Models

from seeme import *

Get all models

Get a list of all models you have access to.

models = client.get_models()

These models are divided in three groups:

public: models that are available to everyone;
owned: models you created;
shared: models that are shared privately with you.

Public models

Public models are provided by SeeMe.ai, other users, or your own organisation.

public_models = [model for model in models if model.public]

Your own models

A list of the models you created:

own_models = [ model for model in models if model.user_id == client.user_id]

Shared models

A list of models that others have shared with you in private.

shared_with_me = [model for model in models if model.shared_with_me]

Create a model

application_id = client.get_application_id(
    Framework.PYTORCH,
    Framework.FASTAI,
    "2.1.0",
    "2.7.13"
)

from seeme.types import Model

my_model = Model(
    name= "Cats and dogs",
    description= "Recognize cats and dogs in pictures.",
    privacy_enabled= False,
    auto_convert= True,
    application_id= application_id
)

my_model = client.create_model(my_model)

Parameter	Type	Description
model	Model	Entire model object

Every model has the following properties:

Property	Type	Description
id	str	Unique id
created_at	str	The creation date
updated_at	str	Last updated date
name	str	The model name
description	str	The model description
notes	str	Notes on the model
user_id	str	The user id of the model creator
can_inference	bool	Flag indicating whether the model can make predictions or not
kind	str	Type of AI application, possible values: “image_classification”, “object_detection”, “text_classification”, “structured”, “language_model”, “ner”, “language_model”, “llm”.
has_logo	bool	Flag indicating whether the model has a logo or not
logo	str	Name and extension of the logo file (mostly for internal purpose)
public	bool	Flag indicating whether the model is public or not
config	str	Additional config stored in a JSON string
active_version_id	str	The id of the current model version (see versions below)
application_id	str	The `application id` of the active model version (see applications)
has_ml_model	bool	Flag indicating whether the model has a Core ML model
has_onnx_model	bool	Flag indicating whether the model has an ONNX model
has_onnx_int8_model	bool	Flag indicating whether the model has an 8-bit quantized model
has_tflite_model	bool	Flag indicating whether the model has a Tensorflow Lite model
has_labels_file	bool	Flag indicating whether a file will all the labels (classes) is available
shared_with_me	bool	Flag indicating whether the model has been shared with you
auto_convert	bool	Flag indicating whether the model will be automatically converted to the supported model formats (see applications). Default value: `True`.
privacy_enabled	bool	Flag indicating whether privacy is enabled. If set to ‘True’, no inputs (images, text files, …) will be stored on the server or the mobile/edge device. Default value: `False`.

Get a model

Use the model id to get all the metadata of the model:

client.get_model(my_model.id)

Parameter	Type	Description
model_id	str	Unique id for the model

Update a model

Update any property of the model:

my_model = client.get_model(my_model.id)

my_model.description = "Updated for documentation purposes"

client.update_model(my_model)

Parameter	Type	Description
model	Model	The entire model object

Delete a model

Delete a model using its id.

client.delete_model(my_model.id)

Parameter	Type	Description
model_id	str	Unique id for the model

Upload a model file

You can upload the model file by calling the upload_model. Make sure the application_id is set to the desired AI application, framework, and version.

my_model = client.upload_model(
    my_model.id,
    folder="directory/to/model",
    filename="your_exported_model_file.pkl"
)

Parameter	Type	Description
model_id	str	Unique id for the model
folder	str	Name of the folder that contains the model file (without trailing ‘/’), default value “data”
filename	str	Name of the file to be uploaded, default value “export.pkl”

This returns an updated my_model, where if successful the can_inference will be set to True.

If auto_convert is enabled, all possible conversions for the selected application_id (see Applications below) will be available.

Download model file(s)

Download the file(s) associated with the current active (production) model.

client.download_active_model(
    my_model,
    asset_type=AssetType.PKL,
    download_folder="."
)

Parameter	Type	Description
model	Model	The entire model object
asset_type	AssetType	The model type you want to download. Default: `PKL`; Possible values: `PKL`, `MLMODEL`, `TFLITE`, `ONNX`, `ONNX_INT8`, `LABELS`, `NAMES`, `WEIGHTS`, `CFG`, `CONVERSION_CFG`, `LOGO`.
download_folder	str	The folder where you would like to download the model. Default: `.` (i.e. current directory)

If the asset_type exists, the model file will be downloaded to my_model.active_model_id.{asset_type}. One exception, the labels file will receive a .txt extension.

If you want to download a specific model version, have a look at Download a model version

Upload model logo

my_model = client.upload_logo(
    my_model.id,
    folder="directory/to/logo", 
    filename="logo_filename.jpg"
)

Parameter	Type	Description
model_id	str	Unique id for the model
folder	str	Name of the folder that contains the logo file (without trailing ‘/’), default value “data”
filename	str	Name of the file to be uploaded, default value “logo.jpg”. Supported formats: `jpg`, `jpeg`, `png`.

Download model logo

client.get_logo(my_model)

Parameter	Type	Description
model	Model	The entire model object

Models Model Versions