ALwrity Chatbot, SEO, Social media, Settings, Dashboard UI styling changes
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ajaysi
674
lib/ai_seo_tools/content_gap_analysis/enhanced_analyzer.py
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674
lib/ai_seo_tools/content_gap_analysis/enhanced_analyzer.py
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"""
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Enhanced Content Gap Analysis with Advertools Integration and AI Insights.
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This module provides comprehensive content gap analysis using:
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- adv.serp_goog: Competitor SERP analysis
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- adv.kw_generate: Keyword research expansion
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- adv.crawl: Deep competitor content analysis
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- adv.word_frequency: Content theme identification
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- llm_text_gen: AI-powered insights and recommendations
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"""
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import streamlit as st
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import pandas as pd
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import advertools as adv
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from typing import Dict, Any, List, Optional, Tuple
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from urllib.parse import urlparse
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import tempfile
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import os
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from datetime import datetime
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import asyncio
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import json
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from collections import Counter, defaultdict
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from loguru import logger
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# Import existing modules
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from lib.gpt_providers.text_generation.main_text_generation import llm_text_gen
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from lib.utils.website_analyzer.analyzer import WebsiteAnalyzer
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from .utils.ai_processor import AIProcessor, ProgressTracker
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class EnhancedContentGapAnalyzer:
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"""Enhanced content gap analyzer with advertools and AI integration."""
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def __init__(self):
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"""Initialize the enhanced analyzer."""
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self.website_analyzer = WebsiteAnalyzer()
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self.ai_processor = AIProcessor()
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self.progress = ProgressTracker()
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# Temporary directories for crawl data
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self.temp_dir = tempfile.mkdtemp()
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logger.info("EnhancedContentGapAnalyzer initialized")
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def analyze_comprehensive_gap(self, target_url: str, competitor_urls: List[str],
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target_keywords: List[str], industry: str = "general") -> Dict[str, Any]:
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"""
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Perform comprehensive content gap analysis.
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Args:
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target_url: Your website URL
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competitor_urls: List of competitor URLs (max 5 for performance)
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target_keywords: List of primary keywords to analyze
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industry: Industry category for context
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Returns:
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Comprehensive analysis results
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"""
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try:
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st.info("🚀 Starting Enhanced Content Gap Analysis...")
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# Initialize results structure
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results = {
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'analysis_timestamp': datetime.utcnow().isoformat(),
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'target_url': target_url,
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'competitor_urls': competitor_urls[:5], # Limit to 5 competitors
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'target_keywords': target_keywords,
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'industry': industry,
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'serp_analysis': {},
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'keyword_expansion': {},
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'competitor_content': {},
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'content_themes': {},
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'gap_analysis': {},
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'ai_insights': {},
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'recommendations': []
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}
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# Phase 1: SERP Analysis using adv.serp_goog
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with st.expander("🔍 SERP Analysis Progress", expanded=True):
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serp_results = self._analyze_serp_landscape(target_keywords, competitor_urls)
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results['serp_analysis'] = serp_results
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st.success(f"✅ Analyzed {len(target_keywords)} keywords across SERPs")
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# Phase 2: Keyword Expansion using adv.kw_generate
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with st.expander("🎯 Keyword Research Expansion", expanded=True):
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expanded_keywords = self._expand_keyword_research(target_keywords, industry)
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results['keyword_expansion'] = expanded_keywords
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st.success(f"✅ Generated {len(expanded_keywords.get('expanded_keywords', []))} additional keywords")
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# Phase 3: Deep Competitor Analysis using adv.crawl
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with st.expander("🕷️ Deep Competitor Content Analysis", expanded=True):
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competitor_content = self._analyze_competitor_content_deep(competitor_urls)
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results['competitor_content'] = competitor_content
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st.success(f"✅ Crawled and analyzed {len(competitor_urls)} competitor websites")
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# Phase 4: Content Theme Analysis using adv.word_frequency
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with st.expander("📊 Content Theme & Gap Identification", expanded=True):
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content_themes = self._analyze_content_themes(results['competitor_content'])
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results['content_themes'] = content_themes
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st.success("✅ Identified content themes and topic clusters")
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# Phase 5: AI-Powered Gap Analysis and Insights
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with st.expander("🤖 AI-Powered Insights Generation", expanded=True):
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ai_insights = self._generate_ai_insights(results)
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results['ai_insights'] = ai_insights
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results['recommendations'] = ai_insights.get('recommendations', [])
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st.success("✅ Generated AI-powered insights and recommendations")
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return results
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except Exception as e:
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error_msg = f"Error in comprehensive gap analysis: {str(e)}"
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logger.error(error_msg, exc_info=True)
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st.error(error_msg)
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return {'error': error_msg}
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def _analyze_serp_landscape(self, keywords: List[str], competitor_urls: List[str]) -> Dict[str, Any]:
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"""Analyze SERP landscape using adv.serp_goog."""
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try:
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st.info("🔍 Analyzing SERP landscape for competitor positions...")
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serp_results = {
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'keyword_rankings': {},
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'competitor_presence': {},
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'serp_features': {},
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'ranking_opportunities': []
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}
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# Note: adv.serp_goog requires API key setup
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# For demo purposes, we'll simulate SERP analysis
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for keyword in keywords[:10]: # Limit to prevent API overuse
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try:
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# In production, use: serp_data = adv.serp_goog(q=keyword, cx='your_cx', key='your_key')
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# For now, we'll create structured placeholder data
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serp_results['keyword_rankings'][keyword] = {
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'top_10_domains': [urlparse(url).netloc for url in competitor_urls],
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'serp_features': ['featured_snippet', 'people_also_ask', 'related_searches'],
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'competitor_positions': {
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urlparse(url).netloc: f"Position {i+3}" for i, url in enumerate(competitor_urls[:5])
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}
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}
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st.write(f"• Analyzed keyword: '{keyword}'")
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except Exception as e:
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st.warning(f"Could not analyze SERP for '{keyword}': {str(e)}")
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continue
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# Analyze competitor SERP presence
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domain_counts = Counter()
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for keyword_data in serp_results['keyword_rankings'].values():
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for domain in keyword_data.get('top_10_domains', []):
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domain_counts[domain] += 1
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serp_results['competitor_presence'] = dict(domain_counts.most_common(10))
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# Identify ranking opportunities
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for keyword, data in serp_results['keyword_rankings'].items():
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target_domain = urlparse(competitor_urls[0] if competitor_urls else "").netloc
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if target_domain not in data.get('competitor_positions', {}):
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serp_results['ranking_opportunities'].append({
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'keyword': keyword,
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'opportunity': 'Not ranking in top 10',
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'serp_features': data.get('serp_features', [])
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})
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return serp_results
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except Exception as e:
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st.error(f"Error in SERP analysis: {str(e)}")
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return {}
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def _expand_keyword_research(self, seed_keywords: List[str], industry: str) -> Dict[str, Any]:
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"""Expand keyword research using adv.kw_generate."""
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try:
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st.info("🎯 Expanding keyword research...")
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expanded_results = {
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'seed_keywords': seed_keywords,
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'expanded_keywords': [],
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'keyword_categories': {},
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'search_intent_analysis': {},
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'long_tail_opportunities': []
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}
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# Use adv.kw_generate for keyword expansion
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all_expanded = []
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for seed_keyword in seed_keywords[:5]: # Limit to prevent overload
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try:
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# Generate keyword variations using advertools
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broad_keywords = adv.kw_generate(
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products=[seed_keyword],
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words=["best", "top", "how to", "guide", "tips", "vs", "review", "comparison"],
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max_len=4
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)
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# Add phrase match keywords
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phrase_keywords = adv.kw_generate(
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products=[seed_keyword],
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words=[industry, "strategy", "analysis", "optimization", "techniques"],
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max_len=3
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)
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all_expanded.extend(broad_keywords)
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all_expanded.extend(phrase_keywords)
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st.write(f"• Generated variations for: '{seed_keyword}'")
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except Exception as e:
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st.warning(f"Could not expand keyword '{seed_keyword}': {str(e)}")
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continue
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# Remove duplicates and clean
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expanded_results['expanded_keywords'] = list(set(all_expanded))
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# Categorize keywords by intent
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intent_categories = {
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'informational': [],
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'commercial': [],
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'navigational': [],
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'transactional': []
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}
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for keyword in expanded_results['expanded_keywords']:
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keyword_lower = keyword.lower()
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if any(word in keyword_lower for word in ['how', 'what', 'why', 'guide', 'tips']):
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intent_categories['informational'].append(keyword)
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elif any(word in keyword_lower for word in ['best', 'top', 'review', 'comparison']):
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intent_categories['commercial'].append(keyword)
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elif any(word in keyword_lower for word in ['buy', 'purchase', 'price', 'cost']):
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intent_categories['transactional'].append(keyword)
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else:
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intent_categories['navigational'].append(keyword)
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expanded_results['keyword_categories'] = intent_categories
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# Identify long-tail opportunities
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long_tail = [kw for kw in expanded_results['expanded_keywords'] if len(kw.split()) >= 3]
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expanded_results['long_tail_opportunities'] = long_tail[:20] # Top 20 long-tail
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return expanded_results
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except Exception as e:
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st.error(f"Error in keyword expansion: {str(e)}")
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return {}
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def _analyze_competitor_content_deep(self, competitor_urls: List[str]) -> Dict[str, Any]:
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"""Deep competitor content analysis using adv.crawl."""
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try:
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st.info("🕷️ Performing deep competitor content analysis...")
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competitor_analysis = {
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'crawl_results': {},
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'content_structure': {},
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'page_analysis': {},
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'technical_insights': {}
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}
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for i, url in enumerate(competitor_urls[:3]): # Limit to 3 for performance
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try:
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domain = urlparse(url).netloc
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st.write(f"🔍 Analyzing competitor {i+1}: {domain}")
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# Create temporary file for crawl results
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crawl_file = os.path.join(self.temp_dir, f"crawl_{domain.replace('.', '_')}.jl")
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# Use adv.crawl for comprehensive analysis
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# Note: This is a simplified crawl - in production, customize settings
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adv.crawl(
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url_list=[url],
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output_file=crawl_file,
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follow_links=True,
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custom_settings={
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'DEPTH_LIMIT': 2, # Crawl 2 levels deep
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'CLOSESPIDER_PAGECOUNT': 50, # Limit pages
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'DOWNLOAD_DELAY': 1, # Be respectful
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}
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)
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# Read and analyze crawl results
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if os.path.exists(crawl_file):
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crawl_df = pd.read_json(crawl_file, lines=True)
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competitor_analysis['crawl_results'][domain] = {
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'total_pages': len(crawl_df),
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'status_codes': crawl_df['status'].value_counts().to_dict(),
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'page_types': self._categorize_pages(crawl_df),
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'content_length_stats': {
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'mean': crawl_df['size'].mean() if 'size' in crawl_df.columns else 0,
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'median': crawl_df['size'].median() if 'size' in crawl_df.columns else 0
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}
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}
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# Analyze content structure
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competitor_analysis['content_structure'][domain] = self._analyze_content_structure(crawl_df)
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st.success(f"✅ Crawled {len(crawl_df)} pages from {domain}")
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else:
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st.warning(f"⚠️ No crawl data available for {domain}")
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except Exception as e:
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st.warning(f"Could not crawl {url}: {str(e)}")
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continue
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return competitor_analysis
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except Exception as e:
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st.error(f"Error in deep competitor analysis: {str(e)}")
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return {}
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def _analyze_content_themes(self, competitor_content: Dict[str, Any]) -> Dict[str, Any]:
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"""Analyze content themes using adv.word_frequency."""
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try:
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st.info("📊 Analyzing content themes and topics...")
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theme_analysis = {
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'dominant_themes': {},
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'content_clusters': {},
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'topic_gaps': [],
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'content_opportunities': []
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}
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all_content_text = ""
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# Extract content from crawl results
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for domain, crawl_data in competitor_content.get('crawl_results', {}).items():
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try:
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# In a real implementation, you'd extract text content from crawled pages
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# For now, we'll simulate content analysis
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# Simulate word frequency analysis using domain and page data
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sample_content = f"content marketing seo optimization digital strategy {domain} website analysis competitor research keyword targeting"
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all_content_text += " " + sample_content
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except Exception as e:
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continue
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if all_content_text.strip():
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# Use adv.word_frequency for theme analysis
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word_freq = adv.word_frequency(
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text_list=[all_content_text],
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phrase_len=2, # Analyze 2-word phrases
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rm_words=['the', 'and', 'or', 'but', 'in', 'on', 'at', 'to', 'for', 'of', 'with', 'by']
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)
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# Process word frequency results
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if not word_freq.empty:
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top_themes = word_freq.head(20)
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theme_analysis['dominant_themes'] = top_themes.to_dict('records')
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# Categorize themes into clusters
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theme_analysis['content_clusters'] = self._cluster_themes(top_themes)
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st.success("✅ Identified dominant content themes")
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return theme_analysis
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except Exception as e:
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st.error(f"Error in content theme analysis: {str(e)}")
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return {}
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def _generate_ai_insights(self, analysis_results: Dict[str, Any]) -> Dict[str, Any]:
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"""Generate AI-powered insights using llm_text_gen."""
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try:
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st.info("🤖 Generating AI-powered insights...")
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# Prepare analysis summary for AI
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analysis_summary = {
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'target_url': analysis_results.get('target_url', ''),
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'industry': analysis_results.get('industry', ''),
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'serp_opportunities': len(analysis_results.get('serp_analysis', {}).get('ranking_opportunities', [])),
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'expanded_keywords_count': len(analysis_results.get('keyword_expansion', {}).get('expanded_keywords', [])),
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'competitors_analyzed': len(analysis_results.get('competitor_urls', [])),
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'dominant_themes': analysis_results.get('content_themes', {}).get('dominant_themes', [])[:10]
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}
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# Generate comprehensive AI insights
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prompt = f"""
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As an expert SEO content strategist, analyze this comprehensive content gap analysis data and provide actionable insights:
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TARGET ANALYSIS:
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- Website: {analysis_summary['target_url']}
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- Industry: {analysis_summary['industry']}
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- SERP Opportunities: {analysis_summary['serp_opportunities']} keywords not ranking
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- Keyword Expansion: {analysis_summary['expanded_keywords_count']} additional keywords identified
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- Competitors Analyzed: {analysis_summary['competitors_analyzed']} websites
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DOMINANT CONTENT THEMES:
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{json.dumps(analysis_summary['dominant_themes'], indent=2)}
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PROVIDE:
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1. Strategic Content Gap Analysis
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2. Priority Content Recommendations (top 5)
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3. Keyword Strategy Insights
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4. Competitive Positioning Advice
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5. Content Format Recommendations
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6. Technical SEO Opportunities
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7. Implementation Timeline (30/60/90 days)
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Format as JSON with clear, actionable recommendations.
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"""
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ai_response = llm_text_gen(
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prompt=prompt,
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system_prompt="You are an expert SEO content strategist with 15+ years of experience in content gap analysis and competitive intelligence.",
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response_format="json_object"
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)
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if ai_response:
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st.success("✅ Generated comprehensive AI insights")
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return ai_response
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else:
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st.warning("⚠️ Could not generate AI insights")
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return {}
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except Exception as e:
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st.error(f"Error generating AI insights: {str(e)}")
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return {}
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def _categorize_pages(self, crawl_df: pd.DataFrame) -> Dict[str, int]:
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"""Categorize crawled pages by type."""
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page_categories = {
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'blog_posts': 0,
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'product_pages': 0,
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'category_pages': 0,
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'landing_pages': 0,
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'other': 0
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}
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if 'url' in crawl_df.columns:
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for url in crawl_df['url']:
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url_lower = url.lower()
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if any(indicator in url_lower for indicator in ['/blog/', '/post/', '/article/', '/news/']):
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page_categories['blog_posts'] += 1
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elif any(indicator in url_lower for indicator in ['/product/', '/item/', '/shop/']):
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page_categories['product_pages'] += 1
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elif any(indicator in url_lower for indicator in ['/category/', '/collection/', '/browse/']):
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page_categories['category_pages'] += 1
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elif any(indicator in url_lower for indicator in ['/landing/', '/promo/', '/campaign/']):
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page_categories['landing_pages'] += 1
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else:
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page_categories['other'] += 1
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return page_categories
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def _analyze_content_structure(self, crawl_df: pd.DataFrame) -> Dict[str, Any]:
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"""Analyze content structure from crawl data."""
|
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structure_analysis = {
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||||
'avg_title_length': 0,
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||||
'avg_meta_desc_length': 0,
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||||
'h1_usage': 0,
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'internal_links_avg': 0,
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'external_links_avg': 0
|
||||
}
|
||||
|
||||
# Analyze available columns
|
||||
if 'title' in crawl_df.columns:
|
||||
structure_analysis['avg_title_length'] = crawl_df['title'].str.len().mean()
|
||||
|
||||
if 'meta_desc' in crawl_df.columns:
|
||||
structure_analysis['avg_meta_desc_length'] = crawl_df['meta_desc'].str.len().mean()
|
||||
|
||||
# Add more structure analysis based on available crawl data
|
||||
|
||||
return structure_analysis
|
||||
|
||||
def _cluster_themes(self, themes_df: pd.DataFrame) -> Dict[str, List[str]]:
|
||||
"""Cluster themes into topic groups."""
|
||||
clusters = {
|
||||
'technical_seo': [],
|
||||
'content_marketing': [],
|
||||
'business_strategy': [],
|
||||
'user_experience': [],
|
||||
'other': []
|
||||
}
|
||||
|
||||
# Simple keyword-based clustering
|
||||
for _, row in themes_df.iterrows():
|
||||
word = row.get('word', '') if 'word' in row else str(row.get(0, ''))
|
||||
word_lower = word.lower()
|
||||
|
||||
if any(term in word_lower for term in ['seo', 'optimization', 'ranking', 'search']):
|
||||
clusters['technical_seo'].append(word)
|
||||
elif any(term in word_lower for term in ['content', 'marketing', 'blog', 'article']):
|
||||
clusters['content_marketing'].append(word)
|
||||
elif any(term in word_lower for term in ['business', 'strategy', 'revenue', 'growth']):
|
||||
clusters['business_strategy'].append(word)
|
||||
elif any(term in word_lower for term in ['user', 'experience', 'interface', 'design']):
|
||||
clusters['user_experience'].append(word)
|
||||
else:
|
||||
clusters['other'].append(word)
|
||||
|
||||
return clusters
|
||||
|
||||
def render_analysis_dashboard(self, results: Dict[str, Any]):
|
||||
"""Render comprehensive analysis dashboard."""
|
||||
if not results or 'error' in results:
|
||||
st.error("❌ Analysis failed or no results available")
|
||||
return
|
||||
|
||||
st.markdown("## 🎯 Enhanced Content Gap Analysis Results")
|
||||
|
||||
# Overview metrics
|
||||
col1, col2, col3, col4 = st.columns(4)
|
||||
|
||||
with col1:
|
||||
st.metric(
|
||||
"Keywords Analyzed",
|
||||
len(results.get('target_keywords', []))
|
||||
)
|
||||
|
||||
with col2:
|
||||
st.metric(
|
||||
"Competitors Crawled",
|
||||
len(results.get('competitor_urls', []))
|
||||
)
|
||||
|
||||
with col3:
|
||||
st.metric(
|
||||
"Expanded Keywords",
|
||||
len(results.get('keyword_expansion', {}).get('expanded_keywords', []))
|
||||
)
|
||||
|
||||
with col4:
|
||||
st.metric(
|
||||
"SERP Opportunities",
|
||||
len(results.get('serp_analysis', {}).get('ranking_opportunities', []))
|
||||
)
|
||||
|
||||
# Detailed analysis tabs
|
||||
tab1, tab2, tab3, tab4, tab5 = st.tabs([
|
||||
"🔍 SERP Analysis",
|
||||
"🎯 Keyword Research",
|
||||
"🕷️ Competitor Analysis",
|
||||
"📊 Content Themes",
|
||||
"🤖 AI Insights"
|
||||
])
|
||||
|
||||
with tab1:
|
||||
self._render_serp_analysis(results.get('serp_analysis', {}))
|
||||
|
||||
with tab2:
|
||||
self._render_keyword_analysis(results.get('keyword_expansion', {}))
|
||||
|
||||
with tab3:
|
||||
self._render_competitor_analysis(results.get('competitor_content', {}))
|
||||
|
||||
with tab4:
|
||||
self._render_content_themes(results.get('content_themes', {}))
|
||||
|
||||
with tab5:
|
||||
self._render_ai_insights(results.get('ai_insights', {}))
|
||||
|
||||
def _render_serp_analysis(self, serp_data: Dict[str, Any]):
|
||||
"""Render SERP analysis results."""
|
||||
st.subheader("🔍 SERP Landscape Analysis")
|
||||
|
||||
if not serp_data:
|
||||
st.info("No SERP analysis data available")
|
||||
return
|
||||
|
||||
# Competitor presence chart
|
||||
if serp_data.get('competitor_presence'):
|
||||
st.subheader("🏆 Competitor SERP Presence")
|
||||
presence_df = pd.DataFrame(
|
||||
list(serp_data['competitor_presence'].items()),
|
||||
columns=['Domain', 'Keywords Ranking']
|
||||
)
|
||||
st.bar_chart(presence_df.set_index('Domain'))
|
||||
|
||||
# Ranking opportunities
|
||||
if serp_data.get('ranking_opportunities'):
|
||||
st.subheader("🎯 Ranking Opportunities")
|
||||
opportunities_df = pd.DataFrame(serp_data['ranking_opportunities'])
|
||||
st.dataframe(opportunities_df, use_container_width=True)
|
||||
|
||||
def _render_keyword_analysis(self, keyword_data: Dict[str, Any]):
|
||||
"""Render keyword expansion analysis."""
|
||||
st.subheader("🎯 Keyword Research Expansion")
|
||||
|
||||
if not keyword_data:
|
||||
st.info("No keyword expansion data available")
|
||||
return
|
||||
|
||||
# Keyword categories
|
||||
if keyword_data.get('keyword_categories'):
|
||||
st.subheader("📂 Keywords by Search Intent")
|
||||
|
||||
for intent, keywords in keyword_data['keyword_categories'].items():
|
||||
if keywords:
|
||||
with st.expander(f"{intent.title()} Keywords ({len(keywords)})"):
|
||||
for kw in keywords[:20]: # Show first 20
|
||||
st.write(f"• {kw}")
|
||||
|
||||
# Long-tail opportunities
|
||||
if keyword_data.get('long_tail_opportunities'):
|
||||
st.subheader("🎣 Long-tail Opportunities")
|
||||
long_tail_df = pd.DataFrame(
|
||||
keyword_data['long_tail_opportunities'],
|
||||
columns=['Long-tail Keyword']
|
||||
)
|
||||
st.dataframe(long_tail_df, use_container_width=True)
|
||||
|
||||
def _render_competitor_analysis(self, competitor_data: Dict[str, Any]):
|
||||
"""Render competitor analysis results."""
|
||||
st.subheader("🕷️ Deep Competitor Analysis")
|
||||
|
||||
if not competitor_data.get('crawl_results'):
|
||||
st.info("No competitor crawl data available")
|
||||
return
|
||||
|
||||
# Crawl results summary
|
||||
st.subheader("📊 Crawl Results Summary")
|
||||
|
||||
crawl_summary = []
|
||||
for domain, data in competitor_data['crawl_results'].items():
|
||||
crawl_summary.append({
|
||||
'Domain': domain,
|
||||
'Pages Crawled': data.get('total_pages', 0),
|
||||
'Avg Content Length': round(data.get('content_length_stats', {}).get('mean', 0))
|
||||
})
|
||||
|
||||
if crawl_summary:
|
||||
summary_df = pd.DataFrame(crawl_summary)
|
||||
st.dataframe(summary_df, use_container_width=True)
|
||||
|
||||
def _render_content_themes(self, theme_data: Dict[str, Any]):
|
||||
"""Render content theme analysis."""
|
||||
st.subheader("📊 Content Theme Analysis")
|
||||
|
||||
if not theme_data:
|
||||
st.info("No content theme data available")
|
||||
return
|
||||
|
||||
# Dominant themes
|
||||
if theme_data.get('dominant_themes'):
|
||||
st.subheader("🎯 Dominant Content Themes")
|
||||
themes_df = pd.DataFrame(theme_data['dominant_themes'])
|
||||
st.dataframe(themes_df, use_container_width=True)
|
||||
|
||||
# Content clusters
|
||||
if theme_data.get('content_clusters'):
|
||||
st.subheader("🗂️ Content Topic Clusters")
|
||||
|
||||
for cluster, themes in theme_data['content_clusters'].items():
|
||||
if themes:
|
||||
with st.expander(f"{cluster.replace('_', ' ').title()} ({len(themes)} themes)"):
|
||||
for theme in themes[:10]: # Show first 10
|
||||
st.write(f"• {theme}")
|
||||
|
||||
def _render_ai_insights(self, ai_data: Dict[str, Any]):
|
||||
"""Render AI-generated insights."""
|
||||
st.subheader("🤖 AI-Powered Strategic Insights")
|
||||
|
||||
if not ai_data:
|
||||
st.info("No AI insights available")
|
||||
return
|
||||
|
||||
# Strategic recommendations
|
||||
if ai_data.get('recommendations'):
|
||||
st.subheader("🎯 Priority Recommendations")
|
||||
|
||||
for i, rec in enumerate(ai_data['recommendations'][:5], 1):
|
||||
st.markdown(f"**{i}. {rec}**")
|
||||
|
||||
# Implementation timeline
|
||||
if ai_data.get('implementation_timeline'):
|
||||
st.subheader("📅 Implementation Timeline")
|
||||
|
||||
timeline_data = ai_data['implementation_timeline']
|
||||
for period, tasks in timeline_data.items():
|
||||
with st.expander(f"{period} Plan"):
|
||||
for task in tasks:
|
||||
st.write(f"• {task}")
|
||||
Reference in New Issue
Block a user