SeeMe.ai Python SDK #

pip install --upgrade seeme

!pip install --upgrade seeme

import seeme
print(seeme.__version__)

from seeme import Client

client = Client()

my_username  = "my_username"
my_email     = "my_email@mydomain.com"
my_password  = "supersecurepassword"
my_firstname = "firstname"
my_name      = "last name"

client.register(
    username = my_username, 
    email=my_email, 
    password=my_password, 
    firstname=my_firstname, 
    name=my_name
)

username = ""
password = ""

client.login(username, password)

my_username = ""
my_apikey = ""

client = Client(username=my_username, apikey=my_apikey)

client = Client(env_file=".my_env_file")

client.logout()

custom_deployment_url = "http://seeme.yourdomain.com/api/v1"

client = Client(backend=custom_deployment_url)

models = client.get_models()

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

public_models

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

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

application_id = client.get_application_id("pytorch", "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)

client.get_model(my_model.id)

my_model = client.get_model(my_model.id)

my_model.description = "Updated for documentation purposes"

client.update_model(my_model)

client.delete_model(my_model.id)

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

client.download_active_model(my_model, asset_type="pkl", download_folder=".")

result = client.predict(model_id, item, application_type=ApplicationType.IMAGE_CLASSIFICATION)

item = "path/to/file.png"

result = client.predict(my_model.id, item)

item = "path/to/file.png"

result = client.predict(my_model.id, item, application_type=ApplicationType.OBJECT_DETECTION)

item = "The text you want to classify."

result = client.predict(my_model.id, item, application_type=ApplicationType.TEXT_CLASSIFICATION)

item = "The text where I want to extract information out of."

result = client.predict(my_model.id, item, application_type=ApplicationType.NER)

inputs = {
    "temperature": "30",
    "day": "Tuesday"
}

result = client.predict(my_model.id, item, application_type=ApplicationType.STRUCTURED)

item = "The story of AI language models is"

result = client.predict(my_model.id, item, application_type=ApplicationType.LANGUAGE_MODEL)

item = "The story of AI language models is"

result = client.predict(my_model.id, item, application_type=ApplicationType.LLM)

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

client.get_logo(my_model)

versions = client.get_model_versions(my_model.id)

new_version = ModelVersion (
    name="A higher accuracy achieved",
    application_id="b4b9aaf0-cb37-4629-8f9b-8877aeb09a53"
)

new_version = client.create_model_version(my_model.id, new_version)

model_version = client.get_model_version(my_model.id, new_version.id)

model_version.description = "SOTA comes and goes, but versions are forever!"

client.update_model_version(model_version)

client.upload_model_version(new_version, folder="directory/to/model", filename="your_exported_model_file_v2.pkl")

client.download_model_version(my_version, asset_type=AssetType.PKL, download_folder="data")

client.version_inference(model_version_id, item, application_type=ApplicationType.IMAGE_CLASSIFICATION)

item = "path/to/file.png"

result = client.version_inference(new_version.id, item)

item = "path/to/file.png"

result = client.version_inference(new_version.id, item, application_type=ApplicationType.OBJECT_DETECTION)

item = "The text you want to classify."

result = client.version_inference(new_version.id, item, application_type=ApplicationType.TEXT_CLASSIFICATION)

inputs = {
    "temperature": "30",
    "day": "Tuesday"
}

result = client.version_inference(my_model.id, item, application_type=ApplicationType.STRUCTURED)

client.delete_model_version(my_model.id, new_version.id)

datasets = client.get_datasets()

my_dataset = Dataset(
    name= "Cats & dogs dataset",
    description= "A dataset with labelled images of cats and dogs.",
    multi_label= False,
    notes= "Cats and dogs is often used as a demo dataset.",
    default_splits= True,
    content_type= ContentType.IMAGES
)

my_dataset = client.create_dataset(my_dataset)

my_dataset = client.get_dataset(my_dataset.id)

my_dataset.notes += "~25k labelled images of cats and dogs; 22500 for training, 2000 for validation."

client.update_dataset(my_dataset)

client.delete_dataset(my_dataset.id)

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

client.get_dataset_logo(my_dataset)

dataset_versions = client.get_dataset_versions(my_dataset.id)

new_dataset_version = DatasetVersion(
    name= "v2",
    description= "Even more images of dogs and cats"
)

new_dataset_version = client.create_dataset_version(my_dataset.id, new_dataset_version)

new_dataset_version

dataset_version = client.get_dataset_version(new_dataset_version.dataset_id, new_dataset_version.id)

new_dataset_version.description = "Even more image of cats and dogs."

client.update_dataset_version(my_dataset.id, new_dataset_version)

client.delete_dataset_version(my_dataset.id, new_dataset_version)

splits = client.get_dataset_splits(my_dataset.id, new_dataset_version.id)

new_split = DatasetSplit(
    name= "train",
    description= "training data for our model to learn from" 
)

new_split = client.create_dataset_split(my_dataset.id, new_dataset_version.id, new_split)

my_split = client.get_dataset_split(my_dataset.id, new_dataset_version.id, new_split.id)

my_split.description = "Training data"

client.update_dataset_split(my_dataset.id, new_dataset_version.id, my_split)

client.delete_dataset_split(my_dataset.id, new_dataset_version.id, my_split)

labels = client.get_dataset_labels(my_dataset.id, new_dataset_version.id.)

new_label = Label(
    name= "Birds",
    description= "Adding birds to the mix"
)

new_label = client.create_dataset_label(my_dataset.id, new_dataset_version.id, new_label)

my_label = client.get_dataset_label(my_dataset.id, new_dataset_version.id, new_label.id)

my_label.color = "#00ff00"

my_label = client.update_dataset_label(my_dataset.id, new_dataset_version.id, my_label.id)

client.delete_dataset_label(my_dataset.id, new_dataset_version.id, my_label)

items = client.get_dataset_items(my_dataset.id, new_dataset_version.id)

# All params are optional, but here we combine them together for demo purposes. 
params = {
    "onlyUnlabelled": True,
    "pageSize": 25,
    "pageCount": 0,
    "labelId": new_label.id,
    "splitId": my_split.id
}

client.get_dataset_items(my_dataset.id, new_dataset_version.id, params)

item = DatasetItem(
    name= "An optional name",
    splits= [my_split]
)

item = client.create_dataset_item(my_dataset.id, new_dataset_version.id, item)

item = client.get_dataset_item(my_dataset["id"], new_dataset_version["id"], item["id"])

item.description = "A better description"

client.update_dataset_item(my_dataset.id, new_dataset_version.id, item)

client.delete_dataset_item(my_dataset.id, new_dataset_version.id, item.id)

client.upload_dataset_item_image(my_dataset.id, new_dataset_version.id, item.id, folder="directory/to/item", filename="item_filename.jpg")

download_location = f"{item.id}.{item.extension}"

client.download_dataset_item_image(my_dataset.id, new_dataset_version.id, item.id, download_location, thumbnail=False)

annotation = Annotation(
    label_id= my_label.id,
    split_id= my_split.id,
    item_id= item.id
)

annotation = client.annotate(my_dataset.id, new_dataset_version.id, annotation)

annotation.coordinates = "14 20 34 48"

annotation = client.update_annotation(my_dataset.id, new_dataset_version.id, annotation)

client.delete_annotation(my_dataset.id, new_dataset_version.id, annotation.id)

client.download_dataset(
    my_dataset.id,
    new_dataset_version.id,
    split_id="",
    extract_to_dir="data",
    download_file="dataset.zip",
    remove_download_file=True,
    export_format=""
)

{ 
    "colors": { 
        "label_1": "#36dfd4" , 
        "label_2": "#f0699e" 
    }
}

{
    "multi_label": false,
    "label_column": "labels",
    "split_column": "split",
    "label_separator": " ",
    "filename": "dataset_version_id.csv",
    "csv_separator": ","
}

{
    "splits": [
        "train",
        "valid",
        "test"
    ],
    "colors": {
        "label_name": "#82ebfd",
        "label_name2": "#e95211"
    }
}

[
    {
        "id": "the_dataset_item_id",
        "name": "the_original_filename" ,
        "text": "The textual content of the file that has been annotated.",
        "annotations": [{
            "start": 4,
            "end": 11,
            "label": "label_name",
        },
         {
             ...
         }
        ]
    },
    {
        ...
    }
]

{ 
    "colors": { 
        "label_name1": "#36dfd4" , 
        "label_name2": "#f0699e" 
    }
}

{
    "multi_label": false,
    "label_column": "labels",
    "split_column": "split",
    "label_separator": " ",
    "filename": "dataset.csv",
    "csv_separator": ","
}

{
    "splits": [
        "train",
        "valid",
        "test"
    ],
    "colors": {
        "label_name": "#82ebfd",
        "label_name2": "#e95211"
    }
}

[{
    "id": "the_dataset_item_id",
    "name": "the_original_filename" ,
    "text": "The textual content of the file that has been annotated.",
    "annotations": [{
        "start": 4,
        "end": 11,
        "label": "label_name",
    },
     {
         ...
     }
    ]
},
{
    ...
}
]

jobs = client.get_jobs(
    application_id="", 
    states=[JobStatus.WAITING, JobStatus.STARTED,
                           JobStatus.FINISHED, JobStatus.ERROR],
    job_types=[JobType.TRAINING]
)

my_job = Job(
    name= "Train a new model for cats and dogs",
    description= "",
    job_type= JobType.TRAINING,
    application_id= "acf26cf4-e19f-425e-b5cb-031830a46df4", # See Applications to get the correct application_id for your job
    dataset_id= "8037b73a-5512-4a45-89e2-29761771fff6", # Update to your dataset_id
    dataset_version_id= "1d3bf8d6-e39b-498e-9c08-680d2f8a3c47", # Update to your dataset_version_id
    items= [
        JobItem(
            name= "image_size",
            value= "224",
            value_type= ValueType.NUMBER,
            label= "Image Size"
        ),
        JobItem(
            name= "arch",
            value= "resnet50",
            value_type= ValueType.TEXT,
            label= "Architecture"
        ),
        JobItem(
            name= "batch_size",
            value= "50",
            value_type= ValueType.NUMBER,
            label= "Batch size"
        )
    ]
)

my_job = client.create_job(my_job)

job_item = JobItem(
    name= "arch",
    value= "resnet50",
    value_type= ValueType.TEXT,
    label= "Architecture"
)

job_item = JobItem(
    name= "image_size",
    value= "224",
    value_type= ValueType.INT,
    label= "Image Size"
)

job_item = JobItem(
    name= "image_resize",
    value= "460",
    value_type= ValueType.INT,
    label= "Image Resize"
)

job_item = JobItem(
    name= "batch_size",
    value= "32",
    value_type= ValueType.INT,
    label= "Batch Size"
)

job_item = JobItem(
    name= "nb_epochs",
    value= "50",
    value_type= ValueType.INT,
    label= "Number of epochs"
)

job_item = JobItem(
    name= "mixup",
    value= "0",
    value_type= ValueType.INT,
    label= "Mix up %"
)

job_item = JobItem(
    name= "seed",
    value= "12",
    value_type= ValueType.INT,
    label= "Seed"
)

job_item = JobItem(
    name= "arch",
    value= "yolov4-tiny",
    value_type= ValueType.INT,
    label= "Architecture"
)

job_item = JobItem(
    name= "image_size",
    value= "416",
    value_type= ValueType.INT,
    label= "Image Size"
)

job_item = JobItem(
    name= "batch_size",
    value= "64",
    value_type= ValueType.INT,
    label= "Batch Size"
)

job_item = JobItem(
    name= "subdivisions",
    value= "8",
    value_type= ValueType.INT,
    label= "Subdivisions"
)

job_item = JobItem(
    name= "language",
    value= "en",
    value_type= ValueType.TEXT,
    label= "Language"
)

job_item = JobItem(
    name= "optimize",
    value= "accuracy",
    value_type= ValueType.MULTI,
    label= "Optimize for"
)

job_item = JobItem(
    name= "arch",
    value= "[200 - 100]",
    value_type= ValueType.TEXT,
    label= "Architecture"
)

job_item = JobItem(
    name "batch_size",
    value= "32",
    value_type= ValueType.INT,
    label= "Batch Size"
)

job_item = JobItem(
    name= "nb_epochs",
    value= "50",
    value_type= Valuetype.INT,
    label= "Number of epochs"
)

job_item = JobItem(
    name= "min_feat_importance",
    value= "0",
    value_type= ValueType.INT,
    label= "Minimal feature importance (%)"
)

job_item = JobItem(
    name= "add_info",
    value= True,
    value_type= ValueType.BOOL,
    label= "Add info"
)

job_item = JobItem(
    name= "booster",
    value= "gbtree", # "gbtree", "dart", "gblinear"
    value_type= ValueType.MULTI,
    label= "Booster"
)

job_item = JobItem(
    name= "nb_estimators",
    value= "100",
    value_type= ValueType.INT,
    label= "Nb Estimators"
)

job_item = JobItem(
    name= "max_depth",
    value= "6",
    value_type= ValueType.INT,
    label= "Number of epochs"
)

job_item = JobItem(
    name= "learning_rate",
    value= "0.3",
    value_type= ValueType.FLOAT,
    label= "Learning rate (eta)"
)

job_item = JobItem(
    name= "subsample",
    value= "0.5",
    value_type= ValueType.FLOAT,
    label= "Subsample (0 - 1)"
)

job_item = JobItem(
    name= "min_feat_importance",
    value= "0",
    value_type= ValueType.INT,
    label= "Minimal feature importance (%)"
)

job_item = JobItem(
    name= "add_info",
    value= True,
    value_type= ValueType.BOOL,
    label= "Add info"
)

job_item = JobItem(
    name= "nb_estimators",
    value= "100",
    value_type= ValueType.INT,
    label= "Nb Estimators"
)

job_item = JobItem(
    name= "learning_rate",
    value= "0.1",
    value_type= ValueType.FLOAT,
    label= "Learning rate"
)

job_item = JobItem(
    name= "min_feat_importance",
    value= "0",
    value_type= ValueType.INT,
    label= "Minimal feature importance (%)"
)

job_item = JobItem(
    name= "add_info",
    value= True,
    value_type= ValueType.BOOL,
    label= "Add info"
)

job_item = JobItem(
    name= "booster",
    value= "gbdt", # "gbdt", "dart", "rf"
    value_type= ValueType.MULTI,
    label= "Boosting Type"
)

job_item = JobItem(
    name= "nb_estimators",
    value= "100",
    value_type= ValueType.INT,
    label= "Nb Estimators"
)

job_item = JobItem(
    name= "max_depth",
    value= "-1",
    value_type= ValueType.INT,
    label= "Number of epochs"
)

job_item = JobItem(
    name= "learning_rate",
    value= "0.1",
    value_type= ValueType.FLOAT,
    label= "Learning rate"
)

job_item = JobItem(
    name= "num_leaves",
    value= "31",
    value_type= ValueType.INT,
    label= "Max number of leaves"
)

job_item = JobItem(
    name= "min_feat_importance",
    value= "0",
    value_type= ValueType.INT,
    label= "Minimal feature importance (%)"
)

job_item = JobItem(
    name= "add_info",
    value= True,
    value_type= ValueType.BOOL,
    label= "Add info"
)

my_job = client.get_job(my_job.id)

my_job.description = "Update any property"

my_job = client.update_job(my_job)

client.delete_job(my_job.id)

client.applications

client.update_applications()

from torch import __version__ as torch_version
from fastai import __version__ as fastai_version

# Get the application_id for your framework (version).
application_id = client.get_application_id(
    base_framework=Framework.PYTORCH,
    framework=Framework.FASTAI,
    base_framework_version=torch_version,
    framework_version=fastai_version,
    application=ApplicationType.IMAGE_CLASSIFICATION
)