import socket

import time

import requests

from datetime import datetime

class ConnectivityAI:

"""

A Kaizen-informed AI assistant for monitoring network integrity.

Inspired by the 7 Archangels: Protective, Swift, and Watchful.

"""

def __init__(self, target_host="8.8.8.8", port=53, timeout=3):

self.target_host = target_host

self.port = port

self.timeout = timeout

self.status = False

def check_pulse(self):

"""Scientific verification of the TCP layer."""

try:

socket.setdefaulttimeout(self.timeout)

# Create a socket connection to verify raw internet access

socket.socket(socket.AF_INET, socket.SOCK_STREAM).connect((self.target_host, self.port))

return True

except socket.error:

return False

def archangel_report(self):

"""Displays status with a HUD-inspired output."""

self.status = self.check_pulse()

timestamp = datetime.now().strftime("%Y-%m-%d %H:%M:%S")

# HUD Interface elements

print(f"--- [ NETWORK HUD ACTIVE ] ---")

print(f"TIME: {timestamp}")

print(f"TARGET: {self.target_host}")

if self.status:

print("STATUS: [ ONLINE ] - Connection is Radiant.")

else:

print("STATUS: [ OFFLINE ] - Seeking restoration...")

self.attempt_self_healing()

def attempt_self_healing(self):

"""Kaizen improvement: Attempt to refresh DNS or log failure."""

print("ACTION: Resetting local DNS cache logic...")

# Add logic here to trigger system-level network restarts if required

if __name__ == "__main__":

ai_monitor = ConnectivityAI()

while True:

ai_monitor.archangel_report()

time.sleep(5) # Efficient polling interval

import numpy as np

import pandas as pd

from sklearn.ensemble import GradientBoostingClassifier

import time

import socket

import logging

class SentinelAI:

"""

Advanced Network AI with Predictive Self-Healing.

Characteristics: Archangel Metatron (The Scribe/Log Keeper)

"""

def __init__(self):

# Training data: [Latency, PacketLoss, Jitter] -> ConnectionState (1=Good, 0=Failing)

self.model = GradientBoostingClassifier()

self.history = []

self.is_trained = False

self._init_hud_logger()

def _init_hud_logger(self):

logging.basicConfig(level=logging.INFO, format='%(asctime)s [SENTINEL-HUD] %(message)s')

self.logger = logging.getLogger("Sentinel")

def collect_telemetry(self):

"""Scientific measurement of TCP/IP handshake latency."""

start = time.perf_counter()

try:

socket.create_connection(("8.8.8.8", 53), timeout=2)

latency = (time.perf_counter() - start) * 1000

status = 1

except Exception:

latency = 2000 # Max timeout

status = 0

return [latency, status]

def kaizen_update(self, new_data):

"""Continuously improve the model with new instances of data."""

self.history.append(new_data)

if len(self.history) > 20: # Start training after 20 samples

df = pd.DataFrame(self.history, columns=['latency', 'status'])

X = df[['latency']]

y = df['status']

self.model.fit(X, y)

self.is_trained = True

def predict_stability(self, current_latency):

if not self.is_trained:

return "ANALYZING..."

prediction = self.model.predict([[current_latency]])[0]

return "STABLE" if prediction == 1 else "IMPENDING FAILURE"

def run_cycle(self):

"""The main HUD-driven execution loop."""

while True:

latency, status = self.collect_telemetry()

self.kaizen_update([latency, status])

stability = self.predict_stability(latency)

self.logger.info(f"LATENCY: {latency:.2f}ms | PRED: {stability} | STATUS: {'🟢' if status else '🔴'}")

if stability == "IMPENDING FAILURE":

self.self_heal()

time.sleep(2)

def self_heal(self):

"""Advanced restoration logic."""

self.logger.warning("ACTIVATE SELF-HEAL: Flushing DNS & Re-routing...")

# Placeholder for os.system commands like 'ipconfig /flushdns'

if __name__ == "__main__":

SentinelAI().run_cycle()