import logging
import networkx as nx
from sklearn.ensemble import RandomForestClassifier, GradientBoostingClassifier, VotingClassifier
from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score
from sklearn.preprocessing import StandardScaler

class VulnerabilityScanner:
    def __init__(self):
        self.scan_results = []
        self.rf_model = RandomForestClassifier(n_estimators=100)
        self.gb_model = GradientBoostingClassifier(n_estimators=100)
        self.ensemble_model = VotingClassifier(estimators=[('rf', self.rf_model), ('gb', self.gb_model)], voting='soft')
        self.data = None
        self.labels = None
        self.scaler = StandardScaler()

    def scan(self, target):
        logging.info(f"Scanning target: {target}")
        vulnerabilities = self.detect_vulnerabilities(target)
        graph_based_vulnerabilities = self.graph_based_detection(target)
        integrated_vulnerabilities = self.integrate_with_security_tools(target)
        all_vulnerabilities = vulnerabilities + graph_based_vulnerabilities + integrated_vulnerabilities
        self.scan_results.append({
            "target": target,
            "vulnerabilities": all_vulnerabilities
        })
        return all_vulnerabilities

    def detect_vulnerabilities(self, target):
        logging.info(f"Detecting vulnerabilities for target: {target}")
        vulnerabilities = []
        # Example vulnerability detection logic
        if "example.com" in target:
            vulnerabilities.append({"id": "CVE-2021-1234", "description": "Sample vulnerability 1", "severity": "High"})
        if "test.com" in target:
            vulnerabilities.append({"id": "CVE-2021-5678", "description": "Sample vulnerability 2", "severity": "Medium"})
        return vulnerabilities

    def graph_based_detection(self, target):
        logging.info(f"Performing graph-based detection for target: {target}")
        vulnerabilities = []
        # Example graph-based detection logic
        G = nx.Graph()
        G.add_node(target)
        # Add more nodes and edges based on the target's network
        # Example: G.add_edge(node1, node2)
        # Analyze the graph for vulnerabilities
        if G.has_node(target):
            vulnerabilities.append({"id": "CVE-2022-0003", "description": "Graph-based vulnerability 1", "severity": "High"})
        return vulnerabilities

    def integrate_with_security_tools(self, target):
        logging.info(f"Integrating with other security tools for target: {target}")
        vulnerabilities = []
        # Example integration with other security tools
        # Placeholder for integration logic
        if "securitytool.com" in target:
            vulnerabilities.append({"id": "CVE-2022-0004", "description": "Security tool vulnerability 1", "severity": "High"})
        return vulnerabilities

    def scan_hak5_vulnerabilities(self, target):
        logging.info(f"Scanning Hak5 vulnerabilities for target: {target}")
        vulnerabilities = []
        # Example Hak5 vulnerability detection logic
        if "hak5.com" in target:
            vulnerabilities.append({"id": "CVE-2022-0001", "description": "Hak5 vulnerability 1", "severity": "High"})
        if "ducky.com" in target:
            vulnerabilities.append({"id": "CVE-2022-0002", "description": "Hak5 vulnerability 2", "severity": "Medium"})
        self.scan_results.append({
            "target": target,
            "vulnerabilities": vulnerabilities
        })
        return vulnerabilities

    def render(self):
        return "Vulnerability Scanner Module: Ready to scan and report vulnerabilities."

    def integrate_with_new_components(self, new_component_data):
        logging.info("Integrating with new components")
        integrated_data = {
            "new_component_vulnerabilities": new_component_data.get("vulnerabilities", {})
        }
        self.scan_results.append(integrated_data)
        return self.scan_results

    def ensure_compatibility(self, existing_data, new_component_data):
        logging.info("Ensuring compatibility with existing vulnerability scanner logic")
        compatible_data = {
            "existing_vulnerabilities": existing_data.get("vulnerabilities", {}),
            "new_component_vulnerabilities": new_component_data.get("vulnerabilities", {})
        }
        return compatible_data

    def preprocess_data(self, data):
        return self.scaler.fit_transform(data)

    def load_data(self, data, labels):
        self.data = self.preprocess_data(data)
        self.labels = labels

    def train_model(self):
        if self.data is None or self.labels is None:
            raise ValueError("Data and labels must be loaded before training the model.")
        
        X_train, X_test, y_train, y_test = train_test_split(self.data, self.labels, test_size=0.2, random_state=42)
        self.ensemble_model.fit(X_train, y_train)
        predictions = self.ensemble_model.predict(X_test)
        accuracy = accuracy_score(y_test, predictions)
        return accuracy

    def predict(self, new_data):
        if self.ensemble_model is None:
            raise ValueError("Model must be trained before making predictions.")
        
        preprocessed_data = self.preprocess_data(new_data)
        return self.ensemble_model.predict(preprocessed_data)