Clarifai Python SDK
Project description
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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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_generator = client.list_apps()
apps = list(apps_generator)
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', input_type='text', csv_type='raw', labels=True)
#upload data from folder
dataset.upload_from_folder(folder_path='folder_path', input_type='text', labels=True)
# Export Dataset
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
Input upload
# 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')
# text upload
input_obj.upload_text(input_id = 'demo', raw_text = 'This is a test')
Input listing
#listing inputs
input_generator = input_obj.list_inputs(page_no=1,per_page=10,input_type='image')
inputs_list = list(input_generator)
#listing annotations
annotation_generator = input_obj.list_annotations(batch_input=inputs_list)
annotations_list = list(annotation_generator)
#listing concepts
all_concepts = list(app.list_concepts())
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 = list(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 = list(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)
all_llm_community_models = list(all_llm_community_models)
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 = list(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 = list(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
all_face_community_workflows = list(all_face_community_workflows)
Workflow Create
Create a new workflow specified by a yaml config file.
# Note: CLARIFAI_PAT must be set as env variable.
from clarifai.client.app import App
app = App(app_id="app_id", user_id="user_id")
workflow = app.create_workflow(config_filepath="config.yml")
Workflow Export
Export an existing workflow from Clarifai as a local yaml file.
# Note: CLARIFAI_PAT must be set as env variable.
from clarifai.client.workflow import Workflow
workflow = Workflow("https://clarifai.com/clarifai/main/workflows/Demographics")
workflow.export('demographics_workflow.yml')
More Examples
See many more code examples in this repo. Also see the official Python SDK docs
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