Recovered state: integrated TrendSurferAgent, restored frontend/backend files, and cleaned up recovery scripts
This commit is contained in:
ajaysi
@@ -5,9 +5,13 @@ Comprehensive on-page SEO analyzer with AI-enhanced insights
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for content optimization and technical improvements.
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"""
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import aiohttp
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from bs4 import BeautifulSoup
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from typing import Dict, Any, List, Optional
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from datetime import datetime
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from loguru import logger
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import re
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from urllib.parse import urlparse
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class OnPageSEOService:
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"""Service for comprehensive on-page SEO analysis"""
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@@ -17,6 +21,155 @@ class OnPageSEOService:
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self.service_name = "on_page_seo_analyzer"
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logger.info(f"Initialized {self.service_name}")
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async def _fetch_page(self, url: str) -> tuple[Optional[str], int]:
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"""Fetch page content"""
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try:
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headers = {
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'User-Agent': 'Mozilla/5.0 (compatible; ALwritySEO/1.0; +https://alwrity.com)'
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}
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async with aiohttp.ClientSession() as session:
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async with session.get(url, headers=headers, timeout=10) as response:
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if response.status == 200:
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return await response.text(), 200
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return None, response.status
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except Exception as e:
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logger.error(f"Error fetching {url}: {str(e)}")
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return None, 500
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def _analyze_meta_tags(self, soup: BeautifulSoup) -> Dict[str, Any]:
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"""Analyze meta tags"""
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title = soup.title.string if soup.title else None
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meta_desc = soup.find('meta', attrs={'name': 'description'})
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viewport = soup.find('meta', attrs={'name': 'viewport'})
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robots = soup.find('meta', attrs={'name': 'robots'})
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charset = soup.find('meta', attrs={'charset': True})
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# Social Tags
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og_title = soup.find('meta', property='og:title')
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og_desc = soup.find('meta', property='og:description')
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og_image = soup.find('meta', property='og:image')
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twitter_card = soup.find('meta', attrs={'name': 'twitter:card'})
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issues = []
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score = 100
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# Title Analysis
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if not title:
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issues.append("Missing title tag")
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score -= 20
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elif len(title) < 30 or len(title) > 60:
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issues.append(f"Title length ({len(title)} chars) should be 30-60 chars")
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score -= 10
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# Description Analysis
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desc_content = meta_desc['content'] if meta_desc else None
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if not desc_content:
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issues.append("Missing meta description")
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score -= 20
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elif len(desc_content) < 70 or len(desc_content) > 160:
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issues.append(f"Description length ({len(desc_content)} chars) should be 70-160 chars")
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score -= 10
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# Viewport
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if not viewport:
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issues.append("Missing viewport meta tag")
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score -= 20
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og_found = list(filter(None, ['Title' if og_title else '', 'Desc' if og_desc else '', 'Image' if og_image else '']))
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return {
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"title_length": f"{len(title)} chars" if title else "Missing",
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"meta_description_length": f"{len(desc_content)} chars" if desc_content else "Missing",
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"has_viewport": bool(viewport),
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"charset": charset['charset'] if charset else "Missing",
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"robots_meta": robots['content'] if robots else "Missing (Default: index, follow)",
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"og_tags": f"Found: {', '.join(og_found)}" if og_found else "None",
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"twitter_card": twitter_card['content'] if twitter_card else "Missing",
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"score": max(0, score),
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"issues": issues
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}
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def _analyze_technical(self, soup: BeautifulSoup, url: str) -> Dict[str, Any]:
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"""Analyze technical SEO elements"""
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canonical = soup.find('link', attrs={'rel': 'canonical'})
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schema = soup.find_all('script', type='application/ld+json')
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issues = []
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score = 100
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if not canonical:
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issues.append("Missing canonical tag")
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score -= 10
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# Check H1
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h1_tags = soup.find_all('h1')
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if len(h1_tags) == 0:
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issues.append("Missing H1 tag")
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score -= 20
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elif len(h1_tags) > 1:
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issues.append(f"Multiple H1 tags found ({len(h1_tags)})")
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score -= 10
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return {
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"canonical_tag": canonical['href'] if canonical else "Missing",
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"schema_markup": f"Found {len(schema)} schema objects",
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"h1_count": len(h1_tags),
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"score": max(0, score),
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"issues": issues
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}
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def _analyze_content(self, soup: BeautifulSoup) -> Dict[str, Any]:
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"""Analyze content quality"""
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# Remove scripts and styles
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for script in soup(["script", "style"]):
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script.extract()
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text = soup.get_text()
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words = len(re.findall(r'\w+', text))
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images = soup.find_all('img')
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images_without_alt = sum(1 for img in images if not img.get('alt'))
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issues = []
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score = 100
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if words < 300:
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issues.append(f"Low word count ({words} words)")
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score -= 20
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if images_without_alt > 0:
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issues.append(f"{images_without_alt} images missing alt text")
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score -= 10
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return {
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"word_count": words,
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"total_images": len(images),
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"images_without_alt": images_without_alt,
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"readability": "Good" if words > 300 else "Needs Improvement", # Placeholder for readability algo
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"score": max(0, score),
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"issues": issues
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}
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def _analyze_url_structure(self, url: str) -> Dict[str, Any]:
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parsed = urlparse(url)
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return {
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"protocol": parsed.scheme,
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"domain": parsed.netloc,
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"path_depth": len(parsed.path.strip('/').split('/')) if parsed.path else 0,
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"is_https": parsed.scheme == 'https'
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}
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def _calculate_overall_score(self, *analyses) -> int:
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total = sum(a.get('score', 0) for a in analyses)
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return round(total / len(analyses))
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def _generate_summary(self, *analyses) -> Dict[str, Any]:
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critical_issues = []
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for a in analyses:
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for issue in a.get('issues', []):
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critical_issues.append({"message": issue, "severity": "critical", "category": "SEO"})
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return {"critical_issues": critical_issues}
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async def analyze_on_page_seo(
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self,
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url: str,
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@@ -25,18 +178,53 @@ class OnPageSEOService:
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analyze_content_quality: bool = True
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) -> Dict[str, Any]:
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"""Analyze on-page SEO factors"""
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# Placeholder implementation
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return {
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"url": url,
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"overall_score": 75,
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"title_analysis": {"score": 80, "issues": [], "recommendations": []},
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"meta_description": {"score": 70, "issues": [], "recommendations": []},
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"heading_structure": {"score": 85, "issues": [], "recommendations": []},
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"content_analysis": {"score": 75, "word_count": 1500, "readability": "Good"},
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"keyword_analysis": {"target_keywords": target_keywords or [], "optimization": "Moderate"},
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"image_analysis": {"total_images": 10, "missing_alt": 2} if analyze_images else {},
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"recommendations": ["Optimize meta description", "Add more target keywords"]
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}
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try:
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# Add protocol if missing
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if not url.startswith(('http://', 'https://')):
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url = 'https://' + url
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html_content, status_code = await self._fetch_page(url)
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if not html_content:
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# Return error structure
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return {
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"url": url,
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"overall_score": 0,
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"summary": {"critical_issues": [{"message": f"Failed to fetch URL (Status: {status_code})", "severity": "critical", "category": "Connectivity"}]},
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"meta": {}, "technical": {}, "content_health": {}, "url_structure": {}, "performance": {}, "accessibility": {}, "ux": {}
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}
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soup = BeautifulSoup(html_content, 'html.parser')
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# Run Analyses
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meta_analysis = self._analyze_meta_tags(soup)
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technical_analysis = self._analyze_technical(soup, url)
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content_analysis = self._analyze_content(soup)
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url_analysis = self._analyze_url_structure(url)
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result = {
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"url": url,
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"overall_score": self._calculate_overall_score(meta_analysis, technical_analysis, content_analysis),
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"meta": meta_analysis,
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"technical": technical_analysis,
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"content_health": content_analysis,
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"url_structure": url_analysis,
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"performance": {"load_time": "Real-time check pending"},
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"accessibility": {"images_without_alt": content_analysis["images_without_alt"]},
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"ux": {"viewport": meta_analysis["has_viewport"], "mobile_friendly": bool(meta_analysis["has_viewport"])},
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"summary": self._generate_summary(meta_analysis, technical_analysis, content_analysis)
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}
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return result
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except Exception as e:
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logger.error(f"Error analyzing {url}: {str(e)}")
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return {
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"url": url,
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"overall_score": 0,
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"summary": {"critical_issues": [{"message": str(e), "severity": "critical", "category": "System"}]},
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"meta": {}, "technical": {}, "content_health": {}, "url_structure": {}, "performance": {}, "accessibility": {}, "ux": {}
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}
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async def health_check(self) -> Dict[str, Any]:
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"""Health check for the on-page SEO service"""
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@@ -44,4 +232,4 @@ class OnPageSEOService:
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"status": "operational",
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"service": self.service_name,
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"last_check": datetime.utcnow().isoformat()
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}
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}
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