Fix new services implementation 5

frontend/src/App.tsx

@@ -13,6 +13,8 @@ import DashboardPage from './pages/dashboard/DashboardPage';
 import ProductionPage from './pages/production/ProductionPage';
 import ForecastPage from './pages/forecast/ForecastPage';
 import OrdersPage from './pages/orders/OrdersPage';
+import InventoryPage from './pages/inventory/InventoryPage';
+import SalesPage from './pages/sales/SalesPage';
 import SettingsPage from './pages/settings/SettingsPage';
 import Layout from './components/layout/Layout';
 
@@ -29,7 +31,7 @@ import './i18n';
 // Global styles
 import './styles/globals.css';
 
-type CurrentPage = 'landing' | 'login' | 'register' | 'onboarding' | 'dashboard' | 'reports' | 'orders' | 'production' | 'settings';
+type CurrentPage = 'landing' | 'login' | 'register' | 'onboarding' | 'dashboard' | 'reports' | 'orders' | 'production' | 'inventory' | 'sales' | 'settings';
 
 interface User {
   id: string;
@@ -291,6 +293,10 @@ const App: React.FC = () => {
           return <OrdersPage />;
         case 'production':
           return <ProductionPage />;
+        case 'inventory':
+          return <InventoryPage />;
+        case 'sales':
+          return <SalesPage />;
         case 'settings':
           return <SettingsPage user={appState.user!} onLogout={handleLogout} />;
         default:
@@ -298,6 +304,8 @@ const App: React.FC = () => {
             onNavigateToOrders={() => navigateTo('orders')}
             onNavigateToReports={() => navigateTo('reports')}
             onNavigateToProduction={() => navigateTo('production')}
+            onNavigateToInventory={() => navigateTo('inventory')}
+            onNavigateToSales={() => navigateTo('sales')}
           />;
       }
     };

frontend/src/api/client/interceptors.ts

@@ -16,13 +16,28 @@ class AuthInterceptor {
   static setup() {
     apiClient.addRequestInterceptor({
       onRequest: async (config: RequestConfig) => {
-        const token = localStorage.getItem('auth_token');
+        let token = localStorage.getItem('auth_token');
+        
+        console.log('🔐 AuthInterceptor: Checking auth token...', token ? 'Found' : 'Missing');
+        if (token) {
+          console.log('🔐 AuthInterceptor: Token preview:', token.substring(0, 20) + '...');
+        }
+        
+        // For development: If no token exists or token is invalid, set a valid demo token
+        if ((!token || token === 'demo-development-token') && window.location.hostname === 'localhost') {
+          console.log('🔧 AuthInterceptor: Development mode - setting valid demo token');
+          token = 'eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9.eyJzdWIiOiIxN2Q1ZTJjZC1hMjk4LTQyNzEtODZjNi01NmEzZGNiNDE0ZWUiLCJ1c2VyX2lkIjoiMTdkNWUyY2QtYTI5OC00MjcxLTg2YzYtNTZhM2RjYjQxNGVlIiwiZW1haWwiOiJ0ZXN0QGRlbW8uY29tIiwidHlwZSI6ImFjY2VzcyIsImV4cCI6MTc1NTI3MzEyNSwiaWF0IjoxNzU1MjcxMzI1LCJpc3MiOiJiYWtlcnktYXV0aCIsImZ1bGxfbmFtZSI6IkRlbW8gVXNlciIsImlzX3ZlcmlmaWVkIjpmYWxzZSwiaXNfYWN0aXZlIjp0cnVlLCJyb2xlIjoidXNlciJ9.RBfzH9L_NKySYkyLzBLYAApnrCFNK4OsGLLO-eCaTSI';
+          localStorage.setItem('auth_token', token);
+        }
         
         if (token) {
           config.headers = {
             ...config.headers,
             Authorization: `Bearer ${token}`,
           };
+          console.log('🔐 AuthInterceptor: Added Authorization header');
+        } else {
+          console.warn('⚠️ AuthInterceptor: No auth token found in localStorage');
         }
 
         return config;

frontend/src/api/hooks/index.ts

@@ -11,7 +11,7 @@ export { useTraining } from './useTraining';
 export { useForecast } from './useForecast';
 export { useNotification } from './useNotification';
 export { useOnboarding, useOnboardingStep } from './useOnboarding';
-export { useInventory, useInventoryDashboard, useInventoryItem } from './useInventory';
+export { useInventory, useInventoryDashboard, useInventoryItem, useInventoryProducts } from './useInventory';
 export { useRecipes, useProduction } from './useRecipes';
 
 // Import hooks for combined usage
@@ -23,6 +23,7 @@ import { useTraining } from './useTraining';
 import { useForecast } from './useForecast';
 import { useNotification } from './useNotification';
 import { useOnboarding } from './useOnboarding';
+import { useInventory } from './useInventory';
 
 // Combined hook for common operations
 export const useApiHooks = () => {
@@ -30,10 +31,11 @@ export const useApiHooks = () => {
   const tenant = useTenant();
   const sales = useSales();
   const external = useExternal();
-  const training = useTraining();
+  const training = useTraining({ disablePolling: true }); // Disable polling by default
   const forecast = useForecast();
   const notification = useNotification();
   const onboarding = useOnboarding();
+  const inventory = useInventory();
 
   return {
     auth,
@@ -44,5 +46,6 @@ export const useApiHooks = () => {
     forecast,
     notification,
     onboarding,
+    inventory
   };
 };

frontend/src/api/hooks/useForecast.ts

@@ -31,7 +31,7 @@ export const useForecast = () => {
       setError(null);
 
       const newForecasts = await forecastingService.createSingleForecast(tenantId, request);
-      setForecasts(prev => [...newForecasts, ...prev]);
+      setForecasts(prev => [...newForecasts, ...(prev || [])]);
       
       return newForecasts;
     } catch (error) {
@@ -52,7 +52,7 @@ export const useForecast = () => {
       setError(null);
 
       const batchForecast = await forecastingService.createBatchForecast(tenantId, request);
-      setBatchForecasts(prev => [batchForecast, ...prev]);
+      setBatchForecasts(prev => [batchForecast, ...(prev || [])]);
       
       return batchForecast;
     } catch (error) {
@@ -90,7 +90,7 @@ export const useForecast = () => {
       const batchForecast = await forecastingService.getBatchForecastStatus(tenantId, batchId);
       
       // Update batch forecast in state
-      setBatchForecasts(prev => prev.map(bf => 
+      setBatchForecasts(prev => (prev || []).map(bf => 
         bf.id === batchId ? batchForecast : bf
       ));
       
@@ -147,7 +147,7 @@ export const useForecast = () => {
       setError(null);
 
       const acknowledgedAlert = await forecastingService.acknowledgeForecastAlert(tenantId, alertId);
-      setAlerts(prev => prev.map(alert => 
+      setAlerts(prev => (prev || []).map(alert => 
         alert.id === alertId ? acknowledgedAlert : alert
       ));
     } catch (error) {

frontend/src/api/hooks/useInventory.ts

@@ -20,6 +20,7 @@ import {
   PaginatedResponse,
   InventoryDashboardData
 } from '../services/inventory.service';
+import type { ProductInfo } from '../types';
 
 import { useTenantId } from '../../hooks/useTenantId';
 
@@ -117,17 +118,29 @@ export const useInventory = (autoLoad = true): UseInventoryReturn => {
     
     try {
       const response = await inventoryService.getInventoryItems(tenantId, params);
-      setItems(response.items);
+      console.log('🔄 useInventory: Loaded items:', response.items);
+      setItems(response.items || []); // Ensure it's always an array
       setPagination({
-        page: response.page,
+        page: response.page || 1,
-        limit: response.limit,
+        limit: response.limit || 20,
-        total: response.total,
+        total: response.total || 0,
-        totalPages: response.total_pages
+        totalPages: response.total_pages || 0
       });
     } catch (err: any) {
+      console.error('❌ useInventory: Error loading items:', err);
       const errorMessage = err.response?.data?.detail || err.message || 'Error loading inventory items';
+      
       setError(errorMessage);
+      setItems([]); // Set empty array on error
+      
+      // Show appropriate error message
+      if (err.response?.status === 401) {
+        console.error('❌ useInventory: Authentication failed');
+      } else if (err.response?.status === 403) {
+        toast.error('No tienes permisos para acceder a este inventario');
+      } else {
         toast.error(errorMessage);
+      }
     } finally {
       setIsLoading(false);
     }
@@ -222,6 +235,7 @@ export const useInventory = (autoLoad = true): UseInventoryReturn => {
       setStockLevels(levelMap);
     } catch (err: any) {
       console.error('Error loading stock levels:', err);
+      // Don't show toast error for this as it's not critical for forecast page
     }
   }, [tenantId]);
 
@@ -273,6 +287,7 @@ export const useInventory = (autoLoad = true): UseInventoryReturn => {
       setAlerts(alertsData);
     } catch (err: any) {
       console.error('Error loading alerts:', err);
+      // Don't show toast error for this as it's not critical for forecast page
     }
   }, [tenantId]);
 
@@ -301,6 +316,7 @@ export const useInventory = (autoLoad = true): UseInventoryReturn => {
       setDashboardData(data);
     } catch (err: any) {
       console.error('Error loading dashboard:', err);
+      // Don't show toast error for this as it's not critical for forecast page
     }
   }, [tenantId]);
 
@@ -508,3 +524,60 @@ export const useInventoryItem = (itemId: string): UseInventoryItemReturn => {
     refresh
   };
 };
+
+// ========== SIMPLE PRODUCTS HOOK FOR FORECASTING ==========
+
+export const useInventoryProducts = () => {
+  const [isLoading, setIsLoading] = useState(false);
+  const [error, setError] = useState<string | null>(null);
+
+  /**
+   * Get Products List for Forecasting
+   */
+  const getProductsList = useCallback(async (tenantId: string): Promise<ProductInfo[]> => {
+    try {
+      setIsLoading(true);
+      setError(null);
+
+      const products = await inventoryService.getProductsList(tenantId);
+      
+      return products;
+    } catch (error) {
+      const message = error instanceof Error ? error.message : 'Failed to get products list';
+      setError(message);
+      throw error;
+    } finally {
+      setIsLoading(false);
+    }
+  }, []);
+
+  /**
+   * Get Product by ID
+   */
+  const getProductById = useCallback(async (tenantId: string, productId: string): Promise<ProductInfo | null> => {
+    try {
+      setIsLoading(true);
+      setError(null);
+
+      const product = await inventoryService.getProductById(tenantId, productId);
+      
+      return product;
+    } catch (error) {
+      const message = error instanceof Error ? error.message : 'Failed to get product';
+      setError(message);
+      throw error;
+    } finally {
+      setIsLoading(false);
+    }
+  }, []);
+
+  return {
+    // State
+    isLoading,
+    error,
+
+    // Actions
+    getProductsList,
+    getProductById,
+  };
+};

frontend/src/api/hooks/useSales.ts

@@ -156,25 +156,6 @@ export const useSales = () => {
     }
   }, []);
 
-  /**
-   * Get Products List
-   */
-  const getProductsList = useCallback(async (tenantId: string): Promise<string[]> => {
-    try {
-      setIsLoading(true);
-      setError(null);
-
-      const products = await salesService.getProductsList(tenantId);
-      
-      return products;
-    } catch (error) {
-      const message = error instanceof Error ? error.message : 'Failed to get products list';
-      setError(message);
-      throw error;
-    } finally {
-      setIsLoading(false);
-    }
-  }, []);
 
   /**
    * Get Sales Analytics
@@ -213,7 +194,6 @@ export const useSales = () => {
     getDashboardStats,
     getRecentActivity,
     exportSalesData,
-    getProductsList,
     getSalesAnalytics,
     clearError: () => setError(null),
   };

frontend/src/api/index.ts

@@ -16,6 +16,7 @@ export {
   trainingService,
   forecastingService,
   notificationService,
+  inventoryService,
   api
 } from './services';
 
@@ -31,6 +32,8 @@ export {
   useNotification,
   useApiHooks,
   useOnboarding,
+  useInventory,
+  useInventoryProducts
 } from './hooks';
 
 // Export WebSocket functionality

frontend/src/api/services/index.ts

@@ -80,6 +80,7 @@ export class HealthService {
       { name: 'Sales', endpoint: '/sales/health' },
       { name: 'External', endpoint: '/external/health' },
       { name: 'Training', endpoint: '/training/health' },
+      { name: 'Inventory', endpoint: '/inventory/health' },
       { name: 'Forecasting', endpoint: '/forecasting/health' },
       { name: 'Notification', endpoint: '/notifications/health' },
     ];

frontend/src/api/services/inventory.service.ts

@@ -5,6 +5,7 @@
  */
 
 import { apiClient } from '../client';
+import type { ProductInfo } from '../types';
 
 // ========== TYPES AND INTERFACES ==========
 
@@ -208,7 +209,7 @@ export interface PaginatedResponse<T> {
 // ========== INVENTORY SERVICE CLASS ==========
 
 export class InventoryService {
-  private baseEndpoint = '/api/v1';
+  private baseEndpoint = '';
 
   // ========== INVENTORY ITEMS ==========
 
@@ -230,16 +231,70 @@ export class InventoryService {
     }
     
     const query = searchParams.toString();
-    const url = `${this.baseEndpoint}/tenants/${tenantId}/inventory/items${query ? `?${query}` : ''}`;
+    const url = `/tenants/${tenantId}/ingredients${query ? `?${query}` : ''}`;
     
-    return apiClient.get(url);
+    console.log('🔍 InventoryService: Fetching inventory items from:', url);
+    
+    try {
+      console.log('🔑 InventoryService: Making request with auth token:', localStorage.getItem('auth_token') ? 'Present' : 'Missing');
+      const response = await apiClient.get(url);
+      console.log('📋 InventoryService: Raw response:', response);
+      console.log('📋 InventoryService: Response type:', typeof response);
+      console.log('📋 InventoryService: Response keys:', response ? Object.keys(response) : 'null');
+      
+      // Handle different response formats
+      if (Array.isArray(response)) {
+        // Direct array response
+        console.log('✅ InventoryService: Array response with', response.length, 'items');
+        return {
+          items: response,
+          total: response.length,
+          page: 1,
+          limit: response.length,
+          total_pages: 1
+        };
+      } else if (response && typeof response === 'object') {
+        // Check if it's already paginated
+        if ('items' in response && Array.isArray(response.items)) {
+          console.log('✅ InventoryService: Paginated response with', response.items.length, 'items');
+          return response;
+        }
+        
+        // Handle object with numeric keys (convert to array)
+        const keys = Object.keys(response);
+        if (keys.length > 0 && keys.every(key => !isNaN(Number(key)))) {
+          const items = Object.values(response);
+          console.log('✅ InventoryService: Numeric keys response with', items.length, 'items');
+          return {
+            items,
+            total: items.length,
+            page: 1,
+            limit: items.length,
+            total_pages: 1
+          };
+        }
+        
+        // Handle empty object - this seems to be what we're getting
+        if (keys.length === 0) {
+          console.log('📭 InventoryService: Empty object response - backend has no inventory items for this tenant');
+          throw new Error('NO_INVENTORY_ITEMS'); // This will trigger fallback in useInventory
+        }
+      }
+      
+      // Fallback: unexpected response format
+      console.warn('⚠️ InventoryService: Unexpected response format, keys:', Object.keys(response || {}));
+      throw new Error('UNEXPECTED_RESPONSE_FORMAT');
+    } catch (error) {
+      console.error('❌ InventoryService: Failed to fetch inventory items:', error);
+      throw error;
+    }
   }
 
   /**
    * Get single inventory item by ID
    */
   async getInventoryItem(tenantId: string, itemId: string): Promise<InventoryItem> {
-    return apiClient.get(`${this.baseEndpoint}/tenants/${tenantId}/inventory/items/${itemId}`);
+    return apiClient.get(`/tenants/${tenantId}/ingredients/${itemId}`);
   }
 
   /**
@@ -249,7 +304,7 @@ export class InventoryService {
     tenantId: string, 
     data: CreateInventoryItemRequest
   ): Promise<InventoryItem> {
-    return apiClient.post(`${this.baseEndpoint}/tenants/${tenantId}/inventory/items`, data);
+    return apiClient.post(`/tenants/${tenantId}/ingredients`, data);
   }
 
   /**
@@ -260,14 +315,14 @@ export class InventoryService {
     itemId: string, 
     data: UpdateInventoryItemRequest
   ): Promise<InventoryItem> {
-    return apiClient.put(`${this.baseEndpoint}/tenants/${tenantId}/inventory/items/${itemId}`, data);
+    return apiClient.put(`/tenants/${tenantId}/ingredients/${itemId}`, data);
   }
 
   /**
    * Delete inventory item (soft delete)
    */
   async deleteInventoryItem(tenantId: string, itemId: string): Promise<void> {
-    return apiClient.delete(`${this.baseEndpoint}/tenants/${tenantId}/inventory/items/${itemId}`);
+    return apiClient.delete(`/tenants/${tenantId}/ingredients/${itemId}`);
   }
 
   /**
@@ -277,7 +332,7 @@ export class InventoryService {
     tenantId: string, 
     updates: { id: string; data: UpdateInventoryItemRequest }[]
   ): Promise<{ success: number; failed: number; errors: string[] }> {
-    return apiClient.post(`${this.baseEndpoint}/tenants/${tenantId}/inventory/items/bulk-update`, {
+    return apiClient.post(`/tenants/${tenantId}/ingredients/bulk-update`, {
       updates
     });
   }
@@ -288,14 +343,16 @@ export class InventoryService {
    * Get current stock level for an item
    */
   async getStockLevel(tenantId: string, itemId: string): Promise<StockLevel> {
-    return apiClient.get(`${this.baseEndpoint}/tenants/${tenantId}/inventory/stock/${itemId}`);
+    return apiClient.get(`/tenants/${tenantId}/ingredients/${itemId}/stock`);
   }
 
   /**
    * Get stock levels for all items
    */
   async getAllStockLevels(tenantId: string): Promise<StockLevel[]> {
-    return apiClient.get(`${this.baseEndpoint}/tenants/${tenantId}/inventory/stock`);
+    // TODO: Map to correct endpoint when available
+    return [];
+    // return apiClient.get(`/stock/summary`);
   }
 
   /**
@@ -307,7 +364,7 @@ export class InventoryService {
     adjustment: StockAdjustmentRequest
   ): Promise<StockMovement> {
     return apiClient.post(
-      `${this.baseEndpoint}/tenants/${tenantId}/inventory/stock/${itemId}/adjust`, 
+      `/stock/consume`, 
       adjustment
     );
   }
@@ -353,7 +410,9 @@ export class InventoryService {
    * Get current stock alerts
    */
   async getStockAlerts(tenantId: string): Promise<StockAlert[]> {
-    return apiClient.get(`${this.baseEndpoint}/tenants/${tenantId}/inventory/alerts`);
+    // TODO: Map to correct endpoint when available
+    return [];
+    // return apiClient.get(`/tenants/${tenantId}/inventory/alerts`);
   }
 
   /**
@@ -378,7 +437,17 @@ export class InventoryService {
    * Get inventory dashboard data
    */
   async getDashboardData(tenantId: string): Promise<InventoryDashboardData> {
-    return apiClient.get(`${this.baseEndpoint}/tenants/${tenantId}/inventory/dashboard`);
+    // TODO: Map to correct endpoint when available
+    return {
+      total_items: 0,
+      low_stock_items: 0,
+      out_of_stock_items: 0,
+      total_value: 0,
+      recent_movements: [],
+      top_products: [],
+      stock_alerts: []
+    };
+    // return apiClient.get(`/tenants/${tenantId}/inventory/dashboard`);
   }
 
   /**
@@ -470,6 +539,87 @@ export class InventoryService {
   async getSupplierSuggestions(tenantId: string): Promise<string[]> {
     return apiClient.get(`${this.baseEndpoint}/tenants/${tenantId}/inventory/suppliers`);
   }
+
+  // ========== PRODUCTS FOR FORECASTING ==========
+
+  /**
+   * Get Products List with IDs for Forecasting
+   */
+  async getProductsList(tenantId: string): Promise<ProductInfo[]> {
+    try {
+      const response = await apiClient.get(`/tenants/${tenantId}/ingredients`, {
+        params: { limit: 100 }, // Get all products
+      });
+      
+      console.log('🔍 Inventory Products API Response:', response);
+      
+      let productsArray: any[] = [];
+      
+      if (Array.isArray(response)) {
+        productsArray = response;
+      } else if (response && typeof response === 'object') {
+        // Handle different response formats
+        const keys = Object.keys(response);
+        if (keys.length > 0 && keys.every(key => !isNaN(Number(key)))) {
+          productsArray = Object.values(response);
+        } else {
+          console.warn('⚠️ Response is object but not with numeric keys:', response);
+          return [];
+        }
+      } else {
+        console.warn('⚠️ Response is not array or object:', response);
+        return [];
+      }
+      
+      // Convert to ProductInfo objects
+      const products: ProductInfo[] = productsArray
+        .map((product: any) => ({
+          inventory_product_id: product.id || product.inventory_product_id,
+          name: product.name || product.product_name || `Product ${product.id || ''}`,
+          category: product.category,
+          // Add additional fields if available from inventory
+          current_stock: product.current_stock,
+          unit: product.unit,
+          cost_per_unit: product.cost_per_unit
+        }))
+        .filter(product => product.inventory_product_id && product.name);
+      
+      console.log('📋 Processed inventory products:', products);
+      
+      return products;
+      
+    } catch (error) {
+      console.error('❌ Failed to fetch inventory products:', error);
+      
+      // Return empty array on error - let dashboard handle fallback
+      return [];
+    }
+  }
+
+  /**
+   * Get Product by ID
+   */
+  async getProductById(tenantId: string, productId: string): Promise<ProductInfo | null> {
+    try {
+      const response = await apiClient.get(`/tenants/${tenantId}/ingredients/${productId}`);
+      
+      if (response) {
+        return {
+          inventory_product_id: response.id || response.inventory_product_id,
+          name: response.name || response.product_name,
+          category: response.category,
+          current_stock: response.current_stock,
+          unit: response.unit,
+          cost_per_unit: response.cost_per_unit
+        };
+      }
+      
+      return null;
+    } catch (error) {
+      console.error('❌ Failed to fetch product by ID:', error);
+      return null;
+    }
+  }
 }
 
 export const inventoryService = new InventoryService();

frontend/src/api/services/sales.service.ts

@@ -181,85 +181,6 @@ export class SalesService {
     });
   }
 
-  /**
-   * Get Products List from Sales Data
-   */
-  async getProductsList(tenantId: string): Promise<string[]> {
-    try {
-      const response = await apiClient.get(`/tenants/${tenantId}/sales/products`);
-      
-      console.log('🔍 Products API Response Analysis:');
-      console.log('- Type:', typeof response);
-      console.log('- Is Array:', Array.isArray(response));
-      console.log('- Keys:', Object.keys(response || {}));
-      console.log('- Response:', response);
-      
-      let productsArray: any[] = [];
-      
-      // ✅ FIX: Handle different response formats
-      if (Array.isArray(response)) {
-        // Standard array response
-        productsArray = response;
-        console.log('✅ Response is already an array');
-      } else if (response && typeof response === 'object') {
-        // Object with numeric keys - convert to array
-        const keys = Object.keys(response);
-        if (keys.length > 0 && keys.every(key => !isNaN(Number(key)))) {
-          // Object has numeric keys like {0: {...}, 1: {...}}
-          productsArray = Object.values(response);
-          console.log('✅ Converted object with numeric keys to array');
-        } else {
-          console.warn('⚠️ Response is object but not with numeric keys:', response);
-          return [];
-        }
-      } else {
-        console.warn('⚠️ Response is not array or object:', response);
-        return [];
-      }
-      
-      console.log('📦 Products array:', productsArray);
-      
-      // Extract product names from the array
-      const productNames = productsArray
-        .map((product: any) => {
-          if (typeof product === 'string') {
-            return product;
-          }
-          if (product && typeof product === 'object') {
-            return product.product_name || 
-                  product.name || 
-                  product.productName ||
-                  null;
-          }
-          return null;
-        })
-        .filter(Boolean) // Remove null/undefined values
-        .filter((name: string) => name.trim().length > 0); // Remove empty strings
-      
-      console.log('📋 Extracted product names:', productNames);
-      
-      if (productNames.length === 0) {
-        console.warn('⚠️ No valid product names extracted from response');
-      }
-      
-      return productNames;
-      
-    } catch (error) {
-      console.error('❌ Failed to fetch products list:', error);
-      
-      // Return fallback products for Madrid bakery
-      return [
-        'Croissants',
-        'Pan de molde', 
-        'Baguettes',
-        'Café',
-        'Napolitanas',
-        'Pan integral',
-        'Magdalenas',
-        'Churros'
-      ];
-    }
-  }
 
   /**
    * Get Sales Summary by Period

frontend/src/api/services/tenant.service.ts

@@ -91,10 +91,21 @@ export class TenantService {
   }
 
   /**
-   * Get User's Tenants
+   * Get User's Tenants - Get tenants where user is owner
    */
   async getUserTenants(): Promise<TenantInfo[]> {
-    return apiClient.get(`/users/me/tenants`);
+    try {
+      // First get current user info to get user ID
+      const currentUser = await apiClient.get(`/users/me`);
+      const userId = currentUser.id;
+      
+      // Then get tenants owned by this user
+      return apiClient.get(`${this.baseEndpoint}/user/${userId}`);
+    } catch (error) {
+      console.error('Failed to get user tenants:', error);
+      // Return empty array if API call fails
+      return [];
+    }
   }
 }
 

frontend/src/api/types/data.ts

@@ -5,6 +5,15 @@
 
 import { BaseQueryParams } from './common';
 
+export interface ProductInfo {
+  inventory_product_id: string;
+  name: string;
+  category?: string;
+  sales_count?: number;
+  total_quantity?: number;
+  last_sale_date?: string;
+}
+
 export interface SalesData {
   id: string;
   tenant_id: string;
@@ -26,7 +35,6 @@ export interface SalesData {
   cost_of_goods?: number;
   revenue?: number;
   quantity_sold?: number;
-  inventory_product_id?: string;
   discount_applied?: number;
   weather_condition?: string;
 }

frontend/src/api/websocket/hooks.ts

@@ -97,21 +97,49 @@ export const useWebSocket = (config: WebSocketConfig) => {
 // Hook for training job updates
 export const useTrainingWebSocket = (jobId: string, tenantId?: string) => {
   const [jobUpdates, setJobUpdates] = useState<any[]>([]);
+  const [connectionError, setConnectionError] = useState<string | null>(null);
+  const [isAuthenticationError, setIsAuthenticationError] = useState(false);
 
-  // Get tenant ID reliably
+  // Get tenant ID reliably with enhanced error handling
   const actualTenantId = tenantId || (() => {
     try {
+      // Try multiple sources for tenant ID
+      const sources = [
+        () => localStorage.getItem('current_tenant_id'),
+        () => {
           const userData = localStorage.getItem('user_data');
           if (userData) {
             const parsed = JSON.parse(userData);
             return parsed.current_tenant_id || parsed.tenant_id;
           }
-      
+          return null;
+        },
+        () => {
           const authData = localStorage.getItem('auth_data');
           if (authData) {
             const parsed = JSON.parse(authData);
             return parsed.tenant_id;
           }
+          return null;
+        },
+        () => {
+          const tenantContext = localStorage.getItem('tenant_context');
+          if (tenantContext) {
+            const parsed = JSON.parse(tenantContext);
+            return parsed.current_tenant_id;
+          }
+          return null;
+        }
+      ];
+      
+      for (const source of sources) {
+        try {
+          const tenantId = source();
+          if (tenantId) return tenantId;
+        } catch (e) {
+          console.warn('Failed to get tenant ID from source:', e);
+        }
+      }
     } catch (e) {
       console.error('Failed to parse tenant ID from storage:', e);
     }
@@ -123,8 +151,10 @@ export const useTrainingWebSocket = (jobId: string, tenantId?: string) => {
       ? `ws://localhost:8000/api/v1/ws/tenants/${actualTenantId}/training/jobs/${jobId}/live`
       : `ws://localhost:8000/api/v1/ws/tenants/unknown/training/jobs/${jobId}/live`,
     reconnect: true,
-    reconnectInterval: 3000,
+    reconnectInterval: 3000, // Faster reconnection for training
-    maxReconnectAttempts: 10
+    maxReconnectAttempts: 20, // More attempts for long training jobs
+    heartbeatInterval: 15000, // Send heartbeat every 15 seconds for training jobs
+    enableLogging: true // Enable logging for debugging
   };
 
   const { 
@@ -137,8 +167,13 @@ export const useTrainingWebSocket = (jobId: string, tenantId?: string) => {
     sendMessage 
   } = useWebSocket(config);
 
-  // Enhanced message handler
+  // Enhanced message handler with error handling
   const handleWebSocketMessage = useCallback((message: any) => {
+    try {
+      // Clear connection error when receiving messages
+      setConnectionError(null);
+      setIsAuthenticationError(false);
+      
       // Handle different message structures
       let processedMessage = message;
       
@@ -147,10 +182,54 @@ export const useTrainingWebSocket = (jobId: string, tenantId?: string) => {
         processedMessage = {
           ...message,
           // Merge data properties to root level for backward compatibility
-        ...message.data
+          ...message.data,
+          // Preserve original structure
+          _originalData: message.data
         };
       }
 
+      // Handle special message types
+      if (message.type === 'connection_established') {
+        console.log('WebSocket training connection established:', message);
+        setJobUpdates(prev => [{
+          type: 'connection_established',
+          message: 'Connected to training service',
+          timestamp: Date.now()
+        }, ...prev.slice(0, 49)]);
+        return;
+      }
+      
+      // Handle keepalive messages (don't show to user, just for connection health)
+      if (message.type === 'pong' || message.type === 'heartbeat') {
+        console.debug('Training WebSocket keepalive received:', message.type);
+        return; // Don't add to jobUpdates
+      }
+      
+      if (message.type === 'authentication_error' || message.type === 'authorization_error') {
+        console.error('WebSocket auth/authorization error:', message);
+        setIsAuthenticationError(true);
+        setConnectionError(message.message || 'Authentication/authorization failed - please refresh and try again');
+        return;
+      }
+      
+      if (message.type === 'connection_error') {
+        console.error('WebSocket connection error:', message);
+        setConnectionError(message.message || 'Connection error');
+        return;
+      }
+      
+      if (message.type === 'connection_timeout') {
+        console.warn('WebSocket connection timeout:', message);
+        // Don't set as error, just log - connection will retry
+        return;
+      }
+      
+      if (message.type === 'job_not_found') {
+        console.error('Training job not found:', message);
+        setConnectionError('Training job not found. Please restart the training process.');
+        return;
+      }
+
       // Comprehensive message type handling
       const trainingMessageTypes = [
         'progress', 'training_progress',
@@ -170,8 +249,12 @@ export const useTrainingWebSocket = (jobId: string, tenantId?: string) => {
         });
       } else {
         // Still add to updates for debugging purposes
+        console.log('Received unknown message type:', message.type, message);
         setJobUpdates(prev => [processedMessage, ...prev.slice(0, 49)]);
       }
+    } catch (error) {
+      console.error('Error processing WebSocket message:', error, message);
+    }
   }, []);
 
   // Set up message handler when hook initializes
@@ -179,21 +262,108 @@ export const useTrainingWebSocket = (jobId: string, tenantId?: string) => {
     addMessageHandler(handleWebSocketMessage);
   }, [addMessageHandler, handleWebSocketMessage]);
 
-  // Send periodic ping to keep connection alive
+  // Enhanced dual ping system for training jobs - prevent disconnection during long training
   useEffect(() => {
     if (isConnected) {
-      const pingInterval = setInterval(() => {
+      // Primary ping system using JSON messages with training info
-        sendMessage({
+      const keepaliveInterval = setInterval(() => {
-          type: 'ping',
+        const success = sendMessage({
-          data: undefined
+          type: 'training_keepalive',
+          data: { 
+            timestamp: Date.now(),
+            job_id: jobId,
+            tenant_id: actualTenantId,
+            status: 'active'
+          }
         });
-      }, 30000); // Every 30 seconds
+        
+        if (!success) {
+          console.warn('Training keepalive failed - connection may be lost');
+        }
+      }, 10000); // Every 10 seconds for training jobs
+
+      // Secondary simple text ping system (more lightweight)
+      const simplePingInterval = setInterval(() => {
+        // Send a simple text ping to keep connection alive
+        const success = sendMessage({
+          type: 'ping',
+          data: { 
+            timestamp: Date.now(),
+            source: 'training_client'
+          }
+        });
+        
+        if (!success) {
+          console.warn('Simple training ping failed');
+        }
+      }, 15000); // Every 15 seconds
 
       return () => {
-        clearInterval(pingInterval);
+        clearInterval(keepaliveInterval);
+        clearInterval(simplePingInterval);
       };
     }
-  }, [isConnected, sendMessage]);
+  }, [isConnected, sendMessage, jobId, actualTenantId]);
+
+  // Define refresh connection function
+  const refreshConnection = useCallback(() => {
+    setConnectionError(null);
+    setIsAuthenticationError(false);
+    disconnect();
+    setTimeout(() => {
+      connect();
+    }, 1000);
+  }, [connect, disconnect]);
+
+  // Enhanced connection monitoring and auto-recovery for training jobs
+  useEffect(() => {
+    if (actualTenantId && jobId !== 'pending') {
+      const healthCheckInterval = setInterval(() => {
+        // If we should be connected but aren't, try to reconnect
+        if (status === 'disconnected' && !connectionError) {
+          console.log('WebSocket health check: reconnecting disconnected training socket');
+          connect();
+        }
+        
+        // More aggressive stale connection detection for training jobs
+        const lastUpdate = jobUpdates.length > 0 ? jobUpdates[0] : null;
+        if (lastUpdate && status === 'connected') {
+          const timeSinceLastMessage = Date.now() - (lastUpdate.timestamp || 0);
+          if (timeSinceLastMessage > 45000) { // 45 seconds without messages during training
+            console.log('WebSocket health check: connection appears stale, refreshing');
+            refreshConnection();
+          }
+        }
+        
+        // If connection is in a failed state for too long, force reconnect
+        if (status === 'failed' && !isAuthenticationError) {
+          console.log('WebSocket health check: recovering from failed state');
+          setTimeout(() => connect(), 2000);
+        }
+      }, 12000); // Check every 12 seconds for training jobs
+
+      return () => clearInterval(healthCheckInterval);
+    }
+  }, [actualTenantId, jobId, status, connectionError, connect, refreshConnection, jobUpdates, isAuthenticationError]);
+
+  // Enhanced connection setup - request current status when connecting
+  useEffect(() => {
+    if (isConnected && jobId !== 'pending') {
+      // Wait a moment for connection to stabilize, then request current status
+      const statusRequestTimer = setTimeout(() => {
+        console.log('Requesting current training status after connection');
+        sendMessage({
+          type: 'get_status',
+          data: {
+            job_id: jobId,
+            tenant_id: actualTenantId
+          }
+        });
+      }, 2000);
+
+      return () => clearTimeout(statusRequestTimer);
+    }
+  }, [isConnected, jobId, actualTenantId, sendMessage]);
 
   return {
     status,
@@ -204,13 +374,33 @@ export const useTrainingWebSocket = (jobId: string, tenantId?: string) => {
     lastMessage,
     tenantId: actualTenantId,
     wsUrl: config.url,
-    // Manual refresh function
+    connectionError,
-    refreshConnection: useCallback(() => {
+    isAuthenticationError,
+    // Enhanced refresh function with status request
+    refreshConnection,
+    // Force retry with new authentication
+    retryWithAuth: useCallback(() => {
+      setConnectionError(null);
+      setIsAuthenticationError(false);
+      // Clear any cached auth data that might be stale
       disconnect();
       setTimeout(() => {
         connect();
-      }, 1000);
+      }, 2000);
-    }, [connect, disconnect])
+    }, [connect, disconnect]),
+    // Manual status request function
+    requestStatus: useCallback(() => {
+      if (isConnected && jobId !== 'pending') {
+        return sendMessage({
+          type: 'get_status',
+          data: {
+            job_id: jobId,
+            tenant_id: actualTenantId
+          }
+        });
+      }
+      return false;
+    }, [isConnected, jobId, actualTenantId, sendMessage])
   };
 };
 

frontend/src/api/websocket/manager.ts

@@ -99,8 +99,17 @@ export class WebSocketManager {
           this.handlers.onClose?.(event);
           
           // Auto-reconnect if enabled and not manually closed
-          if (this.config.reconnect && event.code !== 1000) {
+          // Don't reconnect on authorization failures or job not found (1008) with specific reasons
+          const isAuthorizationError = event.code === 1008 && 
+            (event.reason === 'Authentication failed' || event.reason === 'Authorization failed');
+          const isJobNotFound = event.code === 1008 && event.reason === 'Job not found';
+          
+          if (this.config.reconnect && event.code !== 1000 && !isAuthorizationError && !isJobNotFound) {
             this.scheduleReconnect();
+          } else if (isAuthorizationError || isJobNotFound) {
+            this.log('Connection failed - stopping reconnection attempts:', event.reason);
+            this.status = 'failed';
+            this.handlers.onReconnectFailed?.();
           }
         };
 

frontend/src/components/SimplifiedTrainingProgress.tsx

@@ -1,4 +1,4 @@
-import React, { useState, useEffect } from 'react';
+import React, { useState, useEffect, useRef } from 'react';
 import { 
   Sparkles, CheckCircle, Clock, ArrowRight, Coffee, 
   TrendingUp, Target, Loader, AlertTriangle, Mail,
@@ -18,6 +18,11 @@ interface SimplifiedTrainingProgressProps {
   onTimeout?: () => void;
   onBackgroundMode?: () => void;
   onEmailNotification?: (email: string) => void;
+  // Optional WebSocket debugging info
+  websocketStatus?: string;
+  connectionError?: string;
+  isConnected?: boolean;
+  onRetryConnection?: () => void;
 }
 
 // Proceso simplificado de entrenamiento en 3 etapas
@@ -79,13 +84,18 @@ export default function SimplifiedTrainingProgress({
   progress, 
   onTimeout, 
   onBackgroundMode, 
-  onEmailNotification 
+  onEmailNotification,
+  websocketStatus,
+  connectionError,
+  isConnected,
+  onRetryConnection
 }: SimplifiedTrainingProgressProps) {
   const [showDetails, setShowDetails] = useState(false);
   const [showTimeoutOptions, setShowTimeoutOptions] = useState(false);
   const [emailForNotification, setEmailForNotification] = useState('');
   const [celebratingStage, setCelebratingStage] = useState<string | null>(null);
   const [startTime] = useState(Date.now());
+  const celebratedStagesRef = useRef<Set<string>>(new Set());
 
   // Show timeout options after 7 minutes for better UX
   useEffect(() => {
@@ -98,17 +108,18 @@ export default function SimplifiedTrainingProgress({
     return () => clearTimeout(timer);
   }, [progress.status, progress.progress]);
 
-  // Celebrate stage completions
+  // Celebrate stage completions - fixed to prevent infinite re-renders
   useEffect(() => {
     TRAINING_STAGES.forEach(stage => {
       if (progress.progress >= stage.progressRange[1] && 
-          celebratingStage !== stage.id && 
+          !celebratedStagesRef.current.has(stage.id) && 
           progress.progress > 0) {
         setCelebratingStage(stage.id);
+        celebratedStagesRef.current.add(stage.id);
         setTimeout(() => setCelebratingStage(null), 3000);
       }
     });
-  }, [progress.progress, celebratingStage]);
+  }, [progress.progress]);
 
   const getCurrentStage = () => {
     return TRAINING_STAGES.find(stage => 
@@ -258,6 +269,36 @@ export default function SimplifiedTrainingProgress({
             </p>
           </div>
 
+          {/* Connection Status Debug Info */}
+          {(websocketStatus || connectionError) && (
+            <div className={`mb-4 p-3 rounded-lg text-sm ${
+              connectionError 
+                ? 'bg-red-50 text-red-800 border border-red-200'
+                : isConnected 
+                  ? 'bg-green-50 text-green-800 border border-green-200'
+                  : 'bg-yellow-50 text-yellow-800 border border-yellow-200'
+            }`}>
+              <div className="flex items-center justify-between">
+                <div>
+                  <strong>Estado de conexión:</strong> 
+                  {connectionError 
+                    ? ` Error - ${connectionError}`
+                    : isConnected 
+                      ? ' ✅ Conectado a tiempo real'
+                      : ' ⏳ Conectando...'}
+                </div>
+                {connectionError && onRetryConnection && (
+                  <button
+                    onClick={onRetryConnection}
+                    className="ml-2 px-3 py-1 bg-red-600 text-white text-xs rounded hover:bg-red-700 transition-colors"
+                  >
+                    Reintentar
+                  </button>
+                )}
+              </div>
+            </div>
+          )}
+
           {/* Optional Details */}
           <button
             onClick={() => setShowDetails(!showDetails)}

frontend/src/components/layout/Layout.tsx

@@ -10,7 +10,9 @@ import {
   User,
   Bell,
   ChevronDown,
-  ChefHat
+  ChefHat,
+  Warehouse,
+  ShoppingCart
 } from 'lucide-react';
 
 interface LayoutProps {
@@ -42,6 +44,8 @@ const Layout: React.FC<LayoutProps> = ({
     { id: 'dashboard', label: 'Inicio', icon: Home, href: '/dashboard' },
     { id: 'orders', label: 'Pedidos', icon: Package, href: '/orders' },
     { id: 'production', label: 'Producción', icon: ChefHat, href: '/production' },
+    { id: 'inventory', label: 'Inventario', icon: Warehouse, href: '/inventory' },
+    { id: 'sales', label: 'Ventas', icon: ShoppingCart, href: '/sales' },
     { id: 'reports', label: 'Informes', icon: TrendingUp, href: '/reports' },
     { id: 'settings', label: 'Configuración', icon: Settings, href: '/settings' },
   ];

frontend/src/components/onboarding/SmartHistoricalDataImport.tsx

@@ -245,7 +245,8 @@ const SmartHistoricalDataImport: React.FC<SmartHistoricalDataImportProps> = ({
         color: 'blue',
         bgColor: 'bg-blue-50',
         borderColor: 'border-blue-200',
-        textColor: 'text-blue-900'
+        textColor: 'text-blue-900',
+        businessType: 'bakery'
       },
       central_baker_satellite: {
         icon: Truck,
@@ -254,7 +255,8 @@ const SmartHistoricalDataImport: React.FC<SmartHistoricalDataImportProps> = ({
         color: 'amber',
         bgColor: 'bg-amber-50',
         borderColor: 'border-amber-200',
-        textColor: 'text-amber-900'
+        textColor: 'text-amber-900',
+        businessType: 'bakery'
       },
       retail_bakery: {
         icon: Store,
@@ -263,7 +265,8 @@ const SmartHistoricalDataImport: React.FC<SmartHistoricalDataImportProps> = ({
         color: 'green',
         bgColor: 'bg-green-50',
         borderColor: 'border-green-200',
-        textColor: 'text-green-900'
+        textColor: 'text-green-900',
+        businessType: 'bakery'
       },
       hybrid_bakery: {
         icon: Settings2,
@@ -272,7 +275,28 @@ const SmartHistoricalDataImport: React.FC<SmartHistoricalDataImportProps> = ({
         color: 'purple',
         bgColor: 'bg-purple-50',
         borderColor: 'border-purple-200',
-        textColor: 'text-purple-900'
+        textColor: 'text-purple-900',
+        businessType: 'bakery'
+      },
+      coffee_shop_individual: {
+        icon: Coffee,
+        title: 'Cafetería Individual',
+        description: 'Servicio de bebidas y comida ligera con preparación in-situ',
+        color: 'amber',
+        bgColor: 'bg-amber-50',
+        borderColor: 'border-amber-200',
+        textColor: 'text-amber-900',
+        businessType: 'coffee_shop'
+      },
+      coffee_shop_chain: {
+        icon: Building2,
+        title: 'Cafetería en Cadena',
+        description: 'Múltiples ubicaciones con productos estandarizados',
+        color: 'indigo',
+        bgColor: 'bg-indigo-50',
+        borderColor: 'border-indigo-200',
+        textColor: 'text-indigo-900',
+        businessType: 'coffee_shop'
       },
       // Legacy fallbacks
       production: {
@@ -282,7 +306,8 @@ const SmartHistoricalDataImport: React.FC<SmartHistoricalDataImportProps> = ({
         color: 'blue',
         bgColor: 'bg-blue-50',
         borderColor: 'border-blue-200',
-        textColor: 'text-blue-900'
+        textColor: 'text-blue-900',
+        businessType: 'bakery'
       },
       retail: {
         icon: Store,
@@ -291,7 +316,8 @@ const SmartHistoricalDataImport: React.FC<SmartHistoricalDataImportProps> = ({
         color: 'green',
         bgColor: 'bg-green-50',
         borderColor: 'border-green-200',
-        textColor: 'text-green-900'
+        textColor: 'text-green-900',
+        businessType: 'bakery'
       },
       hybrid: {
         icon: Settings2,
@@ -300,7 +326,8 @@ const SmartHistoricalDataImport: React.FC<SmartHistoricalDataImportProps> = ({
         color: 'purple',
         bgColor: 'bg-purple-50',
         borderColor: 'border-purple-200',
-        textColor: 'text-purple-900'
+        textColor: 'text-purple-900',
+        businessType: 'bakery'
       }
     };
 
@@ -331,13 +358,29 @@ const SmartHistoricalDataImport: React.FC<SmartHistoricalDataImportProps> = ({
                 </span>
               </div>
               <div className="flex items-center space-x-2">
-                <Coffee className="w-4 h-4 text-brown-500" />
+                <Package className="w-4 h-4 text-green-500" />
                 <span className="text-sm text-gray-700">
                   {analysis.finished_product_count} productos finales
                 </span>
               </div>
             </div>
             
+            {/* Enhanced business intelligence insights if available */}
+            {config.businessType === 'coffee_shop' && (
+              <div className="mb-4 p-3 bg-amber-100 border border-amber-200 rounded-lg">
+                <div className="flex items-center space-x-2 mb-2">
+                  <Coffee className="w-4 h-4 text-amber-600" />
+                  <span className="text-sm font-medium text-amber-800">
+                    Negocio de Cafetería Detectado
+                  </span>
+                </div>
+                <p className="text-xs text-amber-700">
+                  Hemos detectado que tu negocio se enfoca principalmente en bebidas y comida ligera.
+                  El sistema se optimizará para gestión de inventario de cafetería.
+                </p>
+              </div>
+            )}
+
             {analysis.recommendations.length > 0 && (
               <div>
                 <h4 className={`text-sm font-medium ${config.textColor} mb-2`}>

frontend/src/components/sales/SalesAnalyticsDashboard.tsx

@@ -19,7 +19,7 @@ import {
 } from 'lucide-react';
 
 import { useSales } from '../../api/hooks/useSales';
-import { useInventory } from '../../api/hooks/useInventory';
+import { useInventory, useInventoryProducts } from '../../api/hooks/useInventory';
 import { useAuth } from '../../api/hooks/useAuth';
 
 import Card from '../ui/Card';
@@ -37,11 +37,14 @@ const SalesAnalyticsDashboard: React.FC = () => {
   const {
     getSalesAnalytics,
     getSalesData,
-    getProductsList,
     isLoading: salesLoading,
     error: salesError
   } = useSales();
   
+  const {
+    getProductsList
+  } = useInventoryProducts();
+
   const {
     items: products,
     loadItems: loadProducts,

frontend/src/hooks/useDashboard.ts

@@ -2,7 +2,8 @@
 // Complete dashboard hook using your API infrastructure
 
 import { useState, useEffect, useCallback } from 'react';
-import { useAuth, useSales, useExternal, useForecast } from '../api';
+import { useAuth, useSales, useExternal, useForecast, useInventoryProducts } from '../api';
+import type { ProductInfo } from '../api/types';
 
 import { useTenantId } from './useTenantId';
 
@@ -14,6 +15,7 @@ interface DashboardData {
   } | null;
   todayForecasts: Array<{
     product: string;
+    inventory_product_id: string;
     predicted: number;
     confidence: 'high' | 'medium' | 'low';
     change: number;
@@ -24,18 +26,22 @@ interface DashboardData {
     accuracy: number;
     stockouts: number;
   } | null;
-  products: string[];
+  products: ProductInfo[];
 }
 
 export const useDashboard = () => {
   const { user } = useAuth();
   const { 
-    getProductsList, 
     getSalesAnalytics,
     getDashboardStats,
     isLoading: salesLoading, 
     error: salesError 
   } = useSales();
+  const { 
+    getProductsList,
+    isLoading: inventoryLoading,
+    error: inventoryError
+  } = useInventoryProducts();
   const { 
     getCurrentWeather,
     isLoading: externalLoading, 
@@ -83,19 +89,31 @@ export const useDashboard = () => {
     setError(null);
 
     try {
-      // 1. Get available products
+      // 1. Get available products from inventory service
-      let products: string[] = [];
+      let products: ProductInfo[] = [];
       try {
         products = await getProductsList(tenantId);
         
         // Fallback to default products if none found
         if (products.length === 0) {
-          products = ['Croissants', 'Pan de molde', 'Baguettes', 'Café', 'Napolitanas'];
+          products = [
-          console.warn('No products found from API, using default products');
+            { inventory_product_id: 'fallback-croissants', name: 'Croissants' },
+            { inventory_product_id: 'fallback-pan', name: 'Pan de molde' },
+            { inventory_product_id: 'fallback-baguettes', name: 'Baguettes' },
+            { inventory_product_id: 'fallback-cafe', name: 'Café' },
+            { inventory_product_id: 'fallback-napolitanas', name: 'Napolitanas' }
+          ];
+          console.warn('No products found from inventory API, using default products');
         }
       } catch (error) {
-        console.warn('Failed to fetch products:', error);
+        console.warn('Failed to fetch products from inventory:', error);
-        products = ['Croissants', 'Pan de molde', 'Baguettes', 'Café', 'Napolitanas'];
+        products = [
+          { inventory_product_id: 'fallback-croissants', name: 'Croissants' },
+          { inventory_product_id: 'fallback-pan', name: 'Pan de molde' },
+          { inventory_product_id: 'fallback-baguettes', name: 'Baguettes' },
+          { inventory_product_id: 'fallback-cafe', name: 'Café' },
+          { inventory_product_id: 'fallback-napolitanas', name: 'Napolitanas' }
+        ];
       }
       // 2. Get weather data (Madrid coordinates)
       let weather = null;
@@ -112,11 +130,10 @@ export const useDashboard = () => {
       }
 
       // 3. Generate forecasts for each product
-      const forecastPromises = products.map(async (product) => {
+      const forecastPromises = products.map(async (productInfo) => {
         try {
           const forecastRequest = {
-            inventory_product_id: product,  // Use product as inventory_product_id
+            inventory_product_id: productInfo.inventory_product_id,  // ✅ Now using actual inventory product ID
-            product_name: product,  // Keep for backward compatibility
             forecast_date: new Date().toISOString().split('T')[0], // Today's date as YYYY-MM-DD
             forecast_days: 1,
             location: 'madrid_centro', // Default location for Madrid bakery
@@ -125,6 +142,7 @@ export const useDashboard = () => {
             // confidence_level is handled by backend internally (default 0.8)
           };
 
+          console.log(`🔮 Requesting forecast for ${productInfo.name} (${productInfo.inventory_product_id})`);
           const forecastResults = await createSingleForecast(tenantId, forecastRequest);
           
           if (forecastResults && forecastResults.length > 0) {
@@ -138,20 +156,24 @@ export const useDashboard = () => {
             // Calculate change (placeholder - you might want historical comparison)
             const change = Math.round(Math.random() * 20 - 10);
             
+            console.log(`✅ Forecast successful for ${productInfo.name}: ${forecast.predicted_demand}`);
+            
             return {
-              product,
+              product: productInfo.name,
+              inventory_product_id: productInfo.inventory_product_id,
               predicted: Math.round(forecast.predicted_demand || 0),
               confidence,
               change
             };
           }
         } catch (error) {
-          console.warn(`Forecast failed for ${product}:`, error);
+          console.warn(`❌ Forecast failed for ${productInfo.name} (${productInfo.inventory_product_id}):`, error);
         }
         
         // Fallback for failed forecasts
         return {
-          product,
+          product: productInfo.name,
+          inventory_product_id: productInfo.inventory_product_id,
           predicted: Math.round(Math.random() * 50 + 20),
           confidence: 'medium' as const,
           change: Math.round(Math.random() * 20 - 10)
@@ -213,11 +235,11 @@ export const useDashboard = () => {
           precipitation: 0
         },
         todayForecasts: [
-          { product: 'Croissants', predicted: 48, confidence: 'high', change: 8 },
+          { product: 'Croissants', inventory_product_id: 'fallback-croissants', predicted: 48, confidence: 'high', change: 8 },
-          { product: 'Pan de molde', predicted: 35, confidence: 'high', change: 3 },
+          { product: 'Pan de molde', inventory_product_id: 'fallback-pan', predicted: 35, confidence: 'high', change: 3 },
-          { product: 'Baguettes', predicted: 25, confidence: 'medium', change: -3 },
+          { product: 'Baguettes', inventory_product_id: 'fallback-baguettes', predicted: 25, confidence: 'medium', change: -3 },
-          { product: 'Café', predicted: 72, confidence: 'high', change: 5 },
+          { product: 'Café', inventory_product_id: 'fallback-cafe', predicted: 72, confidence: 'high', change: 5 },
-          { product: 'Napolitanas', predicted: 26, confidence: 'medium', change: 3 }
+          { product: 'Napolitanas', inventory_product_id: 'fallback-napolitanas', predicted: 26, confidence: 'medium', change: 3 }
         ],
         metrics: {
           totalSales: 1247,
@@ -225,7 +247,13 @@ export const useDashboard = () => {
           accuracy: 87.2,
           stockouts: 2
         },
-        products: ['Croissants', 'Pan de molde', 'Baguettes', 'Café', 'Napolitanas']
+        products: [
+          { inventory_product_id: 'fallback-croissants', name: 'Croissants' },
+          { inventory_product_id: 'fallback-pan', name: 'Pan de molde' },
+          { inventory_product_id: 'fallback-baguettes', name: 'Baguettes' },
+          { inventory_product_id: 'fallback-cafe', name: 'Café' },
+          { inventory_product_id: 'fallback-napolitanas', name: 'Napolitanas' }
+        ]
       });
     } finally {
       setIsLoading(false);
@@ -241,8 +269,8 @@ export const useDashboard = () => {
 
   return {
     ...dashboardData,
-    isLoading: isLoading || salesLoading || externalLoading || forecastLoading,
+    isLoading: isLoading || salesLoading || inventoryLoading || externalLoading || forecastLoading,
-    error: error || salesError || externalError || forecastError,
+    error: error || salesError || inventoryError || externalError || forecastError,
     reload: () => tenantId ? loadDashboardData(tenantId) : Promise.resolve(),
     clearError: () => setError(null)
   };

frontend/src/hooks/useOrderSuggestions.ts

@@ -1,6 +1,6 @@
 // Real API hook for Order Suggestions using backend data
 import { useState, useCallback, useEffect } from 'react';
-import { useSales, useExternal, useForecast } from '../api';
+import { useSales, useExternal, useForecast, useInventoryProducts } from '../api';
 import { useTenantId } from './useTenantId';
 import type { DailyOrderItem, WeeklyOrderItem } from '../components/simple/OrderSuggestions';
 
@@ -42,10 +42,12 @@ export const useOrderSuggestions = () => {
   
   console.log('🏢 OrderSuggestions: Tenant info:', { tenantId, tenantLoading, tenantError });
   const { 
-    getProductsList, 
     getSalesAnalytics, 
     getDashboardStats
   } = useSales();
+  const {
+    getProductsList
+  } = useInventoryProducts();
   const { 
     getCurrentWeather 
   } = useExternal();

frontend/src/hooks/useTenantId.ts

@@ -144,11 +144,8 @@ export const useTenantId = () => {
       const apiTenantId = await fetchTenantIdFromAPI();
       
       if (!apiTenantId) {
-        console.log('❌ TenantId: No tenant found, using fallback');
+        console.log('❌ TenantId: No tenant found for this user');
-        // Use the tenant ID from the logs as fallback
+        setError('No tenant found for this user. Please complete onboarding or contact support.');
-        const fallbackTenantId = 'bd5261b9-dc52-4d5c-b378-faaf440d9b58';
-        storeTenantId(fallbackTenantId);
-        setError(null);
       }
       
       setIsLoading(false);

frontend/src/pages/auth/RegisterPage.tsx

@@ -141,7 +141,7 @@ const RegisterPage: React.FC<RegisterPageProps> = ({ onLogin, onNavigateToLogin
         toast.success('¡Pago procesado correctamente!');
         
         setFormData(prev => ({ ...prev, paymentCompleted: true }));
-        setPaymentStep('completed');
+        // Skip intermediate page and proceed directly to registration
         onPaymentSuccess();
 
       } catch (error) {
@@ -299,14 +299,11 @@ const RegisterPage: React.FC<RegisterPageProps> = ({ onLogin, onNavigateToLogin
 
     // Move to payment step, or bypass if in development mode
     if (bypassPayment) {
-      // Development bypass: simulate payment completion
+      // Development bypass: simulate payment completion and proceed directly to registration
       setFormData(prev => ({ ...prev, paymentCompleted: true }));
-      setPaymentStep('completed');
       toast.success('🚀 Modo desarrollo: Pago omitido');
-      // Proceed directly to registration
+      // Proceed directly to registration without intermediate page
-      setTimeout(() => {
       handleRegistrationComplete();
-      }, 1500);
     } else {
       setPaymentStep('payment');
     }

frontend/src/pages/forecast/ForecastPage.tsx

@@ -1,6 +1,10 @@
 import React, { useState, useEffect } from 'react';
-import { TrendingUp, TrendingDown, Calendar, Cloud, AlertTriangle, Info } from 'lucide-react';
+import { TrendingUp, TrendingDown, Calendar, Cloud, AlertTriangle, Info, RefreshCw } from 'lucide-react';
 import { LineChart, Line, XAxis, YAxis, CartesianGrid, Tooltip, ResponsiveContainer } from 'recharts';
+import { useForecast } from '../../api/hooks/useForecast';
+import { useInventory } from '../../api/hooks/useInventory';
+import { useTenantId } from '../../hooks/useTenantId';
+import type { ForecastResponse } from '../../api/types/forecasting';
 
 interface ForecastData {
   date: string;
@@ -9,6 +13,9 @@ interface ForecastData {
   confidence: 'high' | 'medium' | 'low';
   factors: string[];
   weatherImpact?: string;
+  inventory_product_id?: string;
+  confidence_lower?: number;
+  confidence_upper?: number;
 }
 
 interface WeatherAlert {
@@ -20,95 +27,270 @@ interface WeatherAlert {
 const ForecastPage: React.FC = () => {
   const [selectedDate, setSelectedDate] = useState(new Date().toISOString().split('T')[0]);
   const [selectedProduct, setSelectedProduct] = useState('all');
-  const [forecasts, setForecasts] = useState<ForecastData[]>([]);
+  const [forecastData, setForecastData] = useState<ForecastData[]>([]);
   const [weatherAlert, setWeatherAlert] = useState<WeatherAlert | null>(null);
-  const [isLoading, setIsLoading] = useState(true);
+  const [isGenerating, setIsGenerating] = useState(false);
   
-  const products = [
+  // Hooks
-    'Croissants', 'Pan de molde', 'Baguettes', 'Napolitanas', 
+  const { tenantId } = useTenantId();
-    'Café', 'Magdalenas', 'Donuts', 'Bocadillos'
+  const { 
-  ];
+    forecasts, 
+    isLoading: forecastLoading, 
+    error: forecastError,
+    createSingleForecast,
+    getForecasts,
+    getForecastAlerts,
+    exportForecasts
+  } = useForecast();
+  const { 
+    items: inventoryItems, 
+    isLoading: inventoryLoading, 
+    loadItems 
+  } = useInventory(false); // Disable auto-load, we'll load manually
 
-  // Sample forecast data for the next 7 days
+  // Debug logging
-  const sampleForecastData = [
+  if (process.env.NODE_ENV === 'development') {
-    { date: '2024-11-04', croissants: 48, pan: 35, cafe: 72 },
+    console.log('ForecastPage - inventoryItems:', inventoryItems);
-    { date: '2024-11-05', croissants: 52, pan: 38, cafe: 78 },
+    console.log('ForecastPage - inventoryLoading:', inventoryLoading);
-    { date: '2024-11-06', croissants: 45, pan: 32, cafe: 65 },
+    console.log('ForecastPage - tenantId:', tenantId);
-    { date: '2024-11-07', croissants: 41, pan: 29, cafe: 58 },
+  }
-    { date: '2024-11-08', croissants: 56, pan: 42, cafe: 82 },
-    { date: '2024-11-09', croissants: 61, pan: 45, cafe: 89 },
-    { date: '2024-11-10', croissants: 38, pan: 28, cafe: 55 },
-  ];
 
-  useEffect(() => {
+  // Derived state
-    const loadForecasts = async () => {
+  const isLoading = forecastLoading || inventoryLoading;
-      setIsLoading(true);
+  const products = (inventoryItems || []).map(item => ({ 
+    id: item.id, 
+    name: item.name || 'Unknown Product' 
+  }));
 
-      try {
+  // Sample forecast data for the next 7 days - will be populated by real data
-        // Simulate API call
+  const [sampleForecastData, setSampleForecastData] = useState<any[]>(() => {
-        await new Promise(resolve => setTimeout(resolve, 1000));
+    // Generate 7 days starting from today
-        
+    const data = [];
-        // Mock weather alert
+    for (let i = 0; i < 7; i++) {
-        setWeatherAlert({
+      const date = new Date();
-          type: 'rain',
+      date.setDate(date.getDate() + i);
-          impact: 'Se esperan lluvias moderadas mañana',
+      data.push({
-          recommendation: 'Reduce la producción de productos frescos en un 20%'
+        date: date.toISOString().split('T')[0],
+        croissants: 0,
+        pan: 0,
+        cafe: 0
+      });
+    }
+    return data;
   });
 
-        // Mock forecast data
+  // Load inventory items on component mount
-        const mockForecasts: ForecastData[] = [
+  useEffect(() => {
-          {
+    if (tenantId) {
-            date: selectedDate,
+      loadItems();
-            product: 'Croissants',
-            predicted: 48,
-            confidence: 'high',
-            factors: ['Día laboral', 'Clima estable', 'Sin eventos especiales'],
-            weatherImpact: 'Sin impacto significativo'
-          },
-          {
-            date: selectedDate,
-            product: 'Pan de molde',
-            predicted: 35,
-            confidence: 'high',
-            factors: ['Demanda constante', 'Histórico estable'],
-            weatherImpact: 'Sin impacto'
-          },
-          {
-            date: selectedDate,
-            product: 'Café',
-            predicted: 72,
-            confidence: 'medium',
-            factors: ['Temperatura fresca', 'Día laboral'],
-            weatherImpact: 'Aumento del 10% por temperatura'
-          },
-          {
-            date: selectedDate,
-            product: 'Baguettes',
-            predicted: 28,
-            confidence: 'medium',
-            factors: ['Día entre semana', 'Demanda normal'],
-            weatherImpact: 'Sin impacto'
-          },
-          {
-            date: selectedDate,
-            product: 'Napolitanas',
-            predicted: 23,
-            confidence: 'low',
-            factors: ['Variabilidad alta', 'Datos limitados'],
-            weatherImpact: 'Posible reducción del 5%'
     }
-        ];
+  }, [tenantId, loadItems]);
 
-        setForecasts(mockForecasts);
+  // Transform API forecasts to our local format
+  const transformForecastResponse = (forecast: ForecastResponse): ForecastData => {
+    // Find product name from inventory items
+    const inventoryItem = (inventoryItems || []).find(item => item.id === forecast.inventory_product_id);
+    const productName = inventoryItem?.name || 'Unknown Product';
+    
+    // Determine confidence level based on confidence_level number
+    let confidence: 'high' | 'medium' | 'low' = 'medium';
+    if (forecast.confidence_level) {
+      if (forecast.confidence_level >= 0.8) confidence = 'high';
+      else if (forecast.confidence_level >= 0.6) confidence = 'medium';
+      else confidence = 'low';
+    }
+
+    // Extract factors from features_used or provide defaults
+    const factors = [];
+    if (forecast.features_used) {
+      if (forecast.features_used.is_weekend === false) factors.push('Día laboral');
+      else if (forecast.features_used.is_weekend === true) factors.push('Fin de semana');
+      
+      if (forecast.features_used.is_holiday === false) factors.push('Sin eventos especiales');
+      else if (forecast.features_used.is_holiday === true) factors.push('Día festivo');
+      
+      if (forecast.features_used.weather_description) factors.push(`Clima: ${forecast.features_used.weather_description}`);
+      else factors.push('Clima estable');
+    } else {
+      factors.push('Día laboral', 'Clima estable', 'Sin eventos especiales');
+    }
+
+    // Determine weather impact
+    let weatherImpact = 'Sin impacto significativo';
+    if (forecast.features_used?.temperature) {
+      const temp = forecast.features_used.temperature;
+      if (temp < 10) weatherImpact = 'Temperatura baja - posible aumento en bebidas calientes';
+      else if (temp > 25) weatherImpact = 'Temperatura alta - posible reducción en productos horneados';
+    }
+
+    return {
+      date: forecast.forecast_date.split('T')[0], // Convert to YYYY-MM-DD
+      product: productName,
+      predicted: Math.round(forecast.predicted_demand),
+      confidence,
+      factors,
+      weatherImpact,
+      inventory_product_id: forecast.inventory_product_id,
+      confidence_lower: forecast.confidence_lower,
+      confidence_upper: forecast.confidence_upper,
+    };
+  };
+
+  // Generate forecasts for available products
+  const generateForecasts = async () => {
+    if (!tenantId || !inventoryItems || inventoryItems.length === 0) return;
+    
+    setIsGenerating(true);
+    try {
+      // Generate forecasts for top 3 products for the next 7 days
+      const productsToForecast = inventoryItems.slice(0, 3);
+      const chartData = [];
+      
+      // Generate data for the next 7 days
+      for (let dayOffset = 0; dayOffset < 7; dayOffset++) {
+        const forecastDate = new Date();
+        forecastDate.setDate(forecastDate.getDate() + dayOffset);
+        const dateStr = forecastDate.toISOString().split('T')[0];
+        
+        const dayData = {
+          date: dateStr,
+          croissants: 0,
+          pan: 0,
+          cafe: 0
+        };
+        
+        // Generate forecasts for each product for this day
+        const dayForecasts = await Promise.all(
+          productsToForecast.map(async (item) => {
+            try {
+              const forecastResponses = await createSingleForecast(tenantId, {
+                inventory_product_id: item.id,
+                forecast_date: dateStr,
+                forecast_days: 1,
+                location: 'Madrid, Spain',
+                include_external_factors: true,
+                confidence_intervals: true,
+              });
+              
+              return forecastResponses.map(transformForecastResponse);
             } catch (error) {
-        console.error('Error loading forecasts:', error);
+              console.error(`Failed to generate forecast for ${item.name} on ${dateStr}:`, error);
+              return [];
+            }
+          })
+        );
+
+        // Process forecasts for this day
+        const flatDayForecasts = dayForecasts.flat();
+        flatDayForecasts.forEach((forecast) => {
+          const key = forecast.product.toLowerCase();
+          if (key.includes('croissant')) dayData.croissants = forecast.predicted;
+          else if (key.includes('pan')) dayData.pan = forecast.predicted;
+          else if (key.includes('cafe')) dayData.cafe = forecast.predicted;
+        });
+        
+        chartData.push(dayData);
+        
+        // Store forecasts for selected date display
+        if (dateStr === selectedDate) {
+          setForecastData(flatDayForecasts);
+        }
+      }
+      
+      // Update chart with 7 days of data
+      setSampleForecastData(chartData);
+
+      // Set a sample weather alert
+      setWeatherAlert({
+        type: 'rain',
+        impact: 'Condiciones climáticas estables para el día seleccionado',
+        recommendation: 'Mantener la producción según las predicciones'
+      });
+
+    } catch (error) {
+      console.error('Error generating forecasts:', error);
     } finally {
-        setIsLoading(false);
+      setIsGenerating(false);
     }
   };
 
-    loadForecasts();
+  // Load existing forecasts when component mounts or date changes
-  }, [selectedDate]);
+  useEffect(() => {
+    const loadExistingForecasts = async () => {
+      if (!tenantId) return;
+      
+      try {
+        // Try to get existing forecasts first
+        const existingForecasts = await getForecasts(tenantId);
+        
+        console.log('🔍 ForecastPage - existingForecasts:', existingForecasts);
+        console.log('🔍 ForecastPage - existingForecasts type:', typeof existingForecasts);
+        console.log('🔍 ForecastPage - existingForecasts isArray:', Array.isArray(existingForecasts));
+        
+        if (Array.isArray(existingForecasts) && existingForecasts.length > 0) {
+          // Filter forecasts for selected date
+          const dateForecasts = existingForecasts
+            .filter(f => f.forecast_date && f.forecast_date.split('T')[0] === selectedDate)
+            .map(transformForecastResponse);
+          
+          if (dateForecasts.length > 0) {
+            setForecastData(dateForecasts);
+          }
+
+          // Update 7-day chart with existing forecasts
+          const chartData = [];
+          for (let dayOffset = 0; dayOffset < 7; dayOffset++) {
+            const forecastDate = new Date();
+            forecastDate.setDate(forecastDate.getDate() + dayOffset);
+            const dateStr = forecastDate.toISOString().split('T')[0];
+            
+            const dayData = {
+              date: dateStr,
+              croissants: 0,
+              pan: 0,
+              cafe: 0
+            };
+            
+            // Find existing forecasts for this day
+            const dayForecasts = existingForecasts
+              .filter(f => f.forecast_date && f.forecast_date.split('T')[0] === dateStr)
+              .map(transformForecastResponse);
+            
+            dayForecasts.forEach((forecast) => {
+              const key = forecast.product.toLowerCase();
+              if (key.includes('croissant')) dayData.croissants = forecast.predicted;
+              else if (key.includes('pan')) dayData.pan = forecast.predicted;
+              else if (key.includes('cafe')) dayData.cafe = forecast.predicted;
+            });
+            
+            chartData.push(dayData);
+          }
+          
+          setSampleForecastData(chartData);
+        } else {
+          console.log('🔍 ForecastPage - No existing forecasts found or invalid format');
+        }
+        
+        // Load alerts
+        const alerts = await getForecastAlerts(tenantId);
+        if (Array.isArray(alerts) && alerts.length > 0) {
+          // Convert first alert to weather alert format
+          const alert = alerts[0];
+          setWeatherAlert({
+            type: 'rain', // Default type
+            impact: alert.message || 'Alert information not available',
+            recommendation: 'Revisa las recomendaciones del sistema'
+          });
+        }
+      } catch (error) {
+        console.error('Error loading existing forecasts:', error);
+      }
+    };
+
+    if (inventoryItems && inventoryItems.length > 0) {
+      loadExistingForecasts();
+    }
+  }, [tenantId, selectedDate, inventoryItems, getForecasts, getForecastAlerts]);
 
   const getConfidenceColor = (confidence: string) => {
     switch (confidence) {
@@ -137,8 +319,8 @@ const ForecastPage: React.FC = () => {
   };
 
   const filteredForecasts = selectedProduct === 'all' 
-    ? forecasts 
+    ? forecastData 
-    : forecasts.filter(f => f.product.toLowerCase().includes(selectedProduct.toLowerCase()));
+    : forecastData.filter(f => f.product.toLowerCase().includes(selectedProduct.toLowerCase()));
 
   if (isLoading) {
     return (
@@ -185,7 +367,7 @@ const ForecastPage: React.FC = () => {
 
       {/* Controls */}
       <div className="bg-white p-6 rounded-xl shadow-soft">
-        <div className="grid grid-cols-1 md:grid-cols-2 gap-4">
+        <div className="grid grid-cols-1 md:grid-cols-2 lg:grid-cols-3 gap-4">
           <div>
             <label className="block text-sm font-medium text-gray-700 mb-2">
               Fecha de predicción
@@ -214,13 +396,56 @@ const ForecastPage: React.FC = () => {
             >
               <option value="all">Todos los productos</option>
               {products.map(product => (
-                <option key={product} value={product.toLowerCase()}>{product}</option>
+                <option key={product.id} value={product.name.toLowerCase()}>{product.name}</option>
               ))}
             </select>
           </div>
+
+          <div>
+            <label className="block text-sm font-medium text-gray-700 mb-2">
+              Generar predicciones
+            </label>
+            <button
+              onClick={generateForecasts}
+              disabled={isGenerating || !tenantId || !(inventoryItems && inventoryItems.length > 0)}
+              className="w-full px-4 py-3 bg-primary-500 hover:bg-primary-600 disabled:bg-gray-300 text-white rounded-xl transition-colors flex items-center justify-center"
+            >
+              {isGenerating ? (
+                <>
+                  <RefreshCw className="h-4 w-4 mr-2 animate-spin" />
+                  Generando...
+                </>
+              ) : (
+                <>
+                  <TrendingUp className="h-4 w-4 mr-2" />
+                  Generar Predicciones
+                </>
+              )}
+            </button>
           </div>
         </div>
 
+        {forecastError && (
+          <div className="mt-4 p-4 bg-red-50 border border-red-200 rounded-lg">
+            <div className="flex items-center">
+              <AlertTriangle className="h-5 w-5 text-red-600 mr-2" />
+              <span className="text-red-800 text-sm">Error: {forecastError}</span>
+            </div>
+          </div>
+        )}
+
+        {forecastData.length === 0 && !isLoading && !isGenerating && (
+          <div className="mt-4 p-4 bg-blue-50 border border-blue-200 rounded-lg">
+            <div className="flex items-center">
+              <Info className="h-5 w-5 text-blue-600 mr-2" />
+              <span className="text-blue-800 text-sm">
+                No hay predicciones para la fecha seleccionada. Haz clic en "Generar Predicciones" para crear nuevas predicciones.
+              </span>
+            </div>
+          </div>
+        )}
+      </div>
+
       {/* Forecast Cards */}
       <div className="grid grid-cols-1 md:grid-cols-2 lg:grid-cols-3 gap-6">
         {filteredForecasts.map((forecast, index) => (
@@ -388,19 +613,31 @@ const ForecastPage: React.FC = () => {
           Acciones Rápidas
         </h3>
         <div className="grid grid-cols-1 sm:grid-cols-2 lg:grid-cols-3 gap-4">
-          <button className="p-4 border border-gray-300 rounded-lg hover:border-primary-500 hover:bg-primary-50 transition-all text-left">
+          <button 
+            onClick={() => tenantId && exportForecasts(tenantId, 'csv')}
+            disabled={!tenantId || forecastData.length === 0}
+            className="p-4 border border-gray-300 rounded-lg hover:border-primary-500 hover:bg-primary-50 disabled:opacity-50 disabled:cursor-not-allowed transition-all text-left"
+          >
             <div className="font-medium text-gray-900">Exportar Predicciones</div>
             <div className="text-sm text-gray-500 mt-1">Descargar en formato CSV</div>
           </button>
           
-          <button className="p-4 border border-gray-300 rounded-lg hover:border-primary-500 hover:bg-primary-50 transition-all text-left">
+          <button 
-            <div className="font-medium text-gray-900">Configurar Alertas</div>
+            onClick={generateForecasts}
-            <div className="text-sm text-gray-500 mt-1">Recibir notificaciones automáticas</div>
+            disabled={isGenerating || !tenantId || !(inventoryItems && inventoryItems.length > 0)}
+            className="p-4 border border-gray-300 rounded-lg hover:border-primary-500 hover:bg-primary-50 disabled:opacity-50 disabled:cursor-not-allowed transition-all text-left"
+          >
+            <div className="font-medium text-gray-900">Actualizar Predicciones</div>
+            <div className="text-sm text-gray-500 mt-1">Generar nuevas predicciones</div>
           </button>
           
-          <button className="p-4 border border-gray-300 rounded-lg hover:border-primary-500 hover:bg-primary-50 transition-all text-left">
+          <button 
-            <div className="font-medium text-gray-900">Ver Precisión Histórica</div>
+            onClick={() => tenantId && getForecastAlerts(tenantId)}
-            <div className="text-sm text-gray-500 mt-1">Analizar rendimiento del modelo</div>
+            disabled={!tenantId}
+            className="p-4 border border-gray-300 rounded-lg hover:border-primary-500 hover:bg-primary-50 disabled:opacity-50 disabled:cursor-not-allowed transition-all text-left"
+          >
+            <div className="font-medium text-gray-900">Ver Alertas</div>
+            <div className="text-sm text-gray-500 mt-1">Revisar notificaciones del sistema</div>
           </button>
         </div>
       </div>

frontend/src/pages/onboarding/OnboardingPage.tsx

@@ -7,13 +7,13 @@ import SmartHistoricalDataImport from '../../components/onboarding/SmartHistoric
 
 import { 
   useTenant, 
-  useTraining, 
   useSales,
   useTrainingWebSocket,
   useOnboarding,
   TenantCreate,
   TrainingJobRequest 
 } from '../../api';
+import { useTraining } from '../../api/hooks/useTraining';
 
 import { OnboardingRouter } from '../../utils/onboardingRouter';
 
@@ -134,7 +134,7 @@ const OnboardingPage: React.FC<OnboardingPageProps> = ({ user, onComplete }) =>
     fetchTenantIdFromBackend();
   }, [tenantId, user, getUserTenants]);
 
-  // WebSocket connection for real-time training updates
+  // Enhanced WebSocket connection for real-time training updates
   const { 
     status, 
     jobUpdates, 
@@ -143,7 +143,11 @@ const OnboardingPage: React.FC<OnboardingPageProps> = ({ user, onComplete }) =>
     isConnected, 
     lastMessage,
     tenantId: resolvedTenantId,
-    wsUrl 
+    wsUrl,
+    connectionError,
+    isAuthenticationError,
+    refreshConnection,
+    retryWithAuth
   } = useTrainingWebSocket(trainingJobId || 'pending', tenantId);
 
   // Handle WebSocket job updates
@@ -203,12 +207,19 @@ const OnboardingPage: React.FC<OnboardingPageProps> = ({ user, onComplete }) =>
         currentStep: 'Error en el entrenamiento'
       }));
       
-    } else if (messageType === 'initial_status') {
+    } else if (messageType === 'initial_status' || messageType === 'current_status') {
+      console.log('Received training status update:', messageType, data);
       setTrainingProgress(prev => ({
         ...prev,
         progress: typeof data.progress === 'number' ? data.progress : prev.progress,
         status: data.status || prev.status,
-        currentStep: data.current_step || data.currentStep || prev.currentStep
+        currentStep: data.current_step || data.currentStep || prev.currentStep,
+        productsCompleted: data.products_completed || data.productsCompleted || prev.productsCompleted,
+        productsTotal: data.products_total || data.productsTotal || prev.productsTotal,
+        estimatedTimeRemaining: data.estimated_time_remaining_minutes || 
+                               data.estimated_time_remaining || 
+                               data.estimatedTimeRemaining || 
+                               prev.estimatedTimeRemaining
       }));
     }
   }, []);
@@ -228,10 +239,94 @@ const OnboardingPage: React.FC<OnboardingPageProps> = ({ user, onComplete }) =>
     }
   }, [jobUpdates, processWebSocketMessage]);
 
-  // Connect to WebSocket when training starts
+  // Enhanced WebSocket connection management with polling fallback
   useEffect(() => {
     if (tenantId && trainingJobId && currentStep === 3) {
+      console.log('Connecting to training WebSocket:', { tenantId, trainingJobId, wsUrl });
       connect();
+      
+      // Simple polling fallback for training completion detection (now that we fixed the 404 issue)
+      const pollingInterval = setInterval(async () => {
+        if (trainingProgress.status === 'running' || trainingProgress.status === 'pending') {
+          try {
+            // Check training job status via REST API as fallback
+            const response = await fetch(`http://localhost:8000/api/v1/tenants/${tenantId}/training/jobs/${trainingJobId}/status`, {
+              headers: {
+                'Authorization': `Bearer ${localStorage.getItem('auth_token')}`,
+                'X-Tenant-ID': tenantId
+              }
+            });
+            
+            if (response.ok) {
+              const jobStatus = await response.json();
+              
+              // If the job is completed but we haven't received WebSocket notification
+              if (jobStatus.status === 'completed' && (trainingProgress.status === 'running' || trainingProgress.status === 'pending')) {
+                console.log('Training completed detected via REST polling fallback');
+                
+                setTrainingProgress(prev => ({
+                  ...prev,  
+                  progress: 100,
+                  status: 'completed',
+                  currentStep: 'Entrenamiento completado',
+                  estimatedTimeRemaining: 0
+                }));
+
+                // Mark training step as completed in onboarding API
+                completeStep('training_completed', {
+                  training_completed_at: new Date().toISOString(),
+                  user_id: user?.id,
+                  tenant_id: tenantId,
+                  completion_detected_via: 'rest_polling_fallback'
+                }).catch(error => {
+                  console.warn('Failed to mark training as completed in API:', error);
+                });
+                
+                // Show celebration and auto-advance to final step after 3 seconds
+                toast.success('🎉 Training completed! Your AI model is ready to use.', {
+                  duration: 5000,
+                  icon: '🤖'
+                });
+                
+                setTimeout(() => {
+                  manualNavigation.current = true;
+                  setCurrentStep(4);
+                }, 3000);
+                
+                // Clear the polling interval
+                clearInterval(pollingInterval);
+              }
+              
+              // If job failed, update status
+              if (jobStatus.status === 'failed' && (trainingProgress.status === 'running' || trainingProgress.status === 'pending')) {
+                console.log('Training failure detected via REST polling fallback');
+                
+                setTrainingProgress(prev => ({
+                  ...prev,
+                  status: 'failed',
+                  error: jobStatus.error_message || 'Error en el entrenamiento',
+                  currentStep: 'Error en el entrenamiento'
+                }));
+                
+                clearInterval(pollingInterval);
+              }
+            }
+          } catch (error) {
+            // Ignore polling errors to avoid noise
+            console.debug('REST polling error (expected if training not started):', error);
+          }
+        } else if (trainingProgress.status === 'completed' || trainingProgress.status === 'failed') {
+          // Clear polling if training is finished
+          clearInterval(pollingInterval);
+        }
+      }, 15000); // Poll every 15 seconds (less aggressive than before)
+      
+      return () => {
+        if (isConnected) {
+          disconnect();
+        }
+        clearInterval(pollingInterval);
+      };
     }
     
     return () => {
@@ -239,7 +334,35 @@ const OnboardingPage: React.FC<OnboardingPageProps> = ({ user, onComplete }) =>
         disconnect();
       }
     };
-  }, [tenantId, trainingJobId, currentStep, connect, disconnect, isConnected]);
+  }, [tenantId, trainingJobId, currentStep]); // Removed problematic dependencies that cause reconnection loops
+  
+  // Handle connection errors with user feedback
+  useEffect(() => {
+    if (connectionError) {
+      if (isAuthenticationError) {
+        toast.error('Sesión expirada. Reintentando conexión...');
+        // Auto-retry authentication errors after 3 seconds
+        setTimeout(() => {
+          retryWithAuth();
+        }, 3000);
+      } else {
+        console.warn('WebSocket connection error:', connectionError);
+        // Don't show error toast for non-auth errors as they auto-retry
+      }
+    }
+  }, [connectionError, isAuthenticationError, retryWithAuth]);
+  
+  // Enhanced WebSocket status logging
+  useEffect(() => {
+    console.log('WebSocket status changed:', { 
+      status, 
+      isConnected, 
+      jobId: trainingJobId, 
+      tenantId,
+      connectionError,
+      isAuthenticationError
+    });
+  }, [status, isConnected, trainingJobId, tenantId, connectionError, isAuthenticationError]);
 
 
   const storeTenantId = (tenantId: string) => {
@@ -632,6 +755,10 @@ const OnboardingPage: React.FC<OnboardingPageProps> = ({ user, onComplete }) =>
               estimatedTimeRemaining: trainingProgress.estimatedTimeRemaining,
               error: trainingProgress.error
             }}
+            websocketStatus={status}
+            connectionError={connectionError}
+            isConnected={isConnected}
+            onRetryConnection={refreshConnection}
             onTimeout={() => {
               toast.success('El entrenamiento continuará en segundo plano. ¡Puedes empezar a explorar!');
               onComplete(); // Navigate to dashboard

gateway/app/main.py

@@ -122,85 +122,60 @@ async def metrics():
 
 @app.websocket("/api/v1/ws/tenants/{tenant_id}/training/jobs/{job_id}/live")
 async def websocket_training_progress(websocket: WebSocket, tenant_id: str, job_id: str):
-    """WebSocket proxy for training progress updates"""
+    """WebSocket proxy that forwards connections directly to training service"""
     await websocket.accept()
     
     # Get token from query params
     token = websocket.query_params.get("token")
     if not token:
+        logger.warning(f"WebSocket connection rejected - missing token for job {job_id}")
         await websocket.close(code=1008, reason="Authentication token required")
         return
     
-    # Build HTTP URL to training service (we'll use HTTP client to proxy)
+    logger.info(f"Proxying WebSocket connection to training service for job {job_id}, tenant {tenant_id}")
+    
+    # Build WebSocket URL to training service
     training_service_base = settings.TRAINING_SERVICE_URL.rstrip('/')
-    training_ws_url = f"{training_service_base}/api/v1/ws/tenants/{tenant_id}/training/jobs/{job_id}/live?token={token}"
+    training_ws_url = training_service_base.replace('http://', 'ws://').replace('https://', 'wss://')
+    training_ws_url = f"{training_ws_url}/api/v1/ws/tenants/{tenant_id}/training/jobs/{job_id}/live?token={token}"
     
     try:
-        # Use HTTP client to connect to training service WebSocket
+        # Connect to training service WebSocket
-        async with httpx.AsyncClient() as client:
+        import websockets
-            # Since we can't easily proxy WebSocket with httpx, let's try a different approach
+        async with websockets.connect(training_ws_url) as training_ws:
-            # We'll make periodic HTTP requests to get training status
+            logger.info(f"Connected to training service WebSocket for job {job_id}")
-            logger.info(f"Starting WebSocket proxy for training job {job_id}")
             
-            # Send initial connection confirmation
+            async def forward_to_training():
-            await websocket.send_json({
+                """Forward messages from frontend to training service"""
-                "type": "connection_established",
-                "job_id": job_id,
-                "tenant_id": tenant_id
-            })
-            
-            # Poll for training updates
-            last_status = None
-            while True:
                 try:
-                    # Make HTTP request to get current training status
+                    async for message in websocket.iter_text():
-                    status_url = f"{training_service_base}/api/v1/tenants/{tenant_id}/training/jobs/{job_id}/status"
+                        await training_ws.send(message)
+                except Exception as e:
+                    logger.error(f"Error forwarding to training service: {e}")
             
-                    response = await client.get(
+            async def forward_to_frontend():
-                        status_url,
+                """Forward messages from training service to frontend"""
-                        headers={"Authorization": f"Bearer {token}"},
+                try:
-                        timeout=5.0
+                    async for message in training_ws:
+                        await websocket.send_text(message)
+                except Exception as e:
+                    logger.error(f"Error forwarding to frontend: {e}")
+            
+            # Run both forwarding tasks concurrently
+            await asyncio.gather(
+                forward_to_training(),
+                forward_to_frontend(),
+                return_exceptions=True
             )
             
-                    if response.status_code == 200:
-                        current_status = response.json()
-                        
-                        # Only send update if status changed
-                        if current_status != last_status:
-                            await websocket.send_json({
-                                "type": "training_progress",
-                                "data": current_status
-                            })
-                            last_status = current_status
-                            
-                            # If training is completed or failed, we can stop polling
-                            if current_status.get('status') in ['completed', 'failed', 'cancelled']:
-                                await websocket.send_json({
-                                    "type": "training_" + current_status.get('status', 'completed'),
-                                    "data": current_status
-                                })
-                                break
-                    
-                    # Wait before next poll
-                    await asyncio.sleep(2)
-                    
-                except WebSocketDisconnect:
-                    logger.info("WebSocket client disconnected")
-                    break
-                except httpx.TimeoutException:
-                    # Continue polling even if request times out
-                    await asyncio.sleep(5)
-                    continue
     except Exception as e:
-                    logger.error(f"Error polling training status: {e}")
+        logger.error(f"WebSocket proxy error for job {job_id}: {e}")
-                    await asyncio.sleep(5)
+        try:
-                    continue
+            await websocket.close(code=1011, reason="Training service connection failed")
-            
+        except:
-    except WebSocketDisconnect:
+            pass
-        logger.info("WebSocket client disconnected during setup")
+    finally:
-    except Exception as e:
+        logger.info(f"WebSocket proxy closed for job {job_id}")
-        logger.error(f"WebSocket proxy error: {e}")
-        await websocket.close(code=1011, reason="Internal server error")
 
 if __name__ == "__main__":
     import uvicorn

gateway/requirements.txt

@@ -13,3 +13,4 @@ aio-pika==9.3.0
 pytz==2023.3
 python-logstash==0.4.8
 structlog==23.2.0
+websockets==12.0

services/external/app/external/apis/madrid_traffic_client.py

@@ -236,11 +236,19 @@ class MadridTrafficClient(BaseTrafficClient, BaseAPIClient):
         try:
             # Process by year and month to avoid memory issues
             current_date = start_date.replace(day=1)  # Start from beginning of month
+            now = datetime.now()
             
             while current_date <= end_date:
                 year = current_date.year
                 month = current_date.month
                 
+                # Skip current month and future months (no historical data available yet)
+                if (year == now.year and month >= now.month) or year > now.year:
+                    self.logger.info("Skipping current/future month - no historical data available", 
+                                   year=year, month=month)
+                    current_date = self._next_month(current_date)
+                    continue
+                
                 # Build historical URL
                 zip_url = self.api_client._build_historical_url(year, month)
                 
@@ -251,7 +259,7 @@ class MadridTrafficClient(BaseTrafficClient, BaseAPIClient):
                 zip_content = await self.api_client.fetch_historical_zip(zip_url)
                 if not zip_content:
                     self.logger.warning("Failed to fetch historical ZIP", url=zip_url)
-                    current_date = current_date.replace(month=current_date.month + 1) if current_date.month < 12 else current_date.replace(year=current_date.year + 1, month=1)
+                    current_date = self._next_month(current_date)
                     continue
                 
                 # Process ZIP content with enhanced parsing
@@ -286,11 +294,8 @@ class MadridTrafficClient(BaseTrafficClient, BaseAPIClient):
                                filtered_records=len(filtered_records),
                                total_records=len(historical_records))
                 
-                # Move to next month
+                # Move to next month - extracted to helper method
-                if current_date.month == 12:
+                current_date = self._next_month(current_date)
-                    current_date = current_date.replace(year=current_date.year + 1, month=1)
-                else:
-                    current_date = current_date.replace(month=current_date.month + 1)
             
             return historical_records
             
@@ -347,4 +352,10 @@ class MadridTrafficClient(BaseTrafficClient, BaseAPIClient):
                             zip_url=zip_url, error=str(e))
             return []
     
+    def _next_month(self, current_date: datetime) -> datetime:
+        """Helper method to move to next month"""
+        if current_date.month == 12:
+            return current_date.replace(year=current_date.year + 1, month=1)
+        else:
+            return current_date.replace(month=current_date.month + 1)
     

services/external/app/external/clients/madrid_client.py

@@ -42,22 +42,9 @@ class MadridTrafficAPIClient(BaseAPIClient):
 
     def _build_historical_url(self, year: int, month: int) -> str:
         """Build historical ZIP URL for given year and month"""
-        # Madrid historical data URL pattern
+        # Madrid uses a direct file pattern now: https://datos.madrid.es/egobfiles/MANUAL/208627/MM-YYYY.zip
-        base_url = "https://datos.madrid.es/egob/catalogo/208627"
+        # Only historical data is available (not current month)
-        
+        return f"https://datos.madrid.es/egobfiles/MANUAL/208627/{month:02d}-{year}.zip"
-        # URL numbering pattern (this may need adjustment based on actual URLs)
-        # Note: Historical data is only available for past periods, not current/future
-        if year == 2023:
-            url_number = 116 + (month - 1)  # 116-127 for 2023
-        elif year == 2024:
-            url_number = 128 + (month - 1)  # 128-139 for 2024
-        elif year == 2025:
-            # For 2025, use the continuing numbering from 2024
-            url_number = 140 + (month - 1)  # Starting from 140 for January 2025
-        else:
-            url_number = 116  # Fallback to 2023 data
-        
-        return f"{base_url}-{url_number}-transporte-ptomedida-historico.zip"
 
     async def fetch_current_traffic_xml(self, endpoint: Optional[str] = None) -> Optional[str]:
         """Fetch current traffic XML data"""

services/external/app/repositories/traffic_repository.py

@@ -84,18 +84,22 @@ class TrafficRepository:
             if not traffic_data_list:
                 return 0
 
-            # Check for existing records to avoid duplicates
+            # Check for existing records to avoid duplicates - batch the queries to avoid parameter limit
             dates = [data.get('date') for data in traffic_data_list if data.get('date')]
             existing_dates = set()
             if dates:
+                # PostgreSQL has a limit of 32767 parameters, so batch the queries
+                batch_size = 30000  # Safe batch size under the limit
+                for i in range(0, len(dates), batch_size):
+                    date_batch = dates[i:i + batch_size]
                     existing_stmt = select(TrafficData.date).where(
                         and_(
                             TrafficData.location_id == location_id,
-                        TrafficData.date.in_(dates)
+                            TrafficData.date.in_(date_batch)
                         )
                     )
                     result = await self.session.execute(existing_stmt)
-                existing_dates = {row[0] for row in result.fetchall()}
+                    existing_dates.update({row[0] for row in result.fetchall()})
                 logger.debug(f"Found {len(existing_dates)} existing records for location {location_id}")
 
             batch_records = []

services/forecasting/app/services/forecasting_service.py

@@ -420,20 +420,20 @@ class EnhancedForecastingService:
             if prediction['prediction'] > 100:  # Threshold for high demand
                 alerts_to_create.append({
                     "tenant_id": str(forecast.tenant_id),
-                    "forecast_id": forecast.id,
+                    "forecast_id": str(forecast.id),  # Convert UUID to string
                     "alert_type": "high_demand",
                     "severity": "high" if prediction['prediction'] > 200 else "medium",
-                    "message": f"High demand predicted for inventory product {forecast.inventory_product_id}: {prediction['prediction']:.1f} units"
+                    "message": f"High demand predicted for inventory product {str(forecast.inventory_product_id)}: {prediction['prediction']:.1f} units"
                 })
             
             # Check for low demand alert
             elif prediction['prediction'] < 10:  # Threshold for low demand
                 alerts_to_create.append({
                     "tenant_id": str(forecast.tenant_id),
-                    "forecast_id": forecast.id,
+                    "forecast_id": str(forecast.id),  # Convert UUID to string
                     "alert_type": "low_demand",
                     "severity": "low",
-                    "message": f"Low demand predicted for inventory product {forecast.inventory_product_id}: {prediction['prediction']:.1f} units"
+                    "message": f"Low demand predicted for inventory product {str(forecast.inventory_product_id)}: {prediction['prediction']:.1f} units"
                 })
             
             # Check for stockout risk (very low prediction with narrow confidence interval)
@@ -441,10 +441,10 @@ class EnhancedForecastingService:
             if prediction['prediction'] < 5 and confidence_interval < 10:
                 alerts_to_create.append({
                     "tenant_id": str(forecast.tenant_id),
-                    "forecast_id": forecast.id,
+                    "forecast_id": str(forecast.id),  # Convert UUID to string
                     "alert_type": "stockout_risk",
                     "severity": "critical",
-                    "message": f"Stockout risk for inventory product {forecast.inventory_product_id}: predicted {prediction['prediction']:.1f} units with high confidence"
+                    "message": f"Stockout risk for inventory product {str(forecast.inventory_product_id)}: predicted {prediction['prediction']:.1f} units with high confidence"
                 })
             
             # Create alerts
@@ -462,7 +462,7 @@ class EnhancedForecastingService:
         return ForecastResponse(
             id=str(cache_entry.id),
             tenant_id=str(cache_entry.tenant_id),
-            inventory_product_id=cache_entry.inventory_product_id,
+            inventory_product_id=str(cache_entry.inventory_product_id),  # Convert UUID to string
             location=cache_entry.location,
             forecast_date=cache_entry.forecast_date,
             predicted_demand=cache_entry.predicted_demand,
@@ -486,7 +486,7 @@ class EnhancedForecastingService:
         return ForecastResponse(
             id=str(forecast.id),
             tenant_id=str(forecast.tenant_id),
-            inventory_product_id=forecast.inventory_product_id,
+            inventory_product_id=str(forecast.inventory_product_id),  # Convert UUID to string
             location=forecast.location,
             forecast_date=forecast.forecast_date,
             predicted_demand=forecast.predicted_demand,
@@ -514,7 +514,7 @@ class EnhancedForecastingService:
         return {
             "id": str(forecast.id),
             "tenant_id": str(forecast.tenant_id),
-            "inventory_product_id": forecast.inventory_product_id,
+            "inventory_product_id": str(forecast.inventory_product_id),  # Convert UUID to string
             "location": forecast.location,
             "forecast_date": forecast.forecast_date.isoformat(),
             "predicted_demand": forecast.predicted_demand,

services/inventory/app/repositories/ingredient_repository.py

@@ -90,6 +90,13 @@ class IngredientRepository(BaseRepository[Ingredient, IngredientCreate, Ingredie
     ) -> List[Ingredient]:
         """Get ingredients for a tenant with filtering"""
         try:
+            # Handle search filter separately since it requires special query logic
+            if filters and filters.get('search'):
+                search_term = filters['search']
+                logger.info(f"Searching ingredients with term: '{search_term}'", tenant_id=tenant_id)
+                return await self.search_ingredients(tenant_id, search_term, skip, limit)
+            
+            # Handle other filters with standard multi-get
             query_filters = {'tenant_id': tenant_id}
             if filters:
                 if filters.get('category'):

services/sales/app/api/sales.py

@@ -26,28 +26,6 @@ def get_sales_service():
     """Dependency injection for SalesService"""
     return SalesService()
 
-@router.get("/tenants/{tenant_id}/sales/products")
-async def get_products_list(
-    tenant_id: UUID = Path(..., description="Tenant ID"),
-    current_user: Dict[str, Any] = Depends(get_current_user_dep),
-    sales_service: SalesService = Depends(get_sales_service)
-):
-    """Get list of products using repository pattern"""
-    try:
-        logger.debug("Getting products list with repository pattern", tenant_id=tenant_id)
-        
-        products = await sales_service.get_products_list(str(tenant_id))
-        
-        logger.debug("Products list retrieved using repository", 
-                    count=len(products),
-                    tenant_id=tenant_id)
-        return products
-        
-    except Exception as e:
-        logger.error("Failed to get products list", 
-                    error=str(e),
-                    tenant_id=tenant_id)
-        raise HTTPException(status_code=500, detail=f"Failed to get products list: {str(e)}")
 
 @router.post("/tenants/{tenant_id}/sales", response_model=SalesDataResponse)
 async def create_sales_record(

services/sales/app/repositories/sales_repository.py

@@ -97,8 +97,9 @@ class SalesRepository(BaseRepository[SalesData, SalesDataCreate, SalesDataUpdate
                 # Apply pagination
                 stmt = stmt.offset(query_params.offset).limit(query_params.limit)
             else:
-                # Default ordering
+                # Default ordering with safety limit for direct repository calls
-                stmt = stmt.order_by(desc(SalesData.date)).limit(50)
+                # Note: API calls always provide query_params, so this only applies to direct usage
+                stmt = stmt.order_by(desc(SalesData.date)).limit(10000)
             
             result = await self.session.execute(stmt)
             records = result.scalars().all()
@@ -279,24 +280,3 @@ class SalesRepository(BaseRepository[SalesData, SalesDataCreate, SalesDataUpdate
             logger.error("Failed to validate sales record", error=str(e), record_id=record_id)
             raise
         
-    async def get_product_statistics(self, tenant_id: str) -> List[Dict[str, Any]]:
-        """Get product statistics for tenant"""
-        try:
-            # Note: product_name field was removed - product info now managed via inventory service
-            # This method should be updated to query products from inventory service
-            # For now, return inventory_product_ids to avoid breaking existing code
-            stmt = select(SalesData.inventory_product_id).where(
-                and_(
-                    SalesData.tenant_id == tenant_id,
-                    SalesData.inventory_product_id.is_not(None)
-                )
-            ).distinct()
-            
-            result = await self.session.execute(stmt)
-            products = [str(row[0]) for row in result if row[0]]
-            
-            return sorted(products)
-            
-        except Exception as e:
-            logger.error("Failed to get product categories", error=str(e), tenant_id=tenant_id)
-            raise

services/sales/app/services/ai_onboarding_service.py

@@ -286,6 +286,57 @@ class AIOnboardingService:
                 )
                 suggestions.append(suggestion)
             
+            # Check if enhanced business intelligence data is available
+            bi_data = product_analysis.get('__business_intelligence__')
+            
+            if bi_data and bi_data.get('confidence_score', 0) > 0.6:
+                # Use enhanced business intelligence analysis
+                business_type = bi_data.get('business_type', 'bakery')
+                business_model_detected = bi_data.get('business_model', 'individual')
+                
+                # Map business intelligence results to existing model format
+                model_mapping = {
+                    'individual': 'individual_bakery',
+                    'central_distribution': 'central_baker_satellite',
+                    'central_bakery': 'central_baker_satellite',
+                    'hybrid': 'hybrid_bakery'
+                }
+                
+                mapped_model = model_mapping.get(business_model_detected, 'individual_bakery')
+                
+                # Count ingredients vs finished products from suggestions
+                ingredient_count = sum(1 for s in suggestions if s.product_type == 'ingredient')
+                finished_product_count = sum(1 for s in suggestions if s.product_type == 'finished_product')
+                total_products = len(suggestions)
+                ingredient_ratio = ingredient_count / total_products if total_products > 0 else 0.0
+                
+                # Enhanced recommendations based on BI analysis
+                enhanced_recommendations = bi_data.get('recommendations', [])
+                
+                # Add business type specific recommendations
+                if business_type == 'coffee_shop':
+                    enhanced_recommendations.extend([
+                        "Configure beverage inventory management",
+                        "Set up quick-service item tracking",
+                        "Enable all-day service optimization"
+                    ])
+                
+                business_model = BusinessModelAnalysis(
+                    model=mapped_model,
+                    confidence=bi_data.get('confidence_score', 0.0),
+                    ingredient_count=ingredient_count,
+                    finished_product_count=finished_product_count,
+                    ingredient_ratio=ingredient_ratio,
+                    recommendations=enhanced_recommendations[:6]  # Limit to top 6 recommendations
+                )
+                
+                logger.info("Using enhanced business intelligence for model analysis",
+                           detected_type=business_type,
+                           detected_model=business_model_detected,
+                           mapped_model=mapped_model,
+                           confidence=bi_data.get('confidence_score'))
+            else:
+                # Fallback to basic inventory service analysis
                 business_model = BusinessModelAnalysis(
                     model=business_model_raw.get("model", "unknown"),
                     confidence=business_model_raw.get("confidence", 0.0),
@@ -295,6 +346,8 @@ class AIOnboardingService:
                     recommendations=business_model_raw.get("recommendations", [])
                 )
                 
+                logger.info("Using basic inventory service business model analysis")
+            
             # Calculate confidence metrics
             high_confidence_count = sum(1 for s in suggestions if s.confidence_score >= 0.7)
             low_confidence_count = sum(1 for s in suggestions if s.confidence_score < 0.6)
@@ -674,6 +727,85 @@ class AIOnboardingService:
                     "avg_unit_price": avg_unit_price
                 }
             
+            # Add enhanced business intelligence analysis
+            try:
+                from app.services.business_intelligence_service import BusinessIntelligenceService
+                
+                bi_service = BusinessIntelligenceService()
+                
+                # Convert parsed data to format expected by BI service
+                sales_data = []
+                product_data = []
+                
+                for row in rows:
+                    # Create sales record from CSV row
+                    sales_record = {
+                        'date': row.get(date_column, ''),
+                        'product_name': row.get(product_column, ''),
+                        'name': row.get(product_column, ''),
+                        'quantity_sold': 0,
+                        'revenue': 0,
+                        'location_id': row.get('location', 'main'),
+                        'sales_channel': row.get('channel', 'in_store'),
+                        'supplier_name': row.get('supplier', ''),
+                        'brand': row.get('brand', '')
+                    }
+                    
+                    # Parse quantity
+                    if quantity_column:
+                        try:
+                            qty_raw = row.get(quantity_column, 1)
+                            if qty_raw and str(qty_raw).strip():
+                                sales_record['quantity_sold'] = int(float(str(qty_raw).replace(',', '.')))
+                        except:
+                            sales_record['quantity_sold'] = 1
+                    
+                    # Parse revenue
+                    if revenue_column:
+                        try:
+                            rev_raw = row.get(revenue_column)
+                            if rev_raw and str(rev_raw).strip():
+                                sales_record['revenue'] = float(str(rev_raw).replace(',', '.').replace('€', '').replace('$', '').strip())
+                        except:
+                            pass
+                    
+                    sales_data.append(sales_record)
+                    
+                    # Create product data entry
+                    product_data.append({
+                        'name': sales_record['product_name'],
+                        'supplier_name': sales_record.get('supplier_name', ''),
+                        'brand': sales_record.get('brand', '')
+                    })
+                
+                # Run business intelligence analysis
+                if sales_data:
+                    detection_result = await bi_service.analyze_business_from_sales_data(
+                        sales_data=sales_data,
+                        product_data=product_data
+                    )
+                    
+                    # Store business intelligence results in product_analysis
+                    product_analysis['__business_intelligence__'] = {
+                        "business_type": detection_result.business_type,
+                        "business_model": detection_result.business_model,
+                        "confidence_score": detection_result.confidence_score,
+                        "indicators": detection_result.indicators,
+                        "recommendations": detection_result.recommendations,
+                        "analysis_summary": f"{detection_result.business_type.title()} - {detection_result.business_model.replace('_', ' ').title()}"
+                    }
+                    
+                    logger.info("Enhanced business intelligence analysis completed",
+                               business_type=detection_result.business_type,
+                               business_model=detection_result.business_model,
+                               confidence=detection_result.confidence_score)
+                else:
+                    logger.warning("No sales data available for business intelligence analysis")
+                    
+            except Exception as bi_error:
+                logger.warning("Business intelligence analysis failed", error=str(bi_error))
+                # Continue with basic analysis even if BI fails
+            
             return product_analysis
             
         except Exception as e:

services/sales/app/services/data_import_service.py

@@ -428,7 +428,7 @@ class DataImportService:
         repository: SalesRepository, 
         filename: Optional[str] = None
     ) -> Dict[str, Any]:
-        """Enhanced CSV processing with better data handling"""
+        """Enhanced CSV processing with batch product resolution for better reliability"""
         try:
             reader = csv.DictReader(io.StringIO(csv_content))
             rows = list(reader)
@@ -445,22 +445,41 @@ class DataImportService:
             # Enhanced column mapping
             column_mapping = self._detect_columns(list(rows[0].keys()))
             
-            records_created = 0
+            # Pre-process to extract unique products for batch creation
-            errors = []
+            unique_products = set()
-            warnings = []
+            parsed_rows = []
             
-            logger.info(f"Processing {len(rows)} records from CSV with enhanced mapping")
+            logger.info(f"Pre-processing {len(rows)} records to identify unique products")
             
             for index, row in enumerate(rows):
                 try:
                     # Enhanced data parsing and validation
                     parsed_data = await self._parse_row_data(row, column_mapping, index + 1)
-                    if parsed_data.get("skip"):
+                    if not parsed_data.get("skip"):
-                        errors.extend(parsed_data.get("errors", []))
+                        unique_products.add((
-                        warnings.extend(parsed_data.get("warnings", []))
+                            parsed_data["product_name"],
+                            parsed_data.get("product_category", "general")
+                        ))
+                        parsed_rows.append((index, parsed_data))
+                except Exception as e:
+                    logger.warning(f"Failed to parse row {index + 1}: {e}")
                     continue
             
-                    # Resolve product name to inventory_product_id
+            logger.info(f"Found {len(unique_products)} unique products, attempting batch resolution")
+            
+            # Try to resolve/create all unique products in batch
+            await self._batch_resolve_products(unique_products, tenant_id)
+            
+            # Now process the actual sales records
+            records_created = 0
+            errors = []
+            warnings = []
+            
+            logger.info(f"Processing {len(parsed_rows)} validated records for sales creation")
+            
+            for index, parsed_data in parsed_rows:
+                try:
+                    # Resolve product name to inventory_product_id (should be cached now)
                     inventory_product_id = await self._resolve_product_to_inventory_id(
                         parsed_data["product_name"], 
                         parsed_data.get("product_category"),
@@ -914,31 +933,38 @@ class DataImportService:
         logger.info("Import cache cleared for new session")
     
     async def _resolve_product_to_inventory_id(self, product_name: str, product_category: Optional[str], tenant_id: UUID) -> Optional[UUID]:
-        """Resolve a product name to an inventory_product_id via the inventory service with caching and rate limiting"""
+        """Resolve a product name to an inventory_product_id via the inventory service with improved error handling and fallback"""
         
         # Check cache first
         if product_name in self.product_cache:
             logger.debug("Product resolved from cache", product_name=product_name, tenant_id=tenant_id)
             return self.product_cache[product_name]
         
-        # Skip if this product already failed to resolve
+        # Skip if this product already failed to resolve after all attempts
         if product_name in self.failed_products:
             logger.debug("Skipping previously failed product", product_name=product_name, tenant_id=tenant_id)
             return None
         
-        max_retries = 3
+        max_retries = 5  # Increased retries
-        base_delay = 1.0  # Start with 1 second delay
+        base_delay = 2.0  # Increased base delay
+        fallback_retry_delay = 10.0  # Longer delay for fallback attempts
         
         for attempt in range(max_retries):
             try:
-                # Add delay before API calls to avoid rate limiting
+                # Add progressive delay to avoid rate limiting
                 if attempt > 0:
+                    # Use longer delays for later attempts
+                    if attempt >= 3:
+                        delay = fallback_retry_delay  # Use fallback delay for later attempts
+                    else:
                         delay = base_delay * (2 ** (attempt - 1))  # Exponential backoff
+                    
                     logger.info(f"Retrying product resolution after {delay}s delay", 
                               product_name=product_name, attempt=attempt, tenant_id=tenant_id)
                     await asyncio.sleep(delay)
                 
                 # First try to search for existing product by name
+                try:
                     products = await self.inventory_client.search_products(product_name, tenant_id)
                     
                     if products:
@@ -950,11 +976,14 @@ class DataImportService:
                             logger.info("Resolved product to existing inventory ID", 
                                       product_name=product_name, product_id=product_id, tenant_id=tenant_id)
                             return uuid_id
+                except Exception as search_error:
+                    logger.warning("Product search failed, trying direct creation", 
+                                 product_name=product_name, error=str(search_error), tenant_id=tenant_id)
                 
-                # Add small delay before creation attempt to avoid hitting rate limits
+                # Add delay before creation attempt to avoid hitting rate limits
-                await asyncio.sleep(0.5)
+                await asyncio.sleep(1.0)
                 
-                # If not found, create a new ingredient/product in inventory
+                # If not found or search failed, create a new ingredient/product in inventory
                 ingredient_data = {
                     'name': product_name,
                     'type': 'finished_product',  # Assuming sales are of finished products
@@ -965,6 +994,7 @@ class DataImportService:
                     'category': product_category or 'general'
                 }
                 
+                try:
                     created_product = await self.inventory_client.create_ingredient(ingredient_data, str(tenant_id))
                     if created_product and created_product.get('id'):
                         product_id = created_product['id']
@@ -973,28 +1003,124 @@ class DataImportService:
                         logger.info("Created new inventory product for sales data", 
                                   product_name=product_name, product_id=product_id, tenant_id=tenant_id)
                         return uuid_id
+                except Exception as creation_error:
+                    logger.warning("Product creation failed", 
+                                 product_name=product_name, error=str(creation_error), tenant_id=tenant_id)
                 
                 logger.warning("Failed to resolve or create product in inventory", 
                              product_name=product_name, tenant_id=tenant_id, attempt=attempt)
                              
             except Exception as e:
                 error_str = str(e)
-                if "429" in error_str or "rate limit" in error_str.lower():
+                if "429" in error_str or "rate limit" in error_str.lower() or "too many requests" in error_str.lower():
-                    logger.warning("Rate limit hit, retrying", 
+                    logger.warning("Rate limit or service overload detected, retrying with longer delay", 
                                  product_name=product_name, attempt=attempt, error=error_str, tenant_id=tenant_id)
                     if attempt < max_retries - 1:
                         continue  # Retry with exponential backoff
+                elif "503" in error_str or "502" in error_str or "service unavailable" in error_str.lower():
+                    logger.warning("Service unavailable, retrying with backoff", 
+                                 product_name=product_name, attempt=attempt, error=error_str, tenant_id=tenant_id)
+                    if attempt < max_retries - 1:
+                        continue  # Retry for service unavailable errors
+                elif "timeout" in error_str.lower() or "connection" in error_str.lower():
+                    logger.warning("Network issue detected, retrying", 
+                                 product_name=product_name, attempt=attempt, error=error_str, tenant_id=tenant_id)
+                    if attempt < max_retries - 1:
+                        continue  # Retry for network issues
                 else:
-                    logger.error("Error resolving product to inventory ID", 
+                    logger.error("Non-retryable error resolving product to inventory ID", 
                                error=error_str, product_name=product_name, tenant_id=tenant_id)
-                    break  # Don't retry for non-rate-limit errors
+                    if attempt < max_retries - 1:
+                        # Still retry even for other errors, in case it's transient
+                        continue
+                    else:
+                        break  # Don't retry on final attempt
         
-        # If all retries failed, mark as failed and return None
+        # If all retries failed, log detailed error but don't mark as permanently failed yet
+        # Instead, we'll implement a fallback mechanism
+        logger.error("Failed to resolve product after all retries, attempting fallback", 
+                   product_name=product_name, tenant_id=tenant_id)
+        
+        # FALLBACK: Try to create a temporary product with minimal data
+        try:
+            # Use a simplified approach with minimal data
+            fallback_data = {
+                'name': product_name,
+                'type': 'finished_product',
+                'unit': 'unit',
+                'current_stock': 0,
+                'cost_per_unit': 0
+            }
+            
+            logger.info("Attempting fallback product creation with minimal data", 
+                      product_name=product_name, tenant_id=tenant_id)
+            
+            created_product = await self.inventory_client.create_ingredient(fallback_data, str(tenant_id))
+            if created_product and created_product.get('id'):
+                product_id = created_product['id']
+                uuid_id = UUID(str(product_id))
+                self.product_cache[product_name] = uuid_id
+                logger.info("SUCCESS: Fallback product creation succeeded", 
+                          product_name=product_name, product_id=product_id, tenant_id=tenant_id)
+                return uuid_id
+        except Exception as fallback_error:
+            logger.error("Fallback product creation also failed", 
+                       product_name=product_name, error=str(fallback_error), tenant_id=tenant_id)
+        
+        # Only mark as permanently failed after all attempts including fallback
         self.failed_products.add(product_name)
-        logger.error("Failed to resolve product after all retries", 
+        logger.error("CRITICAL: Permanently failed to resolve product - this will result in missing training data", 
                    product_name=product_name, tenant_id=tenant_id)
         return None
     
+    async def _batch_resolve_products(self, unique_products: set, tenant_id: str) -> None:
+        """Batch resolve/create products to reduce API calls and improve success rate"""
+        
+        if not unique_products:
+            return
+        
+        logger.info(f"Starting batch product resolution for {len(unique_products)} unique products")
+        
+        # Convert set to list for easier handling
+        products_list = list(unique_products)
+        batch_size = 5  # Process in smaller batches to avoid overwhelming the inventory service
+        
+        for i in range(0, len(products_list), batch_size):
+            batch = products_list[i:i + batch_size]
+            logger.info(f"Processing batch {i//batch_size + 1}/{(len(products_list) + batch_size - 1)//batch_size}")
+            
+            # Process each product in the batch with retry logic
+            for product_name, product_category in batch:
+                try:
+                    # Skip if already in cache or failed list
+                    if product_name in self.product_cache or product_name in self.failed_products:
+                        continue
+                    
+                    # Try to resolve the product
+                    await self._resolve_product_to_inventory_id(product_name, product_category, tenant_id)
+                    
+                    # Add small delay between products to be gentle on the API
+                    await asyncio.sleep(0.5)
+                    
+                except Exception as e:
+                    logger.warning(f"Failed to batch process product {product_name}: {e}")
+                    continue
+            
+            # Add delay between batches
+            if i + batch_size < len(products_list):
+                logger.info("Waiting between batches to avoid rate limiting...")
+                await asyncio.sleep(2.0)
+        
+        successful_resolutions = len([p for p, _ in products_list if p in self.product_cache])
+        failed_resolutions = len([p for p, _ in products_list if p in self.failed_products])
+        
+        logger.info(f"Batch product resolution completed: {successful_resolutions} successful, {failed_resolutions} failed")
+        
+        if failed_resolutions > 0:
+            logger.warning(f"ATTENTION: {failed_resolutions} products failed to resolve - these will be missing from training data")
+            
+        return
+    
     def _structure_messages(self, messages: List[Union[str, Dict]]) -> List[Dict[str, Any]]:
         """Convert string messages to structured format"""
         structured = []

services/sales/app/services/sales_service.py

@@ -285,26 +285,6 @@ class SalesService:
             # Don't fail the main operation for auxiliary actions
             logger.warning("Failed to execute post-create actions", error=str(e), record_id=record.id)
             
-    async def get_products_list(self, tenant_id: str) -> List[Dict[str, Any]]:
-        """Get list of all products with sales data for tenant using repository pattern"""
-        try:
-            async with get_db_transaction() as db:
-                repository = SalesRepository(db)
-                
-                # Use repository method for product statistics
-                products = await repository.get_product_statistics(tenant_id)
-                
-                logger.debug("Products list retrieved successfully", 
-                            tenant_id=tenant_id, 
-                            product_count=len(products))
-                
-                return products
-                
-        except Exception as e:
-            logger.error("Failed to get products list", 
-                        error=str(e), 
-                        tenant_id=tenant_id)
-            raise DatabaseError(f"Failed to get products list: {str(e)}")
     
     # New inventory integration methods
     async def search_inventory_products(self, search_term: str, tenant_id: UUID, 

services/training/app/api/training.py

@@ -186,6 +186,15 @@ async def execute_enhanced_training_job_background(
     enhanced_training_service = EnhancedTrainingService(database_manager)
 
     try:
+        # Create initial training log entry first
+        await enhanced_training_service._update_job_status_repository(
+            job_id=job_id,
+            status="pending",
+            progress=0,
+            current_step="Starting enhanced training job",
+            tenant_id=tenant_id
+        )
+        
         # Publish job started event
         await publish_job_started(job_id, tenant_id, {
             "enhanced_features": True,
@@ -214,7 +223,8 @@ async def execute_enhanced_training_job_background(
             job_id=job_id,
             status="running",
             progress=0,
-            current_step="Initializing enhanced training pipeline"
+            current_step="Initializing enhanced training pipeline",
+            tenant_id=tenant_id
         )
         
         # Execute the enhanced training pipeline with repository pattern
@@ -232,7 +242,8 @@ async def execute_enhanced_training_job_background(
             status="completed",
             progress=100,
             current_step="Enhanced training completed successfully",
-            results=result
+            results=result,
+            tenant_id=tenant_id
         )
         
         # Publish enhanced completion event
@@ -262,7 +273,8 @@ async def execute_enhanced_training_job_background(
                 status="failed",
                 progress=0,
                 current_step="Enhanced training failed",
-                error_message=str(training_error)
+                error_message=str(training_error),
+                tenant_id=tenant_id
             )
         except Exception as status_error:
             logger.error("Failed to update job status after training error",

services/training/app/ml/trainer.py

@@ -92,9 +92,27 @@ class EnhancedBakeryMLTrainer:
                 
                 # Get unique products from the sales data
                 products = sales_df['inventory_product_id'].unique().tolist()
-                logger.info("Training enhanced models",
+                
+                # Debug: Log sales data details to understand why only one product is found
+                total_sales_records = len(sales_df)
+                sales_by_product = sales_df.groupby('inventory_product_id').size().to_dict()
+                
+                logger.info("Enhanced training pipeline - Sales data analysis",
+                           total_sales_records=total_sales_records,
                            products_count=len(products),
-                           products=products)
+                           products=products,
+                           sales_by_product=sales_by_product)
+                
+                if len(products) == 1:
+                    logger.warning("Only ONE product found in sales data - this may indicate a data fetching issue",
+                                 tenant_id=tenant_id,
+                                 single_product_id=products[0],
+                                 total_sales_records=total_sales_records)
+                elif len(products) == 0:
+                    raise ValueError("No products found in sales data")
+                else:
+                    logger.info("Multiple products detected for training",
+                               products_count=len(products))
                 
                 self.status_publisher.products_total = len(products)
                 
@@ -512,7 +530,7 @@ class EnhancedBakeryMLTrainer:
                        from_column='quantity_sold',
                        to_column='quantity')
         
-        required_columns = ['date', 'product_name', 'quantity']
+        required_columns = ['date', 'inventory_product_id', 'quantity']
         missing_columns = [col for col in required_columns if col not in sales_df.columns]
         if missing_columns:
             raise ValueError(f"Missing required columns: {missing_columns}")
@@ -541,7 +559,7 @@ class EnhancedBakeryMLTrainer:
         try:
             logger.info("Enhanced model evaluation starting",
                        tenant_id=tenant_id,
-                       product_name=product_name)
+                       inventory_product_id=inventory_product_id)
             
             # Get database session and repositories
             async with self.database_manager.get_session() as db_session:

services/training/app/services/training_orchestrator.py

@@ -574,13 +574,14 @@ class TrainingDataOrchestrator:
             if city_count >= 1:  # At least some city awareness
                 city_aware_records += 1
             
-            # Record is valid if it has basic requirements
+            # Record is valid if it has basic requirements (date + any traffic field)
-            if record_score >= 2:
+            # Lowered requirement from >= 2 to >= 1 to accept records with just date or traffic data
+            if record_score >= 1:
                 valid_records += 1
         
         total_records = len(traffic_data)
-        validity_threshold = 0.3
+        validity_threshold = 0.1  # Reduced from 0.3 to 0.1 - accept if 10% of records are valid
-        enhancement_threshold = 0.2  # Lower threshold for enhanced features
+        enhancement_threshold = 0.1  # Reduced threshold for enhanced features
         
         basic_validity = (valid_records / total_records) >= validity_threshold
         has_enhancements = (enhanced_records / total_records) >= enhancement_threshold

services/training/app/services/training_service.py

@@ -141,6 +141,30 @@ class EnhancedTrainingService:
                     logger.error("Training aborted - no sales data", tenant_id=tenant_id, job_id=job_id)
                     raise ValueError(error_msg)
                 
+                # Debug: Analyze the sales data structure to understand product distribution
+                sales_df_debug = pd.DataFrame(sales_data)
+                if 'inventory_product_id' in sales_df_debug.columns:
+                    unique_products_found = sales_df_debug['inventory_product_id'].unique()
+                    product_counts = sales_df_debug['inventory_product_id'].value_counts().to_dict()
+                    
+                    logger.info("Pre-flight sales data analysis",
+                               tenant_id=tenant_id, 
+                               job_id=job_id,
+                               total_sales_records=len(sales_data),
+                               unique_products_count=len(unique_products_found),
+                               unique_products=unique_products_found.tolist(),
+                               records_per_product=product_counts)
+                    
+                    if len(unique_products_found) == 1:
+                        logger.warning("POTENTIAL ISSUE: Only ONE unique product found in all sales data",
+                                     tenant_id=tenant_id,
+                                     single_product=unique_products_found[0],
+                                     record_count=len(sales_data))
+                else:
+                    logger.warning("No 'inventory_product_id' column found in sales data", 
+                                 tenant_id=tenant_id,
+                                 columns=list(sales_df_debug.columns))
+                
                 logger.info(f"Pre-flight check passed: {len(sales_data)} sales records found", 
                            tenant_id=tenant_id, job_id=job_id)
                 
@@ -536,12 +560,50 @@ class EnhancedTrainingService:
                                           progress: int = None,
                                           current_step: str = None,
                                           error_message: str = None,
-                                          results: Dict = None):
+                                          results: Dict = None,
+                                          tenant_id: str = None):
         """Update job status using repository pattern"""
         try:
             async with self.database_manager.get_session() as session:
                 await self._init_repositories(session)
                 
+                # Check if log exists, create if not
+                existing_log = await self.training_log_repo.get_log_by_job_id(job_id)
+                
+                if not existing_log:
+                    # Create initial log entry
+                    if not tenant_id:
+                        # Extract tenant_id from job_id if not provided
+                        # Format: enhanced_training_{tenant_id}_{job_suffix}
+                        try:
+                            parts = job_id.split('_')
+                            if len(parts) >= 3 and parts[0] == 'enhanced' and parts[1] == 'training':
+                                tenant_id = parts[2]
+                        except Exception:
+                            logger.warning(f"Could not extract tenant_id from job_id {job_id}")
+                    
+                    if tenant_id:
+                        log_data = {
+                            "job_id": job_id,
+                            "tenant_id": tenant_id,
+                            "status": status or "pending",
+                            "progress": progress or 0,
+                            "current_step": current_step or "initializing",
+                            "start_time": datetime.utcnow()
+                        }
+                        
+                        if error_message:
+                            log_data["error_message"] = error_message
+                        if results:
+                            log_data["results"] = results
+                            
+                        await self.training_log_repo.create_training_log(log_data)
+                        logger.info("Created initial training log", job_id=job_id, tenant_id=tenant_id)
+                    else:
+                        logger.error("Cannot create training log without tenant_id", job_id=job_id)
+                        return
+                else:
+                    # Update existing log
                     await self.training_log_repo.update_log_progress(
                         job_id=job_id,
                         progress=progress,
@@ -549,6 +611,19 @@ class EnhancedTrainingService:
                         status=status
                     )
                     
+                    # Update additional fields if provided
+                    if error_message or results:
+                        update_data = {}
+                        if error_message:
+                            update_data["error_message"] = error_message
+                        if results:
+                            update_data["results"] = results
+                        if status in ["completed", "failed"]:
+                            update_data["end_time"] = datetime.utcnow()
+                            
+                        if update_data:
+                            await self.training_log_repo.update(existing_log.id, update_data)
+                
         except Exception as e:
             logger.error("Failed to update job status using repository",
                         job_id=job_id,

shared/clients/training_client.py

@@ -89,13 +89,13 @@ class TrainingServiceClient(BaseServiceClient):
     async def get_active_model_for_product(
         self,
         tenant_id: str,
-        product_name: str
+        inventory_product_id: str
     ) -> Optional[Dict[str, Any]]:
         """
-        Get the active model for a specific product
+        Get the active model for a specific product by inventory product ID
         This is the preferred method since models are stored per product.
         """
-        result = await self.get(f"models/{product_name}/active", tenant_id=tenant_id)
+        result = await self.get(f"models/{inventory_product_id}/active", tenant_id=tenant_id)
         return result
     
     async def deploy_model(self, tenant_id: str, model_id: str) -> Optional[Dict[str, Any]]:

test_onboarding_debug.py

@@ -1,162 +0,0 @@
-#!/usr/bin/env python3
-"""
-Test script to debug onboarding inventory creation step by step
-"""
-
-import asyncio
-import json
-import httpx
-import sys
-from uuid import uuid4
-
-# Test configuration
-GATEWAY_URL = "http://localhost:8000"
-TENANT_ID = "946206b3-7446-436b-b29d-f265b28d9ff5"
-
-# Test token (you'll need to replace this with a real token)
-TEST_TOKEN = "eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9.eyJzdWIiOiIzOTUyYTEwOC1lNWFmLTRlMjktOTJkOC0xMjc0MTBiOWJiYmEiLCJ1c2VyX2lkIjoiMzk1MmExMDgtZTVhZi00ZTI5LTkyZDgtMTI3NDEwYjliYmJhIiwiZW1haWwiOiJkZnNmc2RAdGVzdC5jb20iLCJ0eXBlIjoiYWNjZXNzIiwiZXhwIjoxNzU1MTY3NTk4LCJpYXQiOjE3NTUxNjU3OTgsImlzcyI6ImJha2VyeS1hdXRoIiwiZnVsbF9uYW1lIjoiZGZzZGZzZGYiLCJpc192ZXJpZmllZCI6ZmFsc2UsImlzX2FjdGl2ZSI6dHJ1ZSwicm9sZSI6InVzZXIifQ.hYyRqqqZ-Ud-uzn42l_ic-QjP-NWYvT8RmwmU12uaQU"
-
-async def test_onboarding_flow():
-    """Test the complete onboarding inventory creation flow"""
-    
-    print("🧪 Testing Onboarding Inventory Creation Flow")
-    print("=" * 60)
-    
-    headers = {
-        "Authorization": f"Bearer {TEST_TOKEN}",
-        "Content-Type": "application/json"
-    }
-    
-    async with httpx.AsyncClient(timeout=30.0) as client:
-        
-        # Step 1: Test direct ingredient creation via inventory service
-        print("\n1️⃣ Testing Direct Ingredient Creation via Inventory Service")
-        print("-" * 50)
-        
-        ingredient_data = {
-            "name": "Test Flour",
-            "description": "Test ingredient for debugging",
-            "category": "flour",  # Use valid enum value
-            "unit_of_measure": "kg",  # Use correct field name
-            "brand": "Test Supplier",
-            "is_active": True
-        }
-        
-        ingredient_url = f"{GATEWAY_URL}/api/v1/tenants/{TENANT_ID}/ingredients"
-        print(f"URL: {ingredient_url}")
-        print(f"Data: {json.dumps(ingredient_data, indent=2)}")
-        
-        try:
-            response = await client.post(ingredient_url, json=ingredient_data, headers=headers)
-            print(f"Status: {response.status_code}")
-            print(f"Response: {response.text}")
-            
-            if response.status_code == 201:
-                print("✅ Direct ingredient creation SUCCESS!")
-                created_ingredient = response.json()
-                ingredient_id = created_ingredient.get('id')
-                print(f"Created ingredient ID: {ingredient_id}")
-            else:
-                print("❌ Direct ingredient creation FAILED!")
-                if response.status_code == 401:
-                    print("❌ Authentication failed - token might be expired")
-                    return
-                    
-        except Exception as e:
-            print(f"❌ Direct ingredient creation ERROR: {e}")
-        
-        # Step 2: Test onboarding inventory creation endpoint
-        print("\n2️⃣ Testing Onboarding Inventory Creation Endpoint")
-        print("-" * 50)
-        
-        # Create test suggestions like the frontend sends
-        suggestions = [
-            {
-                "suggestion_id": str(uuid4()),
-                "approved": True,
-                "modifications": {},
-                "original_name": "Pan",
-                "suggested_name": "Pan",
-                "product_type": "finished_product",
-                "category": "other_products",
-                "unit_of_measure": "units",
-                "confidence_score": 0.9,
-                "estimated_shelf_life_days": None,
-                "requires_refrigeration": False,
-                "requires_freezing": False,
-                "is_seasonal": False,
-                "suggested_supplier": None,
-                "notes": "Test bread product"
-            },
-            {
-                "suggestion_id": str(uuid4()),
-                "approved": True,
-                "modifications": {},
-                "original_name": "Test Croissant",
-                "suggested_name": "Test Croissant",
-                "product_type": "finished_product",
-                "category": "pastries",
-                "unit_of_measure": "units",
-                "confidence_score": 0.8,
-                "estimated_shelf_life_days": 2,
-                "requires_refrigeration": False,
-                "requires_freezing": False,
-                "is_seasonal": False,
-                "suggested_supplier": "Test Bakery",
-                "notes": "Test pastry product"
-            }
-        ]
-        
-        onboarding_data = {"suggestions": suggestions}
-        onboarding_url = f"{GATEWAY_URL}/api/v1/tenants/{TENANT_ID}/onboarding/create-inventory"
-        
-        print(f"URL: {onboarding_url}")
-        print(f"Suggestions count: {len(suggestions)}")
-        
-        try:
-            response = await client.post(onboarding_url, json=onboarding_data, headers=headers)
-            print(f"Status: {response.status_code}")
-            print(f"Response: {response.text}")
-            
-            if response.status_code == 200:
-                result = response.json()
-                print(f"✅ Onboarding inventory creation completed!")
-                print(f"Created items: {len(result.get('created_items', []))}")
-                print(f"Failed items: {len(result.get('failed_items', []))}")
-                print(f"Success rate: {result.get('success_rate', 0)}")
-                
-                if result.get('failed_items'):
-                    print("\n❌ Failed items details:")
-                    for item in result['failed_items']:
-                        print(f"  - {item.get('suggestion_id')}: {item.get('error')}")
-                        
-            else:
-                print("❌ Onboarding inventory creation FAILED!")
-                
-        except Exception as e:
-            print(f"❌ Onboarding inventory creation ERROR: {e}")
-        
-        # Step 3: Test service health
-        print("\n3️⃣ Testing Service Health")
-        print("-" * 50)
-        
-        services = [
-            ("Gateway", f"{GATEWAY_URL}/health"),
-            ("Inventory Service", "http://localhost:8008/health"),
-            ("Sales Service", "http://localhost:8004/health")
-        ]
-        
-        for service_name, health_url in services:
-            try:
-                response = await client.get(health_url)
-                status = "✅ Healthy" if response.status_code == 200 else f"❌ Unhealthy ({response.status_code})"
-                print(f"{service_name}: {status}")
-            except Exception as e:
-                print(f"{service_name}: ❌ Error - {e}")
-
-if __name__ == "__main__":
-    print("Starting onboarding flow debug test...")
-    print("Make sure your services are running with docker-compose!")
-    print()
-    
-    asyncio.run(test_onboarding_flow())

test_sales_import_fix.py

@@ -1,153 +0,0 @@
-#!/usr/bin/env python3
-"""
-Test script to verify the sales import fix for inventory_product_id issue
-"""
-
-import asyncio
-import json
-import httpx
-import csv
-import io
-from uuid import uuid4
-
-# Test configuration
-GATEWAY_URL = "http://localhost:8000"
-TENANT_ID = "946206b3-7446-436b-b29d-f265b28d9ff5"
-
-# Test token
-TEST_TOKEN = "eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9.eyJzdWIiOiIzOTUyYTEwOC1lNWFmLTRlMjktOTJkOC0xMjc0MTBiOWJiYmEiLCJ1c2VyX2lkIjoiMzk1MmExMDgtZTVhZi00ZTI5LTkyZDgtMTI3NDEwYjliYmJhIiwiZW1haWwiOiJkZnNmc2RAdGVzdC5jb20iLCJ0eXBlIjoiYWNjZXNzIiwiZXhwIjoxNzU1MTY3NTk4LCJpYXQiOjE3NTUxNjU3OTgsImlzcyI6ImJha2VyeS1hdXRoIiwiZnVsbF9uYW1lIjoiZGZzZGZzZGYiLCJpc192ZXJpZmllZCI6ZmFsc2UsImlzX2FjdGl2ZSI6dHJ1ZSwicm9sZSI6InVzZXIifQ.hYyRqqqZ-Ud-uzn42l_ic-QjP-NWYvT8RmwmU12uaQU"
-
-async def test_sales_import():
-    """Test the sales import functionality with the inventory_product_id fix"""
-    
-    print("🧪 Testing Sales Import Fix for inventory_product_id")
-    print("=" * 60)
-    
-    headers = {
-        "Authorization": f"Bearer {TEST_TOKEN}",
-        "Content-Type": "application/json"
-    }
-    
-    async with httpx.AsyncClient(timeout=60.0) as client:
-        
-        # Step 1: Create test CSV data
-        print("\n1️⃣ Creating Test CSV Data")
-        print("-" * 50)
-        
-        csv_data = [
-            ["date", "product_name", "quantity_sold", "revenue", "location_id"],
-            ["2024-01-15", "Test Bread", "10", "25.50", "store-1"],
-            ["2024-01-15", "Test Croissant", "5", "15.00", "store-1"],
-            ["2024-01-15", "Test Muffin", "8", "20.00", "store-2"]
-        ]
-        
-        # Convert to CSV string
-        csv_string = io.StringIO()
-        writer = csv.writer(csv_string)
-        for row in csv_data:
-            writer.writerow(row)
-        csv_content = csv_string.getvalue()
-        
-        print(f"CSV Content:")
-        print(csv_content)
-        
-        # Step 2: Test the import endpoint
-        print("\n2️⃣ Testing Sales Data Import")
-        print("-" * 50)
-        
-        import_data = {
-            "csv_data": csv_content,
-            "import_type": "enhanced",
-            "overwrite_existing": False
-        }
-        
-        import_url = f"{GATEWAY_URL}/api/v1/tenants/{TENANT_ID}/sales/import/csv"
-        print(f"URL: {import_url}")
-        
-        try:
-            response = await client.post(import_url, json=import_data, headers=headers)
-            print(f"Status: {response.status_code}")
-            
-            if response.status_code == 200:
-                result = response.json()
-                print(f"✅ Sales import completed!")
-                print(f"Records processed: {result.get('records_processed', 0)}")
-                print(f"Records created: {result.get('records_created', 0)}")
-                print(f"Records failed: {result.get('records_failed', 0)}")
-                print(f"Success: {result.get('success', False)}")
-                
-                if result.get('errors'):
-                    print(f"\n❌ Errors ({len(result['errors'])}):")
-                    for error in result['errors'][:5]:  # Show first 5 errors
-                        print(f"  - {error}")
-                        
-                if result.get('warnings'):
-                    print(f"\n⚠️  Warnings ({len(result['warnings'])}):")
-                    for warning in result['warnings'][:3]:  # Show first 3 warnings
-                        print(f"  - {warning}")
-                        
-            else:
-                print("❌ Sales import FAILED!")
-                print(f"Response: {response.text}")
-                
-        except Exception as e:
-            print(f"❌ Sales import ERROR: {e}")
-
-        # Step 3: Check if inventory items were created
-        print("\n3️⃣ Checking Inventory Items Creation")
-        print("-" * 50)
-        
-        try:
-            ingredients_url = f"{GATEWAY_URL}/api/v1/tenants/{TENANT_ID}/ingredients"
-            response = await client.get(ingredients_url, headers=headers)
-            
-            if response.status_code == 200:
-                ingredients = response.json()
-                print(f"✅ Found {len(ingredients)} inventory items")
-                
-                test_products = ["Test Bread", "Test Croissant", "Test Muffin"]
-                for product in test_products:
-                    found = any(ingredient.get('name') == product for ingredient in ingredients)
-                    status = "✅" if found else "❌"
-                    print(f"  {status} {product}")
-                    
-            else:
-                print(f"❌ Failed to fetch inventory items: {response.status_code}")
-                
-        except Exception as e:
-            print(f"❌ Error checking inventory items: {e}")
-
-        # Step 4: Check sales records
-        print("\n4️⃣ Checking Sales Records")
-        print("-" * 50)
-        
-        try:
-            sales_url = f"{GATEWAY_URL}/api/v1/tenants/{TENANT_ID}/sales"
-            response = await client.get(sales_url, headers=headers)
-            
-            if response.status_code == 200:
-                sales_data = response.json()
-                sales_count = len(sales_data) if isinstance(sales_data, list) else sales_data.get('total', 0)
-                print(f"✅ Found {sales_count} sales records")
-                
-                # Show recent records
-                records = sales_data if isinstance(sales_data, list) else sales_data.get('records', [])
-                for i, record in enumerate(records[:3]):  # Show first 3 records
-                    inventory_id = record.get('inventory_product_id')
-                    date = record.get('date', 'Unknown')
-                    quantity = record.get('quantity_sold', 0)
-                    revenue = record.get('revenue', 0)
-                    print(f"  Record {i+1}: Date={date}, Qty={quantity}, Revenue=${revenue}, InventoryID={inventory_id}")
-                    
-            else:
-                print(f"❌ Failed to fetch sales records: {response.status_code}")
-                
-        except Exception as e:
-            print(f"❌ Error checking sales records: {e}")
-
-if __name__ == "__main__":
-    print("Testing sales import fix...")
-    print("Make sure your services are running with docker-compose!")
-    print()
-    
-    asyncio.run(test_sales_import())