dataset ID for the dataset to add the input to.

Returns:

job id for the upload request.

Return type:

input_job_id (str)

Example

>>> from clarifai.client.input import Input
>>> input_obj = Input(user_id = 'user_id', app_id = 'demo_app')
>>> input_obj.upload_text(input_id = 'demo', raw_text = 'This is a test')

Lister

class Lister(page_size: int = 16)

Lister class for obtaining paginated results from the Clarifai API.

__init__(page_size: int = 16)

list_all_pages_generator(endpoint: Callable, proto_message: Any, request_data: Dict[str, Any]) → Generator[Dict[str, Any], None, None]

Lists all pages of a resource.

Parameters:

• endpoint (Callable) – The endpoint to call.

• proto_message (Any) – The proto message to use.

• request_data (dict) – The request data to use.

Yields:

response_dict – The next item in the listing.

Model

class Model(url_init: str = '', model_id: str = '', model_version: Dict = {'id': ''}, output_config: Dict = {'min_value': 0}, **kwargs)

Model is a class that provides access to Clarifai API endpoints related to Model information.

__init__(url_init: str = '', model_id: str = '', model_version: Dict = {'id': ''}, output_config: Dict = {'min_value': 0}, **kwargs)

Initializes a Model object.

Parameters:

• url_init (str) – The URL to initialize the model object.

• model_id (str) – The Model ID to interact with.

• model_version (dict) – The Model Version to interact with.

• output_config (dict) – The output config to interact with. min_value (float): The minimum value of the prediction confidence to filter. max_concepts (int): The maximum number of concepts to return. select_concepts (list[Concept]): The concepts to select. sample_ms (int): The number of milliseconds to sample.

• **kwargs – Additional keyword arguments to be passed to the ClarifaiAuthHelper.

list_versions() → List[Model]

Lists all the versions for the model.

Returns:

A list of Model objects for the versions of the model.

Return type:

List[Model]

Example

>>> from clarifai.client.model import Model
>>> model = Model("model_url") # Example URL: https://clarifai.com/clarifai/main/models/general-image-recognition
            or
>>> model = Model(model_id='model_id', user_id='user_id', app_id='app_id')
>>> all_model_versions = model.list_versions()

predict(inputs: List[Input])

Predicts the model based on the given inputs.

Parameters:

inputs (list[Input]) – The inputs to predict, must be less than 128.

predict_by_bytes(input_bytes: bytes, input_type: str)

Predicts the model based on the given bytes.

Parameters:

• input_bytes (bytes) – File Bytes to predict on.

• input_type (str) – The type of input. Can be ‘image’, ‘text’, ‘video’ or ‘audio’.

Example

>>> from clarifai.client.model import Model
>>> model = Model("https://clarifai.com/anthropic/completion/models/claude-v2")
>>> model_prediction = model.predict_by_bytes(b'Write a tweet on future of AI', 'text')

predict_by_filepath(filepath: str, input_type: str)

Predicts the model based on the given filepath.

Parameters:

• filepath (str) – The filepath to predict.

• input_type (str) – The type of input. Can be ‘image’, ‘text’, ‘video’ or ‘audio.

Example

>>> from clarifai.client.model import Model
>>> model = Model("model_url") # Example URL: https://clarifai.com/clarifai/main/models/general-image-recognition
            or
>>> model = Model(model_id='model_id', user_id='user_id', app_id='app_id')
>>> model_prediction = model.predict_by_filepath('/path/to/image.jpg', 'image')
>>> model_prediction = model.predict_by_filepath('/path/to/text.txt', 'text')

predict_by_url(url: str, input_type: str)

Predicts the model based on the given URL.

Parameters:

• url (str) – The URL to predict.

• input_type (str) – The type of input. Can be ‘image’, ‘text’, ‘video’ or ‘audio.

Example

>>> from clarifai.client.model import Model
>>> model = Model("model_url") # Example URL: https://clarifai.com/clarifai/main/models/general-image-recognition
            or
>>> model = Model(model_id='model_id', user_id='user_id', app_id='app_id')
>>> model_prediction = model.predict_by_url('url', 'image')

Workflow

class Workflow(url_init: str = '', workflow_id: str = '', workflow_version: Dict = {'id': ''}, output_config: Dict = {'min_value': 0}, **kwargs)

Workflow is a class that provides access to Clarifai API endpoints related to Workflow information.

__init__(url_init: str = '', workflow_id: str = '', workflow_version: Dict = {'id': ''}, output_config: Dict = {'min_value': 0}, **kwargs)

Initializes a Workflow object.

Parameters:

• url_init (str) – The URL to initialize the workflow object.

• workflow_id (str) – The Workflow ID to interact with.

• workflow_version (dict) – The Workflow Version to interact with.

• output_config (dict) – The output config to interact with. min_value (float): The minimum value of the prediction confidence to filter. max_concepts (int): The maximum number of concepts to return. select_concepts (list[Concept]): The concepts to select. sample_ms (int): The number of milliseconds to sample.

• **kwargs – Additional keyword arguments to be passed to the ClarifaiAuthHelper.

list_versions() → List[Workflow]

Lists all the versions of the workflow.

Returns:

A list of Workflow objects.

Return type:

list[Workflow]

Example

>>> from clarifai.client.workflow import Workflow
>>> workflow = Workflow(user_id='user_id', app_id='app_id', workflow_id='workflow_id')
>>> workflow_versions = workflow.list_versions()

predict(inputs: List[Input])

Predicts the workflow based on the given inputs.

Parameters:

inputs (list[Input]) – The inputs to predict.

predict_by_bytes(input_bytes: bytes, input_type: str)

Predicts the workflow based on the given bytes.

Parameters:

• input_bytes (bytes) – Bytes to predict on.

• input_type (str) – The type of input. Can be ‘image’, ‘text’, ‘video’ or ‘audio.

predict_by_filepath(filepath: str, input_type: str)

Predicts the workflow based on the given filepath.

Parameters:

• filepath (str) – The filepath to predict.

• input_type (str) – The type of input. Can be ‘image’, ‘text’, ‘video’ or ‘audio.

Example

>>> from clarifai.client.workflow import Workflow
>>> workflow = Workflow("workflow_url") # Example: https://clarifai.com/clarifai/main/workflows/Face-Sentiment
              or
>>> workflow = Workflow(user_id='user_id', app_id='app_id', workflow_id='workflow_id')
>>> workflow_prediction = workflow.predict_by_filepath('filepath', 'image')

predict_by_url(url: str, input_type: str)

Predicts the workflow based on the given URL.

Parameters:

• url (str) – The URL to predict.

• input_type (str) – The type of input. Can be ‘image’, ‘text’, ‘video’ or ‘audio.

Example

>>> from clarifai.client.workflow import Workflow
>>> workflow = Workflow("workflow_url") # Example: https://clarifai.com/clarifai/main/workflows/Face-Sentiment
              or
>>> workflow = Workflow(user_id='user_id', app_id='app_id', workflow_id='workflow_id')
>>> workflow_prediction = workflow.predict_by_url('url', 'image')

Module

class Module(url_init: str = '', module_id: str = '', module_version: Dict = {'id': ''}, **kwargs)

Module is a class that provides access to Clarifai API endpoints related to Module information.

__init__(url_init: str = '', module_id: str = '', module_version: Dict = {'id': ''}, **kwargs)

Initializes a Module object.

Parameters:

• url_init (str) – The URL to initialize the module object.

• module_id (str) – The Module ID to interact with.

• module_version (dict) – The Module Version to interact with.

• **kwargs – Additional keyword arguments to be passed to the ClarifaiAuthHelper.

list_versions() → List[Module]

Lists all the module versions for the module.

Returns:

A list of Module objects for versions of the module.

Return type:

List[Moudle]

Example

>>> from clarifai.client.module import Module
>>> module = Module(module_id='module_id', user_id='user_id', app_id='app_id')
>>> all_Module_versions = module.list_versions()

Utils

class Chunker(seq: List, size: int)

Split an input sequence into small chunks.

__init__(seq: List, size: int) → None

chunk() → List[List]

Chunk input sequence.

Exceptions

class ApiError(resource: str, params: dict, method: str, response: Response = None)

API Server error

class ApiClientError

API Client Error

class UserError

User Error