Fix AI insights feature issues
This commit is contained in:
Urtzi Alfaro
@@ -45,3 +45,55 @@ async def get_sales_analytics(
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except Exception as e:
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logger.error("Failed to get sales analytics", error=str(e), tenant_id=tenant_id)
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raise HTTPException(status_code=500, detail=f"Failed to get sales analytics: {str(e)}")
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@router.get(
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route_builder.build_analytics_route("products/{product_id}/demand-patterns")
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)
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@analytics_tier_required
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async def get_product_demand_patterns(
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tenant_id: UUID = Path(..., description="Tenant ID"),
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product_id: UUID = Path(..., description="Product ID (inventory_product_id)"),
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start_date: Optional[datetime] = Query(None, description="Start date for analysis"),
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end_date: Optional[datetime] = Query(None, description="End date for analysis"),
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min_history_days: int = Query(90, description="Minimum days of history required", ge=30, le=365),
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current_user: Dict[str, Any] = Depends(get_current_user_dep),
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sales_service: SalesService = Depends(get_sales_service)
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):
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"""
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Analyze demand patterns for a specific product (Professional+ tier required).
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Returns:
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- Demand trends (increasing/decreasing/stable)
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- Volatility metrics (coefficient of variation)
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- Weekly seasonal patterns
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- Peak/low demand days
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- Statistical summaries
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"""
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try:
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patterns = await sales_service.analyze_product_demand_patterns(
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tenant_id=tenant_id,
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inventory_product_id=product_id,
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start_date=start_date,
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end_date=end_date,
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min_history_days=min_history_days
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)
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logger.info(
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"Retrieved product demand patterns",
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tenant_id=tenant_id,
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product_id=product_id
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)
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return patterns
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except Exception as e:
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logger.error(
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"Failed to get product demand patterns",
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error=str(e),
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tenant_id=tenant_id,
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product_id=product_id
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)
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raise HTTPException(
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status_code=500,
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detail=f"Failed to analyze demand patterns: {str(e)}"
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)
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@@ -501,15 +501,157 @@ class SalesService:
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error=str(e), product_id=product_id, tenant_id=tenant_id)
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return None
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async def get_inventory_products_by_category(self, category: str, tenant_id: UUID,
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async def get_inventory_products_by_category(self, category: str, tenant_id: UUID,
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product_type: Optional[str] = None) -> List[Dict[str, Any]]:
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"""Get products by category from inventory service"""
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try:
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products = await self.inventory_client.get_products_by_category(category, tenant_id, product_type)
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logger.info("Retrieved inventory products by category", category=category,
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logger.info("Retrieved inventory products by category", category=category,
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count=len(products), tenant_id=tenant_id)
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return products
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except Exception as e:
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logger.error("Failed to get inventory products by category",
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logger.error("Failed to get inventory products by category",
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error=str(e), category=category, tenant_id=tenant_id)
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return []
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return []
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async def analyze_product_demand_patterns(
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self,
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tenant_id: UUID,
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inventory_product_id: UUID,
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start_date: Optional[datetime] = None,
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end_date: Optional[datetime] = None,
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min_history_days: int = 90
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) -> Dict[str, Any]:
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"""
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Analyze demand patterns for a specific product from historical sales data.
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This method provides insights on:
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- Demand trends (increasing/decreasing)
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- Volatility (coefficient of variation)
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- Weekly seasonal patterns
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- Peak/low demand days
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Args:
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tenant_id: Tenant identifier
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inventory_product_id: Product identifier
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start_date: Start date for analysis (optional)
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end_date: End date for analysis (optional)
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min_history_days: Minimum days of history required
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Returns:
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Analysis results with patterns, trends, and statistics
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"""
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try:
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import pandas as pd
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logger.info(
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"Analyzing product demand patterns",
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tenant_id=tenant_id,
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inventory_product_id=inventory_product_id
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)
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# Fetch sales data for the product
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sales_records = await self.get_product_sales(
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tenant_id=tenant_id,
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inventory_product_id=inventory_product_id,
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start_date=start_date,
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end_date=end_date
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)
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if not sales_records or len(sales_records) < min_history_days:
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return {
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'analyzed_at': datetime.utcnow().isoformat(),
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'history_days': len(sales_records) if sales_records else 0,
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'patterns': {},
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'trend_analysis': {},
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'seasonal_factors': {},
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'statistics': {},
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'error': f'Insufficient historical data (need {min_history_days} days, got {len(sales_records) if sales_records else 0})'
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}
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# Convert to DataFrame for analysis
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sales_data = pd.DataFrame([{
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'date': record.date,
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'quantity': record.quantity_sold,
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'revenue': float(record.revenue) if record.revenue else 0
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} for record in sales_records])
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sales_data['date'] = pd.to_datetime(sales_data['date'])
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sales_data = sales_data.sort_values('date')
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# Calculate basic statistics
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mean_demand = sales_data['quantity'].mean()
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std_demand = sales_data['quantity'].std()
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cv = (std_demand / mean_demand) if mean_demand > 0 else 0
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# Trend analysis
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sales_data['days_since_start'] = (sales_data['date'] - sales_data['date'].min()).dt.days
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trend_correlation = sales_data['days_since_start'].corr(sales_data['quantity'])
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is_increasing = trend_correlation > 0.2
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is_decreasing = trend_correlation < -0.2
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# Seasonal pattern detection (day of week)
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sales_data['day_of_week'] = sales_data['date'].dt.dayofweek
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weekly_pattern = sales_data.groupby('day_of_week')['quantity'].mean().to_dict()
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peak_day = max(weekly_pattern, key=weekly_pattern.get)
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low_day = min(weekly_pattern, key=weekly_pattern.get)
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peak_ratio = weekly_pattern[peak_day] / weekly_pattern[low_day] if weekly_pattern[low_day] > 0 else 1.0
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logger.info(
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"Demand pattern analysis complete",
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tenant_id=tenant_id,
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inventory_product_id=inventory_product_id,
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data_points=len(sales_data),
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trend_direction='increasing' if is_increasing else 'decreasing' if is_decreasing else 'stable'
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)
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return {
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'analyzed_at': datetime.utcnow().isoformat(),
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'history_days': len(sales_data),
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'date_range': {
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'start': sales_data['date'].min().isoformat(),
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'end': sales_data['date'].max().isoformat()
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},
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'statistics': {
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'mean_demand': round(mean_demand, 2),
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'std_demand': round(std_demand, 2),
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'coefficient_of_variation': round(cv, 3),
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'total_quantity': round(sales_data['quantity'].sum(), 2),
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'total_revenue': round(sales_data['revenue'].sum(), 2),
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'min_demand': round(sales_data['quantity'].min(), 2),
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'max_demand': round(sales_data['quantity'].max(), 2)
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},
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'trend_analysis': {
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'correlation': round(trend_correlation, 3),
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'is_increasing': is_increasing,
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'is_decreasing': is_decreasing,
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'direction': 'increasing' if is_increasing else 'decreasing' if is_decreasing else 'stable'
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},
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'patterns': {
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'weekly_pattern': {str(k): round(v, 2) for k, v in weekly_pattern.items()},
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'peak_day': int(peak_day),
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'low_day': int(low_day)
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},
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'seasonal_factors': {
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'peak_ratio': round(peak_ratio, 2),
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'has_strong_pattern': peak_ratio > 1.5
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}
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}
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except Exception as e:
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logger.error(
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"Error analyzing product demand patterns",
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tenant_id=tenant_id,
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inventory_product_id=inventory_product_id,
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error=str(e),
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exc_info=True
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)
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return {
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'analyzed_at': datetime.utcnow().isoformat(),
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'history_days': 0,
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'patterns': {},
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'trend_analysis': {},
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'seasonal_factors': {},
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'statistics': {},
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'error': str(e)
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}
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