averian-ai-validator-sdk 1.0.1188

Averian AI Validator SDK

Averian AI Validator SDK

Python SDK for the Averian AI Validator platform. Integrate anomaly detection inference into your application in minutes.

Installation

pip install averian-ai-validator-sdk

Prerequisites

• An API key with inference access — obtained from the Organization Settings page by an admin user
• A project with at least one trained and hosted model ensemble

Quick Start — Inference

This guide assumes your project, input source, and model are already set up in the Averian AI Validator platform.

Step 1 — Connect

from averian_ai_validator_sdk import APIClient

api = APIClient()
api.connect("https://api.yourhost.com", "YOUR_API_KEY")

For servers with a self-signed certificate:

api.connect("https://api.yourhost.com", "YOUR_API_KEY", ca_cert="/path/to/ca.pem")

API keys are created and managed by admin users in the Organization Settings page. Each key has a specific level of access — contact your administrator to obtain a key with inference permissions.

Step 2 — Select Project and Input Source

# List and select a project
projects = api.get_projects()['projects']
project_id = projects[0]['id']  # or match by name

# List and select an input source
sources = api.get_input_sources(project_id)
input_id = sources[0]['id']  # or match by name

Step 3 — Get the Model

The SDK uses the default model ensemble if one has been set for the input source, and falls back to the last active model if not.

source = api.get_input_source_by_id(project_id, input_id)
model_id = source.get('defaultEnsemble')

if not model_id:
    model = api.get_last_active_model(project_id, input_id)
    if not model:
        raise RuntimeError("No hosted model available for this input source.")
    model_id = model['id']

print(f"Using model: {model_id}")

Step 4 — Run Inference

import os

image_path = "part_to_inspect.jpg"
img_name, img_ext = os.path.splitext(os.path.basename(image_path))
img_blob = api.read_file_as_blob(image_path)

result = api.infer(project_id, input_id, model_id, img_name, img_ext.lstrip("."), img_blob)

print(f"Anomalous: {result['isAnomalous']}")
print(f"Result ID: {result['id']}")

Step 5 — Retrieve the Heatmap

heatmap = api.fetch_result_image_with_heatmap(project_id, result['id'])
with open("heatmap.jpg", "wb") as f:
    f.write(heatmap)
print("Heatmap saved to heatmap.jpg")

Step 6 — Clean Up (Optional)

api.del_result(project_id, result['id'])

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For the full API reference, visit the Averian AI Validator documentation.