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import os
import json
import logging
import re
import requests
import hashlib
from urllib.parse import urlparse, urljoin
from typing import List, Dict, Optional, Tuple
from concurrent.futures import ThreadPoolExecutor, as_completed
from bs4 import BeautifulSoup
import PyPDF2
from io import BytesIO
from transformers import pipeline, AutoTokenizer, AutoModelForQuestionAnswering
import numpy as np
from sklearn.feature_extraction.text import TfidfVectorizer
from sentence_transformers import SentenceTransformer
import spacy
import gradio as gr
# Configuración avanzada
logging.basicConfig(level=logging.INFO,
format='%(asctime)s - %(levelname)s - %(message)s')
logger = logging.getLogger(__name__)
class AdvancedSEOAanalyzer:
def __init__(self, sitemap_url: str):
self.sitemap_url = sitemap_url
self.session = self._configure_session()
self.models = self._load_models()
self.processed_urls = set()
self.link_graph = defaultdict(list)
self.content_store = {}
self.documents = []
def _configure_session(self) -> requests.Session:
session = requests.Session()
retry = Retry(
total=5,
backoff_factor=1,
status_forcelist=[500, 502, 503, 504]
)
adapter = HTTPAdapter(max_retries=retry)
session.mount('https://', adapter)
session.headers.update({
'User-Agent': 'Mozilla/5.0 (compatible; SEOBot/1.0; +https://seo.example.com/bot)'
})
return session
def _load_models(self) -> Dict:
return {
'summarization': pipeline("summarization",
model="facebook/bart-large-cnn",
device=0 if torch.cuda.is_available() else -1),
'qa': pipeline("question-answering",
model="deepset/roberta-base-squad2",
tokenizer="deepset/roberta-base-squad2"),
'ner': pipeline("ner",
model="dslim/bert-base-NER",
aggregation_strategy="simple"),
'semantic': SentenceTransformer('sentence-transformers/all-MiniLM-L6-v2'),
'spacy': spacy.load("en_core_web_lg")
}
async def download_content(self, url: str) -> Optional[Dict]:
if url in self.processed_urls:
return None
try:
response = self.session.get(url, timeout=15)
response.raise_for_status()
content_type = response.headers.get('Content-Type', '')
if 'application/pdf' in content_type:
return self._process_pdf(url, response.content)
elif 'text/html' in content_type:
return await self._process_html(url, response.text)
else:
logger.warning(f"Unsupported content type: {content_type}")
return None
except Exception as e:
logger.error(f"Error downloading {url}: {str(e)}")
return None
def _process_pdf(self, url: str, content: bytes) -> Dict:
text = ""
with BytesIO(content) as pdf_file:
reader = PyPDF2.PdfReader(pdf_file)
for page in reader.pages:
text += page.extract_text()
doc_hash = hashlib.sha256(content).hexdigest()
self._save_document(url, content, 'pdf')
return {
'url': url,
'type': 'pdf',
'content': text,
'hash': doc_hash,
'links': []
}
async def _process_html(self, url: str, html: str) -> Dict:
soup = BeautifulSoup(html, 'lxml')
main_content = self._extract_main_content(soup)
links = self._extract_links(url, soup)
self._save_document(url, html.encode('utf-8'), 'html')
return {
'url': url,
'type': 'html',
'content': main_content,
'hash': hashlib.sha256(html.encode()).hexdigest(),
'links': links,
'metadata': self._extract_metadata(soup)
}
def _extract_links(self, base_url: str, soup: BeautifulSoup) -> List[Dict]:
links = []
base_domain = urlparse(base_url).netloc
for tag in soup.find_all(['a', 'link'], href=True):
href = tag['href']
full_url = urljoin(base_url, href)
parsed = urlparse(full_url)
link_type = 'internal' if parsed.netloc == base_domain else 'external'
file_type = 'other'
if parsed.path.lower().endswith(('.pdf', '.doc', '.docx')):
file_type = 'document'
elif parsed.path.lower().endswith(('.jpg', '.png', '.gif')):
file_type = 'image'
links.append({
'url': full_url,
'type': link_type,
'file_type': file_type,
'anchor': tag.text.strip()
})
return links
def _extract_metadata(self, soup: BeautifulSoup) -> Dict:
metadata = {
'title': soup.title.string if soup.title else '',
'description': '',
'keywords': [],
'open_graph': {}
}
meta_tags = soup.find_all('meta')
for tag in meta_tags:
name = tag.get('name', '').lower()
property = tag.get('property', '').lower()
content = tag.get('content', '')
if name == 'description':
metadata['description'] = content
elif name == 'keywords':
metadata['keywords'] = [kw.strip() for kw in content.split(',')]
elif property.startswith('og:'):
key = property[3:]
metadata['open_graph'][key] = content
return metadata
def analyze_content(self, content: Dict) -> Dict:
analysis = {
'summary': self.models['summarization'](content['content'],
max_length=150,
min_length=30)[0]['summary_text'],
'entities': self.models['ner'](content['content']),
'semantic_embedding': self.models['semantic'].encode(content['content']),
'seo_analysis': self._perform_seo_analysis(content)
}
if content['type'] == 'pdf':
analysis['document_analysis'] = self._analyze_document_structure(content)
return analysis
def _perform_seo_analysis(self, content: Dict) -> Dict:
text = content['content']
doc = self.models['spacy'](text)
return {
'readability_score': self._calculate_readability(text),
'keyword_density': self._calculate_keyword_density(text),
'heading_structure': self._analyze_headings(doc),
'content_length': len(text.split()),
'semantic_topics': self._extract_semantic_topics(text)
}
def _extract_semantic_topics(self, text: str) -> List[str]:
vectorizer = TfidfVectorizer(stop_words='english', ngram_range=(1,2))
tfidf = vectorizer.fit_transform([text])
feature_array = np.array(vectorizer.get_feature_names_out())
tfidf_sorting = np.argsort(tfidf.toarray()).flatten()[::-1]
return feature_array[tfidf_sorting][:5].tolist()
def run_analysis(self, max_workers: int = 4) -> Dict:
sitemap_urls = self._parse_sitemap()
results = []
with ThreadPoolExecutor(max_workers=max_workers) as executor:
futures = [executor.submit(self.download_content, url)
for url in sitemap_urls]
for future in as_completed(futures):
result = future.result()
if result:
analyzed = self.analyze_content(result)
results.append({**result, **analyzed})
self._update_link_graph(result)
self._save_full_analysis(results)
return {
'total_pages': len(results),
'document_types': self._count_document_types(results),
'link_analysis': self._analyze_link_graph(),
'content_analysis': self._aggregate_content_stats(results)
}
def _save_document(self, url: str, content: bytes, file_type: str) -> None:
parsed = urlparse(url)
path = parsed.path.lstrip('/')
filename = f"documents/{parsed.netloc}/{path}" if path else f"documents/{parsed.netloc}/index"
os.makedirs(os.path.dirname(filename), exist_ok=True)
with open(filename + f'.{file_type}', 'wb') as f:
f.write(content)
def launch_interface(self):
interface = gr.Interface(
fn=self.run_analysis,
inputs=gr.Textbox(label="Sitemap URL"),
outputs=[
gr.JSON(label="Analysis Results"),
gr.File(label="Download Data")
],
title="Advanced SEO Analyzer",
description="Analyze websites with AI-powered SEO insights"
)
interface.launch()
if __name__ == "__main__":
analyzer = AdvancedSEOAanalyzer("https://www.example.com/sitemap.xml")
analyzer.launch_interface() |