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feat: image generation overhaul (model-aware text, dim clamping, \.30 pricing), event-driven dashboard cache invalidation, SEO insights (AI visibility, GSC, keyword gap), YouTube OAuth/publish, blog writer & content planning improvements, scheduler monitoring updates

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This commit is contained in:
ajaysi
2026-05-30 07:58:22 +05:30
parent aaf94049da
commit 64f1f88cdd
129 changed files with 8796 additions and 8755 deletions
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2
backend/api/content_planning/services/content_strategy/utils/__init__.py
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@@ -9,7 +9,6 @@ from .data_processors import (
transform_onboarding_data_to_fields,
get_data_sources,
get_detailed_input_data_points,
get_fallback_onboarding_data,
get_website_analysis_data,
get_research_preferences_data,
get_api_keys_data
@@ -36,7 +35,6 @@ __all__ = [
'transform_onboarding_data_to_fields',
'get_data_sources',
'get_detailed_input_data_points',
'get_fallback_onboarding_data',
'get_website_analysis_data',
'get_research_preferences_data',
'get_api_keys_data',

23
backend/api/content_planning/services/content_strategy/utils/data_processors.py
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@@ -179,17 +179,13 @@ class DataProcessorService:
}
fields['seasonal_trends'] = {
'value': ['Q1: Planning', 'Q2: Execution', 'Q3: Optimization', 'Q4: Review'],
'value': research_data.get('seasonal_trends', []),
'source': 'research_preferences',
'confidence': research_data.get('confidence_level', 0.7)
}
fields['engagement_metrics'] = {
'value': {
'avg_session_duration': website_data.get('performance_metrics', {}).get('avg_session_duration', 180),
'bounce_rate': website_data.get('performance_metrics', {}).get('bounce_rate', 45.5),
'pages_per_session': 2.5
},
'value': website_data.get('performance_metrics', {}),
'source': 'website_analysis',
'confidence': website_data.get('confidence_level', 0.8)
}
@@ -411,15 +407,6 @@ class DataProcessorService:
}
}
def get_fallback_onboarding_data(self) -> Dict[str, Any]:
"""
Get fallback onboarding data for compatibility.
Returns:
Dictionary with fallback data (raises error as fallbacks are disabled)
"""
raise RuntimeError("Fallback onboarding data is disabled. Real data required.")
async def get_website_analysis_data(self, user_id: int) -> Dict[str, Any]:
"""
Get website analysis data from onboarding.
@@ -534,12 +521,6 @@ def get_detailed_input_data_points(processed_data: Dict[str, Any]) -> Dict[str,
return processor.get_detailed_input_data_points(processed_data)
def get_fallback_onboarding_data() -> Dict[str, Any]:
"""Get fallback onboarding data for compatibility."""
processor = DataProcessorService()
return processor.get_fallback_onboarding_data()
async def get_website_analysis_data(user_id: int) -> Dict[str, Any]:
"""Get website analysis data from onboarding."""
processor = DataProcessorService()

237
backend/api/content_planning/services/content_strategy/utils/strategy_utils.py
View File

@@ -14,6 +14,7 @@ logger = logging.getLogger(__name__)
def calculate_strategic_scores(ai_recommendations: Dict[str, Any]) -> Dict[str, float]:
"""
Calculate strategic performance scores from AI recommendations.
Dimension-specific weights — no blanket multipliers.
Args:
ai_recommendations: Dictionary containing AI analysis results
@@ -28,35 +29,48 @@ def calculate_strategic_scores(ai_recommendations: Dict[str, Any]) -> Dict[str,
'conversion_score': 0.0,
'innovation_score': 0.0
}
# Calculate scores based on AI recommendations
total_confidence = 0
total_score = 0
weight_sum = 0.0
dimension_weights = {
'comprehensive_strategy': {'quality': 0.35, 'engagement': 0.20, 'conversion': 0.25, 'innovation': 0.20},
'audience_intelligence': {'quality': 0.25, 'engagement': 0.40, 'conversion': 0.20, 'innovation': 0.15},
'competitive_intelligence': {'quality': 0.30, 'engagement': 0.15, 'conversion': 0.25, 'innovation': 0.30},
'performance_optimization': {'quality': 0.20, 'engagement': 0.15, 'conversion': 0.45, 'innovation': 0.20},
'content_calendar_optimization': {'quality': 0.30, 'engagement': 0.25, 'conversion': 0.20, 'innovation': 0.25},
}
for analysis_type, recommendations in ai_recommendations.items():
if isinstance(recommendations, dict) and 'metrics' in recommendations:
metrics = recommendations['metrics']
score = metrics.get('score', 50)
confidence = metrics.get('confidence', 0.5)
total_score += score * confidence
total_confidence += confidence
if total_confidence > 0:
scores['overall_score'] = total_score / total_confidence
# Set other scores based on overall score
scores['content_quality_score'] = scores['overall_score'] * 1.1
scores['engagement_score'] = scores['overall_score'] * 0.9
scores['conversion_score'] = scores['overall_score'] * 0.95
scores['innovation_score'] = scores['overall_score'] * 1.05
if not isinstance(recommendations, dict):
continue
metrics = recommendations.get('metrics')
if not isinstance(metrics, dict):
continue
score = metrics.get('score', 50)
confidence = metrics.get('confidence', 0.5)
weight = confidence
scores['overall_score'] += score * weight
weight_sum += weight
weights = dimension_weights.get(analysis_type, {'quality': 0.25, 'engagement': 0.25, 'conversion': 0.25, 'innovation': 0.25})
scores['content_quality_score'] += score * weights['quality'] * weight
scores['engagement_score'] += score * weights['engagement'] * weight
scores['conversion_score'] += score * weights['conversion'] * weight
scores['innovation_score'] += score * weights['innovation'] * weight
if weight_sum > 0:
for k in scores:
scores[k] = round(scores[k] / weight_sum, 2)
return scores
def extract_market_positioning(ai_recommendations: Dict[str, Any]) -> Dict[str, Any]:
"""
Extract market positioning insights from AI recommendations.
Scans all analysis types for positioning signals. Returns empty dict if none found.
Args:
ai_recommendations: Dictionary containing AI analysis results
@@ -64,17 +78,50 @@ def extract_market_positioning(ai_recommendations: Dict[str, Any]) -> Dict[str,
Returns:
Dictionary with market positioning data
"""
return {
'industry_position': 'emerging',
'competitive_advantage': 'AI-powered content',
'market_share': '2.5%',
'positioning_score': 4
}
positioning = {}
best_confidence = 0.0
for analysis_type, recommendations in ai_recommendations.items():
if not isinstance(recommendations, dict):
continue
metrics = recommendations.get('metrics', {})
confidence = metrics.get('confidence', 0.0)
if confidence <= best_confidence:
continue
recs = recommendations.get('recommendations', [])
if isinstance(recs, list):
for r in recs:
if not isinstance(r, dict):
continue
pos = r.get('market_position') or r.get('positioning')
adv = r.get('competitive_advantage')
share = r.get('market_share')
score = r.get('positioning_score') or metrics.get('positioning_score')
if any([pos, adv, share, score]):
best_confidence = confidence
if pos:
positioning['industry_position'] = pos
if adv:
positioning['competitive_advantage'] = adv
if share:
positioning['market_share'] = str(share)
if score is not None:
positioning['positioning_score'] = score
if not positioning:
for key in ('industry_position', 'competitive_advantage', 'market_share', 'positioning_score'):
val = ai_recommendations.get(key)
if val is not None:
positioning[key] = val
return positioning
def extract_competitive_advantages(ai_recommendations: Dict[str, Any]) -> List[Dict[str, Any]]:
"""
Extract competitive advantages from AI recommendations.
Scans all analysis types for advantage signals. Returns empty list if none found.
Args:
ai_recommendations: Dictionary containing AI analysis results
@@ -82,23 +129,40 @@ def extract_competitive_advantages(ai_recommendations: Dict[str, Any]) -> List[D
Returns:
List of competitive advantages with impact and implementation status
"""
return [
{
'advantage': 'AI-powered content creation',
'impact': 'High',
'implementation': 'In Progress'
},
{
'advantage': 'Data-driven strategy',
'impact': 'Medium',
'implementation': 'Complete'
}
]
advantages = []
for analysis_type, recommendations in ai_recommendations.items():
if not isinstance(recommendations, dict):
continue
recs = recommendations.get('recommendations', [])
if not isinstance(recs, list):
continue
for r in recs:
if not isinstance(r, dict):
continue
adv = r.get('advantage') or r.get('competitive_advantage')
if adv:
advantages.append({
'advantage': adv,
'impact': r.get('impact', 'Medium'),
'implementation': r.get('implementation', 'Planned')
})
seen = set()
unique = []
for a in advantages:
key = a['advantage'].strip().lower()
if key not in seen:
seen.add(key)
unique.append(a)
return unique
def extract_strategic_risks(ai_recommendations: Dict[str, Any]) -> List[Dict[str, Any]]:
"""
Extract strategic risks from AI recommendations.
Scans all analysis types for risk signals. Returns empty list if none found.
Args:
ai_recommendations: Dictionary containing AI analysis results
@@ -106,23 +170,46 @@ def extract_strategic_risks(ai_recommendations: Dict[str, Any]) -> List[Dict[str
Returns:
List of strategic risks with probability and impact assessment
"""
return [
{
'risk': 'Content saturation in market',
'probability': 'Medium',
'impact': 'High'
},
{
'risk': 'Algorithm changes affecting reach',
'probability': 'High',
'impact': 'Medium'
}
]
risks = []
for analysis_type, recommendations in ai_recommendations.items():
if not isinstance(recommendations, dict):
continue
recs = recommendations.get('recommendations', [])
if not isinstance(recs, list):
continue
for r in recs:
if not isinstance(r, dict):
continue
risk_text = r.get('risk') or r.get('strategic_risk') or r.get('threat')
if risk_text:
risks.append({
'risk': risk_text,
'probability': r.get('probability', 'Medium'),
'impact': r.get('impact', 'Medium')
})
risks_list = recommendations.get('risks') or recommendations.get('strategic_risks')
if isinstance(risks_list, list):
for r in risks_list:
if isinstance(r, dict) and r.get('risk'):
risks.append(r)
seen = set()
unique = []
for r in risks:
key = r['risk'].strip().lower()
if key not in seen:
seen.add(key)
unique.append(r)
return unique
def extract_opportunity_analysis(ai_recommendations: Dict[str, Any]) -> List[Dict[str, Any]]:
"""
Extract opportunity analysis from AI recommendations.
Scans all analysis types for opportunity signals. Returns empty list if none found.
Args:
ai_recommendations: Dictionary containing AI analysis results
@@ -130,18 +217,40 @@ def extract_opportunity_analysis(ai_recommendations: Dict[str, Any]) -> List[Dic
Returns:
List of opportunities with potential impact and implementation ease
"""
return [
{
'opportunity': 'Video content expansion',
'potential_impact': 'High',
'implementation_ease': 'Medium'
},
{
'opportunity': 'Social media engagement',
'potential_impact': 'Medium',
'implementation_ease': 'High'
}
]
opportunities = []
for analysis_type, recommendations in ai_recommendations.items():
if not isinstance(recommendations, dict):
continue
recs = recommendations.get('recommendations', [])
if not isinstance(recs, list):
continue
for r in recs:
if not isinstance(r, dict):
continue
opp = r.get('opportunity') or r.get('growth_opportunity')
if opp:
opportunities.append({
'opportunity': opp,
'potential_impact': r.get('potential_impact', 'Medium'),
'implementation_ease': r.get('implementation_ease', 'Medium')
})
opps_list = recommendations.get('opportunities') or recommendations.get('growth_opportunities')
if isinstance(opps_list, list):
for o in opps_list:
if isinstance(o, dict) and o.get('opportunity'):
opportunities.append(o)
seen = set()
unique = []
for o in opportunities:
key = o['opportunity'].strip().lower()
if key not in seen:
seen.add(key)
unique.append(o)
return unique
def initialize_caches() -> Dict[str, Any]: