# ============================================================
# PhishGuard AI - gnn/domain_graph_builder.py
# Builds graph representations for GNN inference + training.
#
# Node features (12-dim per URL):
#   [url_len_norm, domain_len_norm, subdomain_count_norm,
#    shannon_entropy_norm, digit_ratio, hyphen_count_norm,
#    phishing_keyword_hits_norm, suspicious_tld_binary,
#    ip_as_hostname_binary, has_https_binary,
#    path_depth_norm, query_string_len_norm]
#
# Edges: shared suspicious TLD + shared IP (async DNS)
# ============================================================

from __future__ import annotations

import re
import math
import asyncio
import logging
import socket
from typing import Dict, List, Optional, Tuple
from urllib.parse import urlparse

import numpy as np

logger = logging.getLogger("phishguard.gnn.graph_builder")

# ── Constants ────────────────────────────────────────────────────────
SUSPICIOUS_TLDS = frozenset({
    ".xyz", ".tk", ".ml", ".ga", ".cf",
    ".gq", ".pw", ".top", ".click",
})

PHISHING_KEYWORDS = frozenset({
    "login", "verify", "secure", "update", "account",
    "banking", "signin", "reset", "confirm", "suspend",
    "webscr", "cmd", "payment", "alert",
})

_re_ip = re.compile(r"^\d{1,3}\.\d{1,3}\.\d{1,3}\.\d{1,3}$")


class DomainGraphBuilder:
    """
    Builds PyTorch Geometric Data objects from URL lists.
    Each URL becomes a node with 12-dim feature vector.
    Edges are created from shared IP addresses and shared TLDs.
    """

    def __init__(self) -> None:
        self._re_ip = _re_ip

    def extract_node_features(self, url: str) -> np.ndarray:
        """
        Extract 12-dim feature vector from a URL.

        Returns np.ndarray of shape (12,) with values in [0, 1].
        """
        try:
            parsed = urlparse(url if "://" in url else f"http://{url}")
        except Exception:
            return np.zeros(12, dtype=np.float32)

        hostname: str = (parsed.hostname or "").lower()
        path: str = parsed.path or ""
        query: str = parsed.query or ""
        scheme: str = parsed.scheme or ""

        # 1. url_len_norm (normalized by 500)
        url_len_norm = min(len(url) / 500.0, 1.0)

        # 2. domain_len_norm (normalized by 100)
        domain_len_norm = min(len(hostname) / 100.0, 1.0)

        # 3. subdomain_count_norm
        parts = hostname.split(".")
        subdomain_count = max(0, len(parts) - 2)
        subdomain_count_norm = min(subdomain_count / 10.0, 1.0)

        # 4. shannon_entropy_norm (normalized by 5.0)
        entropy = self._shannon_entropy(hostname)
        shannon_entropy_norm = min(entropy / 5.0, 1.0)

        # 5. digit_ratio
        digit_ratio = 0.0
        if hostname:
            digits = sum(1 for c in hostname if c.isdigit())
            digit_ratio = digits / len(hostname)

        # 6. hyphen_count_norm
        hyphen_count = hostname.count("-")
        hyphen_count_norm = min(hyphen_count / 10.0, 1.0)

        # 7. phishing_keyword_hits_norm
        url_lower = url.lower()
        keyword_hits = sum(1 for kw in PHISHING_KEYWORDS if kw in url_lower)
        phishing_keyword_hits_norm = min(keyword_hits / 5.0, 1.0)

        # 8. suspicious_tld_binary
        suspicious_tld_binary = 0.0
        for tld in SUSPICIOUS_TLDS:
            if hostname.endswith(tld):
                suspicious_tld_binary = 1.0
                break

        # 9. ip_as_hostname_binary
        ip_as_hostname_binary = 1.0 if self._re_ip.match(hostname) else 0.0

        # 10. has_https_binary
        has_https_binary = 1.0 if scheme == "https" else 0.0

        # 11. path_depth_norm
        path_segments = [s for s in path.split("/") if s]
        path_depth_norm = min(len(path_segments) / 10.0, 1.0)

        # 12. query_string_len_norm
        query_string_len_norm = min(len(query) / 500.0, 1.0)

        features = np.array([
            url_len_norm,
            domain_len_norm,
            subdomain_count_norm,
            shannon_entropy_norm,
            digit_ratio,
            hyphen_count_norm,
            phishing_keyword_hits_norm,
            suspicious_tld_binary,
            ip_as_hostname_binary,
            has_https_binary,
            path_depth_norm,
            query_string_len_norm,
        ], dtype=np.float32)

        return features

    def _shannon_entropy(self, s: str) -> float:
        """Compute Shannon entropy of a string."""
        if not s:
            return 0.0
        length = len(s)
        freq: Dict[str, int] = {}
        for c in s:
            freq[c] = freq.get(c, 0) + 1
        return -sum(
            (count / length) * math.log2(count / length)
            for count in freq.values()
            if count > 0
        )

    async def _resolve_ips(self, domains: List[str]) -> Dict[str, str]:
        """
        Async DNS resolution for a list of domains.
        Returns dict mapping domain → IP address.
        """
        results: Dict[str, str] = {}
        loop = asyncio.get_event_loop()

        async def resolve_one(domain: str) -> Tuple[str, str]:
            try:
                ip = await asyncio.wait_for(
                    loop.run_in_executor(None, socket.gethostbyname, domain),
                    timeout=2.0,
                )
                return domain, ip
            except Exception:
                return domain, ""

        tasks = [resolve_one(d) for d in domains]
        resolved = await asyncio.gather(*tasks, return_exceptions=True)
        for item in resolved:
            if isinstance(item, tuple):
                domain, ip = item
                if ip:
                    results[domain] = ip
        return results

    def _add_shared_ip_edges(
        self, domains: List[str], ips: Dict[str, str]
    ) -> List[Tuple[int, int]]:
        """
        Create edges between nodes that share the same IP address.
        Returns list of (src, dst) index pairs.
        """
        edges: List[Tuple[int, int]] = []
        # Group domain indices by IP
        ip_to_indices: Dict[str, List[int]] = {}
        for idx, domain in enumerate(domains):
            ip = ips.get(domain, "")
            if ip:
                ip_to_indices.setdefault(ip, []).append(idx)

        # Create edges between all nodes sharing an IP
        for ip, indices in ip_to_indices.items():
            for i in range(len(indices)):
                for j in range(i + 1, len(indices)):
                    edges.append((indices[i], indices[j]))
                    edges.append((indices[j], indices[i]))  # bidirectional

        return edges

    def _add_shared_tld_edges(self, domains: List[str]) -> List[Tuple[int, int]]:
        """
        Create edges between nodes that share the same suspicious TLD.
        """
        edges: List[Tuple[int, int]] = []
        tld_to_indices: Dict[str, List[int]] = {}

        for idx, domain in enumerate(domains):
            for tld in SUSPICIOUS_TLDS:
                if domain.endswith(tld):
                    tld_to_indices.setdefault(tld, []).append(idx)
                    break

        for tld, indices in tld_to_indices.items():
            for i in range(len(indices)):
                for j in range(i + 1, len(indices)):
                    edges.append((indices[i], indices[j]))
                    edges.append((indices[j], indices[i]))

        return edges

    def build_graph(self, urls: List[str], resolve_dns: bool = False) -> dict:
        """
        Build a graph dict from a list of URLs.

        Returns dict with:
          - features: np.ndarray of shape (N, 12)
          - edges: List of (src, dst) pairs
          - node_count: int
          - edge_count: int
          - domains: List[str]
        """
        if not urls:
            return {
                "features": np.zeros((1, 12), dtype=np.float32),
                "edges": [],
                "node_count": 0,
                "edge_count": 0,
                "domains": [],
            }

        # Extract features for each URL
        features = np.array(
            [self.extract_node_features(url) for url in urls],
            dtype=np.float32,
        )

        # Extract domains
        domains: List[str] = []
        for url in urls:
            try:
                parsed = urlparse(url if "://" in url else f"http://{url}")
                domains.append((parsed.hostname or "").lower())
            except Exception:
                domains.append("")

        # Build edges from shared TLDs (synchronous, fast)
        edges = self._add_shared_tld_edges(domains)

        # Optionally resolve DNS for shared IP edges
        if resolve_dns and len(domains) > 1:
            try:
                loop = asyncio.get_event_loop()
                if loop.is_running():
                    # Already in async context
                    pass
                else:
                    ips = loop.run_until_complete(self._resolve_ips(domains))
                    edges.extend(self._add_shared_ip_edges(domains, ips))
            except RuntimeError:
                pass  # Cannot resolve in this context

        return {
            "features": features,
            "edges": edges,
            "node_count": len(urls),
            "edge_count": len(edges),
            "domains": domains,
        }

    def build_single_node_graph(self, url: str) -> dict:
        """
        Build a single-node graph for MLP fallback path.
        Used when a graph has fewer than 2 nodes.
        """
        features = self.extract_node_features(url).reshape(1, -1)
        return {
            "features": features,
            "edges": [],
            "node_count": 1,
            "edge_count": 0,
            "domains": [url],
        }


# ── Legacy compatibility wrapper ─────────────────────────────────────
_builder = DomainGraphBuilder()


def build_domain_graph(urls: List[str]) -> dict:
    """Legacy wrapper for backward compatibility."""
    return _builder.build_graph(urls)