search_engine.py

from typing import Dict, List, Any
import requests
from bs4 import BeautifulSoup
from transformers import pipeline
from langchain_community.embeddings import HuggingFaceEmbeddings
import time
import json
import os
from urllib.parse import urlparse, quote_plus
import logging
import random

logger = logging.getLogger(__name__)

class SearchResult:
    def __init__(self, title: str, link: str, snippet: str):
        self.title = title
        self.link = link
        self.snippet = snippet

class ModelManager:
    """Manages different AI models for specific tasks"""
    
    def __init__(self):
        self.device = "cpu"
        self.models = {}
        self.load_models()
        
    def load_models(self):
        # Use smaller models for CPU deployment
        self.models['summarizer'] = pipeline(
            "summarization",
            model="facebook/bart-base",
            device=self.device
        )
        
        self.models['embeddings'] = HuggingFaceEmbeddings(
            model_name="sentence-transformers/all-MiniLM-L6-v2",
            model_kwargs={"device": self.device}
        )

class ContentProcessor:
    """Processes and analyzes different types of content"""
    
    def __init__(self):
        self.model_manager = ModelManager()
        
    def clean_text(self, text: str) -> str:
        """Clean and normalize text content"""
        # Remove extra whitespace and normalize
        lines = [line.strip() for line in text.splitlines()]
        text = ' '.join(line for line in lines if line)
        
        # Remove redundant spaces
        text = ' '.join(text.split())
        
        # Remove common navigation elements
        nav_patterns = [
            "skip to content",
            "search",
            "menu",
            "navigation",
            "subscribe",
            "sign in",
            "log in"
        ]
        for pattern in nav_patterns:
            text = text.replace(pattern, "")
            
        return text
        
    def extract_key_points(self, text: str, max_points: int = 5) -> List[str]:
        """Extract key points from text using the summarizer"""
        try:
            # Split text into chunks of ~1000 characters
            chunks = [text[i:i + 1000] for i in range(0, len(text), 1000)]
            
            all_points = []
            for chunk in chunks[:3]:  # Process first 3 chunks only
                summary = self.model_manager.models['summarizer'](
                    chunk,
                    max_length=100,
                    min_length=30,
                    do_sample=False
                )[0]['summary_text']
                
                # Split into sentences and add as points
                sentences = [s.strip() for s in summary.split('.') if s.strip()]
                all_points.extend(sentences)
            
            # Return unique points, limited to max_points
            unique_points = list(dict.fromkeys(all_points))
            return unique_points[:max_points]
            
        except Exception as e:
            logger.error(f"Error extracting key points: {str(e)}")
            return []
    
    def process_content(self, content: str) -> Dict:
        """Process content and generate insights"""
        try:
            # Clean the text
            cleaned_text = self.clean_text(content)
            
            # Extract key points
            key_points = self.extract_key_points(cleaned_text)
            
            # Generate a concise summary
            summary = self.model_manager.models['summarizer'](
                cleaned_text[:1024],
                max_length=150,
                min_length=50,
                do_sample=False
            )[0]['summary_text']
            
            # Extract potential topics/keywords
            topics = []
            common_topics = [
                "quantum computing", "quantum processors", "quantum bits",
                "quantum algorithms", "quantum supremacy", "quantum advantage",
                "error correction", "quantum hardware", "quantum software",
                "quantum research", "quantum applications"
            ]
            
            for topic in common_topics:
                if topic.lower() in cleaned_text.lower():
                    topics.append(topic)
            
            return {
                'summary': summary,
                'key_points': key_points,
                'topics': topics[:5],  # Limit to top 5 topics
                'content': cleaned_text
            }
            
        except Exception as e:
            return {
                'summary': f"Error processing content: {str(e)}",
                'key_points': [],
                'topics': [],
                'content': content
            }

class WebSearchEngine:
    """Main search engine class"""
    
    def __init__(self):
        self.processor = ContentProcessor()
        self.session = requests.Session()
        self.request_delay = 2.0
        self.last_request_time = 0
        self.max_retries = 3
        self.headers = {
            'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/91.0.4472.124 Safari/537.36',
            'Accept': 'text/html,application/xhtml+xml,application/xml;q=0.9,image/webp,*/*;q=0.8',
            'Accept-Language': 'en-US,en;q=0.5',
            'DNT': '1',
            'Connection': 'keep-alive',
            'Upgrade-Insecure-Requests': '1'
        }

    def safe_get(self, url: str, max_retries: int = 3) -> requests.Response:
        """Make a GET request with retries and error handling"""
        for i in range(max_retries):
            try:
                # Add delay between requests
                current_time = time.time()
                time_since_last = current_time - self.last_request_time
                if time_since_last < self.request_delay:
                    time.sleep(self.request_delay - time_since_last + random.uniform(0.5, 1.5))
                
                response = self.session.get(url, headers=self.headers, timeout=10)
                self.last_request_time = time.time()
                
                if response.status_code == 200:
                    return response
                elif response.status_code == 429:  # Rate limit
                    wait_time = (i + 1) * 5
                    time.sleep(wait_time)
                    continue
                else:
                    response.raise_for_status()
            except Exception as e:
                if i == max_retries - 1:
                    raise
                time.sleep((i + 1) * 2)
        raise Exception(f"Failed to fetch URL after {max_retries} attempts")
        
    def is_valid_url(self, url: str) -> bool:
        """Check if URL is valid for crawling"""
        try:
            parsed = urlparse(url)
            return bool(parsed.netloc and parsed.scheme)
        except:
            return False
    
    def get_metadata(self, soup: BeautifulSoup) -> Dict:
        """Extract metadata from page"""
        title = soup.title.string if soup.title else "No title"
        description = ""
        if soup.find("meta", attrs={"name": "description"}):
            description = soup.find("meta", attrs={"name": "description"}).get("content", "")
        return {
            'title': title,
            'description': description
        }
    
    def process_url(self, url: str) -> Dict:
        """Process a single URL"""
        if not self.is_valid_url(url):
            return {'error': f"Invalid URL: {url}"}
            
        try:
            response = self.safe_get(url)
            soup = BeautifulSoup(response.text, 'lxml')
            
            # Extract text content
            for script in soup(["script", "style"]):
                script.decompose()
            text = soup.get_text()
            lines = (line.strip() for line in text.splitlines())
            chunks = (phrase.strip() for line in lines for phrase in line.split("  "))
            content = ' '.join(chunk for chunk in chunks if chunk)
            
            # Get metadata
            metadata = self.get_metadata(soup)
            
            # Process content
            processed = self.processor.process_content(content)
            
            return {
                'url': url,
                'title': metadata['title'],
                'description': metadata['description'],
                'summary': processed['summary'],
                'key_points': processed['key_points'],
                'topics': processed['topics'],
                'content': processed['content']
            }
            
        except Exception as e:
            return {'error': f"Error processing {url}: {str(e)}"}

    def search_duckduckgo(self, query: str, max_results: int = 5) -> List[Dict]:
        """Search DuckDuckGo and parse HTML results"""
        search_results = []
        
        try:
            # Encode query for URL
            encoded_query = quote_plus(query)
            
            # DuckDuckGo HTML search URL
            search_url = f'https://html.duckduckgo.com/html/?q={encoded_query}'
            
            # Get search results page
            response = self.safe_get(search_url)
            soup = BeautifulSoup(response.text, 'lxml')
            
            # Find all result elements
            results = soup.find_all('div', {'class': 'result'})
            
            for result in results[:max_results]:
                try:
                    # Extract link
                    link_elem = result.find('a', {'class': 'result__a'})
                    if not link_elem:
                        continue
                        
                    link = link_elem.get('href', '')
                    if not link or not self.is_valid_url(link):
                        continue
                        
                    # Extract title
                    title = link_elem.get_text(strip=True)
                    
                    # Extract snippet
                    snippet_elem = result.find('a', {'class': 'result__snippet'})
                    snippet = snippet_elem.get_text(strip=True) if snippet_elem else ""
                    
                    search_results.append({
                        'link': link,
                        'title': title,
                        'snippet': snippet
                    })
                    
                    # Add delay between processing results
                    time.sleep(random.uniform(0.2, 0.5))
                    
                except Exception as e:
                    logger.warning(f"Error processing search result: {str(e)}")
                    continue
                    
            return search_results
            
        except Exception as e:
            logger.error(f"Error during DuckDuckGo search: {str(e)}")
            return []
    
    def search(self, query: str, max_results: int = 5) -> Dict:
        """Perform search and process results"""
        try:
            # Search using DuckDuckGo HTML
            search_results = self.search_duckduckgo(query, max_results)
            
            if not search_results:
                return {'error': 'No results found'}
            
            results = []
            all_key_points = []
            all_topics = set()
            
            for result in search_results:
                if 'link' in result:
                    processed = self.process_url(result['link'])
                    if 'error' not in processed:
                        results.append(processed)
                        # Collect key points and topics
                        if 'key_points' in processed:
                            all_key_points.extend(processed['key_points'])
                        if 'topics' in processed:
                            all_topics.update(processed.get('topics', []))
                        time.sleep(random.uniform(0.5, 1.0))
            
            if not results:
                return {'error': 'Failed to process any search results'}
            
            # Combine all summaries
            all_summaries = " ".join([r['summary'] for r in results if 'summary' in r])
            
            # Generate a meta-summary of all content
            meta_summary = self.processor.model_manager.models['summarizer'](
                all_summaries[:1024],
                max_length=200,
                min_length=100,
                do_sample=False
            )[0]['summary_text']
            
            # Get unique key points
            unique_key_points = list(dict.fromkeys(all_key_points))
            
            return {
                'results': results,
                'insights': {
                    'summary': meta_summary,
                    'key_points': unique_key_points[:7],  # Top 7 key points
                    'topics': list(all_topics)[:5]  # Top 5 topics
                },
                'follow_up_questions': [
                    f"What are the recent breakthroughs in {', '.join(list(all_topics)[:2])}?",
                    f"How do these developments impact the future of quantum computing?",
                    f"What are the practical applications of these quantum computing advances?"
                ]
            }
            
        except Exception as e:
            return {'error': f"Search failed: {str(e)}"}

# Main search function
def search(query: str, max_results: int = 5) -> Dict:
    """Main search function"""
    engine = WebSearchEngine()
    return engine.search(query, max_results)