PII

import json
import pandas as pd
import requests
import time
import fiddler as fdl

# Replace with your actual values
URL = 'https://your_company.fiddler.ai'
TOKEN = 'your_token_here'

# API Configuration
SENSITIVE_INFORMATION_URL = f"{URL}/v3/guardrails/sensitive-information"
FIDDLER_HEADERS = {
    "Authorization": f"Bearer {TOKEN}",
    "Content-Type": "application/json",
}

# Connect to Fiddler
fdl.init(url=URL, token=TOKEN)
print("✅ Connected to Fiddler successfully!")

def get_sensitive_information_response(
    text: str,
    entity_categories: str | list[str] = 'PII',
    custom_entities: list[str] = None,
):
    """
    Detect sensitive information in text.

    Args:
        text: Input text to analyze
        entity_categories: 'PII', 'PHI', 'Custom Entities', or list
        custom_entities: Custom entity patterns (when using 'Custom Entities')

    Returns:
        Tuple of (API response dict, latency in seconds)
    """
    data = {'input': text}

    # Add entity configuration if specified
    if entity_categories != 'PII' or custom_entities:
        data['entity_categories'] = entity_categories
        if custom_entities:
            data['custom_entities'] = custom_entities

    start_time = time.monotonic()

    try:
        response = requests.post(
            SENSITIVE_INFORMATION_URL,
            headers=FIDDLER_HEADERS,
            json={'data': data},
        )
        response.raise_for_status()
        return response.json(), (time.monotonic() - start_time)

    except requests.exceptions.RequestException as e:
        print(f'❌ API call failed: {e}')
        return {}, (time.monotonic() - start_time)

def print_detection_results(response, latency):
    """Display detection results in a formatted way."""
    entities = response.get('fdl_sensitive_information_scores', [])

    print(f"\n🔍 Detection Results (⏱️ {latency:.3f}s)")
    print(f"📊 Total Entities Found: {len(entities)}\n")

    if not entities:
        print("✅ No sensitive information detected.")
        return

    # Group by entity type
    by_type = {}
    for entity in entities:
        label = entity.get('label', 'unknown')
        if label not in by_type:
            by_type[label] = []
        by_type[label].append(entity)

    # Display grouped results
    for label, group in sorted(by_type.items()):
        print(f"🏷️  {label.upper()} ({len(group)} found):")
        for entity in group:
            print(f"   • '{entity['text']}' (confidence: {entity['score']:.3f})")
        print()

# Sample text with various PII types
sample_text = """
I'm John Doe and I live at 1234 Maple Street, Springfield, IL 62704.
You can reach me at john.doe@email.com or call me at (217) 555-1234.
My social security number is 123-45-6789, and I was born on January 15, 1987.
My credit card number is 4111 1111 1111 1111 with CVV 123.
"""

print("🧪 Testing PII Detection")
print("📄 Input Text:")
print(sample_text)

# Call the API with default PII configuration
response, latency = get_sensitive_information_response(sample_text)

# Display results
print_detection_results(response, latency)

🔍 Detection Results (⏱️ 0.125s)
📊 Total Entities Found: 8

🏷️  PERSON (1 found):
   • 'John Doe' (confidence: 0.987)

🏷️  ADDRESS (1 found):
   • '1234 Maple Street, Springfield, IL 62704' (confidence: 0.945)

🏷️  EMAIL (1 found):
   • 'john.doe@email.com' (confidence: 0.998)

🏷️  PHONE NUMBER (1 found):
   • '(217) 555-1234' (confidence: 0.976)

🏷️  SOCIAL SECURITY NUMBER (1 found):
   • '123-45-6789' (confidence: 0.991)

🏷️  CREDIT CARD NUMBER (1 found):
   • '4111 1111 1111 1111' (confidence: 0.989)

🏷️  CVV (1 found):
   • '123' (confidence: 0.892)

🏷️  DATE OF BIRTH (1 found):
   • 'January 15, 1987' (confidence: 0.923)

# Sample text with PHI information
healthcare_text = """
Patient report: John Smith was prescribed metformin for his diabetes condition.
His health insurance number is HI-987654321, and medical record shows
serial number MED-2024-001 for his glucose monitor device.
Birth certificate number is BC-IL-1987-001234.
Current medication includes aspirin and lisinopril for blood pressure management.
"""

print("🏥 Testing PHI Detection for Healthcare Data")
print("📄 Input Text:")
print(healthcare_text)

# Call the API with PHI configuration
response, latency = get_sensitive_information_response(
    healthcare_text,
    entity_categories="PHI"
)

# Display results
print_detection_results(response, latency)

🔍 Detection Results (⏱️ 0.098s)
📊 Total Entities Found: 5

🏷️  PERSON (1 found):
   • 'John Smith' (confidence: 0.976)

🏷️  MEDICATION (3 found):
   • 'metformin' (confidence: 0.945)
   • 'aspirin' (confidence: 0.932)
   • 'lisinopril' (confidence: 0.928)

🏷️  HEALTH INSURANCE NUMBER (1 found):
   • 'HI-987654321' (confidence: 0.887)

# Sample text with custom entities
custom_text = """
Employee ID: EMP-2024-001, Badge Number: BD-789456
Project code: PROJ-AI-2024, Server hostname: srv-prod-01
API key: sk-abc123xyz789
Internal ticket: TICK-2024-5678
"""

# Define custom entities for your organization
custom_entities = [
    'employee id',
    'badge number',
    'project code',
    'api key',
    'server hostname',
    'ticket number'
]

print("🎯 Testing Custom Entity Detection")
print(f"🏷️ Custom entities: {custom_entities}")

# Call the API with custom entity configuration
response, latency = get_sensitive_information_response(
    custom_text,
    entity_categories='Custom Entities',
    custom_entities=custom_entities
)

# Display results
print_detection_results(response, latency)

🔍 Detection Results (⏱️ 0.112s)
📊 Total Entities Found: 6

🏷️  EMPLOYEE ID (1 found):
   • 'EMP-2024-001' (confidence: 0.923)

🏷️  BADGE NUMBER (1 found):
   • 'BD-789456' (confidence: 0.911)

🏷️  PROJECT CODE (1 found):
   • 'PROJ-AI-2024' (confidence: 0.897)

🏷️  API KEY (1 found):
   • 'sk-abc123xyz789' (confidence: 0.945)

🏷️  SERVER HOSTNAME (1 found):
   • 'srv-prod-01' (confidence: 0.878)

🏷️  TICKET NUMBER (1 found):
   • 'TICK-2024-5678' (confidence: 0.902)