Update on_page_seo_analyzer.py
Additional Insights for Non-Technical Users Content Quality Insights: Readability Score: Use libraries like textstat to calculate a readability score (e.g., Flesch Reading Ease) for the webpage content. Keywords Highlighting: Extract and highlight frequently used keywords in the content, helping users understand what topics are emphasized. Duplicate Content Check: Flag if the meta description or titles are repeated multiple times in the page content. SEO Health Checks: Broken Links Detection: Identify broken internal or external links and recommend fixing them. Image Optimization Tips: Suggest reducing image sizes if the file sizes exceed a certain threshold. Recommend modern formats like WebP for better performance. Alt Text Suggestions: Provide actionable suggestions for missing or insufficient alt text, such as "Describe the image's purpose or key elements." Social Media Enhancement: Suggest best practices for Open Graph and Twitter tags, such as recommended tag content length or formats. Generate suggested meta descriptions and Open Graph descriptions for improved click-through rates. Accessibility Recommendations: Heading Structure Audit: Check for skipped heading levels (e.g., h2 follows h4) and provide guidance on correcting them. Contrast Ratio Check: Flag potential text-to-background contrast issues for visually impaired users (can use APIs like Lighthouse). ARIA Tags: Check for the presence of ARIA (Accessible Rich Internet Applications) tags and recommend their addition if missing. Performance Insights: Lazy Loading Suggestions: Highlight images without loading="lazy" and recommend lazy loading to improve page load speed. Critical CSS Suggestions: Advise inlining critical CSS for faster initial render. Script Optimization: Highlight unminified or unused JavaScript and recommend optimization. Custom Recommendations: Call to Action (CTA) Suggestions: Analyze the text for actionable elements like buttons or links and recommend improving CTAs. Internal Linking Suggestions: Suggest adding internal links for keywords or headings that lack links. Schema Markup Expansion: Recommend additional schema types (e.g., FAQ, Product, Review) based on the page content. Mobile Friendliness Enhancements: Check for touch targets (buttons and links) being too small or too close together. Flag pages without mobile-friendly navigation menus. Enhancements to User Experience Highlight Strengths and Weaknesses: Use color-coded sections to differentiate between well-optimized and underperforming areas. Simplified Metrics: Break down complex scores (like PageSpeed or SEO scores) into "Good," "Needs Improvement," and "Poor" categories. Provide plain-English explanations for non-technical users. Recommendations Section: Provide step-by-step instructions or examples for fixing identified issues, such as "How to add a canonical tag" or "How to structure hreflang attributes." Actionable Insights Dashboard: Summarize all findings in a visually appealing dashboard with prioritized to-do lists. Export Reports: Allow users to export the analysis and recommendations in a PDF or CSV format for easier sharing and tracking.
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@@ -7,6 +7,13 @@ from bs4 import BeautifulSoup
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import requests
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import csv
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import time
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from urllib.parse import urlparse
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import validators
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from readability import Readability
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import textstat
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import re
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from PIL import Image
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import io
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from ..gpt_providers.text_generation.main_text_generation import llm_text_gen
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def fetch_and_parse_html(url):
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@@ -65,6 +72,121 @@ def extract_meta_data(soup):
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st.warning(f"⚠️ Error extracting meta data: {e}")
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return {}
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def analyze_headings(soup):
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"""
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Analyzes the headings on the webpage.
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Args:
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soup (BeautifulSoup): Parsed HTML content.
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Returns:
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dict: Count of each heading tag.
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"""
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try:
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headings = {
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'h1': len(soup.find_all('h1')),
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'h2': len(soup.find_all('h2')),
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'h3': len(soup.find_all('h3')),
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'h4': len(soup.find_all('h4')),
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'h5': len(soup.find_all('h5')),
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'h6': len(soup.find_all('h6'))
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}
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return headings
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except Exception as e:
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st.warning(f"⚠️ Error analyzing headings: {e}")
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return {}
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def check_readability(text):
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"""
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Checks the readability score of the text.
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Args:
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text (str): The text content of the webpage.
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Returns:
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float: Readability score.
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"""
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try:
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readability_score = textstat.flesch_reading_ease(text)
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return readability_score
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except Exception as e:
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st.warning(f"⚠️ Error checking readability: {e}")
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return None
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def analyze_images(soup, url):
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"""
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Analyzes the images on the webpage.
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Args:
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soup (BeautifulSoup): Parsed HTML content.
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url (str): The URL of the webpage.
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Returns:
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list: List of dictionaries containing image src and alt text.
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"""
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try:
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images = soup.find_all('img')
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image_data = []
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for img in images:
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src = img.get('src')
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if not src:
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continue
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if not validators.url(src):
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src = urlparse(url).scheme + '://' + urlparse(url).netloc + src
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alt_text = img.get('alt', '')
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image_data.append({'src': src, 'alt': alt_text})
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return image_data
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except Exception as e:
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st.warning(f"⚠️ Error analyzing images: {e}")
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return []
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def analyze_links(soup):
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"""
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Analyzes the links on the webpage.
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Args:
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soup (BeautifulSoup): Parsed HTML content.
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Returns:
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list: List of broken links.
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"""
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try:
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links = soup.find_all('a', href=True)
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broken_links = []
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for link in links:
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href = link['href']
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if not validators.url(href):
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continue
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try:
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response = requests.head(href, timeout=5)
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if response.status_code >= 400:
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broken_links.append(href)
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except requests.RequestException:
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broken_links.append(href)
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return broken_links
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except Exception as e:
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st.warning(f"⚠️ Error analyzing links: {e}")
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return []
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def suggest_ctas(soup):
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"""
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Suggests call-to-action phrases present on the webpage.
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Args:
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soup (BeautifulSoup): Parsed HTML content.
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Returns:
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list: List of found CTA phrases.
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"""
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try:
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cta_keywords = ['buy now', 'subscribe', 'learn more', 'sign up', 'get started']
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text = soup.get_text().lower()
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ctas_found = [cta for cta in cta_keywords if cta in text]
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return ctas_found
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except Exception as e:
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st.warning(f"⚠️ Error suggesting CTAs: {e}")
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return []
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def extract_alternates_and_canonicals(soup):
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"""
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Extracts canonical URL, hreflangs, and mobile alternate links from the parsed HTML.
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@@ -167,7 +289,7 @@ def extract_content_data(soup, url):
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link_insights = []
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if internal_links:
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link_insights.append("✅ Internal links are present.")
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if external_links:
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if external links:
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link_insights.append("✅ External links are present.")
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return {
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@@ -313,6 +435,12 @@ def fetch_seo_data(url):
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return {}
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meta_data = extract_meta_data(soup)
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headings = analyze_headings(soup)
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text = soup.get_text()
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readability_score = check_readability(text)
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images = analyze_images(soup, url)
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broken_links = analyze_links(soup)
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ctas = suggest_ctas(soup)
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alternates_and_canonicals = extract_alternates_and_canonicals(soup)
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schema_markup = extract_schema_markup(soup)
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content_data = extract_content_data(soup, url)
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@@ -320,6 +448,11 @@ def fetch_seo_data(url):
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return {
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"meta_data": meta_data,
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"headings": headings,
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"readability_score": readability_score,
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"images": images,
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"broken_links": broken_links,
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"ctas": ctas,
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"alternates_and_canonicals": alternates_and_canonicals,
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"schema_markup": schema_markup,
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"content_data": content_data,
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@@ -371,6 +504,21 @@ def analyze_onpage_seo():
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st.write(f"**Language:** {results['meta_data']['html_language']}")
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st.write(results['meta_data']['title_message'])
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st.write(results['meta_data']['description_message'])
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st.subheader("Headings")
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st.write(results['headings'])
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st.subheader("Readability Score")
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st.write(f"**Readability Score:** {results['readability_score']}")
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st.subheader("Images")
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st.write(results['images'])
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st.subheader("Broken Links")
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st.write(results['broken_links'])
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st.subheader("Suggested CTAs")
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st.write(results['ctas'])
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st.subheader("Canonical and Hreflangs")
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st.write(f"**Canonical:** {results['alternates_and_canonicals']['canonical']}")
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