Fix AI insights feature issues

services/sales/app/services/sales_service.py

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