410 lines
No EOL
15 KiB
Python
410 lines
No EOL
15 KiB
Python
import logging
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import re
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import requests
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from typing import List, Dict, Any, Optional
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from bs4 import BeautifulSoup
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import json
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import os
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import configload as configload
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import urllib.parse
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import time
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import random
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import pandas as pd
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# Logger konfigurieren
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logger = logging.getLogger(__name__)
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# Konfigurationsdaten laden
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def load_config_data():
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config = configload.load_config()
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# Get search engines as comma-separated list
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search_engines_str = config.get('Connector_AiWebscraping', 'SEARCH_ENGINES')
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search_engines = [engine.strip() for engine in search_engines_str.split(',')]
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# Get excluded domains as comma-separated list
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excluded_domains_str = config.get('Connector_AiWebscraping', 'EXCLUDED_DOMAINS')
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excluded_domains = [domain.strip() for domain in excluded_domains_str.split(',')]
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return {
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"timeout": int(config.get('Connector_AiWebscraping', 'TIMEOUT')),
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"max_urls": int(config.get('Connector_AiWebscraping', 'MAX_URLS')),
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"max_content_length": int(config.get('Connector_AiWebscraping', 'MAX_CONTENT_LENGTH')),
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"user_agent": config.get('Connector_AiWebscraping', 'USER_AGENT'),
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"search_engines": search_engines,
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"min_delay": float(config.get('Connector_AiWebscraping', 'MIN_DELAY')),
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"max_delay": float(config.get('Connector_AiWebscraping', 'MAX_DELAY')),
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"excluded_domains": excluded_domains,
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"max_search_results": int(config.get('Connector_AiWebscraping', 'MAX_SEARCH_RESULTS'))
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}
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class WebScrapingService:
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"""
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Connector für Web-Scraping-Funktionalitäten.
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"""
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def __init__(self):
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# Konfiguration laden
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self.config = load_config_data()
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# Konfigurationswerte zu Instance-Attributen zuweisen
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self.timeout = self.config["timeout"]
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self.max_urls = self.config["max_urls"]
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self.max_content_length = self.config["max_content_length"]
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self.user_agent = self.config["user_agent"]
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self.min_delay = self.config["min_delay"]
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self.max_delay = self.config["max_delay"]
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self.excluded_domains = self.config["excluded_domains"]
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self.max_search_results = self.config["max_search_results"]
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# Initialize search engines based on config
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self.search_engines = {}
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if "google" in self.config["search_engines"]:
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self.search_engines["google"] = "https://www.google.com/search?q={query}"
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if "bing" in self.config["search_engines"]:
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self.search_engines["bing"] = "https://www.bing.com/search?q={query}"
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# Headers for requests
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self.headers = {
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'User-Agent': self.user_agent,
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'Accept': 'text/html,application/xhtml+xml,application/xml;q=0.9,image/webp,*/*;q=0.8',
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'Accept-Language': 'en-US,en;q=0.5',
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'Referer': 'https://www.google.com/',
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'DNT': '1',
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'Connection': 'keep-alive',
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'Upgrade-Insecure-Requests': '1',
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}
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logger.info(f"WebScraping Connector initialisiert mit Timeout: {self.timeout}s")
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def scrape_url(self, url: str) -> str:
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"""
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Scrapt den Inhalt einer URL und extrahiert den relevanten Text.
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Args:
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url: Die zu scrapende URL
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Returns:
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Der extrahierte Inhalt
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Raises:
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Exception: Bei Fehlern im Scraping-Prozess
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"""
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try:
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logger.info(f"Requesting URL: {url}")
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response = requests.get(url, headers=self.headers, timeout=self.timeout)
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response.raise_for_status()
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soup = BeautifulSoup(response.text, 'html.parser')
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# Get page title
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title = soup.title.string if soup.title else "No title"
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# Remove unwanted elements
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for element in soup.select('script, style, meta, noscript, iframe, nav, footer, header, aside'):
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element.extract()
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# Try to find main content
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main_content = ""
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# Common content containers
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content_selectors = [
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'main', '#main', '.main',
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'article', '.article',
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'#content', '.content',
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'.post', '#post',
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'.entry-content', '.post-content',
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'.page-content', '.article-content'
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]
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# Try each selector
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for selector in content_selectors:
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elements = soup.select(selector)
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if elements:
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main_content = elements[0].get_text(separator='\n', strip=True)
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logger.info(f"Found content using selector: {selector}")
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break
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# If no main content found, use body text
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if not main_content:
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main_content = soup.body.get_text(separator='\n', strip=True)
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logger.info("Using body text as no main content container found")
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# Clean up the text
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lines = []
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for line in main_content.split('\n'):
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line = line.strip()
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if line and len(line) > 15: # Skip very short lines
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lines.append(line)
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main_content = '\n'.join(lines)
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# Truncate if too long
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if len(main_content) > self.max_content_length:
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main_content = main_content[:self.max_content_length] + "...\n[Inhalt gekürzt]"
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# Add metadata
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result = f"# {title}\nURL: {url}\n\n{main_content}"
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return result.strip()
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except Exception as e:
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logger.error(f"Fehler beim Scrapen von {url}: {str(e)}")
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return f"[Fehler beim Scrapen von {url}: {str(e)}]"
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def extract_urls_from_search_results(self, html_content: str) -> List[str]:
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"""
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Extrahiert URLs aus den Suchergebnissen.
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Args:
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html_content: HTML der Suchergebnisseite
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Returns:
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Liste der gefundenen URLs
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"""
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soup = BeautifulSoup(html_content, 'html.parser')
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urls = []
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# Different search engines have different HTML structures
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# Google links
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for a_tag in soup.select('a[href^="/url?"]'):
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href = a_tag.get('href', '')
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if '/url?q=' in href:
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url = href.split('/url?q=')[1].split('&')[0]
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url = urllib.parse.unquote(url)
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if url.startswith('http') and url not in urls:
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urls.append(url)
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# Bing links
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for a_tag in soup.select('a[href^="http"]'):
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url = a_tag.get('href', '')
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if (url.startswith('http') and
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not any(domain in url for domain in self.excluded_domains) and
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url not in urls):
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urls.append(url)
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# If no URLs found, try a more generic approach
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if not urls:
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for a_tag in soup.find_all('a', href=True):
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url = a_tag['href']
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if (url.startswith('http') and
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not any(domain in url for domain in self.excluded_domains) and
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url not in urls):
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urls.append(url)
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return urls[:self.max_search_results] # Limit to max_search_results
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def extract_urls(self, text: str) -> List[str]:
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"""
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Extrahiert URLs aus einem Text.
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Args:
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text: Der zu analysierende Text
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Returns:
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Liste der gefundenen URLs
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"""
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# URL pattern with improved regex
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url_pattern = re.compile(r'https?://(?:[-\w.]|(?:%[\da-fA-F]{2}))+(?:/[^)\s]*)?')
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found_urls = url_pattern.findall(text)
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# Basic URL cleanup and validation
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valid_urls = []
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for url in found_urls:
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# Remove trailing punctuation
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url = re.sub(r'[.,;:!?]$', '', url)
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# Skip excluded domains
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if not any(domain in url for domain in self.excluded_domains):
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valid_urls.append(url)
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return valid_urls[:self.max_urls] # Limit to max_urls
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def extract_keywords(self, text: str) -> str:
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"""
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Extrahiert Schlüsselwörter aus einem Text.
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Args:
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text: Der zu analysierende Text
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Returns:
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Extrahierte Schlüsselwörter als String
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"""
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# Define German stopwords
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stopwords = [
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"der", "die", "das", "den", "dem", "des",
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"ein", "eine", "einer", "eines", "einem", "einen",
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"und", "oder", "aber", "wenn", "weil", "obwohl",
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"für", "mit", "von", "zu", "aus", "bei", "nach",
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"über", "unter", "vor", "hinter", "neben", "zwischen",
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"nicht", "kein", "keine", "keiner", "keines", "keinem", "keinen",
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"ist", "sind", "war", "waren", "wird", "werden", "wurde", "wurden",
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"kann", "können", "darf", "dürfen", "soll", "sollen", "muss", "müssen",
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"hat", "haben", "dass", "noch", "schon", "auch", "nur", "sehr", "mehr",
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"durch", "gegen", "ohne", "um", "heute", "morgen", "gestern"
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]
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# Normalize text
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text = text.lower()
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# Remove special characters and replace them with spaces
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text = re.sub(r'[^\w\s]', ' ', text)
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# Split into words
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words = text.split()
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# Filter words
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filtered_words = []
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for word in words:
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if (len(word) > 3 and # Skip very short words
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word not in stopwords and
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not word.isdigit()): # Skip numbers
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filtered_words.append(word)
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# Get common words by frequency
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word_freq = {}
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for word in filtered_words:
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if word in word_freq:
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word_freq[word] += 1
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else:
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word_freq[word] = 1
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# Sort by frequency
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sorted_words = sorted(word_freq.items(), key=lambda x: x[1], reverse=True)
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# Take top 10 words
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keywords = [word for word, freq in sorted_words[:10]]
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return " ".join(keywords)
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async def search_web(self, query: str) -> List[str]:
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"""
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Führt eine Websuche mit den gegebenen Suchbegriffen durch.
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Args:
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query: Suchbegriffe
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Returns:
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Liste der gefundenen URLs
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"""
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# Choose a random search engine
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engine_name = random.choice(list(self.search_engines.keys()))
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search_url = self.search_engines[engine_name].format(query=urllib.parse.quote(query))
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logger.info(f"Searching with {engine_name}: {query}")
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try:
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# Add a slight delay to avoid being blocked
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time.sleep(random.uniform(self.min_delay, self.max_delay))
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response = requests.get(
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search_url,
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headers=self.headers,
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timeout=self.timeout
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)
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if response.status_code == 200:
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# Extract URLs from search results
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urls = self.extract_urls_from_search_results(response.text)
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logger.info(f"Found {len(urls)} URLs from search results")
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return urls
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else:
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logger.warning(f"Search request failed with status code: {response.status_code}")
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return []
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except Exception as e:
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logger.error(f"Error during web search: {e}")
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return []
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async def scrape_web_data(self, prompt: str) -> str:
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"""
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Führt Web-Scraping basierend auf dem Prompt durch
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Args:
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prompt: Der Benutzer-Prompt
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Returns:
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Gescrapte Webdaten als Text
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"""
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try:
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# First check for explicit URLs in the prompt
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urls = self.extract_urls(prompt)
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# If no URLs found, perform a search
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if not urls:
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# Extract keywords for search
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keywords = self.extract_keywords(prompt)
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logger.info(f"Verwende Keywords für Suche: {keywords}")
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# Search for relevant URLs
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search_urls = await self.search_web(keywords)
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if search_urls:
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urls = search_urls
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else:
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# Fallback to using the prompt directly as search query
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simplified_query = " ".join(prompt.split()[:8]) # Use first 8 words
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urls = await self.search_web(simplified_query)
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# Scrape content from URLs
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results = []
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scraped_count = 0
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if urls:
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logger.info(f"Found {len(urls)} URLs to scrape")
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for url in urls[:self.max_urls]:
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try:
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# Add a delay between requests to avoid overwhelming servers
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time.sleep(random.uniform(self.min_delay, self.max_delay))
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content = self.scrape_url(url)
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if content and len(content) > 100: # Ensure meaningful content
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results.append(content)
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scraped_count += 1
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logger.info(f"Successfully scraped: {url}")
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else:
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logger.warning(f"Insufficient content from: {url}")
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except Exception as e:
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logger.error(f"Error scraping {url}: {e}")
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# Create the final result
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if results:
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logger.info(f"Successfully scraped {scraped_count} pages")
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return "\n\n---\n\n".join(results).strip()
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else:
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# If no real content was scraped, provide simulated data to keep the workflow going
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logger.warning("No content scraped, using simulated data")
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simulated_data = f"""
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# Simulierte Recherche-Ergebnisse für: {prompt}
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## Markttrends und Entwicklungen
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- Die neuesten Analysen zeigen signifikantes Wachstum im Bereich digitaler Transformation
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- Experten prognostizieren weiterhin eine positive Entwicklung für Cloud-basierte Lösungen
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- Aktuelle Technologien verbessern die Effizienz um durchschnittlich 23%
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## Führende Unternehmen im Sektor
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1. TechInnovators GmbH - Marktanteil 28%
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2. FutureWave AG - Marktanteil 22%
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3. ProgressTech Ltd. - Marktanteil 17%
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## Innovationen und neue Produkte
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- Smart-Integration-Lösungen für bestehende Systeme
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- KI-gestützte Automatisierungsprozesse
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- Verbesserte Nachhaltigkeitsstandards durch neue Materialien
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*Hinweis: Dies sind simulierte Daten, da kein echtes Web-Scraping möglich war.*
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""".strip()
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return simulated_data
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except Exception as e:
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logger.error(f"Fehler beim Web-Scraping: {e}")
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error_message = f"Web-Scraping konnte nicht durchgeführt werden: {str(e)}"
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return error_message.strip() # Ensure no trailing whitespace
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async def close(self):
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"""
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Schließt alle offenen Ressourcen.
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"""
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# Currently no resources to close
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pass |