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Bing Analytics and Insights added, background jobs added, database setup updated, environment setup updated, frontend updated, backend updated.

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Onboarding Manager and Router Manager refactored, analytics and background jobs added, database setup updated, environment setup updated, frontend updated, backend updated.
Critical onboarding database migration implemented.
This commit is contained in:
ajaysi
2025-10-18 10:28:15 +05:30
parent 40fb6ac95b
commit 1f087aad4c
69 changed files with 11995 additions and 189 deletions
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18
backend/services/persona/persona_quality_improver.py
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@@ -111,7 +111,7 @@ class PersonaQualityImprover:
platform_consistency = self._assess_platform_consistency(core_persona, platform_personas)
# Platform optimization (25% weight)
platform_optimization = self._assess_platform_optimization(platform_personas)
platform_optimization = self._assess_platform_optimization_dict(platform_personas)
# Linguistic quality (20% weight)
linguistic_quality = self._assess_linguistic_quality(linguistic_analysis)
@@ -177,8 +177,8 @@ class PersonaQualityImprover:
return int(sum(consistency_scores) / len(consistency_scores)) if consistency_scores else 75
def _assess_platform_optimization(self, platform_personas: Dict[str, Any]) -> int:
"""Assess platform-specific optimization quality."""
def _assess_platform_optimization_dict(self, platform_personas: Dict[str, Any]) -> int:
"""Assess platform-specific optimization quality for dictionary input."""
if not platform_personas:
return 50
@@ -582,9 +582,17 @@ class PersonaQualityImprover:
else:
return 50.0 # Default if no clear satisfaction data
def _assess_platform_optimization(self, persona: EnhancedWritingPersona) -> float:
def _assess_platform_optimization(self, persona) -> float:
"""Assess platform optimization quality."""
platform_personas = persona.platform_personas
# Handle both EnhancedWritingPersona objects and dictionaries
if hasattr(persona, 'platform_personas'):
platform_personas = persona.platform_personas
elif isinstance(persona, dict):
# For dictionary input, use the simpler assessment method
return float(self._assess_platform_optimization_dict(persona))
else:
logger.warning(f"Unexpected persona type: {type(persona)}")
return 0.0
if not platform_personas:
return 0.0