ALwrity Chatbot, SEO, Social media, Settings, Dashboard UI styling changes
This commit is contained in:
ajaysi
787
lib/ai_seo_tools/content_gap_analysis/enhanced_ui.py
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787
lib/ai_seo_tools/content_gap_analysis/enhanced_ui.py
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"""
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Enhanced UI for Content Gap Analysis with Advertools Integration.
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This module provides a comprehensive Streamlit interface for content gap analysis
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using the EnhancedContentGapAnalyzer with advertools and AI insights.
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"""
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import streamlit as st
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import pandas as pd
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from typing import Dict, Any, List
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import json
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from datetime import datetime
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import io
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import base64
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from .enhanced_analyzer import EnhancedContentGapAnalyzer
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from lib.alwrity_ui.dashboard_styles import apply_dashboard_style, render_dashboard_header
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class EnhancedContentGapAnalysisUI:
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"""Enhanced UI for content gap analysis."""
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def __init__(self):
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"""Initialize the enhanced UI."""
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self.analyzer = EnhancedContentGapAnalyzer()
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# Apply dashboard styling
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apply_dashboard_style()
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def render(self):
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"""Render the enhanced content gap analysis interface."""
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# Enhanced dashboard header
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render_dashboard_header(
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"🎯 Enhanced Content Gap Analysis",
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"Discover content opportunities with AI-powered insights using advertools, SERP analysis, competitor crawling, and strategic recommendations."
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)
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# Main content area
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with st.container():
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# Analysis input form
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self._render_analysis_form()
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# Session state for results
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if 'gap_analysis_results' in st.session_state and st.session_state.gap_analysis_results:
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st.markdown("---")
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self._render_results_dashboard(st.session_state.gap_analysis_results)
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def _render_analysis_form(self):
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"""Render the analysis input form."""
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st.markdown("## 🚀 Setup Your Content Gap Analysis")
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with st.form("enhanced_gap_analysis_form"):
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# Target website input
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col1, col2 = st.columns([2, 1])
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with col1:
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target_url = st.text_input(
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"🎯 Your Website URL",
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placeholder="https://yourwebsite.com",
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help="Enter your website URL to analyze"
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)
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with col2:
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industry = st.selectbox(
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"🏭 Industry",
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options=[
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"general", "technology", "healthcare", "finance",
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"ecommerce", "education", "real estate", "travel",
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"food", "fitness", "marketing", "consulting"
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],
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help="Select your industry for better analysis context"
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)
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# Competitor URLs
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st.markdown("### 🏆 Competitor Analysis")
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competitor_urls_text = st.text_area(
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"Competitor URLs (one per line, max 5)",
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placeholder="https://competitor1.com\nhttps://competitor2.com\nhttps://competitor3.com",
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height=120,
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help="Enter up to 5 competitor URLs for comprehensive analysis"
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)
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# Target keywords
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st.markdown("### 🎯 Keyword Focus")
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target_keywords_text = st.text_input(
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"Primary Keywords (comma-separated)",
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placeholder="seo, content marketing, digital marketing",
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help="Enter your main keywords to analyze and expand"
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)
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# Analysis options
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st.markdown("### ⚙️ Analysis Options")
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col1, col2, col3 = st.columns(3)
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with col1:
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enable_serp = st.checkbox(
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"🔍 SERP Analysis",
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value=True,
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help="Analyze competitor positions in search results"
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)
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with col2:
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enable_crawling = st.checkbox(
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"🕷️ Deep Crawling",
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value=True,
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help="Perform comprehensive competitor content crawling"
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)
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with col3:
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enable_ai_insights = st.checkbox(
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"🤖 AI Insights",
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value=True,
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help="Generate AI-powered strategic recommendations"
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)
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# Submit button
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submitted = st.form_submit_button(
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"🚀 Start Enhanced Analysis",
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use_container_width=True,
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type="primary"
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)
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if submitted:
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# Validate inputs
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if not target_url or not target_url.startswith(('http://', 'https://')):
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st.error("❌ Please enter a valid target URL starting with http:// or https://")
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return
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if not target_keywords_text.strip():
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st.error("❌ Please enter at least one target keyword")
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return
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# Process inputs
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competitor_urls = [
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url.strip() for url in competitor_urls_text.split('\n')
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if url.strip() and url.strip().startswith(('http://', 'https://'))
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]
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if not competitor_urls:
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st.error("❌ Please enter at least one valid competitor URL")
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return
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target_keywords = [
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kw.strip() for kw in target_keywords_text.split(',')
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if kw.strip()
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]
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# Run analysis
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self._run_enhanced_analysis(
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target_url=target_url,
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competitor_urls=competitor_urls,
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target_keywords=target_keywords,
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industry=industry,
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options={
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'enable_serp': enable_serp,
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'enable_crawling': enable_crawling,
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'enable_ai_insights': enable_ai_insights
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}
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)
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def _run_enhanced_analysis(self, target_url: str, competitor_urls: List[str],
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target_keywords: List[str], industry: str, options: Dict[str, bool]):
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"""Run the enhanced content gap analysis."""
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try:
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with st.spinner("🔄 Running Enhanced Content Gap Analysis..."):
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# Initialize progress tracking
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progress_bar = st.progress(0)
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status_text = st.empty()
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# Update progress
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progress_bar.progress(10)
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status_text.text("🚀 Initializing analysis...")
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# Run comprehensive analysis
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results = self.analyzer.analyze_comprehensive_gap(
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target_url=target_url,
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competitor_urls=competitor_urls,
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target_keywords=target_keywords,
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industry=industry
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)
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progress_bar.progress(100)
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status_text.text("✅ Analysis complete!")
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# Store results in session state
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st.session_state.gap_analysis_results = results
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# Clear progress indicators
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progress_bar.empty()
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status_text.empty()
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if 'error' in results:
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st.error(f"❌ Analysis failed: {results['error']}")
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else:
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st.success("🎉 Enhanced Content Gap Analysis completed successfully!")
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st.balloons()
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# Rerun to show results
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st.rerun()
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except Exception as e:
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st.error(f"❌ Error running analysis: {str(e)}")
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def _render_results_dashboard(self, results: Dict[str, Any]):
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"""Render the comprehensive results dashboard."""
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if 'error' in results:
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st.error(f"❌ Analysis Error: {results['error']}")
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return
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# Results header
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st.markdown("## 📊 Enhanced Content Gap Analysis Results")
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# Key metrics overview
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self._render_metrics_overview(results)
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# Detailed analysis tabs
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self._render_detailed_analysis(results)
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# Export functionality
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self._render_export_options(results)
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def _render_metrics_overview(self, results: Dict[str, Any]):
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"""Render key metrics overview."""
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st.markdown("### 📈 Analysis Overview")
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# Create metrics columns
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col1, col2, col3, col4, col5 = st.columns(5)
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with col1:
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st.metric(
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"🎯 Keywords Analyzed",
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len(results.get('target_keywords', [])),
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help="Number of primary keywords analyzed"
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)
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with col2:
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st.metric(
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"🏆 Competitors Crawled",
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len(results.get('competitor_urls', [])),
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help="Number of competitor websites analyzed"
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)
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with col3:
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expanded_keywords = results.get('keyword_expansion', {}).get('expanded_keywords', [])
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st.metric(
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"🔍 Keywords Discovered",
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len(expanded_keywords),
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help="Additional keywords discovered through expansion"
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)
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with col4:
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ranking_opportunities = results.get('serp_analysis', {}).get('ranking_opportunities', [])
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st.metric(
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"🚀 SERP Opportunities",
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len(ranking_opportunities),
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help="Keywords with ranking opportunities identified"
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)
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with col5:
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recommendations = results.get('recommendations', [])
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st.metric(
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"💡 AI Recommendations",
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len(recommendations),
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help="AI-generated strategic recommendations"
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)
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# Analysis timestamp
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if results.get('analysis_timestamp'):
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timestamp = datetime.fromisoformat(results['analysis_timestamp'].replace('Z', '+00:00'))
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st.caption(f"📅 Analysis completed: {timestamp.strftime('%Y-%m-%d %H:%M:%S UTC')}")
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def _render_detailed_analysis(self, results: Dict[str, Any]):
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"""Render detailed analysis in tabs."""
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# Create main analysis tabs
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tab1, tab2, tab3, tab4, tab5, tab6 = st.tabs([
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"🔍 SERP Analysis",
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"🎯 Keyword Research",
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"🕷️ Competitor Intelligence",
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"📊 Content Themes",
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"🤖 AI Strategic Insights",
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"📋 Action Plan"
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])
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with tab1:
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self._render_serp_analysis(results.get('serp_analysis', {}))
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with tab2:
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self._render_keyword_research(results.get('keyword_expansion', {}))
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with tab3:
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self._render_competitor_intelligence(results.get('competitor_content', {}))
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with tab4:
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self._render_content_themes(results.get('content_themes', {}))
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with tab5:
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self._render_ai_insights(results.get('ai_insights', {}))
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with tab6:
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self._render_action_plan(results)
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def _render_serp_analysis(self, serp_data: Dict[str, Any]):
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"""Render SERP analysis results."""
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st.markdown("### 🔍 Search Engine Results Analysis")
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if not serp_data:
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st.info("No SERP analysis data available")
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return
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# Competitor SERP presence
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if serp_data.get('competitor_presence'):
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st.markdown("#### 🏆 Competitor SERP Dominance")
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presence_data = serp_data['competitor_presence']
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presence_df = pd.DataFrame(
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list(presence_data.items()),
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columns=['Domain', 'Keywords Ranking']
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)
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# Display as chart
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st.bar_chart(presence_df.set_index('Domain'))
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# Top performers
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st.markdown("**🥇 Top Performing Competitors:**")
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for domain, count in list(presence_data.items())[:3]:
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st.write(f"• **{domain}**: Ranking for {count} keywords")
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# Ranking opportunities
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if serp_data.get('ranking_opportunities'):
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st.markdown("#### 🚀 Ranking Opportunities")
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opportunities = serp_data['ranking_opportunities']
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if opportunities:
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opp_df = pd.DataFrame(opportunities)
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st.dataframe(opp_df, use_container_width=True)
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st.info(f"💡 Found {len(opportunities)} keywords where you're not ranking in top 10!")
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else:
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st.success("🎉 You're already ranking well for your target keywords!")
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# SERP features analysis
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if serp_data.get('keyword_rankings'):
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st.markdown("#### 🎯 SERP Features Opportunities")
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all_features = []
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for keyword_data in serp_data['keyword_rankings'].values():
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all_features.extend(keyword_data.get('serp_features', []))
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if all_features:
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feature_counts = pd.Series(all_features).value_counts()
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st.bar_chart(feature_counts)
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st.markdown("**🎯 Focus on these SERP features:**")
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for feature, count in feature_counts.head(3).items():
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st.write(f"• **{feature.replace('_', ' ').title()}**: Appears in {count} keyword searches")
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def _render_keyword_research(self, keyword_data: Dict[str, Any]):
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"""Render keyword research results."""
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st.markdown("### 🎯 Advanced Keyword Research")
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if not keyword_data:
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st.info("No keyword expansion data available")
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return
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# Seed vs expanded keywords
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seed_keywords = keyword_data.get('seed_keywords', [])
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expanded_keywords = keyword_data.get('expanded_keywords', [])
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col1, col2 = st.columns(2)
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with col1:
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st.metric("🌱 Seed Keywords", len(seed_keywords))
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if seed_keywords:
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for kw in seed_keywords:
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st.write(f"• {kw}")
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with col2:
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st.metric("🔍 Expanded Keywords", len(expanded_keywords))
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st.write(f"**Expansion Factor:** {len(expanded_keywords) / len(seed_keywords) if seed_keywords else 0:.1f}x")
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# Search intent categorization
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if keyword_data.get('keyword_categories'):
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st.markdown("#### 🧠 Search Intent Analysis")
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categories = keyword_data['keyword_categories']
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# Create intent distribution chart
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intent_counts = {intent: len(keywords) for intent, keywords in categories.items() if keywords}
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if intent_counts:
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intent_df = pd.DataFrame(
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list(intent_counts.items()),
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columns=['Search Intent', 'Keywords']
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)
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st.bar_chart(intent_df.set_index('Search Intent'))
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# Detailed breakdown
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for intent, keywords in categories.items():
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if keywords:
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with st.expander(f"📂 {intent.title()} Keywords ({len(keywords)})"):
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for kw in keywords[:20]: # Show first 20
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st.write(f"• {kw}")
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# Long-tail opportunities
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if keyword_data.get('long_tail_opportunities'):
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st.markdown("#### 🎣 Long-tail Keyword Opportunities")
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long_tail = keyword_data['long_tail_opportunities']
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if long_tail:
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st.info(f"🎯 Found {len(long_tail)} long-tail opportunities with lower competition!")
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# Display in expandable format
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with st.expander("View Long-tail Keywords"):
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for i, kw in enumerate(long_tail, 1):
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st.write(f"{i}. {kw}")
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else:
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st.warning("No long-tail opportunities identified")
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def _render_competitor_intelligence(self, competitor_data: Dict[str, Any]):
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"""Render competitor intelligence results."""
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st.markdown("### 🕷️ Competitive Intelligence")
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if not competitor_data.get('crawl_results'):
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st.info("No competitor crawl data available")
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return
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# Crawl summary
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crawl_results = competitor_data['crawl_results']
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st.markdown("#### 📊 Competitor Content Overview")
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# Create summary table
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summary_data = []
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for domain, data in crawl_results.items():
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summary_data.append({
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'Competitor': domain,
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'Pages Crawled': data.get('total_pages', 0),
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'Avg Content Length': f"{data.get('content_length_stats', {}).get('mean', 0):,.0f} chars",
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'Success Rate': f"{data.get('status_codes', {}).get(200, 0) / data.get('total_pages', 1) * 100:.1f}%"
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})
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if summary_data:
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summary_df = pd.DataFrame(summary_data)
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st.dataframe(summary_df, use_container_width=True)
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# Page type analysis
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st.markdown("#### 📄 Content Type Distribution")
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for domain, data in crawl_results.items():
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page_types = data.get('page_types', {})
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if page_types:
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with st.expander(f"📊 {domain} Content Types"):
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# Create chart data
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types_df = pd.DataFrame(
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list(page_types.items()),
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columns=['Page Type', 'Count']
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)
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if not types_df.empty:
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st.bar_chart(types_df.set_index('Page Type'))
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# Key insights
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total_pages = sum(page_types.values())
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if total_pages > 0:
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blog_ratio = page_types.get('blog_posts', 0) / total_pages * 100
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product_ratio = page_types.get('product_pages', 0) / total_pages * 100
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st.write("**Content Strategy Insights:**")
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st.write(f"• Blog content: {blog_ratio:.1f}% of pages")
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st.write(f"• Product focus: {product_ratio:.1f}% of pages")
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# Content structure insights
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if competitor_data.get('content_structure'):
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st.markdown("#### 🏗️ Content Structure Analysis")
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structure_data = competitor_data['content_structure']
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for domain, structure in structure_data.items():
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with st.expander(f"🔍 {domain} Structure Analysis"):
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col1, col2 = st.columns(2)
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with col1:
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st.metric("Avg Title Length", f"{structure.get('avg_title_length', 0):.0f} chars")
|
||||
st.metric("H1 Usage", f"{structure.get('h1_usage', 0):.1f}%")
|
||||
|
||||
with col2:
|
||||
st.metric("Avg Meta Desc Length", f"{structure.get('avg_meta_desc_length', 0):.0f} chars")
|
||||
st.metric("Internal Links", f"{structure.get('internal_links_avg', 0):.1f} avg")
|
||||
|
||||
def _render_content_themes(self, theme_data: Dict[str, Any]):
|
||||
"""Render content theme analysis."""
|
||||
|
||||
st.markdown("### 📊 Content Theme Intelligence")
|
||||
|
||||
if not theme_data:
|
||||
st.info("No content theme data available")
|
||||
return
|
||||
|
||||
# Dominant themes
|
||||
if theme_data.get('dominant_themes'):
|
||||
st.markdown("#### 🎯 Dominant Content Themes")
|
||||
|
||||
themes = theme_data['dominant_themes']
|
||||
|
||||
if themes:
|
||||
themes_df = pd.DataFrame(themes)
|
||||
st.dataframe(themes_df, use_container_width=True)
|
||||
|
||||
# Top themes highlight
|
||||
st.markdown("**🔥 Top Content Themes:**")
|
||||
for i, theme in enumerate(themes[:5], 1):
|
||||
word = theme.get('word', theme.get('text', 'Unknown'))
|
||||
freq = theme.get('freq', theme.get('frequency', 0))
|
||||
st.write(f"{i}. **{word}** (appears {freq} times)")
|
||||
|
||||
# Content clusters
|
||||
if theme_data.get('content_clusters'):
|
||||
st.markdown("#### 🗂️ Topic Cluster Analysis")
|
||||
|
||||
clusters = theme_data['content_clusters']
|
||||
|
||||
# Cluster distribution
|
||||
cluster_counts = {name: len(themes) for name, themes in clusters.items() if themes}
|
||||
|
||||
if cluster_counts:
|
||||
cluster_df = pd.DataFrame(
|
||||
list(cluster_counts.items()),
|
||||
columns=['Topic Cluster', 'Theme Count']
|
||||
)
|
||||
st.bar_chart(cluster_df.set_index('Topic Cluster'))
|
||||
|
||||
# Detailed cluster view
|
||||
for cluster_name, themes in clusters.items():
|
||||
if themes:
|
||||
with st.expander(f"📂 {cluster_name.replace('_', ' ').title()} ({len(themes)} themes)"):
|
||||
for theme in themes[:15]: # Show first 15
|
||||
st.write(f"• {theme}")
|
||||
|
||||
# Content gaps and opportunities
|
||||
if theme_data.get('content_opportunities'):
|
||||
st.markdown("#### 🎯 Content Gap Opportunities")
|
||||
|
||||
opportunities = theme_data['content_opportunities']
|
||||
|
||||
if opportunities:
|
||||
for opp in opportunities:
|
||||
st.write(f"🎯 **{opp}**")
|
||||
else:
|
||||
st.info("No specific content opportunities identified in theme analysis")
|
||||
|
||||
def _render_ai_insights(self, ai_data: Dict[str, Any]):
|
||||
"""Render AI-generated strategic insights."""
|
||||
|
||||
st.markdown("### 🤖 AI-Powered Strategic Insights")
|
||||
|
||||
if not ai_data:
|
||||
st.info("No AI insights available")
|
||||
return
|
||||
|
||||
# Strategic recommendations
|
||||
if ai_data.get('recommendations'):
|
||||
st.markdown("#### 🎯 Priority Strategic Recommendations")
|
||||
|
||||
recommendations = ai_data['recommendations']
|
||||
|
||||
for i, rec in enumerate(recommendations[:5], 1):
|
||||
with st.expander(f"🎯 Recommendation {i}"):
|
||||
st.markdown(rec)
|
||||
|
||||
# Competitive positioning
|
||||
if ai_data.get('competitive_positioning'):
|
||||
st.markdown("#### 🏆 Competitive Positioning Insights")
|
||||
st.markdown(ai_data['competitive_positioning'])
|
||||
|
||||
# Content strategy insights
|
||||
if ai_data.get('content_strategy'):
|
||||
st.markdown("#### 📝 Content Strategy Recommendations")
|
||||
st.markdown(ai_data['content_strategy'])
|
||||
|
||||
# Implementation timeline
|
||||
if ai_data.get('implementation_timeline'):
|
||||
st.markdown("#### 📅 Implementation Roadmap")
|
||||
|
||||
timeline = ai_data['implementation_timeline']
|
||||
|
||||
for period, tasks in timeline.items():
|
||||
with st.expander(f"📅 {period.replace('_', ' ').title()} Plan"):
|
||||
for task in tasks:
|
||||
st.write(f"• {task}")
|
||||
|
||||
# Technical SEO opportunities
|
||||
if ai_data.get('technical_opportunities'):
|
||||
st.markdown("#### ⚙️ Technical SEO Opportunities")
|
||||
|
||||
tech_opps = ai_data['technical_opportunities']
|
||||
|
||||
for opp in tech_opps:
|
||||
st.write(f"⚙️ {opp}")
|
||||
|
||||
def _render_action_plan(self, results: Dict[str, Any]):
|
||||
"""Render actionable implementation plan."""
|
||||
|
||||
st.markdown("### 📋 Your Content Gap Action Plan")
|
||||
|
||||
# Quick wins section
|
||||
st.markdown("#### 🚀 Quick Wins (Week 1-2)")
|
||||
|
||||
quick_wins = []
|
||||
|
||||
# SERP opportunities
|
||||
serp_opportunities = results.get('serp_analysis', {}).get('ranking_opportunities', [])
|
||||
if serp_opportunities:
|
||||
quick_wins.append(f"🎯 Target {len(serp_opportunities)} keywords where you're not ranking")
|
||||
|
||||
# Long-tail keywords
|
||||
long_tail = results.get('keyword_expansion', {}).get('long_tail_opportunities', [])
|
||||
if long_tail:
|
||||
quick_wins.append(f"🎣 Create content for {min(5, len(long_tail))} high-potential long-tail keywords")
|
||||
|
||||
# Content themes
|
||||
themes = results.get('content_themes', {}).get('dominant_themes', [])
|
||||
if themes:
|
||||
top_theme = themes[0].get('word', 'top theme') if themes else 'content optimization'
|
||||
quick_wins.append(f"📊 Optimize existing content around '{top_theme}' theme")
|
||||
|
||||
for i, win in enumerate(quick_wins, 1):
|
||||
st.write(f"{i}. {win}")
|
||||
|
||||
# Medium-term strategy
|
||||
st.markdown("#### 📈 Medium-term Strategy (Month 1-3)")
|
||||
|
||||
medium_term = [
|
||||
"🕷️ Conduct regular competitor content audits",
|
||||
"🎯 Develop content calendar based on keyword gaps",
|
||||
"📊 Implement content theme clusters",
|
||||
"🤖 Set up automated SERP monitoring"
|
||||
]
|
||||
|
||||
for i, strategy in enumerate(medium_term, 1):
|
||||
st.write(f"{i}. {strategy}")
|
||||
|
||||
# Long-term vision
|
||||
st.markdown("#### 🎯 Long-term Vision (Quarter 2+)")
|
||||
|
||||
long_term = [
|
||||
"🏆 Establish thought leadership in identified content gaps",
|
||||
"🌐 Build comprehensive content hub around dominant themes",
|
||||
"📈 Scale content production based on proven gaps",
|
||||
"🤝 Develop strategic partnerships for content collaboration"
|
||||
]
|
||||
|
||||
for i, vision in enumerate(long_term, 1):
|
||||
st.write(f"{i}. {vision}")
|
||||
|
||||
# Success metrics
|
||||
st.markdown("#### 📊 Success Metrics to Track")
|
||||
|
||||
metrics = [
|
||||
"🎯 Keyword ranking improvements for target terms",
|
||||
"📈 Organic traffic growth from new content",
|
||||
"🔍 SERP feature acquisitions (featured snippets, etc.)",
|
||||
"🏆 Competitive ranking gains in content themes",
|
||||
"📊 Content engagement metrics and user behavior"
|
||||
]
|
||||
|
||||
for metric in metrics:
|
||||
st.write(f"• {metric}")
|
||||
|
||||
def _render_export_options(self, results: Dict[str, Any]):
|
||||
"""Render export options for analysis results."""
|
||||
|
||||
st.markdown("---")
|
||||
st.markdown("### 📥 Export Analysis Results")
|
||||
|
||||
col1, col2, col3 = st.columns(3)
|
||||
|
||||
with col1:
|
||||
# JSON export
|
||||
if st.button("📄 Export as JSON", use_container_width=True):
|
||||
json_data = json.dumps(results, indent=2, default=str)
|
||||
|
||||
st.download_button(
|
||||
label="⬇️ Download JSON Report",
|
||||
data=json_data,
|
||||
file_name=f"content_gap_analysis_{datetime.now().strftime('%Y%m%d_%H%M%S')}.json",
|
||||
mime="application/json",
|
||||
use_container_width=True
|
||||
)
|
||||
|
||||
with col2:
|
||||
# CSV export for keywords
|
||||
if st.button("📊 Export Keywords CSV", use_container_width=True):
|
||||
expanded_keywords = results.get('keyword_expansion', {}).get('expanded_keywords', [])
|
||||
|
||||
if expanded_keywords:
|
||||
keywords_df = pd.DataFrame(expanded_keywords, columns=['Keyword'])
|
||||
csv_data = keywords_df.to_csv(index=False)
|
||||
|
||||
st.download_button(
|
||||
label="⬇️ Download Keywords CSV",
|
||||
data=csv_data,
|
||||
file_name=f"discovered_keywords_{datetime.now().strftime('%Y%m%d_%H%M%S')}.csv",
|
||||
mime="text/csv",
|
||||
use_container_width=True
|
||||
)
|
||||
else:
|
||||
st.warning("No keywords available for export")
|
||||
|
||||
with col3:
|
||||
# Summary report
|
||||
if st.button("📋 Generate Summary Report", use_container_width=True):
|
||||
summary = self._generate_summary_report(results)
|
||||
|
||||
st.download_button(
|
||||
label="⬇️ Download Summary Report",
|
||||
data=summary,
|
||||
file_name=f"content_gap_summary_{datetime.now().strftime('%Y%m%d_%H%M%S')}.txt",
|
||||
mime="text/plain",
|
||||
use_container_width=True
|
||||
)
|
||||
|
||||
def _generate_summary_report(self, results: Dict[str, Any]) -> str:
|
||||
"""Generate a text summary report."""
|
||||
|
||||
target_url = results.get('target_url', 'Unknown')
|
||||
timestamp = results.get('analysis_timestamp', datetime.now().isoformat())
|
||||
|
||||
summary = f"""
|
||||
ENHANCED CONTENT GAP ANALYSIS REPORT
|
||||
=====================================
|
||||
|
||||
Target Website: {target_url}
|
||||
Analysis Date: {timestamp}
|
||||
Industry: {results.get('industry', 'General')}
|
||||
|
||||
EXECUTIVE SUMMARY
|
||||
-----------------
|
||||
Keywords Analyzed: {len(results.get('target_keywords', []))}
|
||||
Competitors Analyzed: {len(results.get('competitor_urls', []))}
|
||||
Keywords Discovered: {len(results.get('keyword_expansion', {}).get('expanded_keywords', []))}
|
||||
SERP Opportunities: {len(results.get('serp_analysis', {}).get('ranking_opportunities', []))}
|
||||
|
||||
RANKING OPPORTUNITIES
|
||||
---------------------
|
||||
"""
|
||||
|
||||
# Add ranking opportunities
|
||||
opportunities = results.get('serp_analysis', {}).get('ranking_opportunities', [])
|
||||
for i, opp in enumerate(opportunities[:10], 1):
|
||||
summary += f"{i}. {opp.get('keyword', 'Unknown keyword')}\n"
|
||||
|
||||
# Add top keywords discovered
|
||||
summary += "\nTOP DISCOVERED KEYWORDS\n-----------------------\n"
|
||||
expanded_keywords = results.get('keyword_expansion', {}).get('expanded_keywords', [])
|
||||
for i, kw in enumerate(expanded_keywords[:20], 1):
|
||||
summary += f"{i}. {kw}\n"
|
||||
|
||||
# Add AI recommendations
|
||||
recommendations = results.get('ai_insights', {}).get('recommendations', [])
|
||||
if recommendations:
|
||||
summary += "\nAI STRATEGIC RECOMMENDATIONS\n----------------------------\n"
|
||||
for i, rec in enumerate(recommendations[:5], 1):
|
||||
summary += f"{i}. {rec}\n"
|
||||
|
||||
summary += f"\n\nReport generated by ALwrity Enhanced Content Gap Analysis\nTimestamp: {datetime.now().strftime('%Y-%m-%d %H:%M:%S')}"
|
||||
|
||||
return summary
|
||||
|
||||
# Render function for integration with main dashboard
|
||||
def render_enhanced_content_gap_analysis():
|
||||
"""Render the enhanced content gap analysis UI."""
|
||||
ui = EnhancedContentGapAnalysisUI()
|
||||
ui.render()
|
||||
Reference in New Issue
Block a user