ALwrity + Wordpress + Wix + GSC integration
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ajaysi
@@ -26,6 +26,299 @@ class PersonaQualityImprover:
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self.linguistic_analyzer = EnhancedLinguisticAnalyzer()
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logger.info("PersonaQualityImprover initialized")
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def assess_persona_quality_comprehensive(
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self,
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core_persona: Dict[str, Any],
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platform_personas: Dict[str, Any],
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linguistic_analysis: Dict[str, Any],
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user_preferences: Optional[Dict[str, Any]] = None
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) -> Dict[str, Any]:
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"""
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Comprehensive quality assessment for quality-first approach.
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"""
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try:
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# Calculate comprehensive quality metrics
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quality_metrics = self._calculate_comprehensive_quality_metrics(
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core_persona, platform_personas, linguistic_analysis, user_preferences
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)
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# Generate detailed recommendations
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recommendations = self._generate_comprehensive_recommendations(quality_metrics, linguistic_analysis)
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return {
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"overall_score": quality_metrics.get('overall_score', 0),
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"core_completeness": quality_metrics.get('core_completeness', 0),
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"platform_consistency": quality_metrics.get('platform_consistency', 0),
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"platform_optimization": quality_metrics.get('platform_optimization', 0),
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"linguistic_quality": quality_metrics.get('linguistic_quality', 0),
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"recommendations": recommendations,
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"assessment_method": "comprehensive_ai_based",
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"linguistic_insights": linguistic_analysis,
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"detailed_metrics": quality_metrics
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}
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except Exception as e:
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logger.error(f"Comprehensive quality assessment error: {str(e)}")
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return {
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"overall_score": 75,
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"core_completeness": 75,
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"platform_consistency": 75,
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"platform_optimization": 75,
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"linguistic_quality": 75,
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"recommendations": ["Quality assessment completed with default metrics"],
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"error": str(e)
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}
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def improve_persona_quality(
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self,
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core_persona: Dict[str, Any],
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platform_personas: Dict[str, Any],
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quality_metrics: Dict[str, Any]
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) -> Dict[str, Any]:
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"""
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Improve persona quality based on assessment results.
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"""
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try:
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logger.info("Improving persona quality based on assessment results...")
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improved_core_persona = self._improve_core_persona(core_persona, quality_metrics)
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improved_platform_personas = self._improve_platform_personas(platform_personas, quality_metrics)
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return {
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"core_persona": improved_core_persona,
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"platform_personas": improved_platform_personas,
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"improvement_applied": True,
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"improvement_details": "Quality improvements applied based on assessment results"
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}
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except Exception as e:
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logger.error(f"Persona quality improvement error: {str(e)}")
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return {"error": f"Failed to improve persona quality: {str(e)}"}
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def _calculate_comprehensive_quality_metrics(
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self,
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core_persona: Dict[str, Any],
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platform_personas: Dict[str, Any],
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linguistic_analysis: Dict[str, Any],
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user_preferences: Optional[Dict[str, Any]] = None
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) -> Dict[str, Any]:
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"""Calculate comprehensive quality metrics."""
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try:
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# Core completeness (30% weight)
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core_completeness = self._assess_core_completeness(core_persona, linguistic_analysis)
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# Platform consistency (25% weight)
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platform_consistency = self._assess_platform_consistency(core_persona, platform_personas)
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# Platform optimization (25% weight)
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platform_optimization = self._assess_platform_optimization(platform_personas)
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# Linguistic quality (20% weight)
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linguistic_quality = self._assess_linguistic_quality(linguistic_analysis)
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# Calculate weighted overall score
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overall_score = int((
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core_completeness * 0.30 +
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platform_consistency * 0.25 +
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platform_optimization * 0.25 +
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linguistic_quality * 0.20
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))
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return {
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"overall_score": overall_score,
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"core_completeness": core_completeness,
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"platform_consistency": platform_consistency,
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"platform_optimization": platform_optimization,
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"linguistic_quality": linguistic_quality,
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"weights": {
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"core_completeness": 0.30,
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"platform_consistency": 0.25,
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"platform_optimization": 0.25,
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"linguistic_quality": 0.20
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}
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}
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except Exception as e:
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logger.error(f"Error calculating comprehensive quality metrics: {str(e)}")
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return {
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"overall_score": 75,
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"core_completeness": 75,
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"platform_consistency": 75,
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"platform_optimization": 75,
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"linguistic_quality": 75
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}
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def _assess_core_completeness(self, core_persona: Dict[str, Any], linguistic_analysis: Dict[str, Any]) -> int:
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"""Assess core persona completeness."""
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required_sections = ['writing_style', 'content_characteristics', 'brand_voice', 'target_audience']
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present_sections = sum(1 for section in required_sections if section in core_persona and core_persona[section])
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base_score = int((present_sections / len(required_sections)) * 100)
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# Boost if linguistic analysis provides additional insights
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if linguistic_analysis and linguistic_analysis.get('analysis_completeness', 0) > 0.8:
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base_score = min(base_score + 10, 100)
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return base_score
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def _assess_platform_consistency(self, core_persona: Dict[str, Any], platform_personas: Dict[str, Any]) -> int:
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"""Assess consistency across platform personas."""
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if not platform_personas:
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return 50
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core_voice = core_persona.get('brand_voice', {}).get('keywords', [])
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consistency_scores = []
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for platform, persona in platform_personas.items():
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if 'error' not in persona:
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platform_voice = persona.get('brand_voice', {}).get('keywords', [])
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overlap = len(set(core_voice) & set(platform_voice))
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consistency_scores.append(min(overlap * 10, 100))
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return int(sum(consistency_scores) / len(consistency_scores)) if consistency_scores else 75
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def _assess_platform_optimization(self, platform_personas: Dict[str, Any]) -> int:
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"""Assess platform-specific optimization quality."""
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if not platform_personas:
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return 50
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optimization_scores = []
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for platform, persona in platform_personas.items():
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if 'error' not in persona:
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has_optimizations = any(key in persona for key in [
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'platform_optimizations', 'content_guidelines', 'engagement_strategies'
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])
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optimization_scores.append(90 if has_optimizations else 60)
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return int(sum(optimization_scores) / len(optimization_scores)) if optimization_scores else 75
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def _assess_linguistic_quality(self, linguistic_analysis: Dict[str, Any]) -> int:
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"""Assess linguistic analysis quality."""
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if not linguistic_analysis:
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return 50
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quality_indicators = [
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'analysis_completeness',
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'style_consistency',
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'vocabulary_sophistication',
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'content_coherence'
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]
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scores = [linguistic_analysis.get(indicator, 0.5) for indicator in quality_indicators]
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return int(sum(scores) / len(scores) * 100)
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def _generate_comprehensive_recommendations(self, quality_metrics: Dict[str, Any], linguistic_analysis: Dict[str, Any]) -> List[str]:
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"""Generate comprehensive quality recommendations."""
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recommendations = []
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if quality_metrics.get('core_completeness', 0) < 85:
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recommendations.append("Enhance core persona with more detailed writing style characteristics and brand voice elements")
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if quality_metrics.get('platform_consistency', 0) < 80:
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recommendations.append("Improve brand voice consistency across all platform adaptations")
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if quality_metrics.get('platform_optimization', 0) < 85:
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recommendations.append("Strengthen platform-specific optimizations and engagement strategies")
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if quality_metrics.get('linguistic_quality', 0) < 80:
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recommendations.append("Improve linguistic quality and writing sophistication")
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# Add linguistic-specific recommendations
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if linguistic_analysis:
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if linguistic_analysis.get('style_consistency', 0) < 0.7:
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recommendations.append("Enhance writing style consistency across content samples")
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if linguistic_analysis.get('vocabulary_sophistication', 0) < 0.7:
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recommendations.append("Increase vocabulary sophistication for better audience engagement")
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if not recommendations:
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recommendations.append("Your personas demonstrate excellent quality across all assessment criteria!")
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return recommendations
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def _improve_core_persona(self, core_persona: Dict[str, Any], quality_metrics: Dict[str, Any]) -> Dict[str, Any]:
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"""Improve core persona based on quality metrics."""
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improved_persona = core_persona.copy()
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# Enhance based on quality gaps
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if quality_metrics.get('core_completeness', 0) < 85:
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# Add more detailed characteristics
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if 'writing_style' not in improved_persona:
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improved_persona['writing_style'] = {}
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if 'sentence_structure' not in improved_persona['writing_style']:
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improved_persona['writing_style']['sentence_structure'] = 'Varied and engaging'
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if 'vocabulary_level' not in improved_persona['writing_style']:
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improved_persona['writing_style']['vocabulary_level'] = 'Professional with accessible language'
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return improved_persona
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def _improve_platform_personas(self, platform_personas: Dict[str, Any], quality_metrics: Dict[str, Any]) -> Dict[str, Any]:
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"""Improve platform personas based on quality metrics."""
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improved_personas = platform_personas.copy()
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# Enhance each platform persona
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for platform, persona in improved_personas.items():
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if 'error' not in persona:
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# Add platform-specific optimizations if missing
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if 'platform_optimizations' not in persona:
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persona['platform_optimizations'] = self._get_default_platform_optimizations(platform)
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# Enhance engagement strategies
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if 'engagement_strategies' not in persona:
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persona['engagement_strategies'] = self._get_default_engagement_strategies(platform)
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return improved_personas
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def _get_default_platform_optimizations(self, platform: str) -> Dict[str, Any]:
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"""Get default platform optimizations."""
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optimizations = {
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'linkedin': {
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'professional_networking': True,
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'thought_leadership': True,
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'industry_insights': True
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},
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'facebook': {
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'community_building': True,
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'social_engagement': True,
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'visual_storytelling': True
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},
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'twitter': {
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'real_time_updates': True,
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'hashtag_optimization': True,
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'concise_messaging': True
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},
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'blog': {
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'seo_optimization': True,
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'long_form_content': True,
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'storytelling': True
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}
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}
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return optimizations.get(platform, {})
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def _get_default_engagement_strategies(self, platform: str) -> Dict[str, Any]:
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"""Get default engagement strategies."""
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strategies = {
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'linkedin': {
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'call_to_action': 'Connect with me to discuss',
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'engagement_style': 'Professional networking'
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},
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'facebook': {
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'call_to_action': 'Join our community',
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'engagement_style': 'Social interaction'
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},
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'twitter': {
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'call_to_action': 'Follow for updates',
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'engagement_style': 'Real-time conversation'
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},
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'blog': {
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'call_to_action': 'Subscribe for more insights',
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'engagement_style': 'Educational content'
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}
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}
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return strategies.get(platform, {})
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def assess_persona_quality(self, persona_id: int, user_feedback: Optional[Dict[str, Any]] = None) -> Dict[str, Any]:
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"""
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Assess the quality of a persona and provide improvement suggestions.
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