ALwrity Chatbot, SEO, Social media, Settings, Dashboard UI styling changes
This commit is contained in:
ajaysi
@@ -7,13 +7,16 @@ 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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from urllib.parse import urlparse, urljoin
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import validators
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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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import advertools as adv
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import pandas as pd
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from collections import Counter
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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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@@ -421,6 +424,314 @@ def check_alt_text(soup):
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st.warning(f"⚠️ Error checking alt text: {e}")
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return {}
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def analyze_keyword_density(text, url=None):
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"""
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Analyze keyword density and word frequency using advertools for comprehensive SEO insights.
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Args:
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text (str): The main content text from the webpage
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url (str): Optional URL for additional context
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Returns:
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dict: Comprehensive keyword density analysis
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"""
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try:
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# Use advertools word_frequency for professional analysis
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word_freq_df = adv.word_frequency(text)
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if word_freq_df.empty:
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return {
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"word_frequency": [],
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"keyword_density": {},
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"top_keywords": [],
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"analysis_message": "⚠️ Unable to analyze content - no words found",
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"recommendations": []
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}
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# Get top 20 most frequent words (excluding very common words)
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# Filter out common stopwords and very short words
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common_stopwords = {'the', 'and', 'or', 'but', 'in', 'on', 'at', 'to', 'for', 'of', 'with', 'by', 'from', 'up', 'about', 'into', 'through', 'during', 'before', 'after', 'above', 'below', 'between', 'among', 'this', 'that', 'these', 'those', 'is', 'are', 'was', 'were', 'be', 'been', 'being', 'have', 'has', 'had', 'do', 'does', 'did', 'will', 'would', 'could', 'should', 'may', 'might', 'must', 'can', 'a', 'an', 'i', 'you', 'he', 'she', 'it', 'we', 'they', 'me', 'him', 'her', 'us', 'them'}
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# Filter and process the word frequency data
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filtered_words = []
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total_words = len(text.split())
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for idx, row in word_freq_df.iterrows():
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word = row['word'].lower().strip()
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count = row['abs_freq']
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# Filter criteria
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if (len(word) >= 3 and
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word not in common_stopwords and
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word.isalpha() and
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count >= 2): # Minimum frequency of 2
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density = (count / total_words) * 100
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filtered_words.append({
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'word': word,
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'count': count,
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'density': round(density, 2)
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})
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# Sort by frequency and take top 15
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top_keywords = sorted(filtered_words, key=lambda x: x['count'], reverse=True)[:15]
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# Calculate keyword density categories
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keyword_density = {
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'high_density': [kw for kw in top_keywords if kw['density'] > 3],
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'medium_density': [kw for kw in top_keywords if 1 <= kw['density'] <= 3],
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'low_density': [kw for kw in top_keywords if kw['density'] < 1]
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}
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# Generate analysis messages and recommendations
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analysis_messages = []
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recommendations = []
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if len(top_keywords) == 0:
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analysis_messages.append("⚠️ No significant keywords found in content")
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recommendations.append("Add more descriptive and relevant keywords to your content")
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else:
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analysis_messages.append(f"✅ Found {len(top_keywords)} significant keywords")
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# Check for keyword stuffing
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if keyword_density['high_density']:
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high_density_words = [kw['word'] for kw in keyword_density['high_density']]
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analysis_messages.append(f"⚠️ Potential keyword stuffing detected: {', '.join(high_density_words[:3])}")
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recommendations.append("Consider reducing frequency of over-optimized keywords (>3% density)")
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# Check for good keyword distribution
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if len(keyword_density['medium_density']) >= 3:
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analysis_messages.append("✅ Good keyword distribution found")
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else:
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recommendations.append("Consider adding more medium-density keywords (1-3% density)")
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# Check total word count
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if total_words < 300:
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recommendations.append("Content is quite short - consider expanding to at least 300 words")
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elif total_words > 2000:
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recommendations.append("Content is quite long - ensure it's well-structured with headings")
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return {
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"word_frequency": word_freq_df.to_dict('records') if not word_freq_df.empty else [],
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"keyword_density": keyword_density,
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"top_keywords": top_keywords,
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"total_words": total_words,
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"analysis_message": " | ".join(analysis_messages) if analysis_messages else "✅ Keyword analysis complete",
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"recommendations": recommendations
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}
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except Exception as e:
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st.warning(f"⚠️ Error in keyword density analysis: {e}")
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return {
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"word_frequency": [],
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"keyword_density": {},
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"top_keywords": [],
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"total_words": 0,
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"analysis_message": f"⚠️ Error analyzing keywords: {str(e)}",
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"recommendations": []
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}
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def analyze_url_structure_with_advertools(text, url):
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"""
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Analyze URL structure and extract URLs using advertools for comprehensive link analysis.
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Args:
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text (str): The main content text from the webpage
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url (str): The current webpage URL for context
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Returns:
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dict: Comprehensive URL analysis using advertools
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"""
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try:
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# Use advertools extract_urls for professional URL extraction
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extracted_urls = adv.extract_urls(text)
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if not extracted_urls:
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return {
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"extracted_urls": [],
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"url_analysis": {},
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"link_insights": [],
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"recommendations": ["No URLs found in content text"]
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}
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# Convert to DataFrame for easier analysis
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urls_df = pd.DataFrame(extracted_urls, columns=['urls'])
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# Analyze URL patterns and structure
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current_domain = urlparse(url).netloc.lower()
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# Categorize URLs
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internal_urls = []
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external_urls = []
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social_urls = []
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email_urls = []
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file_urls = []
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# Social media domains for classification
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social_domains = ['facebook.com', 'twitter.com', 'linkedin.com', 'instagram.com',
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'youtube.com', 'pinterest.com', 'tiktok.com', 'snapchat.com']
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# File extensions to identify downloadable content
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file_extensions = ['.pdf', '.doc', '.docx', '.xls', '.xlsx', '.ppt', '.pptx',
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'.zip', '.rar', '.mp4', '.mp3', '.jpg', '.png', '.gif']
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for extracted_url in extracted_urls:
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url_lower = extracted_url.lower()
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parsed_url = urlparse(extracted_url)
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domain = parsed_url.netloc.lower()
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# Categorize URLs
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if extracted_url.startswith('mailto:'):
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email_urls.append(extracted_url)
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elif any(ext in url_lower for ext in file_extensions):
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file_urls.append(extracted_url)
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elif any(social in domain for social in social_domains):
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social_urls.append(extracted_url)
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elif current_domain in domain or domain == '':
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internal_urls.append(extracted_url)
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else:
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external_urls.append(extracted_url)
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# Generate insights and recommendations
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insights = []
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recommendations = []
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# URL distribution analysis
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total_urls = len(extracted_urls)
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if total_urls > 0:
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insights.append(f"✅ Found {total_urls} URLs in content")
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# Internal vs External ratio analysis
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internal_ratio = (len(internal_urls) / total_urls) * 100
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external_ratio = (len(external_urls) / total_urls) * 100
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if internal_ratio > 70:
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insights.append(f"✅ Good internal linking: {len(internal_urls)} internal URLs ({internal_ratio:.1f}%)")
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elif internal_ratio < 30:
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insights.append(f"⚠️ Low internal linking: {len(internal_urls)} internal URLs ({internal_ratio:.1f}%)")
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recommendations.append("Consider adding more internal links to improve site structure")
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else:
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insights.append(f"✅ Balanced linking: {len(internal_urls)} internal, {len(external_urls)} external URLs")
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# External links analysis
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if external_urls:
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insights.append(f"🔗 {len(external_urls)} external links found ({external_ratio:.1f}%)")
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if len(external_urls) > 10:
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recommendations.append("Consider reviewing external links - too many might dilute page authority")
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else:
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recommendations.append("Consider adding relevant external links to authoritative sources")
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# Social media presence
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if social_urls:
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insights.append(f"📱 {len(social_urls)} social media links found")
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else:
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recommendations.append("Consider adding social media links for better engagement")
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# File downloads
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if file_urls:
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insights.append(f"📄 {len(file_urls)} downloadable files linked")
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# Email links
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if email_urls:
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insights.append(f"📧 {len(email_urls)} email links found")
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# URL quality analysis
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broken_or_suspicious = []
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for extracted_url in extracted_urls:
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# Check for common issues
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if extracted_url.count('http') > 1:
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broken_or_suspicious.append(f"Malformed URL: {extracted_url}")
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elif len(extracted_url) > 200:
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broken_or_suspicious.append(f"Very long URL: {extracted_url[:100]}...")
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if broken_or_suspicious:
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insights.append(f"⚠️ {len(broken_or_suspicious)} potentially problematic URLs found")
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recommendations.extend(broken_or_suspicious[:3]) # Show first 3
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# Performance insights
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if total_urls > 50:
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recommendations.append("High number of URLs - ensure they're all necessary for user experience")
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elif total_urls < 5:
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recommendations.append("Consider adding more relevant links to improve content value")
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return {
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"extracted_urls": extracted_urls,
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"url_analysis": {
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"total_urls": total_urls,
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"internal_urls": internal_urls,
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"external_urls": external_urls,
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"social_urls": social_urls,
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"email_urls": email_urls,
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"file_urls": file_urls,
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"internal_ratio": round((len(internal_urls) / total_urls) * 100, 1) if total_urls > 0 else 0,
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"external_ratio": round((len(external_urls) / total_urls) * 100, 1) if total_urls > 0 else 0
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},
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"link_insights": insights,
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"recommendations": recommendations,
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"problematic_urls": broken_or_suspicious
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}
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except Exception as e:
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st.warning(f"⚠️ Error in URL analysis: {e}")
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return {
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"extracted_urls": [],
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"url_analysis": {},
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"link_insights": [f"⚠️ Error analyzing URLs: {str(e)}"],
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"recommendations": []
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}
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def enhanced_content_analysis(soup, url):
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"""
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Enhanced content analysis that includes advertools word frequency and URL analysis.
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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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dict: Enhanced content analysis data
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"""
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try:
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# Get the main content text (excluding navigation, footers, etc.)
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# Remove script and style elements
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for script in soup(["script", "style", "nav", "footer", "header"]):
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script.decompose()
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# Get text content
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main_text = soup.get_text()
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# Clean up the text
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lines = (line.strip() for line in main_text.splitlines())
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chunks = (phrase.strip() for line in lines for phrase in line.split(" "))
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clean_text = ' '.join(chunk for chunk in chunks if chunk)
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# Perform keyword density analysis
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keyword_analysis = analyze_keyword_density(clean_text, url)
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# Perform URL analysis using advertools
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url_analysis = analyze_url_structure_with_advertools(clean_text, url)
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# Get existing content data
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content_data = extract_content_data(soup, url)
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# Enhance with keyword and URL analysis
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content_data.update({
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"keyword_analysis": keyword_analysis,
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"url_analysis": url_analysis,
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"clean_text_length": len(clean_text),
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"clean_word_count": len(clean_text.split())
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})
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# Update link insights with advertools analysis
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if url_analysis.get('link_insights'):
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content_data['link_insights'] = url_analysis['link_insights']
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return content_data
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except Exception as e:
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st.warning(f"⚠️ Error in enhanced content analysis: {e}")
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return extract_content_data(soup, url) # Fallback to original
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def fetch_seo_data(url):
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"""
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Fetches SEO-related data from the provided URL and returns a dictionary with results.
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@@ -444,7 +755,7 @@ def fetch_seo_data(url):
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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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content_data = enhanced_content_analysis(soup, url)
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open_graph = extract_open_graph(soup)
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return {
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@@ -481,10 +792,11 @@ def analyze_onpage_seo():
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"""
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Main function to analyze on-page SEO using Streamlit.
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"""
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st.title("ALwrity On Page SEO Analyzer")
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st.title("🔍 ALwrity On-Page SEO Analyzer")
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st.write("Enhanced with AI-powered keyword density and URL analysis")
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url = st.text_input("Enter URL to Analyze", "")
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if st.button("Analyze"):
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if st.button("🚀 Analyze"):
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if not url:
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st.error("⚠️ Please enter a URL.")
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else:
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@@ -496,72 +808,263 @@ def analyze_onpage_seo():
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alt_text = check_alt_text(fetch_and_parse_html(url))
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if results:
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st.subheader("Meta Data")
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st.write(f"**Title:** {results['meta_data']['metatitle']}")
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st.write(f"**Description:** {results['meta_data']['metadescription']}")
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st.write(f"**Robots Directives:** {', '.join(results['meta_data']['robots_directives'])}")
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st.write(f"**Viewport:** {results['meta_data']['viewport']}")
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st.write(f"**Charset:** {results['meta_data']['charset']}")
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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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# Create tabs for better organization
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tab1, tab2, tab3, tab4, tab5 = st.tabs([
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"📄 Meta & Content",
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"🔤 Keywords & Density",
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"🖼️ Media & Links",
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"📱 Technical",
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"📊 Performance"
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])
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with tab1:
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st.subheader("Meta Data")
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col1, col2 = st.columns(2)
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with col1:
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st.write(f"**Title:** {results['meta_data']['metatitle']}")
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st.write(f"**Description:** {results['meta_data']['metadescription']}")
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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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with col2:
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st.write(f"**Robots Directives:** {', '.join(results['meta_data']['robots_directives'])}")
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st.write(f"**Viewport:** {results['meta_data']['viewport']}")
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st.write(f"**Charset:** {results['meta_data']['charset']}")
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st.subheader("Headings")
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st.write(results['headings'])
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st.subheader("Content Overview")
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col1, col2, col3 = st.columns(3)
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with col1:
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st.metric("Text Length", f"{results['content_data']['text_length']} chars")
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with col2:
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if 'clean_word_count' in results['content_data']:
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st.metric("Word Count", results['content_data']['clean_word_count'])
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with col3:
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st.metric("Readability Score", f"{results['readability_score']:.1f}")
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st.write(results['content_data']['h1_message'])
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st.write(results['content_data']['content_message'])
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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("Headings Structure")
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if results['headings']:
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headings_df = pd.DataFrame(results['headings'])
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st.dataframe(headings_df, use_container_width=True)
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else:
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st.write("No headings found")
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st.subheader("Images")
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st.write(results['images'])
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with tab2:
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st.subheader("🎯 Keyword Density Analysis")
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if 'keyword_analysis' in results['content_data']:
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keyword_data = results['content_data']['keyword_analysis']
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# Display analysis message
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st.write(keyword_data['analysis_message'])
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# Show recommendations if any
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if keyword_data['recommendations']:
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st.write("**💡 Recommendations:**")
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for rec in keyword_data['recommendations']:
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st.write(f"• {rec}")
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# Display top keywords
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if keyword_data['top_keywords']:
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st.subheader("📈 Top Keywords")
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# Create a DataFrame for better visualization
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keywords_df = pd.DataFrame(keyword_data['top_keywords'])
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# Color code by density
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def highlight_density(val):
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if val > 3:
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return 'background-color: #ffcccc' # Light red for high density
|
||||
elif val >= 1:
|
||||
return 'background-color: #ccffcc' # Light green for good density
|
||||
else:
|
||||
return 'background-color: #ffffcc' # Light yellow for low density
|
||||
|
||||
styled_df = keywords_df.style.applymap(highlight_density, subset=['density'])
|
||||
st.dataframe(styled_df, use_container_width=True)
|
||||
|
||||
# Keyword density categories
|
||||
col1, col2, col3 = st.columns(3)
|
||||
|
||||
with col1:
|
||||
st.write("**🔴 High Density (>3%)**")
|
||||
if keyword_data['keyword_density']['high_density']:
|
||||
for kw in keyword_data['keyword_density']['high_density']:
|
||||
st.write(f"• {kw['word']}: {kw['density']}%")
|
||||
else:
|
||||
st.write("None found ✅")
|
||||
|
||||
with col2:
|
||||
st.write("**🟢 Good Density (1-3%)**")
|
||||
if keyword_data['keyword_density']['medium_density']:
|
||||
for kw in keyword_data['keyword_density']['medium_density'][:5]:
|
||||
st.write(f"• {kw['word']}: {kw['density']}%")
|
||||
else:
|
||||
st.write("None found")
|
||||
|
||||
with col3:
|
||||
st.write("**🟡 Low Density (<1%)**")
|
||||
if keyword_data['keyword_density']['low_density']:
|
||||
for kw in keyword_data['keyword_density']['low_density'][:5]:
|
||||
st.write(f"• {kw['word']}: {kw['density']}%")
|
||||
else:
|
||||
st.write("None found")
|
||||
|
||||
else:
|
||||
st.warning("No significant keywords found in content")
|
||||
else:
|
||||
st.warning("Keyword analysis not available")
|
||||
|
||||
st.subheader("Broken Links")
|
||||
st.write(results['broken_links'])
|
||||
with tab3:
|
||||
st.subheader("Images Analysis")
|
||||
st.write(results['content_data']['alt_text_message'])
|
||||
|
||||
if results['images']:
|
||||
st.write(f"**Total Images:** {len(results['images'])}")
|
||||
with st.expander("View Image Details"):
|
||||
for i, img in enumerate(results['images'][:10]): # Show first 10
|
||||
st.write(f"**Image {i+1}:** {img}")
|
||||
|
||||
st.subheader("🔗 Advanced Link Analysis")
|
||||
|
||||
# Display advertools URL analysis if available
|
||||
if 'url_analysis' in results['content_data']:
|
||||
url_data = results['content_data']['url_analysis']
|
||||
|
||||
# URL Statistics
|
||||
st.subheader("📊 URL Statistics")
|
||||
col1, col2, col3, col4 = st.columns(4)
|
||||
|
||||
with col1:
|
||||
st.metric("Total URLs", url_data['url_analysis'].get('total_urls', 0))
|
||||
with col2:
|
||||
st.metric("Internal Links", len(url_data['url_analysis'].get('internal_urls', [])))
|
||||
with col3:
|
||||
st.metric("External Links", len(url_data['url_analysis'].get('external_urls', [])))
|
||||
with col4:
|
||||
st.metric("Social Links", len(url_data['url_analysis'].get('social_urls', [])))
|
||||
|
||||
# Link Distribution
|
||||
if url_data['url_analysis'].get('total_urls', 0) > 0:
|
||||
st.subheader("🎯 Link Distribution")
|
||||
col1, col2 = st.columns(2)
|
||||
|
||||
with col1:
|
||||
st.write("**Internal vs External Ratio:**")
|
||||
internal_ratio = url_data['url_analysis'].get('internal_ratio', 0)
|
||||
external_ratio = url_data['url_analysis'].get('external_ratio', 0)
|
||||
st.write(f"• Internal: {internal_ratio}%")
|
||||
st.write(f"• External: {external_ratio}%")
|
||||
|
||||
with col2:
|
||||
st.write("**Link Categories:**")
|
||||
if url_data['url_analysis'].get('email_urls'):
|
||||
st.write(f"• Email: {len(url_data['url_analysis']['email_urls'])}")
|
||||
if url_data['url_analysis'].get('file_urls'):
|
||||
st.write(f"• Files: {len(url_data['url_analysis']['file_urls'])}")
|
||||
if url_data['url_analysis'].get('social_urls'):
|
||||
st.write(f"• Social: {len(url_data['url_analysis']['social_urls'])}")
|
||||
|
||||
# URL Insights and Recommendations
|
||||
if url_data.get('link_insights'):
|
||||
st.subheader("💡 Link Analysis Insights")
|
||||
for insight in url_data['link_insights']:
|
||||
st.write(f"• {insight}")
|
||||
|
||||
if url_data.get('recommendations'):
|
||||
st.subheader("🎯 Link Optimization Recommendations")
|
||||
for rec in url_data['recommendations']:
|
||||
st.write(f"• {rec}")
|
||||
|
||||
# Show extracted URLs
|
||||
if url_data.get('extracted_urls'):
|
||||
with st.expander(f"📋 View All Extracted URLs ({len(url_data['extracted_urls'])})"):
|
||||
# Categorize and display URLs
|
||||
internal_urls = url_data['url_analysis'].get('internal_urls', [])
|
||||
external_urls = url_data['url_analysis'].get('external_urls', [])
|
||||
social_urls = url_data['url_analysis'].get('social_urls', [])
|
||||
|
||||
if internal_urls:
|
||||
st.write("**🏠 Internal URLs:**")
|
||||
for url in internal_urls[:10]: # Show first 10
|
||||
st.write(f"• {url}")
|
||||
|
||||
if external_urls:
|
||||
st.write("**🌐 External URLs:**")
|
||||
for url in external_urls[:10]: # Show first 10
|
||||
st.write(f"• {url}")
|
||||
|
||||
if social_urls:
|
||||
st.write("**📱 Social Media URLs:**")
|
||||
for url in social_urls:
|
||||
st.write(f"• {url}")
|
||||
|
||||
else:
|
||||
# Fallback to original link analysis
|
||||
st.subheader("Links Analysis")
|
||||
for insight in results['content_data']['link_insights']:
|
||||
st.write(f"- {insight}")
|
||||
|
||||
st.write(results['content_data']['internal_links_message'])
|
||||
st.write(results['content_data']['external_links_message'])
|
||||
|
||||
if results['broken_links']:
|
||||
st.subheader("⚠️ Broken Links")
|
||||
for link in results['broken_links'][:5]: # Show first 5
|
||||
st.write(f"• {link}")
|
||||
else:
|
||||
st.success("✅ No broken links detected")
|
||||
|
||||
st.subheader("Suggested CTAs")
|
||||
st.write(results['ctas'])
|
||||
with tab4:
|
||||
st.subheader("Schema Markup")
|
||||
st.write(f"**Schema Types:** {results['schema_markup']['schema_types']}")
|
||||
st.write(results['schema_markup']['schema_message'])
|
||||
|
||||
st.subheader("Canonical and Hreflangs")
|
||||
st.write(f"**Canonical:** {results['alternates_and_canonicals']['canonical']}")
|
||||
st.write(f"**Hreflangs:** {results['alternates_and_canonicals']['hreflangs']}")
|
||||
st.write(f"**Mobile Alternate:** {results['alternates_and_canonicals']['mobile_alternate']}")
|
||||
st.write(results['alternates_and_canonicals']['canonical_message'])
|
||||
st.write(results['alternates_and_canonicals']['hreflangs_message'])
|
||||
|
||||
st.subheader("Open Graph & Social")
|
||||
st.write(f"**Open Graph Tags:** {results['open_graph']['open_graph']}")
|
||||
st.write(results['open_graph']['open_graph_message'])
|
||||
|
||||
st.write(f"**Twitter Cards:** {social_tags['twitter_cards']}")
|
||||
st.write(social_tags['twitter_message'])
|
||||
st.write(f"**Facebook Open Graph:** {social_tags['facebook_open_graph']}")
|
||||
st.write(social_tags['facebook_message'])
|
||||
|
||||
with tab5:
|
||||
st.subheader("Performance & Usability")
|
||||
|
||||
col1, col2 = st.columns(2)
|
||||
|
||||
with col1:
|
||||
st.write("**Page Speed**")
|
||||
st.write(speed['speed_message'])
|
||||
|
||||
st.write("**Mobile Usability**")
|
||||
st.write(mobile_usability['mobile_message'])
|
||||
|
||||
with col2:
|
||||
st.write("**Accessibility**")
|
||||
st.write(alt_text['alt_text_message'])
|
||||
|
||||
st.write("**CTAs Found**")
|
||||
if results['ctas']:
|
||||
for cta in results['ctas']:
|
||||
st.write(f"• {cta}")
|
||||
else:
|
||||
st.write("No common CTAs detected")
|
||||
|
||||
st.subheader("Canonical and Hreflangs")
|
||||
st.write(f"**Canonical:** {results['alternates_and_canonicals']['canonical']}")
|
||||
st.write(f"**Hreflangs:** {results['alternates_and_canonicals']['hreflangs']}")
|
||||
st.write(f"**Mobile Alternate:** {results['alternates_and_canonicals']['mobile_alternate']}")
|
||||
st.write(results['alternates_and_canonicals']['canonical_message'])
|
||||
st.write(results['alternates_and_canonicals']['hreflangs_message'])
|
||||
|
||||
st.subheader("Schema Markup")
|
||||
st.write(f"**Schema Types:** {results['schema_markup']['schema_types']}")
|
||||
st.write(results['schema_markup']['schema_message'])
|
||||
|
||||
st.subheader("Content Data")
|
||||
st.write(f"**Text Length:** {results['content_data']['text_length']} characters")
|
||||
st.write(results['content_data']['h1_message'])
|
||||
st.write(results['content_data']['content_message'])
|
||||
st.write(results['content_data']['alt_text_message'])
|
||||
|
||||
for insight in results['content_data']['link_insights']:
|
||||
st.write(f"- {insight}")
|
||||
|
||||
st.write(results['content_data']['internal_links_message'])
|
||||
st.write(results['content_data']['external_links_message'])
|
||||
|
||||
st.subheader("Open Graph Data")
|
||||
st.write(f"**Open Graph Tags:** {results['open_graph']['open_graph']}")
|
||||
st.write(results['open_graph']['open_graph_message'])
|
||||
|
||||
st.subheader("Social Tags")
|
||||
st.write(f"**Twitter Cards:** {social_tags['twitter_cards']}")
|
||||
st.write(social_tags['twitter_message'])
|
||||
st.write(f"**Facebook Open Graph:** {social_tags['facebook_open_graph']}")
|
||||
st.write(social_tags['facebook_message'])
|
||||
|
||||
st.subheader("Performance Metrics")
|
||||
st.write(speed['speed_message'])
|
||||
|
||||
st.subheader("Mobile Usability")
|
||||
st.write(mobile_usability['mobile_message'])
|
||||
|
||||
st.subheader("Accessibility")
|
||||
st.write(alt_text['alt_text_message'])
|
||||
|
||||
if st.button("Download CSV"):
|
||||
# Export functionality
|
||||
st.subheader("📥 Export Data")
|
||||
if st.button("Download Complete Analysis as CSV"):
|
||||
download_csv(results)
|
||||
|
||||
Reference in New Issue
Block a user