API Reference

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

Application

class clarifai.rest.client.ClarifaiApp(app_id=None, app_secret=None, base_url=None, api_key=None, quiet=True, log_level=None)

Clarifai Application Object

This is the entry point of the Clarifai Client API. With authentication to an application, you can access all the models, concepts, and inputs in this application through the attributes of this class.
To access the models: use app.models
To access the inputs: use app.inputs
To access the concepts: use app.concepts

wait_until_inputs_delete_finish()

Block until a current inputs deletion operation finishes

The criteria for unblocking is 0 inputs returned from GET /inputs

Returns:None
wait_until_inputs_upload_finish(max_wait=666666)

Block until the inputs upload finishes

The criteria for unblocking is 0 “to_process” inputs from GET /inputs/status

Returns:None
wait_until_models_delete_finish()

Block until the inputs deletion finishes

The criteria for unblocking is 0 models returned from GET /models

Returns:None

Concepts

class clarifai.rest.client.Concepts(api)
bulk_create(concept_ids, concept_names=None)

Bulk create concepts

When the concept name is not set, it will be set as the same as concept ID.

Parameters:
  • concept_ids – a list of concept IDs, in sequence
  • concept_names – a list of corresponding concept names, in the same sequence
Returns:

A list of Concept objects

Examples

>>> app.concepts.bulk_create(['id1', 'id2'], ['cute cat', 'cute dog'])
bulk_update(concept_ids, concept_names, action='overwrite')

Patch multiple concepts

Parameters:
  • concept_ids – a list of concept IDs, in sequence
  • concept_names – a list of corresponding concept names, in the same sequence
  • action – the type of update
Returns:

the new Concept object

Examples

>>> app.concepts.bulk_update(concept_ids=['myid1', 'myid2'],
>>>                          concept_names=['name2', 'name3'])
create(concept_id, concept_name=None)

Create a new concept

Parameters:
  • concept_id – concept ID, the unique identifier of the concept
  • concept_name – name of the concept. If name is not specified, it will be set to the same as the concept ID
Returns:

the new Concept object

get(concept_id)

Get a concept by id

Parameters:concept_id – concept ID, the unique identifier of the concept
Returns:If found, return the Concept object. Otherwise, return None

Examples

>>> app.concepts.get('id')
get_all()

Get all concepts associated with the application

Returns:all concepts in a generator function
get_by_page(page=1, per_page=20)

get concept with pagination

Parameters:
  • page – page number
  • per_page – number of concepts to retrieve per page
Returns:

a list of Concept objects

Examples

>>> for concept in app.concepts.get_by_page(2, 10):
>>>     print(concept.concept_id)
search(term, lang=None)

search concepts by concept name with wildcards

Parameters:
  • term – search term with wildcards allowed
  • lang – language to search, if none is specified the default for the application will be used
Returns:

a generator function with all concepts pertaining to the search term

Examples

>>> app.concepts.search('cat')
>>> # search for Chinese label name
>>> app.concepts.search(u'狗*', lang='zh')
update(concept_id, concept_name, action='overwrite')

Patch concept

Parameters:
  • concept_id – id of the concept
  • concept_name – the new name for the concept
Returns:

the new Concept object

Examples

>>> app.concepts.update(concept_id='myid1', concept_name='new_concept_name2')

Inputs

class clarifai.rest.client.Inputs(api)
add_concepts(input_id, concepts, not_concepts)

Add concepts for one input

This is just an alias of merge_concepts for easier understanding when you try to add some new concepts to an image

Parameters:
  • input_id – the unique ID of the input
  • concepts – the list of concepts
  • not_concepts – the list of negative concepts
Returns:

an Input object

Examples

>>> app.inputs.add_concepts('id', ['cat', 'kitty'], ['dog'])
bulk_create_images(images)

Create images in bulk

Parameters:images – a list of Image objects
Returns:a list of the Image objects that were just created

Examples

>>> img1 = Image(url="", concepts=['cat', 'kitty'])
>>> img2 = Image(url="", concepts=['dog'], not_concepts=['cat'])
>>> app.inputs.bulk_create_images([img1, img2])
bulk_delete_concepts(input_ids, concept_lists)

bulk delete concepts from a list of input ids

Parameters:
  • input_ids – a list of input IDs
  • concept_lists – a list of concept lists, each one corresponding to a listed input ID and
  • with concepts to be deleted from that input (filled) –
Returns:

an Input object

Examples

>>> app.inputs.bulk_delete_concepts(['id'], [['cat', 'dog']])
bulk_merge_concepts(input_ids, concept_lists)

bulk merge concepts from a list of input ids

Parameters:
  • input_ids – a list of input IDs
  • concept_lists – a list of concept lists, each one corresponding to a listed input ID and
  • with concepts to be added to that input (filled) –
Returns:

an Input object

Examples

>>> app.inputs.bulk_merge_concepts('id', [[('cat',True), ('dog',False)]])
bulk_update(images, action='merge')

Update the input update the information of an input/image

Parameters:
  • images – a list of Image objects that have concepts, metadata, etc.
  • action

    one of [‘merge’, ‘overwrite’]

    ’merge’ is to append the info onto the exising info, for either concept or metadata

    ’overwrite’ is to overwrite the existing metadata and concepts with the existing ones

Returns:

an Image object

Examples

>>> new_img1 = Image(image_id="abc1", concepts=['c1', 'c2'], not_concepts=['c3'],
>>>                  metadata={'key':'val'})
>>> new_img2 = Image(image_id="abc2", concepts=['c1', 'c2'], not_concepts=['c3'],
>>>                  metadata={'key':'val'})
>>> app.inputs.update([new_img1, new_img2], action='overwrite')
check_status()

check the input upload status

Returns:InputCounts object

Examples

>>> status = app.inputs.check_status()
>>> print(status.code)
>>> print(status.description)
create_image(image)

create an image from Image object

Parameters:image – a Clarifai Image object
Returns:the Image object that just got created and uploaded

Examples

>>> app.inputs.create_image(Image(url='https://samples.clarifai.com/metro-north.jpg'))
create_image_from_base64(base64_bytes, image_id=None, concepts=None, not_concepts=None, crop=None, metadata=None, geo=None, allow_duplicate_url=False)

create an image by base64 bytes

Parameters:
  • base64_bytes – base64 encoded image bytes
  • image_id – ID of the image
  • concepts – a list of concept names this image is associated with
  • not_concepts – a list of concept names this image is not associated with
  • crop – crop information, with four corner coordinates
  • metadata – meta data with a dictionary
  • geo – geo info with a dictionary
  • allow_duplicate_url – True or False, the flag to allow a duplicate url to be imported
Returns:

the Image object that just got created and uploaded

Examples

>>> app.inputs.create_image_bytes(base64_bytes="base64 encoded image bytes...")
create_image_from_bytes(img_bytes, image_id=None, concepts=None, not_concepts=None, crop=None, metadata=None, geo=None, allow_duplicate_url=False)

create an image by image bytes

Parameters:
  • img_bytes – raw bytes of an image
  • image_id – ID of the image
  • concepts – a list of concept names this image is associated with
  • not_concepts – a list of concept names this image is not associated with
  • crop – crop information, with four corner coordinates
  • metadata – meta data with a dictionary
  • geo – geo info with a dictionary
  • allow_duplicate_url – True or False, the flag to allow a duplicate url to be imported
Returns:

the Image object that just got created and uploaded

Examples

>>> app.inputs.create_image_bytes(img_bytes="raw image bytes...")
create_image_from_filename(filename, image_id=None, concepts=None, not_concepts=None, crop=None, metadata=None, geo=None, allow_duplicate_url=False)

create an image by local filename

Parameters:
  • filename – local filename
  • image_id – ID of the image
  • concepts – a list of concept names this image is associated with
  • not_concepts – a list of concept names this image is not associated with
  • crop – crop information, with four corner coordinates
  • metadata – meta data with a dictionary
  • geo – geo info with a dictionary
  • allow_duplicate_url – True or False, the flag to allow a duplicate url to be imported
Returns:

the Image object that just got created and uploaded

Examples

>>> app.inputs.create_image_filename(filename="a.jpeg")
create_image_from_url(url, image_id=None, concepts=None, not_concepts=None, crop=None, metadata=None, geo=None, allow_duplicate_url=False)

create an image from Image url

Parameters:
  • url – image url
  • image_id – ID of the image
  • concepts – a list of concept names this image is associated with
  • not_concepts – a list of concept names this image is not associated with
  • crop – crop information, with four corner coordinates
  • metadata – meta data with a dictionary
  • geo – geo info with a dictionary
  • allow_duplicate_url – True or False, the flag to allow a duplicate url to be imported
Returns:

the Image object that just got created and uploaded

Examples

>>> app.inputs.create_image_from_url(url='https://samples.clarifai.com/metro-north.jpg')
>>>
>>> # create image with geo point
>>> app.inputs.create_image_from_url(url='https://samples.clarifai.com/metro-north.jpg',
>>>                                  geo=Geo(geo_point=GeoPoint(22.22, 44.44))
delete(input_id)

delete an input with input ID

Parameters:input_id – the unique input ID
Returns:ApiStatus object

Examples

>>> ret = app.inputs.delete('id1')
>>> print(ret.code)
delete_all()

delete all inputs from the application

delete_concepts(input_id, concepts)

delete concepts from an input/image

Parameters:
  • input_id – unique ID of the input
  • concepts – a list of concept names
Returns:

an Image object

get(input_id)

get an Input object by input ID

Parameters:input_id – the unique identifier of the input
Returns:an Image/Input object

Examples

>>> image = app.inputs.get('id1')
>>> print(image.input_id)
get_all(ignore_error=False)

Get all inputs

Parameters:ignore_error – ignore errored inputs. For example some images may fail to be imported due to bad url
Returns:a generator function that yields Input objects

Examples

>>> for image in app.inputs.get_all():
>>>     print(image.input_id)
get_by_page(page=1, per_page=20, ignore_error=False)

Get inputs with pagination

Parameters:
  • page – page number
  • per_page – number of inputs to retrieve per page
  • ignore_error – ignore errored inputs. For example some images may fail to be imported due to bad url
Returns:

a list of Input objects

Examples

>>> for image in app.inputs.get_by_page(2, 10):
>>>     print(image.input_id)
merge_concepts(input_id, concepts, not_concepts, overwrite=False)

Merge concepts for one input

Parameters:
  • input_id – the unique ID of the input
  • concepts – the list of concepts
  • not_concepts – the list of negative concepts
  • overwrite – if True, this operation will replace the previous concepts. If False,
  • will append them. (it) –
Returns:

an Input object

Examples

>>> app.inputs.merge_concepts('id', ['cat', 'kitty'], ['dog'])
merge_metadata(input_id, metadata)

merge metadata for the image

This is to merge/update the metadata of the given image

Parameters:
  • input_id – the unique ID of the input
  • metadata – the metadata dictionary

Examples

>>> # merge the metadata
>>> # metadata will be appended to the existing key/value pairs
>>> app.inputs.merge_metadata('id', {'key1':'value1', 'key2':'value2'})
search(qb, page=1, per_page=20, raw=False)

search with a clarifai image query builder

WARNING: this is the advanced search function. You will need to build a query builder in order to use this.
There are a few simple search functions:
search_by_annotated_concepts() search_by_predicted_concepts() search_by_image() search_by_metadata()
Parameters:
  • qb – clarifai query builder
  • raw – raw result indicator
Returns:

a list of Input/Image object

search_by_annotated_concepts(concept=None, concepts=None, value=True, values=None, concept_id=None, concept_ids=None, page=1, per_page=20, raw=False)

search using the concepts the user has manually specified

Parameters:
  • concept – concept name to search
  • concepts – a list of concept name to search
  • concept_id – concept IDs to search
  • concept_ids – a list of concept IDs to search
  • value – whether the concept should be a positive tag or negative
  • values – the list of values corresponding to the concepts
  • page – page number
  • per_page – number of images to return per page
  • raw – raw result indicator
Returns:

a list of Image objects

Examples

>>> app.inputs.search_by_annotated_concepts(concept='cat')
search_by_geo(geo_point=None, geo_limit=None, geo_box=None, page=1, per_page=20, raw=False)

search by geo point and geo limit

Parameters:
  • geo_point – A GeoPoint object, which represents the (longitude, latitude) of a location
  • geo_limit – A GeoLimit object, which represents a range to a GeoPoint
  • geo_box – A GeoBox object, which represents a box area
  • page – page number
  • per_page – number of images to return per page
  • raw – raw result indicator
Returns:

a list of Image objects

Examples

>>> app.inputs.search_by_geo(GeoPoint(30, 40), GeoLimit("mile", 10))
search_by_image(image_id=None, image=None, url=None, imgbytes=None, base64bytes=None, fileobj=None, filename=None, crop=None, page=1, per_page=20, raw=False)

Search for visually similar images

By passing image_id, raw image bytes, base64 encoded bytes, image file io stream, image filename, or Clarifai Image object, you can use the visual search power of the Clarifai API.

You can specify a crop of the image to search over

Parameters:
  • image_id – unique ID of the image for search
  • image – Image object for search
  • imgbytes – raw image bytes for search
  • base64bytes – base63 encoded image bytes
  • fileobj – file io stream, like open(file)
  • filename – filename on local filesystem
  • crop – crop of the image as a list of four floats representing the corner coordinates
  • page – page number
  • per_page – number of images returned per page
  • raw – raw result indicator
Returns:

a list of Image object

Examples

>>> # search by image url
>>> app.inputs.search_by_image(url='http://blabla')
>>> # search by local filename
>>> app.inputs.search_by_image(filename='bla')
>>> # search by raw image bytes
>>> app.inputs.search_by_image(imgbytes='data')
>>> # search by base64 encoded image bytes
>>> app.inputs.search_by_image(base64bytes='data')
>>> # search by file stream io
>>> app.inputs.search_by_image(fileobj=open('file'))
search_by_metadata(metadata, page=1, per_page=20, raw=False)

search by meta data of the image rather than concept

Parameters:
  • metadata – a dictionary for meta data search. The dictionary could be a simple one with only one key and value, Or a nested dictionary with multi levels.
  • page – page number
  • per_page – the number of images to return per page
  • raw – raw result indicator
Returns:

a list of Image objects

Examples

>>> app.inputs.search_by_metadata(metadata={'name':'bla'})
>>> app.inputs.search_by_metadata(metadata={'my_class1': { 'name' : 'bla' }})
search_by_original_url(url, page=1, per_page=20, raw=False)

search by the original url of the uploaded images

Parameters:
  • url – url of the image
  • page – page number
  • per_page – the number of images to return per page
  • raw – raw result indicator
Returns:

a list of Image objects

Examples

>>> app.inputs.search_by_original_url(url='http://bla')
search_by_predicted_concepts(concept=None, concepts=None, value=True, values=None, concept_id=None, concept_ids=None, page=1, per_page=20, lang=None, raw=False)

search over the predicted concepts

Parameters:
  • concept – concept name to search
  • concepts – a list of concept names to search
  • concept_id – concept id to search
  • concept_ids – a list of concept ids to search
  • value – whether the concept should be a positive tag or negative
  • values – the list of values corresponding to the concepts
  • page – page number
  • per_page – number of images to return per page
  • lang – language to search over for translated concepts
  • raw – raw result indicator
Returns:

a list of Image objects

Examples

>>> app.inputs.search_by_predicted_concepts(concept='cat')
>>> # search over simplified Chinese label
>>> app.inputs.search_by_predicted_concepts(concept=u'狗', lang='zh')
send_search_feedback(input_id, feedback_info=None)

Send feedback for search

Parameters:input_id – unique identifier for the input
Returns:None
update(image, action='merge')

Update the information of an input/image

Parameters:
  • image – an Image object that has concepts, metadata, etc.
  • action

    one of [‘merge’, ‘overwrite’]

    ’merge’ is to append the info onto the existing info, for either concept or metadata

    ’overwrite’ is to overwrite the existing metadata and concepts with the existing ones

Returns:

an Image object

Examples

>>> new_img = Image(image_id="abc", concepts=['c1', 'c2'], not_concepts=['c3'],
>>>                 metadata={'key':'val'})
>>> app.inputs.update(new_img, action='overwrite')

Models

class clarifai.rest.client.Models(api, solutions)
bulk_delete(model_ids)

Delete multiple models.

Parameters:model_ids – a list of unique IDs of the models to delete
Returns:the raw JSON response from the server

Examples

>>> app.models.delete_models(['model_id1', 'model_id2'])
clear_model_cache()

clear model_name -> model_id cache

WARNING: This is an internal function, user should not call this

We cache model_name to model_id mapping for API efficiency. The first time you call a models.get() by name, the name to ID mapping is saved so next time there is no query. Then user does not have to query the model ID every time when they want to work on it.

create(model_id, model_name=None, concepts=None, concepts_mutually_exclusive=False, closed_environment=False, hyper_parameters=None)

Create a new model

Parameters:
  • model_id – ID of the model
  • model_name – optional name of the model
  • concepts – optional concepts to be associated with this model
  • concepts_mutually_exclusive – True or False, whether concepts are mutually exclusive
  • closed_environment – True or False, whether to use negatives for prediction
  • hyper_parameters – hyper parameters for the model, with a json object
Returns:

Model object

Examples

>>> # create a model with no concepts
>>> app.models.create('my_model1')
>>> # create a model with a few concepts
>>> app.models.create('my_model2', concepts=['bird', 'fish'])
>>> # create a model with closed environment
>>> app.models.create('my_model3', closed_environment=True)
delete(model_id, version_id=None)

delete the model, or a specific version of the model

Without model version id specified, all the versions associated with this model will be deleted as well.

With model version id specified, it will delete a particular model version from the model

Parameters:
  • model_id – the unique ID of the model
  • version_id – the unique ID of the model version
Returns:

the raw JSON response from the server

Examples

>>> # delete a model
>>> app.models.delete('model_id1')
>>> # delete a model version
>>> app.models.delete('model_id1', version_id='version1')
delete_all()

Delete all models and the versions associated with each one

After this operation, you will have no models in the application

Returns:the raw JSON response from the server

Examples

>>> app.models.delete_all()
get(model_name=None, model_id=None, model_type=None)

Get a model, by ID or name

Parameters:
  • model_name – name of the model
  • model_id – unique identifier of the model
  • model_type – type of the model
Returns:

the Model object

Examples

>>> # get general-v1.3 model
>>> app.models.get('general-v1.3')
get_all(public_only=False, private_only=False)

Get all models in the application

Parameters:
  • public_only – only yield public models
  • private_only – only yield private models that tie to your own account
Returns:

a generator function that yields Model objects

Examples

>>> for model in app.models.get_all():
>>>     print(model.model_name)
get_by_page(public_only=False, private_only=False, page=1, per_page=20)

get paginated models from the application

When the number of models gets high, you may want to get the paginated results from all the models

Parameters:
  • public_only – only yield public models
  • private_only – only yield private models that tie to your own account
  • page – page number
  • per_page – number of models returned in one page
Returns:

a list of Model objects

Examples

>>> models = app.models.get_by_page(2, 20)
init_model_cache()

Initialize the model cache for the public models

This will go through all public models and cache them

Returns:JSON object containing the name, type, and id of all cached models
search(model_name, model_type=None)

Search the model by name and optionally type. Default is to search concept models only. All the custom model trained are concept models.

Parameters:
  • model_name – name of the model. name is not unique.
  • model_type – default to None, equivalent to wildcards search
Returns:

a list of Model objects or None

Examples

>>> # search for general-v1.3 models
>>> app.models.search('general-v1.3')
>>>
>>> # search for color model
>>> app.models.search('color', model_type='color')
>>>
>>> # search for face model
>>> app.models.search('face-v1.3', model_type='facedetect')

Model

class clarifai.rest.client.Model(api, item=None, model_id=None, solutions=None)
add_concepts(concept_ids)

merge concepts into a model

This is just an alias of merge_concepts, for easier understanding of adding new concepts to the model without overwritting them.

Parameters:concept_ids – a list of concept IDs
Returns:the Model object

Examples

>>> model = self.app.models.get('model_id')
>>> model.add_concepts(['cat', 'dog'])
delete_concepts(concept_ids)

delete concepts from a model

Parameters:concept_ids – a list of concept IDs to be removed
Returns:the Model object

Examples

>>> model = self.app.models.get('model_id')
>>> model.delete_concepts(['cat', 'dog'])
delete_version(version_id)

delete model version by version_id

Parameters:version_id – version id of the model version
Returns:the JSON response

Examples

>>> model = self.app.models.get('model_id')
>>> model.delete_version('model_version_id')
evaluate()

run model evaluation

Returns:the model version data with evaluation metrics in JSON format
get_concept_ids()

get concepts IDs associated with the model

Returns:a list of concept IDs

Examples

>>> ids = model.get_concept_ids()
get_info(verbose=False)

get model info, with or without the concepts associated with the model.

Parameters:verbose – default is False. True will yield output_info, with concepts of the model
Returns:raw json of the response

Examples

>>> # with basic model info
>>> model.get_info()
>>> # model info with concepts
>>> model.get_info(verbose=True)
get_inputs(version_id=None, page=1, per_page=20)

Get all the inputs from the model or a specific model version. Without specifying a model version id, this will yield all inputs

Parameters:
  • version_id – model version id
  • page – page number
  • per_page – number of inputs to return for each page
Returns:

A list of Input objects

get_version(version_id)

get model version info for a particular version

Parameters:version_id – version id of the model version
Returns:the JSON response

Examples

>>> model = self.app.models.get('model_id')
>>> model.get_version('model_version_id')
list_versions()

list all model versions

Returns:the JSON response

Examples

>>> model = self.app.models.get('model_id')
>>> model.list_versions()
merge_concepts(concept_ids, overwrite=False)

merge concepts in a model

When overwrite is False, if the concept does not exist in the model it will be appended. Otherwise, the original one will be kept.

Parameters:
  • concept_ids – a list of concept id
  • overwrite – True or False. If True, the existing concepts will be replaced
Returns:

the Model object

predict(inputs, model_output_info=None)

predict with multiple images

Parameters:inputs – a list of Image objects
Returns:the prediction of the model in JSON format
predict_by_base64(base64_bytes, lang=None, is_video=False, min_value=None, max_concepts=None, select_concepts=None, sample_ms=None)

predict a model with base64 encoded image bytes

Parameters:
  • base64_bytes – base64 encoded image bytes
  • lang – language to predict, if the translation is available
  • is_video – whether this is a video
  • min_value – threshold to cut the predictions, 0-1.0
  • max_concepts – max concepts to keep in the predictions, 0-200
  • select_concepts – a list of concepts that are selected to be exposed
  • sample_ms – video frame prediction every sample_ms milliseconds
Returns:

the prediction of the model in JSON format

predict_by_bytes(raw_bytes, lang=None, is_video=False, min_value=None, max_concepts=None, select_concepts=None, sample_ms=None)

predict a model with image raw bytes

Parameters:
  • raw_bytes – raw bytes of an image
  • lang – language to predict, if the translation is available
  • is_video – whether this is a video
  • min_value – threshold to cut the predictions, 0-1.0
  • max_concepts – max concepts to keep in the predictions, 0-200
  • select_concepts – a list of concepts that are selected to be exposed
  • sample_ms – video frame prediction every sample_ms milliseconds
Returns:

the prediction of the model in JSON format

predict_by_filename(filename, lang=None, is_video=False, min_value=None, max_concepts=None, select_concepts=None, sample_ms=None)

predict a model with a local filename

Parameters:
  • filename – filename on local filesystem
  • lang – language to predict, if the translation is available
  • is_video – whether this is a video
  • min_value – threshold to cut the predictions, 0-1.0
  • max_concepts – max concepts to keep in the predictions, 0-200
  • select_concepts – a list of concepts that are selected to be exposed
  • sample_ms – video frame prediction every sample_ms milliseconds
Returns:

the prediction of the model in JSON format

predict_by_url(url, lang=None, is_video=False, min_value=None, max_concepts=None, select_concepts=None, sample_ms=None)

predict a model with url

Parameters:
  • url – publicly accessible url of an image
  • lang – language to predict, if the translation is available
  • is_video – whether this is a video
  • min_value – threshold to cut the predictions, 0-1.0
  • max_concepts – max concepts to keep in the predictions, 0-200
  • select_concepts – a list of concepts that are selected to be exposed
  • sample_ms – video frame prediction every sample_ms milliseconds
Returns:

the prediction of the model in JSON format

send_concept_feedback(input_id, url, concepts=None, not_concepts=None, feedback_info=None)

Send feedback for this model

Parameters:
  • input_id – input id for the feedback
  • url – the url of the input
  • concepts – concepts that are present
  • not_concepts – concepts that aren’t present
  • feedback_info – feedback info
Returns:

None

send_region_feedback(input_id, url, concepts=None, not_concepts=None, regions=None, feedback_info=None)

Send feedback for this model

Parameters:
  • input_id – input id for the feedback
  • url – the input url
Returns:

None

train(sync=True, timeout=60)

train the model in synchronous or asynchronous mode. Synchronous will block until the model is trained, async will not.

Parameters:
  • sync – indicating synchronous or asynchronous, default is True
  • timeout – Used when sync=True. Num. of seconds we should wait for training to complete.
Returns:

the Model object

update(action='merge', model_name=None, concepts_mutually_exclusive=None, closed_environment=None, concept_ids=None)

Update the model attributes. The name of the model, list of concepts, and the attributes concepts_mutually_exclusive and closed_environment can be changed. Note this is a overwriting change. For a valid call, at least one or more attributes should be specified. Otherwise the call will be just skipped without error.

Parameters:
  • action – the way to patch the model: [‘merge’, ‘remove’, ‘overwrite’]
  • model_name – name of the model
  • concepts_mutually_exclusive – whether the concepts are mutually exclusive
  • closed_environment – whether negative concepts should be taken into account during training
  • concept_ids – a list of concept ids
Returns:

the Model object

Examples

>>> model = self.app.models.get('model_id')
>>> model.update(model_name="new_model_name")
>>> model.update(concepts_mutually_exclusive=False)
>>> model.update(closed_environment=True)
>>> model.update(concept_ids=["bird", "hurd"])
>>> model.update(concepts_mutually_exclusive=True, concept_ids=["bird", "hurd"])

Search Syntax

class clarifai.rest.client.SearchTerm

Clarifai search term interface. This is the base class for InputSearchTerm and OutputSearchTerm

It is used to build SearchQueryBuilder

class clarifai.rest.client.InputSearchTerm(url=None, input_id=None, concept=None, concept_id=None, value=True, metadata=None, geo=None)

Clarifai Input Search Term for an image search. For input search, you can specify search terms for url string match, input_id string match, concept string match, concept_id string match, and geographic information. Value indicates whether the concept search is a NOT search

Examples

>>> # search for url, string match
>>> InputSearchTerm(url='http://blabla')
>>> # search for input ID, string match
>>> InputSearchTerm(input_id='site1_bla')
>>> # search for annotated concept
>>> InputSearchTerm(concept='tag1')
>>> # search for not the annotated concept
>>> InputSearchTerm(concept='tag1', value=False)
>>> # search for metadata
>>> InputSearchTerm(metadata={'key':'value'})
>>> # search for geo
>>> InputSearchTerm(geo=Geo(geo_point=GeoPoint(-40, 30),
>>>                 geo_limit=GeoLimit('withinMiles', 10)))
class clarifai.rest.client.OutputSearchTerm(url=None, base64=None, input_id=None, concept=None, concept_id=None, value=True, crop=None)

Clarifai Output Search Term for image search. For output search, you can specify search term for url, base64, and input_id for visual search, or specify concept and concept_id for string match. Value indicates whether the concept search is a NOT search

Examples

>>> # search for visual similarity from url
>>> OutputSearchTerm(url='http://blabla')
>>> # search for visual similarity from base64 encoded image
>>> OutputSearchTerm(base64='sdfds')
>>> # search for visual similarity from input id
>>> OutputSearchTerm(input_id='site1_bla')
>>> # search for predicted concept
>>> OutputSearchTerm(concept='tag1')
>>> # search for not the predicted concept
>>> OutputSearchTerm(concept='tag1', value=False)
class clarifai.rest.client.SearchQueryBuilder(language=None)

Clarifai Image Search Query Builder

This builder is for advanced search use ONLY.

If you are looking for simple concept search, or simple image similarity search, you should use one of the existing functions search_by_annotated_concepts, search_by_predicted_concepts, search_by_image or search_by_metadata

Currently the query builder only supports a list of query terms with AND. InputSearchTerm and OutputSearchTerm are the only terms supported by the query builder

Examples

>>> qb = SearchQueryBuilder()
>>> qb.add_term(term1)
>>> qb.add_term(term2)
>>>
>>> app.inputs.search(qb)
>>>
>>> # for search over translated output concepts
>>> qb = SearchQueryBuilder(language='zh')
>>> qb.add_term(term1)
>>> qb.add_term(term2)
>>>
>>> app.inputs.search(qb)
add_term(term)

add a search term to the query. This can search by input or by output. Construct the term argument with an InputSearchTerm or OutputSearchTerm object.

dict()

construct the raw query for the RESTful API

Exceptions

class clarifai.rest.client.ApiError(resource, params, method, response=None)

API Server error

class clarifai.rest.client.ApiClientError

API Client Error

class clarifai.rest.client.UserError

User Error