Getting Started with Python

Safe API Key Storage

In a production setup, do not store API Keys in the host's environment variables or in the source code.

Requirements

Requires Python v3.8 or later

Getting Started

First, install Opportify SDK via PyPI manager:

pip install opportify-sdk

Single Analysis

Calling Email Insights

from opportify_sdk import EmailInsights

# Initialize the wrapper with your API key
api_key = "<YOUR-API-KEY-HERE>"
email_insights = EmailInsights(api_key)

# Optional: Configure host, version, and debug mode
email_insights.set_version("v1")

# Define request parameters
params = {
    "email": "<SOME-EMAIL-HERE>",
    "enableAutoCorrection": True,
    "enableAi": True 
}

# Call the API
try:
    result = email_insights.analyze(params)
    print("Response:", result)
except Exception as e:
    print(f"Error: {e}")

Calling IP Insights

from opportify_sdk import IpInsights

# Initialize the wrapper with your API key
api_key = "<YOUR-API-KEY-HERE>"
ip_insights = IpInsights(api_key)

# Optional: Configure host, version, and debug mode
ip_insights.set_version("v1")

# Define request parameters
params = {
    "ip": "<SOME-IP-HERE>",
    "enableAi": True 
}

# Call the API
try:
    result = ip_insights.analyze(params)
    print("Response:", result)
except Exception as e:
    print(f"Error: {e}")

Batch Analysis

For processing large volumes of emails or IP addresses, use the batch analysis methods:

Batch Email Analysis

from opportify_sdk import EmailInsights

# Initialize the wrapper with your API key
api_key = "<YOUR-API-KEY-HERE>"
email_insights = EmailInsights(api_key)

# Define batch request parameters
batch_params = {
    "emails": [
        "user1@company.com",
        "user2@domain.org",
        "test@example.com"
    ],
    "enableAutoCorrection": True,
    "enableAi": True 
}

# Start batch analysis
try:
    batch_response = email_insights.batch_analyze(batch_params)
    job_id = batch_response['job_id']
    print(f"Batch job started: {job_id}")
    
    # Check batch status
    status = email_insights.get_batch_status(job_id)
    print(f"Status: {status['status']}, Progress: {status['progress']}%")
    
    # When completed, download URLs will be available
    if status['status'] == 'COMPLETED':
        download_urls = status['download_urls']
        print(f"Results ready: {download_urls['csv']}")
        
except Exception as e:
    print(f"Error: {e}")

Batch IP Analysis

from opportify_sdk import IpInsights

# Initialize the wrapper with your API key
api_key = "<YOUR-API-KEY-HERE>"
ip_insights = IpInsights(api_key)

# Define batch request parameters
batch_params = {
    "ips": [
        "8.8.8.8",
        "1.1.1.1",
        "192.168.1.1"
    ],
    "enableAi": True 
}

# Start batch analysis
try:
    batch_response = ip_insights.batch_analyze(batch_params)
    job_id = batch_response['job_id']
    print(f"Batch job started: {job_id}")
    
    # Check batch status
    status = ip_insights.get_batch_status(job_id)
    print(f"Status: {status['status']}, Progress: {status['progress']}%")
    
    # When completed, download URLs will be available
    if status['status'] == 'COMPLETED':
        download_urls = status['download_urls']
        print(f"Results ready: {download_urls['csv']}")
        
except Exception as e:
    print(f"Error: {e}")

Configuration

Enable Debug Mode

ip_insights.set_version("v1").set_debug_mode(True)
email_insights.set_version("v1").set_debug_mode(True)

Batch Status Polling

For long-running batch jobs, poll the status periodically:

import time

def wait_for_batch_completion(client, job_id, max_wait=300):
    """Wait for batch job to complete with polling."""
    start_time = time.time()
    
    while time.time() - start_time < max_wait:
        status = client.get_batch_status(job_id)
        
        if status['status'] == 'COMPLETED':
            return status
        elif status['status'] == 'ERROR':
            raise Exception(f"Batch job failed: {status.get('status_description')}")
        
        print(f"Progress: {status.get('progress', 0)}%")
        time.sleep(10)  # Wait 10 seconds before next check
    
    raise TimeoutError("Batch job did not complete within the timeout period")

# Usage
try:
    batch_response = email_insights.batch_analyze(batch_params)
    final_status = wait_for_batch_completion(email_insights, batch_response['job_id'])
    print("Batch completed successfully!")
except Exception as e:
    print(f"Error: {e}")