"""
PII (Personally Identifiable Information) Filter Module
=========================================================
Regex-based detection and masking for emails, phone numbers,
CNIC/SSN-like patterns, API keys, and addresses.
"""

import re
from dataclasses import dataclass
from typing import List, Dict, Tuple
import pandas as pd


@dataclass
class PIIFilterConfig:
    """Configuration for PII filtering."""
    filter_emails: bool = False
    filter_phones: bool = False
    filter_id_numbers: bool = False   # CNIC / SSN patterns
    filter_api_keys: bool = False
    filter_addresses: bool = False
    mask_char: str = "[REDACTED]"


# ---------------------------------------------------------------------------
# Detection + Masking patterns
# ---------------------------------------------------------------------------

_EMAIL_PATTERN = re.compile(
    r'\b[A-Za-z0-9._%+-]+@[A-Za-z0-9.-]+\.[A-Z|a-z]{2,}\b'
)

_PHONE_PATTERN = re.compile(
    r'(?:\+?\d{1,3}[-.\s]?)?\(?\d{2,4}\)?[-.\s]?\d{3,4}[-.\s]?\d{3,4}'
)

# SSN: 123-45-6789, CNIC: 12345-1234567-1
_ID_NUMBER_PATTERN = re.compile(
    r'\b\d{3}-\d{2}-\d{4}\b'        # US SSN
    r'|\b\d{5}-\d{7}-\d{1}\b'       # PK CNIC
    r'|\b\d{13}\b'                   # 13-digit ID
)

# Long hex or base64 strings that look like API keys / secrets
_API_KEY_PATTERN = re.compile(
    r'\b(?:sk|pk|api|key|secret|token)[_-]?[A-Za-z0-9]{20,}\b'
    r'|[A-Fa-f0-9]{32,}'
    r'|[A-Za-z0-9+/]{40,}={0,2}',
    re.IGNORECASE,
)

# Basic address patterns (US-style zip, PO Box, street numbers)
_ADDRESS_PATTERN = re.compile(
    r'\b\d{1,5}\s+\w+\s+(?:St|Street|Ave|Avenue|Blvd|Boulevard|Dr|Drive|Rd|Road|Ln|Lane|Way|Ct|Court)\b'
    r'|\bP\.?O\.?\s*Box\s+\d+\b'
    r'|\b\d{5}(?:-\d{4})?\b',         # Zip code
    re.IGNORECASE,
)


def detect_emails(text: str) -> List[str]:
    """Find all email addresses in text."""
    return _EMAIL_PATTERN.findall(text) if isinstance(text, str) else []


def mask_emails(text: str, mask: str = "[REDACTED_EMAIL]") -> str:
    """Replace email addresses with mask."""
    return _EMAIL_PATTERN.sub(mask, text) if isinstance(text, str) else text


def detect_phones(text: str) -> List[str]:
    """Find all phone numbers in text."""
    return _PHONE_PATTERN.findall(text) if isinstance(text, str) else []


def mask_phones(text: str, mask: str = "[REDACTED_PHONE]") -> str:
    """Replace phone numbers with mask."""
    return _PHONE_PATTERN.sub(mask, text) if isinstance(text, str) else text


def detect_id_numbers(text: str) -> List[str]:
    """Find SSN/CNIC-like patterns in text."""
    return _ID_NUMBER_PATTERN.findall(text) if isinstance(text, str) else []


def mask_id_numbers(text: str, mask: str = "[REDACTED_ID]") -> str:
    """Replace ID number patterns with mask."""
    return _ID_NUMBER_PATTERN.sub(mask, text) if isinstance(text, str) else text


def detect_api_keys(text: str) -> List[str]:
    """Find API key / secret patterns in text."""
    return _API_KEY_PATTERN.findall(text) if isinstance(text, str) else []


def mask_api_keys(text: str, mask: str = "[REDACTED_KEY]") -> str:
    """Replace API key patterns with mask."""
    return _API_KEY_PATTERN.sub(mask, text) if isinstance(text, str) else text


def detect_addresses(text: str) -> List[str]:
    """Find address-like patterns in text."""
    return _ADDRESS_PATTERN.findall(text) if isinstance(text, str) else []


def mask_addresses(text: str, mask: str = "[REDACTED_ADDR]") -> str:
    """Replace address patterns with mask."""
    return _ADDRESS_PATTERN.sub(mask, text) if isinstance(text, str) else text


def apply_pii_filter(
    text: str,
    config: PIIFilterConfig,
) -> str:
    """Apply all enabled PII filters to a single text string."""
    mask = config.mask_char

    if config.filter_emails:
        text = mask_emails(text, mask)
    if config.filter_phones:
        text = mask_phones(text, mask)
    if config.filter_id_numbers:
        text = mask_id_numbers(text, mask)
    if config.filter_api_keys:
        text = mask_api_keys(text, mask)
    if config.filter_addresses:
        text = mask_addresses(text, mask)

    return text


def apply_pii_filter_df(
    df: pd.DataFrame,
    columns: List[str],
    config: PIIFilterConfig,
) -> pd.DataFrame:
    """Apply PII filtering to specified columns of a DataFrame."""
    df = df.copy()
    for col in columns:
        if col in df.columns:
            df[col] = df[col].apply(lambda t: apply_pii_filter(str(t), config))
    return df


def detect_pii_summary(
    df: pd.DataFrame,
    columns: List[str],
) -> Dict[str, int]:
    """
    Scan columns and count PII instances found.
    Returns dict like {"emails": 5, "phones": 2, ...}.
    """
    summary = {"emails": 0, "phones": 0, "id_numbers": 0, "api_keys": 0, "addresses": 0}

    for col in columns:
        if col not in df.columns:
            continue
        for text in df[col].astype(str):
            summary["emails"] += len(detect_emails(text))
            summary["phones"] += len(detect_phones(text))
            summary["id_numbers"] += len(detect_id_numbers(text))
            summary["api_keys"] += len(detect_api_keys(text))
            summary["addresses"] += len(detect_addresses(text))

    return summary