Tutorial

Each of the examples below is a small independent code snippet within 10 lines that could work by copy and paste to a python source code file. By playing with them, you should be getting started with Clarifai API. For more information about the API, check the API Reference.

• Predict Tutorial
  • Predict with Models
  • Predict with Workflow

Create App

# Note: CLARIFAI_PAT must be set as env variable.
from clarifai.client.user import User
client = User(user_id="user_id")

# Get all apps
apps = client.list_apps()

# Create app and dataset
app = client.create_app(app_id="demo_app", base_workflow="Universal")

Create Dataset

# Note: CLARIFAI_PAT must be set as env variable.
from clarifai.client.app import App

# Get an app
app = App(user_id="user_id", app_id="app_id")

 # Create a dataset
dataset = app.create_dataset(dataset_id="demo_dataset")

Upload Dataset

# Note: CLARIFAI_PAT must be set as env variable.
from clarifai.client.dataset import Dataset

# Get a dataset
dataset = Dataset(user_id="user_id", app_id="app_id", dataset_id="dataset_id")

# Upload dataset from loaders
dataset.upload_dataset(task='visual_segmentation', split="train", dataset_loader='coco_segmentation')

# Upload a dataset from local folder
dataset.upload_from_folder(folder_path='folder_path', input_type='text', labels=True)

# Upload a text dataset from csv file
dataset.upload_from_csv(csv_path='csv_path', labels=True)

Upload Inputs

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

#input upload from url
input_obj.upload_from_url(input_id = 'demo', image_url='https://samples.clarifai.com/metro-north.jpg')

#input upload from filename
input_obj.upload_from_file(input_id = 'demo', video_file='demo.mp4')

# text upload
input_obj.upload_text(input_id = 'demo', raw_text = 'This is a test')

List Apps

# Note: CLARIFAI_PAT must be set as env variable.
from clarifai.client.user import User
client = User(user_id="user_id")

# Get all apps
apps = client.list_apps()

List Datasets

# Note: CLARIFAI_PAT must be set as env variable.
from clarifai.client.app import App

# Get an app
app = App(user_id="user_id", app_id="app_id")

# Get all datasets
datasets = app.list_datasets()

List Models

# Note: CLARIFAI_PAT must be set as env variable.
from clarifai.client.app import App

# Get an app
app = App(user_id="user_id", app_id="app_id")

# Get all models
models = app.list_models()

# List all models in community filtered by model_type, description
all_llm_community_models = App().list_models(filter_by={"query": "LLM",
                                                     "model_type_id": "text-to-text"}, only_in_app=False)

List Workflows

# Note: CLARIFAI_PAT must be set as env variable.
from clarifai.client.app import App

# Get an app
app = App(user_id="user_id", app_id="app_id")

# List all workflow in an app
workflows = app.list_workflows()

# List all workflow in community filtered by description
all_face_community_workflows = App().list_workflows(filter_by={"query": "face"}, only_in_app=False) # Get all face related workflows

Delete App

# Note: CLARIFAI_PAT must be set as env variable.
from clarifai.client.user import User
client = User(user_id="user_id")

# Delete an app
client.delete_app(app_id="app_id")