resistant-documents-client 2.4.3

Resistant.ai document forjerry python client for convenient integration with REST API service.

Resistant documents client

This library provides a Python client for a Resistant.ai document forgery analysis service. For a detailed description of the API please see API reference docs.

Prerequisites

During the customer onboarding process, you should be provided with the following:

• CLIENT_ID : str
• CLIENT_SECRET : str

Note: Those credentials are connected with specific environment (e.g. test, production etc.). Together with secret credentials for non-production environment you should be provided with url links:

• api_url
• token_url

Basic usage

Submit a document for analysis with default pipeline configuration.

from resistant_documents_client.client import ResistantDocumentsClient

client = ResistantDocumentsClient(client_id="CLIENT_ID", client_secret="CLIENT_SECRET")
with open("local_file.pdf", "rb") as fp:
    report = client.analyze(fp.read(), query_id="local_file.pdf")
print(report["score"])

For non-production environment, provide api_url and token_url provided together with client secrets to client creation (ResistantDocumentsClient).

Customized usage

Submit a document for analysis with customized process parameters or select a different type of analysis.

Step 1: Create a client with your credentials

client = ResistantDocumentsClient(client_id="CLIENT_ID", client_secret="CLIENT_SECRET")

Note: If your client secrets are for different environment than production you have to provide following api_url token_url. To setup proxy server URL, you can provide optional proxy.

Step 2: Create submission with pipeline setup

with open("local_file.pdf", "rb") as fp:
    my_submission_id = client.submit(fp.read(), query_id="local_file.pdf", pipeline_configuration="CONTENT_AFTER_FRAUD_AFTER_QUALITY")

Possible pipeline configurations are listed in REST API docs

Step 3: Retrieve analysis result

You can retrieve only those types of analysis which were requested in the previous step as pipeline_configuration option.

result_content = client.content(submission_id=submission_id)
result_fraud = client.content(submission_id=submission_id)
result_quality = client.quality(submission_id=submission_id)
result_classification = client.classification(submission_id=submission_id)

print(result_content)
print(result_fraud)
print(result_quality)
print(result_classification)

These methods also accept max_num_retries, which represents how many times the client will poll the server before failing (because the communication is asynchronous). It might be customized but has a default value. Other parameters correspond to the ones in the REST API docs.

Other supported endpoints

• submission/{submission_id}/decision
• submission/{submission_id}/feedback
• submission/{submission_id}/characteristics