"""
Extract HTML Features - Direct from Files (No Metadata Needed)
Simplified version that scans directories directly
WITH QUALITY FILTERING to remove low-quality HTML files
"""
import pandas as pd
from pathlib import Path
import logging
from tqdm import tqdm
import sys
import re
from bs4 import BeautifulSoup

# Add scripts directory to path
sys.path.append(str(Path(__file__).parent))

from html_features import HTMLFeatureExtractor

# Setup logging
logging.basicConfig(
    level=logging.INFO,
    format='%(asctime)s - %(levelname)s - %(message)s',
    datefmt='%H:%M:%S'
)
logger = logging.getLogger(__name__)

# Quality filter constants
MIN_FILE_SIZE = 1000  # Minimum 1KB
MIN_WORDS = 50  # Minimum 50 words of text content
MIN_TAGS = 10  # Minimum 10 HTML tags
ERROR_PATTERNS = [
    'page not found', '404', '403', 'forbidden', 'access denied',
    'error occurred', 'server error', 'not available', 'suspended',
    'domain for sale', 'this site can', 'website expired',
    'coming soon', 'under construction', 'parked domain',
    'buy this domain', 'this domain', 'domain has expired'
]


def is_quality_html(html_content, filename=""):
    """
    Check if HTML file meets quality criteria.
    
    Returns:
        tuple: (is_valid, reason)
    """
    # Check 1: Minimum file size
    if len(html_content) < MIN_FILE_SIZE:
        return False, f"Too small ({len(html_content)} bytes)"
    
    try:
        soup = BeautifulSoup(html_content, 'html.parser')
        
        # Check 2: Has body tag (basic HTML structure)
        if not soup.find('body'):
            return False, "No body tag"
        
        # Check 3: Minimum number of tags
        num_tags = len(soup.find_all())
        if num_tags < MIN_TAGS:
            return False, f"Too few tags ({num_tags})"
        
        # Check 4: Get text content and check word count
        text = soup.get_text(separator=' ', strip=True).lower()
        words = text.split()
        if len(words) < MIN_WORDS:
            return False, f"Too few words ({len(words)})"
        
        # Check 5: Not an error page
        text_lower = text[:2000]  # Check first 2000 chars
        for pattern in ERROR_PATTERNS:
            if pattern in text_lower:
                return False, f"Error page pattern: '{pattern}'"
        
        # Check 6: Has some interactive elements OR substantial content
        has_links = len(soup.find_all('a')) > 0
        has_forms = len(soup.find_all('form')) > 0
        has_inputs = len(soup.find_all('input')) > 0
        has_images = len(soup.find_all('img')) > 0
        has_divs = len(soup.find_all('div')) > 3
        
        if not (has_links or has_forms or has_inputs or has_images or has_divs):
            return False, "No interactive elements"
        
        # Check 7: Not mostly JavaScript (JS-only pages are hard to analyze)
        script_content = ''.join([s.string or '' for s in soup.find_all('script')])
        if len(script_content) > len(text) * 3 and len(text) < 200:
            return False, "Mostly JavaScript, little content"
        
        return True, "OK"
        
    except Exception as e:
        return False, f"Parse error: {str(e)[:50]}"


def extract_features_from_directory(html_dir, label, limit=None, apply_filter=True):
    """
    Extract features from all HTML files in a directory.
    
    Args:
        html_dir: Directory containing HTML files
        label: Label for these files (0=legitimate, 1=phishing)
        limit: Maximum number of files to process (None = all)
        apply_filter: Apply quality filter to remove bad HTML files
        
    Returns:
        List of feature dictionaries
    """
    html_dir = Path(html_dir)
    logger.info(f"\nProcessing: {html_dir}")
    logger.info(f"  Label: {'Phishing' if label == 1 else 'Legitimate'}")
    logger.info(f"  Quality filter: {'ENABLED' if apply_filter else 'DISABLED'}")
    
    # Get all HTML files
    html_files = sorted(html_dir.glob('*.html'))
    total_files = len(html_files)
    logger.info(f"  Found {total_files:,} HTML files")
    
    # Initialize extractor
    extractor = HTMLFeatureExtractor()
    
    results = []
    errors = 0
    filtered_out = 0
    filter_reasons = {}
    
    # Process each HTML file
    for html_path in tqdm(html_files, 
                        desc=f"Extracting {'Phishing' if label == 1 else 'Legitimate'} features"):
        try:
            # Read HTML content
            with open(html_path, 'r', encoding='utf-8', errors='ignore') as f:
                html_content = f.read()
            
            # Apply quality filter if enabled
            if apply_filter:
                is_valid, reason = is_quality_html(html_content, html_path.name)
                if not is_valid:
                    filtered_out += 1
                    filter_reasons[reason] = filter_reasons.get(reason, 0) + 1
                    continue
            
            # Extract features
            features = extractor.extract_features(html_content, url=None)
            
            # Add metadata
            features['filename'] = html_path.name # type: ignore
            features['label'] = label
            
            results.append(features)
            
            # Check if we reached the limit
            if limit and len(results) >= limit:
                logger.info(f"  Reached limit of {limit:,} quality files")
                break
            
        except Exception as e:
            errors += 1
            if errors < 10:  # Show first 10 errors
                logger.warning(f"  Error processing {html_path.name}: {e}")
    
    logger.info(f"  Quality files extracted: {len(results):,}")
    logger.info(f"  Filtered out (low quality): {filtered_out:,} ({filtered_out/total_files*100:.1f}%)")
    
    if filter_reasons and apply_filter:
        logger.info(f"  Filter reasons (top 5):")
        for reason, count in sorted(filter_reasons.items(), key=lambda x: -x[1])[:5]:
            logger.info(f"    - {reason}: {count:,}")
    
    if errors > 0:
        logger.warning(f"  Errors: {errors:,}")
    
    return results


def main():
    logger.info("="*80)
    logger.info("BALANCED HTML FEATURES EXTRACTION (WITH QUALITY FILTER)")
    logger.info("="*80)
    
    # Quality filter info
    logger.info("\nQuality Filter Criteria:")
    logger.info(f"  - Minimum file size: {MIN_FILE_SIZE} bytes")
    logger.info(f"  - Minimum word count: {MIN_WORDS} words")
    logger.info(f"  - Minimum HTML tags: {MIN_TAGS}")
    logger.info(f"  - Must have body tag")
    logger.info(f"  - Not an error/parked page")
    logger.info(f"  - Has interactive elements (links/forms/images)")
    
    # Paths
    phishing_html_dir = Path('data/html/phishing_v1')
    legit_html_dir = Path('data/html/legitimate_v1')
    output_path = Path('data/features/html_features_old.csv')
    
    # Check directories exist
    if not phishing_html_dir.exists():
        logger.error(f"Phishing directory not found: {phishing_html_dir}")
        return
    
    if not legit_html_dir.exists():
        logger.error(f"Legitimate directory not found: {legit_html_dir}")
        return
    
    # Count files
    logger.info("\n1. Checking available HTML files...")
    phishing_files = list(phishing_html_dir.glob('*.html'))
    legit_files = list(legit_html_dir.glob('*.html'))
    
    phishing_count = len(phishing_files)
    legit_count = len(legit_files)
    
    logger.info(f"   Phishing HTML files: {phishing_count:,}")
    logger.info(f"   Legitimate HTML files: {legit_count:,}")
    
    # Extract phishing features (with quality filter)
    logger.info("\n2. Extracting PHISHING HTML features (with quality filter)...")
    phishing_features = extract_features_from_directory(
        phishing_html_dir, 
        label=1,  # Phishing
        limit=None,  # Get all quality files first
        apply_filter=True
    )
    
    # Extract legitimate features (with quality filter)
    logger.info("\n3. Extracting LEGITIMATE HTML features (with quality filter)...")
    legit_features = extract_features_from_directory(
        legit_html_dir,
        label=0,  # Legitimate
        limit=None,  # Get all quality files first
        apply_filter=True
    )
    
    # Balance the dataset
    logger.info("\n4. Balancing dataset...")
    min_count = min(len(phishing_features), len(legit_features))
    logger.info(f"   Quality phishing samples: {len(phishing_features):,}")
    logger.info(f"   Quality legitimate samples: {len(legit_features):,}")
    logger.info(f"   Balancing to: {min_count:,} per class")
    
    # Truncate to balanced size
    phishing_features = phishing_features[:min_count]
    legit_features = legit_features[:min_count]
    
    # Combine results
    logger.info("\n5. Combining datasets...")
    all_features = phishing_features + legit_features
    
    if len(all_features) == 0:
        logger.error("No features extracted! Check error messages above.")
        return
    
    # Create DataFrame
    logger.info("\n6. Creating features DataFrame...")
    features_df = pd.DataFrame(all_features)
    
    # Reorder columns (filename and label first, then features)
    feature_cols = [col for col in features_df.columns if col not in ['filename', 'label']]
    features_df = features_df[['filename', 'label'] + feature_cols]
    
    # Shuffle dataset
    features_df = features_df.sample(frac=1, random_state=42).reset_index(drop=True)
    
    logger.info(f"   Shape: {features_df.shape}")
    logger.info(f"   Features: {len(feature_cols)}")
    
    # Show label distribution
    logger.info(f"\n   Label distribution:")
    label_counts = features_df['label'].value_counts()
    for label, count in label_counts.items():
        label_name = 'Phishing' if label == 1 else 'Legitimate'
        logger.info(f"     {label_name}: {count:,} ({count/len(features_df)*100:.1f}%)")
    
    # Save to CSV
    logger.info(f"\n7. Saving features to: {output_path}")
    output_path.parent.mkdir(parents=True, exist_ok=True)
    features_df.to_csv(output_path, index=False)
    logger.info(f"   ✓ Saved!")
    
    # Show statistics
    logger.info("\n" + "="*80)
    logger.info("EXTRACTION SUMMARY")
    logger.info("="*80)
    logger.info(f"\nTotal samples: {len(features_df):,}")
    logger.info(f"  Phishing: {len(phishing_features):,}")
    logger.info(f"  Legitimate: {len(legit_features):,}")
    logger.info(f"\nFeatures extracted: {len(feature_cols)}")
    logger.info(f"Dataset balance: {(label_counts[0]/label_counts[1])*100:.1f}%")
    
    # Show sample statistics
    logger.info(f"\nFeature statistics (first 10 features):")
    numeric_cols = features_df.select_dtypes(include=['int64', 'float64']).columns[:10]
    stats = features_df[numeric_cols].describe()
    logger.info(f"\n{stats.to_string()}")
    
    logger.info("\n" + "="*80)
    logger.info("✓ QUALITY-FILTERED HTML FEATURES EXTRACTION COMPLETE!")
    logger.info("="*80)
    logger.info(f"\nOutput file: {output_path}")
    logger.info(f"Shape: {features_df.shape}")
    logger.info(f"Quality filter removed low-quality HTML files")
    logger.info("="*80)


if __name__ == '__main__':
    main()