127 lines
4.3 KiB
Python
127 lines
4.3 KiB
Python
"""
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Gap analyzer integration for content calendar.
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"""
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import streamlit as st
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from typing import Dict, Any, List, Optional
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from loguru import logger
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from lib.utils.website_analyzer.analyzer import WebsiteAnalyzer
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from lib.ai_seo_tools.content_gap_analysis.main import ContentGapAnalysis
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import asyncio
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import sys
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import os
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import json
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from datetime import datetime
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# Configure logger for content calendar debugging
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logger.remove() # Remove default handler
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logger.add(
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sys.stdout,
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level="DEBUG",
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format="<green>{time:YYYY-MM-DD HH:mm:ss}</green> | <level>{level: <8}</level> | <cyan>{name}</cyan> | <yellow>{function}</yellow> | {message}",
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filter=lambda record: "content_calendar" in record["name"].lower()
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)
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class GapAnalyzerIntegration:
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"""Integrates content gap analysis with content calendar."""
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def __init__(self):
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"""Initialize the gap analyzer integration."""
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self.gap_analyzer = ContentGapAnalysis()
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logger.debug("GapAnalyzerIntegration initialized for content calendar")
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def analyze_gaps(self, data: Dict[str, Any]) -> Dict[str, Any]:
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"""
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Analyze content gaps.
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Args:
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data: Dictionary containing content data
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Returns:
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Dictionary containing gap analysis results
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"""
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try:
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logger.debug(f"Starting gap analysis with data: {json.dumps(data, indent=2)}")
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# Run gap analysis
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results = self.gap_analyzer.analyze(data)
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logger.debug(f"Gap analysis completed with results: {json.dumps(results, indent=2)}")
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return results
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except Exception as e:
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error_msg = f"Error analyzing content gaps: {str(e)}"
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logger.error(error_msg, exc_info=True)
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return {
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'error': error_msg,
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'gaps': [],
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'recommendations': []
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}
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def get_topic_suggestions(
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self,
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gap_analysis: Dict[str, Any],
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platform: str,
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count: int = 5
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) -> List[Dict[str, Any]]:
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"""
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Get topic suggestions for a specific platform based on gap analysis.
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Args:
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gap_analysis: Results from gap analysis
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platform: Target platform for content
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count: Number of suggestions to generate
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Returns:
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List of topic suggestions
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"""
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try:
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logger.debug(f"Generating topic suggestions for platform: {platform}, count: {count}")
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suggestions = []
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for gap in gap_analysis.get('processed_gaps', []):
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# Generate platform-specific topics
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platform_topics = self.ai_processor.generate_platform_topics(
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gap=gap,
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platform=platform,
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count=count
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)
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logger.debug(f"Generated topics for gap: {json.dumps(platform_topics, indent=2)}")
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suggestions.extend(platform_topics)
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logger.debug(f"Total suggestions generated: {len(suggestions)}")
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return suggestions
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except Exception as e:
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logger.error(f"Error generating topic suggestions: {str(e)}")
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return []
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def analyze_topic_relevance(
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self,
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topic: Dict[str, Any],
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gap_analysis: Dict[str, Any]
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) -> Dict[str, Any]:
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"""
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Analyze how well a topic addresses content gaps.
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Args:
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topic: Topic to analyze
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gap_analysis: Results from gap analysis
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Returns:
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Dictionary containing relevance analysis
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"""
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try:
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logger.debug(f"Analyzing topic relevance: {json.dumps(topic, indent=2)}")
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relevance = self.ai_processor.analyze_topic_relevance(
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topic=topic,
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gaps=gap_analysis.get('gaps', [])
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)
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logger.debug(f"Topic relevance analysis completed: {json.dumps(relevance, indent=2)}")
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return relevance
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except Exception as e:
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logger.error(f"Error analyzing topic relevance: {str(e)}")
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return {
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'error': str(e),
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'score': 0
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} |