main.py

import requests
from bs4 import BeautifulSoup, Comment
import pandas as pd
import os, schedule, time, logging, re, nltk, json, random
from nltk.corpus import stopwords, wordnet
from nltk.stem import PorterStemmer
from concurrent.futures import ThreadPoolExecutor
from urllib.robotparser import RobotFileParser
from sklearn.metrics import precision_score, recall_score, f1_score, confusion_matrix
import matplotlib.pyplot as plt
import numpy as np

# Initialize logging
logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s')

# Load or set default configuration
config = {
    "MAX_VECTOR_SIZE": 10000,  # Hypervector dimensionality
    "TEST_SPLIT": 0.1,
    "URLS": [
        'https://www.bbc.com',
        'https://www.cnn.com',
    ],
    "RATE_LIMIT": 1  # seconds between requests to prevent overloading
}

# Load configuration from file if exists
try:
    with open('config.json', 'r') as f:
        config.update(json.load(f))
except FileNotFoundError:
    logging.info("No config file found, using default configuration.")

# Apply configuration
MAX_VECTOR_SIZE = config['MAX_VECTOR_SIZE']
TEST_SPLIT = config['TEST_SPLIT']
URLS = config['URLS']
RATE_LIMIT = config['RATE_LIMIT']

# Prepare data storage
DATA_DIR = 'data'
os.makedirs(DATA_DIR, exist_ok=True)
EXISTING_FEATURES_PATH = os.path.join(DATA_DIR, 'existing_features.pkl')
EXISTING_LABELS_PATH = os.path.join(DATA_DIR, 'existing_labels.pkl')
LABEL_MAP_PATH = os.path.join(DATA_DIR, 'label_map.pkl')

# NLTK setup for enhanced preprocessing
nltk.download('stopwords')
nltk.download('wordnet')
nltk.download('averaged_perceptron_tagger')
nltk.download('punkt')
stop_words = set(stopwords.words('english'))
porter = PorterStemmer()
visited_urls = set()
robot_cache = {}

# Initialize or load label map
if os.path.exists(LABEL_MAP_PATH):
    label_map = pd.read_pickle(LABEL_MAP_PATH)
else:
    label_map = {'Long Text': 0, 'Short Text': 1, 'Media Rich': 2}

# Function to respect rate limiting
def rate_limit_delay():
    time.sleep(RATE_LIMIT)

# Retry mechanism with exponential backoff
def fetch_with_retries(url, retries=3, backoff=2):
    for attempt in range(retries):
        try:
            response = requests.get(url, timeout=10)
            response.raise_for_status()
            return response
        except requests.exceptions.RequestException as e:
            if attempt == retries - 1:
                logging.error(f"Request failed after {retries} retries for {url}: {e}")
                return None
            else:
                logging.warning(f"Retrying {url} in {backoff ** attempt} seconds due to: {e}")
                time.sleep(backoff ** attempt)

# Caching robots.txt
def can_fetch(url):
    domain = re.findall(r'(https?://[^/]+)', url)[0]
    if domain not in robot_cache:
        rp = RobotFileParser()
        rp.set_url(domain + '/robots.txt')
        rp.read()
        robot_cache[domain] = rp
    return robot_cache[domain].can_fetch('*', url)

# Get internal links with base URL
def get_internal_links(soup, base_url):
    internal_links = set()
    for link in soup.find_all('a', href=True):
        href = link['href']
        if href.startswith('/') or not href.startswith(('http://', 'https://')):
            internal_link = base_url + href
            internal_links.add(internal_link)
        elif href.startswith(base_url):
            internal_links.add(href)
    return internal_links

# Web Scraping with Error Handling, Retry, and Recursive Depth
def scrape_website(url, depth=0, max_depth=2):
    print(f"Scraping {url} at depth {depth}...")
    if depth > max_depth:
        return None, None

    rate_limit_delay()  # Apply rate limiting

    response = fetch_with_retries(url)
    if response:
        soup = BeautifulSoup(response.content, 'html.parser')
        body_tag = soup.find('body')
        if body_tag:
            for element in body_tag(text=lambda text: isinstance(text, Comment)):
                element.extract()
            body_content = ' '.join(body_tag.stripped_strings)

            if depth < max_depth:
                internal_links = get_internal_links(soup, url)
                for link in internal_links:
                    if can_fetch(link) and link not in visited_urls:
                        link_content, _ = scrape_website(link, depth + 1)
                        if link_content:
                            body_content += ' ' + link_content
                            visited_urls.add(link)
                        else:
                            logging.error(f"Failed to scrape {link}")
                    elif not can_fetch(link):
                        logging.info(f"Scraping of {link} disallowed by robots.txt")
            print(f"Scraped successfully: {url}")
            return body_content, soup
    return None, None

# Heuristic Labeling with Improved Strategy
def generate_label(soup):
    if soup is None:
        return None
    text_length = len(soup.get_text())
    num_images = len(soup.find_all('img'))
    num_videos = len(soup.find_all('video'))

    if num_images > 5 or num_videos > 0:
        return 'Media Rich'
    elif text_length > 1000:
        return 'Long Text'
    else:
        return 'Short Text'

# Hyperdimensional Computing (HDC) based data preprocessing
def generate_random_hypervector(size):
    """Generate a random hypervector of +1/-1 with the specified dimensionality."""
    return np.random.choice([-1, 1], size=size)

def superposition(vectors):
    """Perform superposition (element-wise summing) of multiple hypervectors."""
    return np.sign(np.sum(vectors, axis=0))

def bind(vectors):
    """Perform binding (element-wise multiplication) of hypervectors."""
    result = vectors[0]
    for vector in vectors[1:]:
        result = result * vector
    return result

def text_to_hypervector(text):
    """Convert text to a hypervector using bundling and binding."""
    words = clean_text(text).split()
    word_vectors = [generate_random_hypervector(MAX_VECTOR_SIZE) for _ in words]
    return superposition(word_vectors)  # Hyperdimensional encoding through superposition

# Text cleaning
def clean_text(text):
    text = text.lower()
    text = re.sub(r'\[.*?\]', '', text)
    text = re.sub(r'https?://\S+|www\.\S+', '', text)
    text = re.sub(r'<.*?>+', '', text)
    text = re.sub(r'[^a-zA-Z\s]', '', text)
    text = ' '.join([word for word in text.split() if word not in stop_words])
    return text

# Symbolic Inference: Neuro-Symbolic Classification
def symbolic_inference(features):
    """Perform neuro-symbolic reasoning based on the hypervector input."""
    # Example neuro-symbolic logic:
    # 1. Check for certain characteristics (e.g., length, specific patterns).
    # 2. Use logical rules for classification (e.g., length-based or content-based rules).
    
    predictions = []
    for feature in features:
        magnitude = np.sum(np.abs(feature))  # Example: Summing absolute values as a proxy for content magnitude
        if magnitude > 5000:
            predictions.append(0)  # 'Long Text'
        elif magnitude < 3000 and random.random() > 0.5:
            predictions.append(1)  # 'Short Text'
        else:
            predictions.append(2)  # 'Media Rich'
    
    return predictions

# Data Augmentation
def augment_text(text):
    words = text.split()
    augmented_text = words.copy()
    
    # Synonym replacement
    n_replace = max(1, int(len(words) * 0.1))
    for _ in range(n_replace):
        idx_to_replace = random.randint(0, len(words) - 1)
        synonyms = get_synonyms(words[idx_to_replace])
        if synonyms:
            synonym = random.choice(synonyms)
            augmented_text[idx_to_replace] = synonym

    return ' '.join(augmented_text)

def get_synonyms(word):
    synonyms = set()
    for syn in wordnet.synsets(word):
        for lemma in syn.lemmas():
            synonym = lemma.name().replace('_', ' ')
            if synonym != word:
                synonyms.add(synonym)
    return list(synonyms)

def random_deletion(text, p=0.1):
    words = text.split()
    if len(words) == 1:
        return text
    retained_words = [word for word in words if random.random() > p]
    return ' '.join(retained_words)

def random_swap(text, n_swaps=1):
    words = text.split()
    if len(words) < 2:
        return text
    for _ in range(n_swaps):
        idx1, idx2 = random.sample(range(len(words)), 2)
        words[idx1], words[idx2] = words[idx2], words[idx1]
    return ' '.join(words)

# Preprocessing Data
def preprocess_data(raw_data):
    augmented_data = []
    for text in raw_data:
        clean_text_data = clean_text(text)
        augmented_data.append(clean_text_data)
        augmented_data.append(augment_text(clean_text_data))
        augmented_data.append(random_deletion(clean_text_data))
        augmented_data.append(random_swap(clean_text_data))

    hypervectors = [text_to_hypervector(text) for text in augmented_data]
    return hypervectors

# Data integration with Error Handling
def integrate_data(new_data, existing_data_path):
    if new_data is None:
        return None

    if os.path.exists(existing_data_path):
        existing_data = pd.read_pickle(existing_data_path)
    else:
        existing_data = pd.DataFrame()

    integrated_data = pd.concat([existing_data, pd.DataFrame(new_data)], ignore_index=True)
    integrated_data.to_pickle(existing_data_path)
    return integrated_data

# Evaluation and Performance Metrics
def evaluate_model(predictions, labels):
    precision = precision_score(labels, predictions, average='weighted')
    recall = recall_score(labels, predictions, average='weighted')
    f1 = f1_score(labels, predictions, average='weighted')

    logging.info(f'Precision: {precision}, Recall: {recall}, F1 Score: {f1}')
    cm = confusion_matrix(labels, predictions)
    plot_confusion_matrix(cm)

# Plot Confusion Matrix
def plot_confusion_matrix(cm):
    plt.figure(figsize=(10, 7))
    plt.imshow(cm, interpolation='nearest', cmap=plt.cm.Blues)
    plt.title('Confusion Matrix')
    plt.colorbar()
    plt.tight_layout()
    plt.ylabel('True label')
    plt.xlabel('Predicted label')
    plt.show()

# Concurrency and Automated Retraining Setup
def automated_retraining():
    print("Starting automated retraining...")
    with ThreadPoolExecutor(max_workers=len(URLS)) as executor:
        futures = [executor.submit(scrape_website, url, 0) for url in URLS]
        for future in futures:
            body_content, soup = future.result()
            if body_content and soup:
                label = generate_label(soup)
                update_label_map(label)
                preprocessed_content = preprocess_data([body_content])
                integrated_features = integrate_data(preprocessed_content, EXISTING_FEATURES_PATH)
                integrated_labels = integrate_data([label], EXISTING_LABELS_PATH)
                if integrated_features is not None and integrated_labels is not None:
                    predictions = symbolic_inference(integrated_features)  # Neuro-symbolic reasoning for classification
                    evaluate_model(predictions, integrated_labels)

def update_label_map(label):
    global label_map
    if label not in label_map:
        new_index = max(label_map.values()) + 1
        label_map[label] = new_index
        pd.to_pickle(label_map, LABEL_MAP_PATH)
        print(f"Updated label map with new label: {label}")

# Run schedule
if __name__ == "__main__":
    automated_retraining()
    schedule.every().day.at("01:00").do(automated_retraining)

    try:
        while True:
            schedule.run_pending()
            time.sleep(1)
    except KeyboardInterrupt:
        logging.info("Program interrupted by user. Exiting...")