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Recovered state: integrated TrendSurferAgent, restored frontend/backend files, and cleaned up recovery scripts

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ajaysi
2026-02-08 13:56:57 +05:30
parent 1db10ccd0f
commit e404a86502
333 changed files with 42223 additions and 10875 deletions
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65
backend/services/ai_analytics_service.py
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@@ -10,7 +10,7 @@ from loguru import logger
import asyncio
from sqlalchemy.orm import Session
from services.database import get_db_session
from services.database import get_session_for_user
from models.content_planning import ContentAnalytics, ContentStrategy, CalendarEvent
from services.content_gap_analyzer.ai_engine_service import AIEngineService
@@ -19,19 +19,17 @@ class AIAnalyticsService:
def __init__(self):
self.ai_engine = AIEngineService()
self.db_session = None
def _get_db_session(self) -> Session:
def _get_db_session(self, user_id: int) -> Session:
"""Get database session."""
if not self.db_session:
self.db_session = get_db_session()
return self.db_session
return get_session_for_user(str(user_id))
async def analyze_content_evolution(self, strategy_id: int, time_period: str = "30d") -> Dict[str, Any]:
async def analyze_content_evolution(self, user_id: int, strategy_id: int, time_period: str = "30d") -> Dict[str, Any]:
"""
Analyze content evolution over time for a specific strategy.
Args:
user_id: User ID
strategy_id: Content strategy ID
time_period: Analysis period (7d, 30d, 90d, 1y)
@@ -39,10 +37,10 @@ class AIAnalyticsService:
Content evolution analysis results
"""
try:
logger.info(f"Analyzing content evolution for strategy {strategy_id}")
logger.info(f"Analyzing content evolution for strategy {strategy_id} (user {user_id})")
# Get analytics data for the strategy
analytics_data = await self._get_analytics_data(strategy_id, time_period)
analytics_data = await self._get_analytics_data(user_id, strategy_id, time_period)
# Analyze content performance trends
performance_trends = await self._analyze_performance_trends(analytics_data)
@@ -72,11 +70,12 @@ class AIAnalyticsService:
logger.error(f"Error analyzing content evolution: {str(e)}")
raise
async def analyze_performance_trends(self, strategy_id: int, metrics: List[str] = None) -> Dict[str, Any]:
async def analyze_performance_trends(self, user_id: int, strategy_id: int, metrics: List[str] = None) -> Dict[str, Any]:
"""
Analyze performance trends for content strategy.
Args:
user_id: User ID
strategy_id: Content strategy ID
metrics: List of metrics to analyze (engagement, reach, conversion, etc.)
@@ -84,13 +83,13 @@ class AIAnalyticsService:
Performance trend analysis results
"""
try:
logger.info(f"Analyzing performance trends for strategy {strategy_id}")
logger.info(f"Analyzing performance trends for strategy {strategy_id} (user {user_id})")
if not metrics:
metrics = ['engagement_rate', 'reach', 'conversion_rate', 'click_through_rate']
# Get performance data
performance_data = await self._get_performance_data(strategy_id, metrics)
performance_data = await self._get_performance_data(user_id, strategy_id, metrics)
# Analyze trends for each metric
trend_analysis = {}
@@ -120,12 +119,13 @@ class AIAnalyticsService:
logger.error(f"Error analyzing performance trends: {str(e)}")
raise
async def predict_content_performance(self, content_data: Dict[str, Any],
async def predict_content_performance(self, user_id: int, content_data: Dict[str, Any],
strategy_id: int) -> Dict[str, Any]:
"""
Predict content performance using AI models.
Args:
user_id: User ID
content_data: Content details (title, description, type, platform, etc.)
strategy_id: Content strategy ID
@@ -133,10 +133,10 @@ class AIAnalyticsService:
Performance prediction results
"""
try:
logger.info(f"Predicting performance for content in strategy {strategy_id}")
logger.info(f"Predicting performance for content in strategy {strategy_id} (user {user_id})")
# Get historical performance data
historical_data = await self._get_historical_performance_data(strategy_id)
historical_data = await self._get_historical_performance_data(user_id, strategy_id)
# Analyze content characteristics
content_analysis = await self._analyze_content_characteristics(content_data)
@@ -166,12 +166,13 @@ class AIAnalyticsService:
logger.error(f"Error predicting content performance: {str(e)}")
raise
async def generate_strategic_intelligence(self, strategy_id: int,
async def generate_strategic_intelligence(self, user_id: int, strategy_id: int,
market_data: Dict[str, Any] = None) -> Dict[str, Any]:
"""
Generate strategic intelligence for content planning.
Args:
user_id: User ID
strategy_id: Content strategy ID
market_data: Additional market data for analysis
@@ -179,10 +180,10 @@ class AIAnalyticsService:
Strategic intelligence results
"""
try:
logger.info(f"Generating strategic intelligence for strategy {strategy_id}")
logger.info(f"Generating strategic intelligence for strategy {strategy_id} (user {user_id})")
# Get strategy data
strategy_data = await self._get_strategy_data(strategy_id)
strategy_data = await self._get_strategy_data(user_id, strategy_id)
# Analyze market positioning
market_positioning = await self._analyze_market_positioning(strategy_data, market_data)
@@ -213,10 +214,11 @@ class AIAnalyticsService:
raise
# Helper methods for data retrieval and analysis
async def _get_analytics_data(self, strategy_id: int, time_period: str) -> List[Dict[str, Any]]:
async def _get_analytics_data(self, user_id: int, strategy_id: int, time_period: str) -> List[Dict[str, Any]]:
"""Get analytics data for the specified strategy and time period."""
session = None
try:
session = self._get_db_session()
session = self._get_db_session(user_id)
# Calculate date range
end_date = datetime.utcnow()
@@ -243,6 +245,9 @@ class AIAnalyticsService:
except Exception as e:
logger.error(f"Error getting analytics data: {str(e)}")
return []
finally:
if session:
session.close()
async def _analyze_performance_trends(self, analytics_data: List[Dict[str, Any]]) -> Dict[str, Any]:
"""Analyze performance trends from analytics data."""
@@ -404,10 +409,10 @@ class AIAnalyticsService:
logger.error(f"Error generating evolution recommendations: {str(e)}")
return [{'error': str(e)}]
async def _get_performance_data(self, strategy_id: int, metrics: List[str]) -> List[Dict[str, Any]]:
async def _get_performance_data(self, user_id: int, strategy_id: int, metrics: List[str]) -> List[Dict[str, Any]]:
"""Get performance data for specified metrics."""
try:
session = self._get_db_session()
session = self._get_db_session(user_id)
# Get analytics data for the strategy
analytics = session.query(ContentAnalytics).filter(
@@ -695,10 +700,11 @@ class AIAnalyticsService:
logger.error(f"Error generating competitor recommendations: {str(e)}")
return [{'error': str(e)}]
async def _get_historical_performance_data(self, strategy_id: int) -> List[Dict[str, Any]]:
async def _get_historical_performance_data(self, user_id: int, strategy_id: int) -> List[Dict[str, Any]]:
"""Get historical performance data for the strategy."""
session = None
try:
session = self._get_db_session()
session = self._get_db_session(user_id)
analytics = session.query(ContentAnalytics).filter(
ContentAnalytics.strategy_id == strategy_id
@@ -709,6 +715,9 @@ class AIAnalyticsService:
except Exception as e:
logger.error(f"Error getting historical performance data: {str(e)}")
return []
finally:
if session:
session.close()
async def _analyze_content_characteristics(self, content_data: Dict[str, Any]) -> Dict[str, Any]:
"""Analyze content characteristics for performance prediction."""
@@ -801,10 +810,11 @@ class AIAnalyticsService:
logger.error(f"Error generating optimization recommendations: {str(e)}")
return [{'error': str(e)}]
async def _get_strategy_data(self, strategy_id: int) -> Dict[str, Any]:
async def _get_strategy_data(self, user_id: int, strategy_id: int) -> Dict[str, Any]:
"""Get strategy data for analysis."""
session = None
try:
session = self._get_db_session()
session = self._get_db_session(user_id)
strategy = session.query(ContentStrategy).filter(
ContentStrategy.id == strategy_id
@@ -818,6 +828,9 @@ class AIAnalyticsService:
except Exception as e:
logger.error(f"Error getting strategy data: {str(e)}")
return {}
finally:
if session:
session.close()
async def _analyze_market_positioning(self, strategy_data: Dict[str, Any],
market_data: Dict[str, Any] = None) -> Dict[str, Any]: