main.py

import pytorch
import torch
from transformers import BertTokenizer, BertForMaskedLM, Trainer, TrainingArguments
from bs4 import BeautifulSoup
from selenium import webdriver
from selenium.webdriver.chrome.options import Options
from selenium.common.exceptions import WebDriverException
import logging
import nltk
from nltk.corpus import stopwords
from nltk.tokenize import word_tokenize
from nltk.stem import WordNetLemmatizer
from qiskit import Aer, IBMQ
from qiskit import IBMQAccountError
from qiskit.optimization import QuadraticProgram
from qiskit.optimization.algorithms import MinimumEigenOptimizer
from qiskit.algorithms import QAOA
from qiskit.utils import QuantumInstance
import openai
from datasets import Dataset, DatasetDict
import time
import os

# Configure logging
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)

def download_nltk_resources():
    """Downloads required NLTK resources."""
    nltk.download('punkt', quiet=True)
    nltk.download('stopwords', quiet=True)
    nltk.download('wordnet', quiet=True)

def clean_data(data: str) -> str:
    """Clean and preprocess the scraped data."""
    soup = BeautifulSoup(data, 'html.parser')
    text = soup.get_text()
    tokens = word_tokenize(text)
    tokens = [word for word in tokens if word.isalpha()]
    stop_words = set(stopwords.words('english'))
    tokens = [word for word in tokens if word.lower() not in stop_words]
    lemmatizer = WordNetLemmatizer()
    tokens = [lemmatizer.lemmatize(word) for word in tokens]
    return ' '.join(tokens)

def handle_webdriver_exception(e):
    """Handles WebDriver exceptions and logs the error."""
    logger.error("Error in data collection: %s", e)
    return ""

def collect_and_preprocess_data(url: str) -> str:
    """Collect and preprocess data from a given URL."""
    options = Options()
    options.add_argument("--headless")  # Run in headless mode

    try:
        with webdriver.Chrome(options=options) as driver:
            driver.get(url)
            time.sleep(3)  # Adjust as necessary to wait for the page to load
            scraped_data = driver.page_source
            cleaned_data = clean_data(scraped_data)
            print("Data collection and preprocessing completed.")
            return cleaned_data
    except WebDriverException as e:
        return handle_webdriver_exception(e)

def train_model(train_data: str):
    """Train the DarkBERT model."""
    tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')
    train_encodings = tokenizer(train_data, truncation=True, padding=True, return_tensors='pt')

    # For MLM, labels are needed. Mask some of the tokens for training.
    inputs = train_encodings['input_ids']
    labels = inputs.clone()

    # We sample a few tokens in each sequence for MLM training (with probability 0.15)
    rand = torch.rand(labels.shape)
    mask_arr = (rand < 0.15) * (inputs != tokenizer.cls_token_id) * \
               (inputs != tokenizer.pad_token_id) * (inputs != tokenizer.sep_token_id)
    labels[~mask_arr] = -100  # We only compute loss on masked tokens

    dataset = Dataset.from_dict({
        'input_ids': inputs,
        'attention_mask': train_encodings['attention_mask'],
        'labels': labels
    })

    train_dataset = DatasetDict({
        'train': dataset
    })

    model = BertForMaskedLM.from_pretrained('bert-base-uncased')

    training_args = TrainingArguments(
        output_dir='./results_darkbert',
        num_train_epochs=3,
        per_device_train_batch_size=8,
        save_steps=10_000,
        save_total_limit=2,
    )

    trainer = Trainer(
        model=model,
        args=training_args,
        train_dataset=train_dataset['train'],
    )

    trainer.train()
    model.save_pretrained('./trained_darkbert')
    tokenizer.save_pretrained('./trained_darkbert')
    print("Model training completed and model saved.")

def select_model():
    """Select the model for advanced search."""
    model_choice = input("Select the model for advanced search (1. BERT, 2. DarkBERT): ")
    while model_choice not in ['1', '2']:
        print("Invalid choice. Please enter 1 or 2.")
        model_choice = input("Select the model for advanced search (1. BERT, 2. DarkBERT): ")

    if model_choice == '1':
        model_name = 'bert-base-uncased'
    elif model_choice == '2':
        model_name = './trained_darkbert'
    return model_name

def integrate_quantum_optimization(data, retry=False):
    """Integrate quantum computing for optimization tasks."""
    try:
        IBMQ.load_account()
        provider = IBMQ.get_provider(hub='ibm-q')

        backend = Aer.get_backend('qasm_simulator')
        quantum_instance = QuantumInstance(backend, shots=1024)
        qaoa = QAOA(quantum_instance=quantum_instance)
        optimizer = MinimumEigenOptimizer(qaoa)

        problem = create_optimization_problem(data)  # Create
        result = optimizer.solve(problem)
        print("Quantum optimization completed. Result:", result)
        return result
    except IBMQAccountError:
        logger.error("Error: IBM Quantum account credentials are missing or invalid.")
        if not retry:
            api_token = input("Enter your IBM Quantum API token: ")
            try:
                IBMQ.save_account(api_token)
                IBMQ.load_account()
                integrate_quantum_optimization(data, retry=True)
            except IBMQAccountError:
                logger.error("Failed to load IBM Quantum account with provided token.")
        else:
            logger.error("IBMQAccountError occurred even after retry.")
    except Exception as e:
        logger.error("Unexpected error in quantum integration: %s", e)

def create_optimization_problem(data):
    """Create a QuadraticProgram for a specific optimization task."""
    # Placeholder: Depending on the task, define the problem with appropriate constraints and objectives
    # For example, for text analysis, it could involve optimizing parameters for NLP models or clustering algorithms
    pass

def advanced_search(query: str, model: BertForMaskedLM, tokenizer: BertTokenizer):
    """Perform advanced search using the specified model."""
    try:
        inputs = tokenizer(query, return_tensors='pt')
        outputs = model(**inputs)
        return outputs
    except Exception as e:
        logger.error("Error in advanced search: %s", e)
        return None

def scrape_hacker_forums_with_quantum(query: str) -> list:
    """Scrape hacker forums for code snippets using OpenAI Codex with quantum assistance."""
    try:
        response = openai.Completion.create(
            engine="davinci-codex",
            prompt=query,
            max_tokens=100,
            n=5,
            stop=None,
            temperature=0.7
        )
        return [choice.text.strip() for choice in response.choices]
    except Exception as e:
        logger.error("Error in Codex integration: %s", e)
        return []

def show_menu():
    """Display the main menu options."""
    print("Main Menu:")
    print("1. Collect and preprocess data from Dark Web URL")
    print("2. Train DarkBERT model")
    print("3. Select model for advanced search")
    print("4. Perform advanced search")
    print("5. Scrape hacker forums with quantum assistance")
    print("6. Integrate quantum optimization for specific tasks")
    print("7. Exit")

def main():
    """Main function to execute the program."""
    download_nltk_resources()
    while True:
        show_menu()
        choice = input("Enter your choice: ")
        if choice == '1':
            url = input("Enter the URL to collect data from: ")
            cleaned_data = collect_and_preprocess_data(url)
            # Placeholder: Add functionality for malware detection
            # Placeholder: Add functionality for code review
        elif choice == '2':
            train_data = input("Enter training data for DarkBERT: ")
            train_model(train_data)
        elif choice == '3':
            model_name = select_model()
            tokenizer = BertTokenizer.from_pretrained(model_name)
            model = BertForMaskedLM.from_pretrained(model_name)
        elif choice == '4':
            query = input("Enter your query for advanced search: ")
            if 'model' not in locals():
                print("Please select a model first (Option 3).")
                continue
            outputs = advanced_search(query, model, tokenizer)
            if outputs:
                print(outputs)
        elif choice == '5':
            query = input("Enter your query for hacker forums: ")
            code_snippets = scrape_hacker_forums_with_quantum(query)
            for snippet in code_snippets:
                print(snippet)
        elif choice == '6':
            data = input("Enter data for optimization task: ")
            integrate_quantum_optimization(data)
        elif choice == '7':
            print("Exiting the program...")
            break
        else:
            print("Invalid choice. Please enter a number from 1 to 7.")

if __name__ == "__main__":
    main()