Content Calendar, Content Gap Analysis, and Content Optimization
This commit is contained in:
ajaysi
769
lib/ai_seo_tools/content_gap_analysis/ui.py
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769
lib/ai_seo_tools/content_gap_analysis/ui.py
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"""
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Streamlit UI for Content Gap Analysis workflow.
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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 plotly.express as px
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import plotly.graph_objects as go
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import json
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from datetime import datetime
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from .main import ContentGapAnalysis
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from .keyword_researcher import KeywordResearcher
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from .competitor_analyzer import CompetitorAnalyzer
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from .website_analyzer import WebsiteAnalyzer
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from .recommendation_engine import RecommendationEngine
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from .utils.ai_processor import AIProcessor
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from .navigation import show_content_gap_analysis_nav
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from typing import Dict, Any
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import logging
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# Configure logging
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logging.basicConfig(level=logging.DEBUG)
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logger = logging.getLogger(__name__)
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class ContentGapAnalysisUI:
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"""Streamlit UI for Content Gap Analysis workflow."""
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def __init__(self):
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"""Initialize the UI components."""
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# Initialize session state for progress tracking
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if 'current_step' not in st.session_state:
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st.session_state.current_step = 1
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if 'analysis_results' not in st.session_state:
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st.session_state.analysis_results = {}
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# Initialize analysis components
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self.analyzer = ContentGapAnalysis()
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self.keyword_researcher = KeywordResearcher()
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self.competitor_analyzer = CompetitorAnalyzer()
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self.website_analyzer = WebsiteAnalyzer()
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self.recommendation_engine = RecommendationEngine()
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self.ai_processor = AIProcessor()
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def run(self):
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"""Run the Streamlit interface."""
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try:
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# Show navigation
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nav_option = show_content_gap_analysis_nav()
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# Main content area
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st.title("Content Gap Analysis")
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st.markdown("""
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This tool helps you identify content gaps and opportunities by analyzing your website,
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competitors, and market trends. Follow the steps below to get started.
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""")
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# Progress tracking
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self._show_progress()
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# Main workflow steps
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if nav_option == "Website Analysis" or st.session_state.current_step == 1:
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self._website_analysis_step()
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elif nav_option == "Competitor Analysis" or st.session_state.current_step == 2:
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self._competitor_analysis_step()
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elif nav_option == "Keyword Research" or st.session_state.current_step == 3:
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self._keyword_research_step()
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elif nav_option == "Recommendations" or st.session_state.current_step == 4:
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self._recommendations_step()
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else:
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self._export_results()
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except Exception as e:
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logger.error(f"Error in run method: {str(e)}", exc_info=True)
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st.error(f"An error occurred: {str(e)}")
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def _show_progress(self):
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"""Display progress tracking."""
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steps = [
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"Website Analysis",
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"Competitor Analysis",
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"Keyword Research",
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"Recommendations",
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"Export Results"
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]
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progress = st.session_state.current_step / len(steps)
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st.progress(progress)
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cols = st.columns(len(steps))
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for i, col in enumerate(cols):
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with col:
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if i + 1 < st.session_state.current_step:
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st.success(f"✓ {steps[i]}")
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elif i + 1 == st.session_state.current_step:
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st.info(f"→ {steps[i]}")
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else:
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st.text(f"○ {steps[i]}")
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def _website_analysis_step(self):
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"""Website analysis step UI."""
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try:
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st.header("Step 1: Website Analysis")
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# Display previous results if they exist
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if 'website' in st.session_state.analysis_results:
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st.info("Previous analysis results found. You can analyze a new website or proceed to the next step.")
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self._display_website_analysis(st.session_state.analysis_results['website'])
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col1, col2 = st.columns(2)
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with col1:
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if st.button("Analyze New Website"):
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st.session_state.analysis_results.pop('website', None)
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st.rerun()
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with col2:
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if st.button("Proceed to Competitor Analysis"):
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st.session_state.current_step = 2
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st.rerun()
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return
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# Create form for new analysis
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with st.form("website_analysis_form"):
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website_url = st.text_input("Enter your website URL")
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industry = st.text_input("Enter your industry/niche")
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submitted = st.form_submit_button("Analyze Website")
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# Handle form submission outside the form
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if submitted and website_url and industry:
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# Initialize progress tracking
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if 'analysis_progress' not in st.session_state:
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st.session_state.analysis_progress = {
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'status': 'initializing',
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'current_step': 'Starting Analysis',
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'progress': 0,
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'details': 'Initializing analysis...'
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}
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# Create progress container
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progress_container = st.empty()
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status_container = st.empty()
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details_container = st.empty()
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# Update progress display
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def update_progress_display():
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progress = st.session_state.analysis_progress
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# Update progress bar
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with progress_container:
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st.progress(progress['progress'] / 100)
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# Update status
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with status_container:
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if progress['status'] == 'error':
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st.error(f"Error: {progress['current_step']}")
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elif progress['status'] == 'completed':
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st.success(f"✓ {progress['current_step']}")
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else:
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st.info(f"→ {progress['current_step']}")
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# Update details
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with details_container:
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st.write(progress['details'])
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# Initial progress display
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update_progress_display()
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try:
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# Get basic analysis
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results = self.website_analyzer.analyze(website_url)
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# Update progress from analyzer
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st.session_state.analysis_progress = self.website_analyzer.progress.get_progress()
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update_progress_display()
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if isinstance(results, dict) and 'error' in results:
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st.error(f"Error in website analysis: {results['error']}")
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return
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# Get AI-enhanced analysis
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st.session_state.analysis_progress.update({
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'current_step': 'AI Analysis',
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'progress': 95,
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'details': 'Performing AI-enhanced analysis...'
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})
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update_progress_display()
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ai_analysis = self.ai_processor.analyze_content({
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'url': website_url,
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'industry': industry,
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'content': results
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})
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# Combine results
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if isinstance(results, dict):
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results.update(ai_analysis)
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else:
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results = {'error': 'Invalid analysis results format'}
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# Store results in session state
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st.session_state.analysis_results['website'] = results
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# Update final progress
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st.session_state.analysis_progress.update({
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'status': 'completed',
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'current_step': 'Analysis Complete',
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'progress': 100,
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'details': 'Analysis completed successfully!'
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})
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update_progress_display()
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# Display results
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self._display_website_analysis(results)
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except Exception as e:
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logger.error(f"Error during website analysis: {str(e)}", exc_info=True)
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st.session_state.analysis_progress.update({
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'status': 'error',
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'current_step': 'Analysis Failed',
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'details': f"Error during website analysis: {str(e)}"
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})
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update_progress_display()
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st.error(f"Error during website analysis: {str(e)}")
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return
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except Exception as e:
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logger.error(f"Error in website analysis step: {str(e)}", exc_info=True)
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st.error(f"Error in website analysis: {str(e)}")
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def _display_website_analysis(self, results: Dict[str, Any]):
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"""Display website analysis results."""
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try:
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if not isinstance(results, dict):
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st.error("Invalid analysis results format")
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return
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if 'error' in results:
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st.error(f"Error in analysis: {results['error']}")
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return
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# Content Metrics
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st.subheader("Content Metrics")
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content_metrics = results.get('content_metrics', {})
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if content_metrics:
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# Basic metrics in columns
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col1, col2, col3, col4 = st.columns(4)
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with col1:
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st.metric("Word Count", f"{content_metrics.get('word_count', 0):,}")
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with col2:
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st.metric("Headings", f"{content_metrics.get('heading_count', 0):,}")
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with col3:
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st.metric("Images", f"{content_metrics.get('image_count', 0):,}")
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with col4:
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st.metric("Links", f"{content_metrics.get('link_count', 0):,}")
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# Content Structure Visualization
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st.write("Content Structure")
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heading_data = {
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'Type': ['H1', 'H2', 'H3', 'Paragraphs'],
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'Count': [
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content_metrics.get('h1_count', 0),
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content_metrics.get('h2_count', 0),
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content_metrics.get('h3_count', 0),
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content_metrics.get('paragraph_count', 0)
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]
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}
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fig = px.bar(
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heading_data,
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x='Type',
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y='Count',
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title="Content Structure Distribution",
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color='Type',
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color_discrete_sequence=px.colors.qualitative.Set3
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)
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st.plotly_chart(fig, use_container_width=True)
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# Content Features
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st.write("Content Features")
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features = {
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'Feature': ['Meta Description', 'Robots.txt', 'Sitemap'],
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'Status': [
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content_metrics.get('has_meta_description', False),
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content_metrics.get('has_robots_txt', False),
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content_metrics.get('has_sitemap', False)
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]
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}
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fig = px.bar(
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features,
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x='Feature',
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y='Status',
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title="Content Features Status",
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color='Status',
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color_discrete_sequence=['red', 'green']
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)
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st.plotly_chart(fig, use_container_width=True)
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# SEO Metrics
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st.subheader("SEO Metrics")
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seo_metrics = results.get('seo_metrics', {})
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if seo_metrics:
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# Basic metrics in columns
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col1, col2, col3, col4 = st.columns(4)
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with col1:
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st.metric("Overall Score", f"{seo_metrics.get('overall_score', 0):.1f}%")
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with col2:
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content_quality = seo_metrics.get('content', {}).get('content_quality_score', 0)
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st.metric("Content Quality", f"{content_quality:.1f}%")
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with col3:
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readability = seo_metrics.get('content', {}).get('readability_score', 0)
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st.metric("Readability", f"{readability:.1f}%")
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with col4:
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keyword_density = seo_metrics.get('content', {}).get('keyword_density', 0)
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st.metric("Keyword Density", f"{keyword_density:.1f}%")
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# SEO Scores Radar Chart
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seo_scores = {
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'Metric': ['Overall', 'Content Quality', 'Readability', 'Keyword Density'],
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'Score': [
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seo_metrics.get('overall_score', 0),
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content_quality,
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readability,
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keyword_density
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]
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}
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fig = px.line_polar(
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seo_scores,
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r='Score',
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theta='Metric',
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line_close=True,
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title="SEO Performance Overview"
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)
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fig.update_traces(fill='toself')
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st.plotly_chart(fig, use_container_width=True)
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# Meta Tags Analysis
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st.write("Meta Tags Analysis")
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meta_tags = seo_metrics.get('meta_tags', {})
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if meta_tags:
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# Title Analysis
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title = meta_tags.get('title', {})
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st.write("Title Tag")
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st.write(f"Status: {'✅' if title.get('status') == 'good' else '❌'}")
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st.write(f"Value: {title.get('value', 'N/A')}")
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st.write(f"Length: {title.get('length', 0)} characters")
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st.write(f"Score: {title.get('score', 0)}%")
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if title.get('recommendation'):
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st.warning(title.get('recommendation'))
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# Description Analysis
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desc = meta_tags.get('description', {})
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st.write("Meta Description")
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st.write(f"Status: {'✅' if desc.get('status') == 'good' else '❌'}")
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st.write(f"Value: {desc.get('value', 'N/A')}")
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st.write(f"Length: {desc.get('length', 0)} characters")
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st.write(f"Score: {desc.get('score', 0)}%")
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if desc.get('recommendation'):
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st.warning(desc.get('recommendation'))
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# Keywords Analysis
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keywords = meta_tags.get('keywords', {})
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st.write("Meta Keywords")
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st.write(f"Status: {'✅' if keywords.get('status') == 'good' else '❌'}")
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st.write(f"Value: {keywords.get('value', 'N/A')}")
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if keywords.get('recommendation'):
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st.warning(keywords.get('recommendation'))
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# Technical Metrics
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st.subheader("Technical Metrics")
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technical_info = results.get('technical_info', {})
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if technical_info:
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col1, col2 = st.columns(2)
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with col1:
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st.write("Basic Information")
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st.metric("Status Code", technical_info.get('status_code', 'N/A'))
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st.metric("Server", technical_info.get('server_info', {}).get('server', 'N/A'))
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st.metric("Content Type", technical_info.get('server_info', {}).get('content_type', 'N/A'))
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with col2:
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st.write("Security Information")
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security_info = technical_info.get('security_info', {})
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security_data = {
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'Feature': ['SSL', 'HSTS', 'XSS Protection'],
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'Status': [
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security_info.get('ssl', False),
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security_info.get('hsts', False),
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security_info.get('xss_protection', False)
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]
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}
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fig = px.bar(
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security_data,
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x='Feature',
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y='Status',
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title="Security Features Status",
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color='Status',
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color_discrete_sequence=['red', 'green']
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)
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st.plotly_chart(fig, use_container_width=True)
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# Performance Metrics
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st.subheader("Performance Metrics")
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performance = results.get('performance', {})
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if performance:
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# Basic metrics in columns
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col1, col2, col3, col4 = st.columns(4)
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with col1:
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st.metric("Load Time", f"{performance.get('load_time', 0):.2f}s")
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with col2:
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st.metric("Page Size", f"{performance.get('page_size', 0):.1f} KB")
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with col3:
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st.metric("Status Code", performance.get('status_code', 'N/A'))
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with col4:
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st.metric("Response Time", f"{performance.get('response_time', 0):.2f}s")
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# Insights and Recommendations
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st.subheader("Insights and Recommendations")
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insights = results.get('insights', [])
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if insights:
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for insight in insights:
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st.info(f"• {insight}")
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else:
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st.info("No specific insights available")
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except Exception as e:
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logger.error(f"Error displaying website analysis: {str(e)}", exc_info=True)
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st.error(f"Error displaying website analysis: {str(e)}")
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def _competitor_analysis_step(self):
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"""Competitor analysis step UI."""
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try:
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st.header("Step 2: Competitor Analysis")
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with st.form("competitor_analysis_form"):
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competitors = st.text_area(
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"Enter competitor URLs (one per line)",
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help="Enter the URLs of your main competitors"
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)
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submitted = st.form_submit_button("Analyze Competitors")
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if submitted and competitors:
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with st.spinner("Analyzing competitors..."):
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competitor_urls = [url.strip() for url in competitors.split('\n') if url.strip()]
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results = self.competitor_analyzer.analyze(competitor_urls)
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# Get AI-enhanced competitor analysis
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ai_analysis = self.ai_processor.analyze_competitors({
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'competitors': competitor_urls,
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'analysis': results
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})
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# Combine results
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results.update(ai_analysis)
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st.session_state.analysis_results['competitors'] = results
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# Display results
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self._display_competitor_analysis(results)
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# Move to next step
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st.session_state.current_step = 3
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st.rerun()
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except Exception as e:
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logger.error(f"Error in competitor analysis step: {str(e)}", exc_info=True)
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st.error(f"Error in competitor analysis: {str(e)}")
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def _display_competitor_analysis(self, results: dict):
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"""Display competitor analysis results."""
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st.subheader("Competitor Analysis Results")
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# Competitor comparison
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||||
st.subheader("Competitor Comparison")
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comp_data = pd.DataFrame(results.get('comparison', []))
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if not comp_data.empty:
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fig = px.bar(
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||||
comp_data,
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x='competitor',
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||||
y='score',
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||||
color='metric',
|
||||
title="Competitor Comparison"
|
||||
)
|
||||
st.plotly_chart(fig)
|
||||
|
||||
# AI-Enhanced Competitor Analysis
|
||||
st.subheader("AI-Enhanced Competitor Analysis")
|
||||
|
||||
# Competitor Trend Analysis
|
||||
trend_data = results.get('competitor_trends', {})
|
||||
if trend_data:
|
||||
fig = go.Figure()
|
||||
for competitor, trends in trend_data.items():
|
||||
fig.add_trace(go.Scatter(
|
||||
x=trends.get('timeline', []),
|
||||
y=trends.get('scores', []),
|
||||
name=competitor,
|
||||
mode='lines+markers'
|
||||
))
|
||||
fig.update_layout(
|
||||
title="Competitor Performance Trends",
|
||||
xaxis_title="Timeline",
|
||||
yaxis_title="Score"
|
||||
)
|
||||
st.plotly_chart(fig)
|
||||
|
||||
# Content gaps
|
||||
st.subheader("Content Gaps")
|
||||
gaps = results.get('content_gaps', [])
|
||||
for gap in gaps:
|
||||
st.info(f"• {gap}")
|
||||
|
||||
# AI-Generated Competitive Insights
|
||||
st.subheader("Competitive Insights")
|
||||
insights = results.get('competitive_insights', {})
|
||||
if insights:
|
||||
for category, points in insights.items():
|
||||
with st.expander(f"{category.title()} Analysis"):
|
||||
for point in points:
|
||||
st.success(f"• {point}")
|
||||
|
||||
def _keyword_research_step(self):
|
||||
"""Keyword research step UI."""
|
||||
try:
|
||||
st.header("Step 3: Keyword Research")
|
||||
|
||||
with st.form("keyword_research_form"):
|
||||
industry = st.text_input(
|
||||
"Enter your industry/niche",
|
||||
value=st.session_state.analysis_results.get('website', {}).get('industry', '')
|
||||
)
|
||||
|
||||
submitted = st.form_submit_button("Research Keywords")
|
||||
|
||||
if submitted and industry:
|
||||
with st.spinner("Researching keywords..."):
|
||||
results = self.keyword_researcher.research(industry)
|
||||
|
||||
# Get AI-enhanced keyword analysis
|
||||
ai_analysis = self.ai_processor.analyze_keywords({
|
||||
'industry': industry,
|
||||
'keywords': results
|
||||
})
|
||||
|
||||
# Combine results
|
||||
results.update(ai_analysis)
|
||||
st.session_state.analysis_results['keywords'] = results
|
||||
|
||||
# Display results
|
||||
self._display_keyword_research(results)
|
||||
|
||||
# Move to next step
|
||||
st.session_state.current_step = 4
|
||||
st.rerun()
|
||||
except Exception as e:
|
||||
logger.error(f"Error in keyword research step: {str(e)}", exc_info=True)
|
||||
st.error(f"Error in keyword research: {str(e)}")
|
||||
|
||||
def _display_keyword_research(self, results: dict):
|
||||
"""Display keyword research results."""
|
||||
st.subheader("Keyword Research Results")
|
||||
|
||||
# Keyword metrics
|
||||
st.subheader("Keyword Metrics")
|
||||
keyword_data = pd.DataFrame(results.get('keywords', []))
|
||||
if not keyword_data.empty:
|
||||
fig = px.scatter(
|
||||
keyword_data,
|
||||
x='search_volume',
|
||||
y='difficulty',
|
||||
size='relevance_score',
|
||||
hover_data=['keyword'],
|
||||
title="Keyword Opportunities"
|
||||
)
|
||||
st.plotly_chart(fig)
|
||||
|
||||
# AI-Enhanced Keyword Analysis
|
||||
st.subheader("AI-Enhanced Keyword Analysis")
|
||||
|
||||
# Keyword Trend Analysis
|
||||
trend_data = results.get('keyword_trends', {})
|
||||
if trend_data:
|
||||
fig = go.Figure()
|
||||
for keyword, trends in trend_data.items():
|
||||
fig.add_trace(go.Scatter(
|
||||
x=trends.get('timeline', []),
|
||||
y=trends.get('scores', []),
|
||||
name=keyword,
|
||||
mode='lines+markers'
|
||||
))
|
||||
fig.update_layout(
|
||||
title="Keyword Trend Analysis",
|
||||
xaxis_title="Timeline",
|
||||
yaxis_title="Trend Score"
|
||||
)
|
||||
st.plotly_chart(fig)
|
||||
|
||||
# Search intent distribution
|
||||
st.subheader("Search Intent Distribution")
|
||||
intent_data = pd.DataFrame(results.get('search_intent', {}).get('summary', {}))
|
||||
if not intent_data.empty:
|
||||
fig = px.pie(
|
||||
intent_data,
|
||||
values='count',
|
||||
names='intent',
|
||||
title="Search Intent Distribution"
|
||||
)
|
||||
st.plotly_chart(fig)
|
||||
|
||||
# Content format suggestions
|
||||
st.subheader("Content Format Suggestions")
|
||||
formats = results.get('content_formats', [])
|
||||
for format in formats:
|
||||
st.info(f"• {format}")
|
||||
|
||||
# AI-Generated Keyword Insights
|
||||
st.subheader("Keyword Insights")
|
||||
insights = results.get('keyword_insights', {})
|
||||
if insights:
|
||||
for category, points in insights.items():
|
||||
with st.expander(f"{category.title()} Insights"):
|
||||
for point in points:
|
||||
st.success(f"• {point}")
|
||||
|
||||
def _recommendations_step(self):
|
||||
"""Recommendations step UI."""
|
||||
try:
|
||||
st.header("Step 4: Content Recommendations")
|
||||
|
||||
with st.spinner("Generating recommendations..."):
|
||||
results = self.recommendation_engine.generate_recommendations(
|
||||
st.session_state.analysis_results
|
||||
)
|
||||
|
||||
# Get AI-enhanced recommendations
|
||||
ai_recommendations = self.ai_processor.analyze_recommendations({
|
||||
'recommendations': results,
|
||||
'analysis': st.session_state.analysis_results
|
||||
})
|
||||
|
||||
# Combine results
|
||||
results.update(ai_recommendations)
|
||||
st.session_state.analysis_results['recommendations'] = results
|
||||
|
||||
# Display results
|
||||
self._display_recommendations(results)
|
||||
|
||||
# Move to next step
|
||||
st.session_state.current_step = 5
|
||||
st.rerun()
|
||||
except Exception as e:
|
||||
logger.error(f"Error in recommendations step: {str(e)}", exc_info=True)
|
||||
st.error(f"Error in recommendations: {str(e)}")
|
||||
|
||||
def _display_recommendations(self, results: dict):
|
||||
"""Display content recommendations."""
|
||||
st.subheader("Content Recommendations")
|
||||
|
||||
# Priority recommendations
|
||||
st.subheader("Priority Recommendations")
|
||||
priorities = results.get('priorities', [])
|
||||
for priority in priorities:
|
||||
st.success(f"• {priority}")
|
||||
|
||||
# AI-Enhanced Recommendations
|
||||
st.subheader("AI-Enhanced Recommendations")
|
||||
|
||||
# Recommendation Impact Analysis
|
||||
impact_data = results.get('impact_analysis', {})
|
||||
if impact_data:
|
||||
fig = go.Figure()
|
||||
for metric, values in impact_data.items():
|
||||
fig.add_trace(go.Bar(
|
||||
name=metric,
|
||||
x=values.get('categories', []),
|
||||
y=values.get('scores', [])
|
||||
))
|
||||
fig.update_layout(
|
||||
title="Recommendation Impact Analysis",
|
||||
xaxis_title="Categories",
|
||||
yaxis_title="Impact Score",
|
||||
barmode='group'
|
||||
)
|
||||
st.plotly_chart(fig)
|
||||
|
||||
# Implementation timeline
|
||||
st.subheader("Implementation Timeline")
|
||||
timeline = results.get('timeline', [])
|
||||
for item in timeline:
|
||||
st.info(f"• {item}")
|
||||
|
||||
# Expected impact
|
||||
st.subheader("Expected Impact")
|
||||
impact = results.get('impact', {})
|
||||
for metric, value in impact.items():
|
||||
st.metric(metric, value)
|
||||
|
||||
# AI-Generated Strategic Insights
|
||||
st.subheader("Strategic Insights")
|
||||
insights = results.get('strategic_insights', {})
|
||||
if insights:
|
||||
for category, points in insights.items():
|
||||
with st.expander(f"{category.title()} Strategy"):
|
||||
for point in points:
|
||||
st.success(f"• {point}")
|
||||
|
||||
def _export_results(self):
|
||||
"""Export results step UI."""
|
||||
st.header("Step 5: Export Results")
|
||||
|
||||
# Export options
|
||||
export_format = st.radio(
|
||||
"Choose export format",
|
||||
["JSON", "CSV", "PDF"]
|
||||
)
|
||||
|
||||
if st.button("Export Results"):
|
||||
if export_format == "JSON":
|
||||
self._export_json()
|
||||
elif export_format == "CSV":
|
||||
self._export_csv()
|
||||
else:
|
||||
st.info("PDF export coming soon!")
|
||||
|
||||
def _export_json(self):
|
||||
"""Export results as JSON."""
|
||||
results = st.session_state.analysis_results
|
||||
timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
|
||||
filename = f"content_gap_analysis_{timestamp}.json"
|
||||
|
||||
st.download_button(
|
||||
"Download JSON",
|
||||
data=json.dumps(results, indent=2),
|
||||
file_name=filename,
|
||||
mime="application/json"
|
||||
)
|
||||
|
||||
def _export_csv(self):
|
||||
"""Export results as CSV."""
|
||||
results = st.session_state.analysis_results
|
||||
timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
|
||||
|
||||
# Convert results to CSV format
|
||||
csv_data = []
|
||||
for section, data in results.items():
|
||||
if isinstance(data, list):
|
||||
for item in data:
|
||||
if isinstance(item, dict):
|
||||
item['section'] = section
|
||||
csv_data.append(item)
|
||||
elif isinstance(data, dict):
|
||||
data['section'] = section
|
||||
csv_data.append(data)
|
||||
|
||||
if csv_data:
|
||||
df = pd.DataFrame(csv_data)
|
||||
filename = f"content_gap_analysis_{timestamp}.csv"
|
||||
|
||||
st.download_button(
|
||||
"Download CSV",
|
||||
data=df.to_csv(index=False),
|
||||
file_name=filename,
|
||||
mime="text/csv"
|
||||
)
|
||||
|
||||
def main():
|
||||
"""Main entry point for the Streamlit app."""
|
||||
ui = ContentGapAnalysisUI()
|
||||
ui.run()
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
Reference in New Issue
Block a user