clarifai 9.8.0

Clarifai Python SDK

Clarifai Python SDK

This is the official Python client for interacting with our powerful API. The Clarifai Python SDK offers a comprehensive set of tools to integrate Clarifai's AI platform to leverage computer vision capabiities like classification , detection ,segementation and natural language capabilities like classification , summarisation , generation , Q&A ,etc into your applications. With just a few lines of code, you can leverage cutting-edge artificial intelligence to unlock valuable insights from visual and textual content.

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Website: https://www.clarifai.com

Demo: https://clarifai.com/demo

Sign up for a free Account: https://clarifai.com/signup

Developer Guide: https://docs.clarifai.com

Clarifai Community: https://clarifai.com/explore

Python SDK Docs: https://clarifai-python.readthedocs.io/en/latest/index.html

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Installation

Install from PyPi:

pip install -U clarifai

Install from Source:

git clone https://github.com/Clarifai/clarifai-python.git
cd clarifai-python
python3 -m venv env
source env/bin/activate
pip3 install -r requirements.txt

Getting started

Clarifai uses Personal Access Tokens(PATs) to validate requests. You can create and manage PATs under your Clarifai account security settings.

Export your PAT as an environment variable. Then, import and initialize the API Client.

export CLARIFAI_PAT={your personal access token}

# 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()

Interacting with Datasets

# Note: CLARIFAI_PAT must be set as env variable.

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

# execute data upload to Clarifai app dataset
dataset.upload_dataset(task='visual_segmentation', split="train", dataset_loader='coco_segmentation')

#upload text from csv
dataset.upload_from_csv(csv_path='csv_path', labels=True)

#upload data from folder
dataset.upload_from_folder(folder_path='folder_path', input_type='text', labels=True)

# Export Dataset Inputs
from clarifai.client.dataset import Dataset
# Note: clarifai-data-protobuf.zip is acquired through exporting datasets within the Clarifai Platform.
Dataset().export(save_path='output.zip', local_archive_path='clarifai-data-protobuf.zip')

Interacting with Inputs

# Note: CLARIFAI_PAT must be set as env variable.
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')

#listing inputs
input_obj.list_inputs()

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

Interacting with Models

Model Predict

# Note: CLARIFAI_PAT must be set as env variable.
from clarifai.client.model import Model

# Model Predict
model_prediction = Model("https://clarifai.com/anthropic/completion/models/claude-v2").predict_by_bytes(b"Write a tweet on future of AI", "text")

model = Model(user_id="user_id", app_id="app_id", model_id="model_id")
model_prediction = model.predict_by_url(url="url", input_type="image") # Supports image, text, audio, video

# Customizing Model Inference Output
model = Model(user_id="user_id", app_id="app_id", model_id="model_id",
                  output_config={"min_value": 0.98}) # Return predictions having prediction confidence > 0.98
model_prediction = model.predict_by_filepath(filepath="local_filepath", input_type="text") # Supports image, text, audio, video

model = Model(user_id="user_id", app_id="app_id", model_id="model_id",
                    output_config={"sample_ms": 2000}) # Return predictions for specified interval
model_prediction = model.predict_by_url(url="VIDEO_URL", input_type="video")

Models Listing

# Note: CLARIFAI_PAT must be set as env variable.

# List all model versions
all_model_versions = model.list_versions()

# Go to specific model version
model_v1 = client.app("app_id").model(model_id="model_id", model_version_id="model_version_id")

# List all models in an app
all_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)

Interacting with Workflows

Workflow Predict

# Note: CLARIFAI_PAT must be set as env variable.
from clarifai.client.workflow import Workflow

# Workflow Predict
workflow = Workflow("workflow_url") # Example: https://clarifai.com/clarifai/main/workflows/Face-Sentiment
workflow_prediction = workflow.predict_by_url(url="url", input_type="image") # Supports image, text, audio, video

# Customizing Workflow Inference Output
workflow = Workflow(user_id="user_id", app_id="app_id", workflow_id="workflow_id",
                  output_config={"min_value": 0.98}) # Return predictions having prediction confidence > 0.98
workflow_prediction = workflow.predict_by_filepath(filepath="local_filepath", input_type="text") # Supports image, text, audio, video

Workflows Listing

# Note: CLARIFAI_PAT must be set as env variable.

# List all workflow versions
all_workflow_versions = workflow.list_versions()

# Go to specific workflow version
workflow_v1 = Workflow(workflow_id="workflow_id", workflow_version=dict(id="workflow_version_id"), app_id="app_id", user_id="user_id")

# List all workflow in an app
all_workflow = app.list_workflow()

# 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

More Examples

See many more code examples in this repo. Also see the official Python SDK docs