Fix new services implementation 1

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

@@ -56,6 +56,28 @@ export interface BusinessModelAnalysis {
   recommendations: string[];
 }
 
+// Step 1: File validation result
+export interface FileValidationResult {
+  is_valid: boolean;
+  total_records: number;
+  unique_products: number;
+  product_list: string[];
+  validation_errors: any[];
+  validation_warnings: any[];
+  summary: Record<string, any>;
+}
+
+// Step 2: AI suggestions result
+export interface ProductSuggestionsResult {
+  suggestions: InventorySuggestion[];
+  business_model_analysis: BusinessModelAnalysis;
+  total_products: number;
+  high_confidence_count: number;
+  low_confidence_count: number;
+  processing_time_seconds: number;
+}
+
+// Legacy support - will be deprecated
 export interface OnboardingAnalysisResult {
   total_products_found: number;
   inventory_suggestions: InventorySuggestion[];
@@ -143,16 +165,16 @@ export class OnboardingService {
     return apiClient.get(`${this.baseEndpoint}/can-access/${stepName}`);
   }
 
-  // ========== AUTOMATED INVENTORY CREATION METHODS ==========
+  // ========== NEW 4-STEP AUTOMATED INVENTORY CREATION METHODS ==========
 
   /**
-   * Phase 1: Analyze sales data and get AI suggestions
+   * Step 1: Validate file and extract unique products
    */
-  async analyzeSalesDataForOnboarding(tenantId: string, file: File): Promise<OnboardingAnalysisResult> {
+  async validateFileAndExtractProducts(tenantId: string, file: File): Promise<FileValidationResult> {
     const formData = new FormData();
     formData.append('file', file);
     
-    return apiClient.post(`/tenants/${tenantId}/onboarding/analyze`, formData, {
+    return apiClient.post(`/tenants/${tenantId}/onboarding/validate-file`, formData, {
       headers: {
         'Content-Type': 'multipart/form-data',
       },
@@ -160,7 +182,26 @@ export class OnboardingService {
   }
 
   /**
-   * Phase 2: Create inventory from approved suggestions
+   * Step 2: Generate AI-powered inventory suggestions
+   */
+  async generateInventorySuggestions(
+    tenantId: string, 
+    file: File, 
+    productList: string[]
+  ): Promise<ProductSuggestionsResult> {
+    const formData = new FormData();
+    formData.append('file', file);
+    formData.append('product_list', JSON.stringify(productList));
+    
+    return apiClient.post(`/tenants/${tenantId}/onboarding/generate-suggestions`, formData, {
+      headers: {
+        'Content-Type': 'multipart/form-data',
+      },
+    });
+  }
+
+  /**
+   * Step 3: Create inventory from approved suggestions
    */
   async createInventoryFromSuggestions(
     tenantId: string, 
@@ -176,7 +217,7 @@ export class OnboardingService {
   }
 
   /**
-   * Phase 3: Import sales data with inventory mapping
+   * Step 4: Final sales data import with inventory mapping
    */
   async importSalesWithInventory(
     tenantId: string, 
@@ -194,6 +235,35 @@ export class OnboardingService {
     });
   }
 
+  // ========== LEGACY METHODS (for backward compatibility) ==========
+
+  /**
+   * @deprecated Use the new 4-step flow instead
+   * Phase 1: Analyze sales data and get AI suggestions (OLD METHOD)
+   */
+  async analyzeSalesDataForOnboarding(tenantId: string, file: File): Promise<OnboardingAnalysisResult> {
+    // This method will use the new flow under the hood for backward compatibility
+    const validationResult = await this.validateFileAndExtractProducts(tenantId, file);
+    
+    if (!validationResult.is_valid) {
+      throw new Error(`File validation failed: ${validationResult.validation_errors.map(e => e.message || e).join(', ')}`);
+    }
+
+    const suggestionsResult = await this.generateInventorySuggestions(tenantId, file, validationResult.product_list);
+
+    // Convert to legacy format
+    return {
+      total_products_found: suggestionsResult.total_products,
+      inventory_suggestions: suggestionsResult.suggestions,
+      business_model_analysis: suggestionsResult.business_model_analysis,
+      import_job_id: `legacy-${Date.now()}`,
+      status: 'completed',
+      processed_rows: validationResult.total_records,
+      errors: validationResult.validation_errors.map(e => e.message || String(e)),
+      warnings: validationResult.validation_warnings.map(w => w.message || String(w))
+    };
+  }
+
   /**
    * Get business model guidance based on analysis
    */

frontend/src/components/onboarding/SmartHistoricalDataImport.tsx

@@ -21,6 +21,8 @@ import {
 import toast from 'react-hot-toast';
 
 import { 
+  FileValidationResult,
+  ProductSuggestionsResult,
   OnboardingAnalysisResult, 
   InventorySuggestion, 
   BusinessModelAnalysis,
@@ -35,12 +37,13 @@ interface SmartHistoricalDataImportProps {
   onBack?: () => void;
 }
 
-type ImportPhase = 'upload' | 'analysis' | 'review' | 'creation' | 'import' | 'complete';
+type ImportPhase = 'upload' | 'validation' | 'suggestions' | 'review' | 'creation' | 'import' | 'complete';
 
 interface PhaseState {
   phase: ImportPhase;
   file?: File;
-  analysisResult?: OnboardingAnalysisResult;
+  validationResult?: FileValidationResult;
+  suggestionsResult?: ProductSuggestionsResult;
   reviewedSuggestions?: InventorySuggestion[];
   creationResult?: InventoryCreationResult;
   importResult?: SalesImportResult;
@@ -57,22 +60,56 @@ const SmartHistoricalDataImport: React.FC<SmartHistoricalDataImportProps> = ({
   const [showAllSuggestions, setShowAllSuggestions] = useState(false);
 
   const handleFileUpload = useCallback(async (file: File) => {
-    setState(prev => ({ ...prev, file, phase: 'analysis' }));
+    setState(prev => ({ ...prev, file, phase: 'validation' }));
     setIsProcessing(true);
     
     try {
-      toast.loading('🧠 Analizando tu archivo con IA...', { id: 'analysis' });
+      // Step 1: Validate file and extract products
+      toast.loading('📋 Validando archivo...', { id: 'validation' });
       
-      const analysisResult = await onboardingService.analyzeSalesDataForOnboarding(tenantId, file);
+      const validationResult = await onboardingService.validateFileAndExtractProducts(tenantId, file);
       
-      toast.success(`¡Análisis completado! ${analysisResult.total_products_found} productos encontrados`, { 
-        id: 'analysis' 
+      if (!validationResult.is_valid) {
+        throw new Error(`Archivo inválido: ${validationResult.validation_errors.map(e => e.message || e).join(', ')}`);
+      }
+
+      toast.success(`¡Archivo válido! ${validationResult.unique_products} productos únicos encontrados`, { 
+        id: 'validation' 
+      });
+
+      setState(prev => ({ ...prev, validationResult, phase: 'suggestions' }));
+      
+      // Step 2: Generate AI suggestions
+      setTimeout(() => handleGenerateSuggestions(file, validationResult.product_list), 1000);
+      
+    } catch (error: any) {
+      toast.error('Error al validar el archivo', { id: 'validation' });
+      setState(prev => ({ 
+        ...prev, 
+        error: error.message || 'Error de validación', 
+        phase: 'upload' 
+      }));
+    } finally {
+      setIsProcessing(false);
+    }
+  }, [tenantId]);
+
+  const handleGenerateSuggestions = useCallback(async (file: File, productList: string[]) => {
+    setIsProcessing(true);
+    
+    try {
+      toast.loading('🧠 Generando sugerencias con IA...', { id: 'suggestions' });
+      
+      const suggestionsResult = await onboardingService.generateInventorySuggestions(tenantId, file, productList);
+      
+      toast.success(`¡${suggestionsResult.total_products} productos clasificados! ${suggestionsResult.high_confidence_count} con alta confianza`, { 
+        id: 'suggestions' 
       });
 
       setState(prev => ({ 
         ...prev, 
-        analysisResult,
-        reviewedSuggestions: analysisResult.inventory_suggestions.map(s => ({ 
+        suggestionsResult,
+        reviewedSuggestions: suggestionsResult.suggestions.map(s => ({ 
           ...s, 
           user_approved: s.confidence_score >= 0.7 
         })),
@@ -80,11 +117,11 @@ const SmartHistoricalDataImport: React.FC<SmartHistoricalDataImportProps> = ({
       }));
       
     } catch (error: any) {
-      toast.error('Error al analizar el archivo', { id: 'analysis' });
+      toast.error('Error al generar sugerencias', { id: 'suggestions' });
       setState(prev => ({ 
         ...prev, 
-        error: error.message || 'Error desconocido', 
-        phase: 'upload' 
+        error: error.message || 'Error en sugerencias de IA', 
+        phase: 'validation' 
       }));
     } finally {
       setIsProcessing(false);
@@ -475,17 +512,17 @@ const SmartHistoricalDataImport: React.FC<SmartHistoricalDataImportProps> = ({
         </div>
       );
 
-    case 'analysis':
+    case 'validation':
       return (
         <div className="text-center py-12">
-          <div className="w-20 h-20 bg-gradient-to-r from-blue-500 to-purple-500 rounded-full flex items-center justify-center mx-auto mb-6 animate-pulse">
-            <Brain className="w-10 h-10 text-white" />
+          <div className="w-20 h-20 bg-gradient-to-r from-blue-500 to-green-500 rounded-full flex items-center justify-center mx-auto mb-6 animate-pulse">
+            <CheckCircle2 className="w-10 h-10 text-white" />
           </div>
           <h2 className="text-xl font-semibold text-gray-900 mb-3">
-            🧠 Analizando tu archivo con IA...
+            📋 Validando archivo...
           </h2>
           <p className="text-gray-600 mb-6">
-            Esto puede tomar unos momentos mientras clasificamos tus productos
+            Verificando formato y extrayendo productos únicos
           </p>
           <div className="bg-white rounded-lg shadow-sm p-4 max-w-md mx-auto">
             <div className="flex items-center justify-between text-sm text-gray-600">
@@ -493,16 +530,48 @@ const SmartHistoricalDataImport: React.FC<SmartHistoricalDataImportProps> = ({
               <span className="font-medium">{state.file?.name}</span>
             </div>
             <div className="mt-2 bg-gray-200 rounded-full h-2">
-              <div className="bg-gradient-to-r from-blue-500 to-purple-500 h-2 rounded-full w-1/2 animate-pulse"></div>
+              <div className="bg-gradient-to-r from-blue-500 to-green-500 h-2 rounded-full w-1/3 animate-pulse"></div>
             </div>
+            <div className="mt-2 text-xs text-gray-500">Paso 1 de 4: Validación</div>
+          </div>
+        </div>
+      );
+
+    case 'suggestions':
+      return (
+        <div className="text-center py-12">
+          <div className="w-20 h-20 bg-gradient-to-r from-purple-500 to-pink-500 rounded-full flex items-center justify-center mx-auto mb-6 animate-pulse">
+            <Brain className="w-10 h-10 text-white" />
+          </div>
+          <h2 className="text-xl font-semibold text-gray-900 mb-3">
+            🧠 Generando sugerencias con IA...
+          </h2>
+          <p className="text-gray-600 mb-6">
+            Clasificando productos y analizando tu modelo de negocio
+          </p>
+          <div className="bg-white rounded-lg shadow-sm p-4 max-w-md mx-auto">
+            {state.validationResult && (
+              <div className="mb-4">
+                <div className="flex items-center justify-center space-x-2 text-green-600 mb-2">
+                  <CheckCircle2 className="w-4 h-4" />
+                  <span className="text-sm font-medium">
+                    {state.validationResult.unique_products} productos únicos encontrados
+                  </span>
+                </div>
+              </div>
+            )}
+            <div className="bg-gray-200 rounded-full h-2">
+              <div className="bg-gradient-to-r from-purple-500 to-pink-500 h-2 rounded-full w-2/3 animate-pulse"></div>
+            </div>
+            <div className="mt-2 text-xs text-gray-500">Paso 2 de 4: Clasificación IA</div>
           </div>
         </div>
       );
 
     case 'review':
-      if (!state.analysisResult) return null;
+      if (!state.suggestionsResult) return null;
       
-      const { analysisResult, reviewedSuggestions } = state;
+      const { suggestionsResult, reviewedSuggestions } = state;
       const approvedCount = reviewedSuggestions?.filter(s => s.user_approved).length || 0;
       const highConfidenceCount = reviewedSuggestions?.filter(s => s.confidence_score >= 0.7).length || 0;
       const visibleSuggestions = showAllSuggestions 
@@ -519,12 +588,15 @@ const SmartHistoricalDataImport: React.FC<SmartHistoricalDataImportProps> = ({
               ¡Análisis Completado! 🎉
             </h2>
             <p className="text-gray-600">
-              Hemos encontrado <strong>{analysisResult.total_products_found} productos</strong> y 
+              Hemos encontrado <strong>{suggestionsResult.total_products} productos</strong> y 
               sugerimos <strong>{approvedCount} para tu inventario</strong>
             </p>
+            <div className="mt-2 text-sm text-gray-500">
+              ⚡ Procesado en {suggestionsResult.processing_time_seconds.toFixed(1)}s
+            </div>
           </div>
 
-          {renderBusinessModelInsight(analysisResult.business_model_analysis)}
+          {renderBusinessModelInsight(suggestionsResult.business_model_analysis)}
 
           <div className="bg-white border rounded-xl p-6">
             <div className="flex items-center justify-between mb-4">
@@ -579,15 +651,15 @@ const SmartHistoricalDataImport: React.FC<SmartHistoricalDataImportProps> = ({
               {visibleSuggestions?.map(renderSuggestionCard)}
             </div>
 
-            {analysisResult.warnings.length > 0 && (
+            {state.validationResult?.validation_warnings && state.validationResult.validation_warnings.length > 0 && (
               <div className="bg-amber-50 border border-amber-200 rounded-lg p-4 mb-4">
                 <div className="flex">
                   <AlertTriangle className="h-5 w-5 text-amber-400" />
                   <div className="ml-3">
-                    <h4 className="text-sm font-medium text-amber-800">Advertencias</h4>
+                    <h4 className="text-sm font-medium text-amber-800">Advertencias de Validación</h4>
                     <ul className="mt-2 text-sm text-amber-700 space-y-1">
-                      {analysisResult.warnings.map((warning, idx) => (
-                        <li key={idx}>• {warning}</li>
+                      {state.validationResult.validation_warnings.map((warning, idx) => (
+                        <li key={idx}>• {warning.message || warning}</li>
                       ))}
                     </ul>
                   </div>
@@ -630,6 +702,9 @@ const SmartHistoricalDataImport: React.FC<SmartHistoricalDataImportProps> = ({
     case 'creation':
     case 'import':
       const isCreating = state.phase === 'creation';
+      const stepNumber = isCreating ? 3 : 4;
+      const stepProgress = isCreating ? 75 : 90;
+      
       return (
         <div className="text-center py-12">
           <div className="w-20 h-20 bg-gradient-to-r from-green-400 to-blue-500 rounded-full flex items-center justify-center mx-auto mb-6 animate-pulse">
@@ -662,11 +737,17 @@ const SmartHistoricalDataImport: React.FC<SmartHistoricalDataImportProps> = ({
             )}
             
             <div className="bg-gray-200 rounded-full h-3">
-              <div className="bg-gradient-to-r from-green-400 to-blue-500 h-3 rounded-full w-3/4 animate-pulse"></div>
+              <div 
+                className="bg-gradient-to-r from-green-400 to-blue-500 h-3 rounded-full animate-pulse transition-all duration-500"
+                style={{ width: `${stepProgress}%` }}
+              />
             </div>
-            <p className="text-sm text-gray-500 mt-2">
+            <div className="flex justify-between items-center mt-2">
+              <p className="text-sm text-gray-500">
                 {isCreating ? 'Creando inventario...' : 'Procesando importación final...'}
               </p>
+              <span className="text-xs text-gray-400">Paso {stepNumber} de 4</span>
+            </div>
           </div>
         </div>
       );

frontend/src/pages/inventory/InventoryPage.tsx

@@ -5,7 +5,7 @@ import {
   Filter,
   Download,
   Upload,
-  Grid3X3,
+  LayoutGrid,
   List,
   Package,
   TrendingDown,
@@ -143,6 +143,14 @@ const InventoryPage: React.FC = () => {
     console.log('View details:', item);
   };
 
+  // Handle view item by ID (for alerts)
+  const handleViewItemById = (itemId: string) => {
+    const item = items.find(item => item.id === itemId);
+    if (item) {
+      handleItemViewDetails(item);
+    }
+  };
+
   // Handle alert acknowledgment
   const handleAcknowledgeAlert = async (alertId: string) => {
     await acknowledgeAlert(alertId);
@@ -302,7 +310,7 @@ const InventoryPage: React.FC = () => {
                           : 'bg-white text-gray-600 hover:bg-gray-50'
                       }`}
                     >
-                      <Grid3X3 className="w-4 h-4" />
+                      <LayoutGrid className="w-4 h-4" />
                     </button>
                     <button
                       onClick={() => setViewMode('list')}
@@ -529,7 +537,7 @@ const InventoryPage: React.FC = () => {
                 alerts={alerts}
                 onAcknowledge={handleAcknowledgeAlert}
                 onAcknowledgeAll={handleBulkAcknowledgeAlerts}
-                onViewItem={handleItemViewDetails}
+                onViewItem={handleViewItemById}
               />
             </div>
           )}

frontend/src/pages/recipes/RecipesPage.tsx

@@ -4,7 +4,7 @@ import {
   Search,
   Plus,
   Filter,
-  Grid3X3,
+  LayoutGrid,
   List,
   ChefHat,
   TrendingUp,
@@ -304,7 +304,7 @@ const RecipesPage: React.FC = () => {
                       : 'bg-white text-gray-600 hover:bg-gray-50'
                   }`}
                 >
-                  <Grid3X3 className="w-4 h-4" />
+                  <LayoutGrid className="w-4 h-4" />
                 </button>
                 <button
                   onClick={() => setViewMode('list')}

gateway/app/main.py

@@ -17,7 +17,7 @@ from app.core.service_discovery import ServiceDiscovery
 from app.middleware.auth import AuthMiddleware
 from app.middleware.logging import LoggingMiddleware
 from app.middleware.rate_limit import RateLimitMiddleware
-from app.routes import auth, tenant, notification, nominatim, user
+from app.routes import auth, tenant, notification, nominatim, user, inventory
 from shared.monitoring.logging import setup_logging
 from shared.monitoring.metrics import MetricsCollector
 
@@ -60,6 +60,7 @@ app.include_router(user.router, prefix="/api/v1/users", tags=["users"])
 app.include_router(tenant.router, prefix="/api/v1/tenants", tags=["tenants"])
 app.include_router(notification.router, prefix="/api/v1/notifications", tags=["notifications"])
 app.include_router(nominatim.router, prefix="/api/v1/nominatim", tags=["location"])
+app.include_router(inventory.router, prefix="/api/v1/inventory", tags=["inventory"])
 
 @app.on_event("startup")
 async def startup_event():

gateway/app/routes/inventory.py

@@ -0,0 +1,216 @@
+# gateway/app/routes/inventory.py
+"""
+Inventory routes for API Gateway - Handles inventory management endpoints
+"""
+
+from fastapi import APIRouter, Request, Response, HTTPException, Path
+from fastapi.responses import JSONResponse
+import httpx
+import logging
+from typing import Optional
+
+from app.core.config import settings
+
+logger = logging.getLogger(__name__)
+router = APIRouter()
+
+# Inventory service URL - add to settings
+INVENTORY_SERVICE_URL = "http://inventory-service:8000"
+
+# ================================================================
+# TENANT-SCOPED INVENTORY ENDPOINTS
+# ================================================================
+
+@router.api_route("/{tenant_id}/inventory/ingredients{path:path}", methods=["GET", "POST", "PUT", "DELETE", "OPTIONS"])
+async def proxy_tenant_ingredients(request: Request, tenant_id: str = Path(...), path: str = ""):
+    """Proxy tenant ingredient requests to inventory service"""
+    base_path = f"/api/v1/ingredients"
+    
+    # If path is empty or just "/", use base path
+    if not path or path == "/" or path == "":
+        target_path = base_path
+    else:
+        # Ensure path starts with "/"
+        if not path.startswith("/"):
+            path = "/" + path
+        target_path = base_path + path
+    
+    return await _proxy_to_inventory_service(request, target_path)
+
+@router.api_route("/{tenant_id}/inventory/stock{path:path}", methods=["GET", "POST", "PUT", "DELETE", "OPTIONS"])
+async def proxy_tenant_stock(request: Request, tenant_id: str = Path(...), path: str = ""):
+    """Proxy tenant stock requests to inventory service"""
+    base_path = f"/api/v1/stock"
+    
+    # If path is empty or just "/", use base path
+    if not path or path == "/" or path == "":
+        target_path = base_path
+    else:
+        # Ensure path starts with "/"
+        if not path.startswith("/"):
+            path = "/" + path
+        target_path = base_path + path
+    
+    return await _proxy_to_inventory_service(request, target_path)
+
+@router.api_route("/{tenant_id}/inventory/alerts{path:path}", methods=["GET", "POST", "PUT", "DELETE", "OPTIONS"])
+async def proxy_tenant_alerts(request: Request, tenant_id: str = Path(...), path: str = ""):
+    """Proxy tenant inventory alert requests to inventory service"""
+    base_path = f"/api/v1/alerts"
+    
+    # If path is empty or just "/", use base path  
+    if not path or path == "/" or path == "":
+        target_path = base_path
+    else:
+        # Ensure path starts with "/"
+        if not path.startswith("/"):
+            path = "/" + path
+        target_path = base_path + path
+    
+    return await _proxy_to_inventory_service(request, target_path)
+
+@router.api_route("/{tenant_id}/inventory/dashboard{path:path}", methods=["GET", "OPTIONS"])
+async def proxy_tenant_inventory_dashboard(request: Request, tenant_id: str = Path(...), path: str = ""):
+    """Proxy tenant inventory dashboard requests to inventory service"""
+    base_path = f"/api/v1/dashboard"
+    
+    # If path is empty or just "/", use base path
+    if not path or path == "/" or path == "":
+        target_path = base_path
+    else:
+        # Ensure path starts with "/"
+        if not path.startswith("/"):
+            path = "/" + path
+        target_path = base_path + path
+    
+    return await _proxy_to_inventory_service(request, target_path)
+
+# ================================================================
+# DIRECT INVENTORY ENDPOINTS (for backward compatibility)
+# ================================================================
+
+@router.api_route("/ingredients{path:path}", methods=["GET", "POST", "PUT", "DELETE", "OPTIONS"])
+async def proxy_ingredients(request: Request, path: str = ""):
+    """Proxy ingredient requests to inventory service"""
+    base_path = f"/api/v1/ingredients"
+    
+    if not path or path == "/" or path == "":
+        target_path = base_path
+    else:
+        if not path.startswith("/"):
+            path = "/" + path
+        target_path = base_path + path
+    
+    return await _proxy_to_inventory_service(request, target_path)
+
+@router.api_route("/stock{path:path}", methods=["GET", "POST", "PUT", "DELETE", "OPTIONS"])
+async def proxy_stock(request: Request, path: str = ""):
+    """Proxy stock requests to inventory service"""
+    base_path = f"/api/v1/stock"
+    
+    if not path or path == "/" or path == "":
+        target_path = base_path
+    else:
+        if not path.startswith("/"):
+            path = "/" + path
+        target_path = base_path + path
+    
+    return await _proxy_to_inventory_service(request, target_path)
+
+@router.api_route("/alerts{path:path}", methods=["GET", "POST", "PUT", "DELETE", "OPTIONS"])
+async def proxy_alerts(request: Request, path: str = ""):
+    """Proxy inventory alert requests to inventory service"""
+    base_path = f"/api/v1/alerts"
+    
+    if not path or path == "/" or path == "":
+        target_path = base_path
+    else:
+        if not path.startswith("/"):
+            path = "/" + path
+        target_path = base_path + path
+    
+    return await _proxy_to_inventory_service(request, target_path)
+
+# ================================================================
+# PROXY HELPER FUNCTION
+# ================================================================
+
+async def _proxy_to_inventory_service(request: Request, target_path: str):
+    """Proxy request to inventory service with enhanced error handling"""
+    
+    # Handle OPTIONS requests directly for CORS
+    if request.method == "OPTIONS":
+        return Response(
+            status_code=200,
+            headers={
+                "Access-Control-Allow-Origin": "*",
+                "Access-Control-Allow-Methods": "GET, POST, PUT, DELETE, OPTIONS",
+                "Access-Control-Allow-Headers": "Content-Type, Authorization, X-Tenant-ID",
+                "Access-Control-Allow-Credentials": "true",
+                "Access-Control-Max-Age": "86400"
+            }
+        )
+    
+    try:
+        url = f"{INVENTORY_SERVICE_URL}{target_path}"
+        
+        # Forward headers and add user/tenant context
+        headers = dict(request.headers)
+        headers.pop("host", None)
+        
+        # Get request body if present
+        body = None
+        if request.method in ["POST", "PUT", "PATCH"]:
+            body = await request.body()
+        
+        # Add query parameters
+        params = dict(request.query_params)
+        
+        timeout_config = httpx.Timeout(
+            connect=30.0,    # Connection timeout
+            read=600.0,      # Read timeout: 10 minutes
+            write=30.0,      # Write timeout
+            pool=30.0        # Pool timeout
+        )
+        
+        async with httpx.AsyncClient(timeout=timeout_config) as client:
+            response = await client.request(
+                method=request.method,
+                url=url,
+                headers=headers,
+                content=body,
+                params=params
+            )
+            
+            # Handle different response types
+            if response.headers.get("content-type", "").startswith("application/json"):
+                try:
+                    content = response.json()
+                except:
+                    content = {"message": "Invalid JSON response from inventory service"}
+            else:
+                content = response.text
+            
+            return JSONResponse(
+                status_code=response.status_code,
+                content=content
+            )
+            
+    except httpx.ConnectTimeout:
+        logger.error(f"Connection timeout to inventory service: {INVENTORY_SERVICE_URL}{target_path}")
+        raise HTTPException(
+            status_code=503,
+            detail="Inventory service temporarily unavailable"
+        )
+    except httpx.ReadTimeout:
+        logger.error(f"Read timeout from inventory service: {INVENTORY_SERVICE_URL}{target_path}")
+        raise HTTPException(
+            status_code=504,
+            detail="Inventory service response timeout"
+        )
+    except Exception as e:
+        logger.error(f"Unexpected error proxying to inventory service {INVENTORY_SERVICE_URL}{target_path}: {e}")
+        raise HTTPException(
+            status_code=500,
+            detail="Internal gateway error"
+        )

services/recipes/app/services/inventory_client.py

@@ -3,40 +3,27 @@
 Client for communicating with Inventory Service
 """
 
-import httpx
 import logging
 from typing import List, Optional, Dict, Any
 from uuid import UUID
 
+from shared.clients.inventory_client import InventoryServiceClient as SharedInventoryClient
 from ..core.config import settings
 
 logger = logging.getLogger(__name__)
 
 
 class InventoryClient:
-    """Client for inventory service communication"""
+    """Client for inventory service communication via shared client"""
     
     def __init__(self):
-        self.base_url = settings.INVENTORY_SERVICE_URL
-        self.timeout = 30.0
+        self._shared_client = SharedInventoryClient(settings)
     
     async def get_ingredient_by_id(self, tenant_id: UUID, ingredient_id: UUID) -> Optional[Dict[str, Any]]:
         """Get ingredient details from inventory service"""
         try:
-            async with httpx.AsyncClient(timeout=self.timeout) as client:
-                response = await client.get(
-                    f"{self.base_url}/api/v1/ingredients/{ingredient_id}",
-                    headers={"X-Tenant-ID": str(tenant_id)}
-                )
-                
-                if response.status_code == 200:
-                    return response.json()
-                elif response.status_code == 404:
-                    return None
-                else:
-                    logger.error(f"Failed to get ingredient {ingredient_id}: {response.status_code}")
-                    return None
-                    
+            result = await self._shared_client.get_ingredient_by_id(ingredient_id, str(tenant_id))
+            return result
         except Exception as e:
             logger.error(f"Error getting ingredient {ingredient_id}: {e}")
             return None
@@ -44,19 +31,13 @@ class InventoryClient:
     async def get_ingredients_by_ids(self, tenant_id: UUID, ingredient_ids: List[UUID]) -> List[Dict[str, Any]]:
         """Get multiple ingredients by IDs"""
         try:
-            async with httpx.AsyncClient(timeout=self.timeout) as client:
-                response = await client.post(
-                    f"{self.base_url}/api/v1/ingredients/batch",
-                    headers={"X-Tenant-ID": str(tenant_id)},
-                    json={"ingredient_ids": [str(id) for id in ingredient_ids]}
-                )
-                
-                if response.status_code == 200:
-                    return response.json()
-                else:
-                    logger.error(f"Failed to get ingredients batch: {response.status_code}")
-                    return []
-                    
+            # For now, get ingredients individually - could be optimized with batch endpoint
+            results = []
+            for ingredient_id in ingredient_ids:
+                ingredient = await self._shared_client.get_ingredient_by_id(ingredient_id, str(tenant_id))
+                if ingredient:
+                    results.append(ingredient)
+            return results
         except Exception as e:
             logger.error(f"Error getting ingredients batch: {e}")
             return []
@@ -64,20 +45,16 @@ class InventoryClient:
     async def get_ingredient_stock_level(self, tenant_id: UUID, ingredient_id: UUID) -> Optional[Dict[str, Any]]:
         """Get current stock level for ingredient"""
         try:
-            async with httpx.AsyncClient(timeout=self.timeout) as client:
-                response = await client.get(
-                    f"{self.base_url}/api/v1/stock/ingredient/{ingredient_id}",
-                    headers={"X-Tenant-ID": str(tenant_id)}
-                )
-                
-                if response.status_code == 200:
-                    return response.json()
-                elif response.status_code == 404:
+            stock_entries = await self._shared_client.get_ingredient_stock(ingredient_id, str(tenant_id))
+            if stock_entries:
+                # Calculate total available stock from all entries
+                total_stock = sum(entry.get('available_quantity', 0) for entry in stock_entries)
+                return {
+                    'ingredient_id': str(ingredient_id),
+                    'total_available': total_stock,
+                    'stock_entries': stock_entries
+                }
             return None
-                else:
-                    logger.error(f"Failed to get stock level for {ingredient_id}: {response.status_code}")
-                    return None
-                    
         except Exception as e:
             logger.error(f"Error getting stock level for {ingredient_id}: {e}")
             return None
@@ -114,22 +91,18 @@ class InventoryClient:
     ) -> Dict[str, Any]:
         """Record ingredient consumption for production"""
         try:
-            async with httpx.AsyncClient(timeout=self.timeout) as client:
-                response = await client.post(
-                    f"{self.base_url}/api/v1/stock/consume",
-                    headers={"X-Tenant-ID": str(tenant_id)},
-                    json={
+            consumption_data = {
                 "consumptions": consumptions,
                 "reference_number": str(production_batch_id),
                 "movement_type": "production_use"
             }
-                )
             
-                if response.status_code == 200:
-                    return {"success": True, "data": response.json()}
+            result = await self._shared_client.consume_stock(consumption_data, str(tenant_id))
+            
+            if result:
+                return {"success": True, "data": result}
             else:
-                    logger.error(f"Failed to consume ingredients: {response.status_code}")
-                    return {"success": False, "error": response.text}
+                return {"success": False, "error": "Failed to consume ingredients"}
                 
         except Exception as e:
             logger.error(f"Error consuming ingredients: {e}")
@@ -142,18 +115,12 @@ class InventoryClient:
     ) -> Dict[str, Any]:
         """Add finished product to inventory after production"""
         try:
-            async with httpx.AsyncClient(timeout=self.timeout) as client:
-                response = await client.post(
-                    f"{self.base_url}/api/v1/stock/add",
-                    headers={"X-Tenant-ID": str(tenant_id)},
-                    json=product_data
-                )
+            result = await self._shared_client.receive_stock(product_data, str(tenant_id))
             
-                if response.status_code == 200:
-                    return {"success": True, "data": response.json()}
+            if result:
+                return {"success": True, "data": result}
             else:
-                    logger.error(f"Failed to add finished product: {response.status_code}")
-                    return {"success": False, "error": response.text}
+                return {"success": False, "error": "Failed to add finished product"}
                 
         except Exception as e:
             logger.error(f"Error adding finished product: {e}")

services/sales/app/api/onboarding.py

@@ -10,11 +10,12 @@ from uuid import UUID
 from pydantic import BaseModel, Field
 import structlog
 
-from app.services.onboarding_import_service import (
-    OnboardingImportService, 
+from app.services.ai_onboarding_service import (
+    AIOnboardingService,
+    OnboardingValidationResult,
+    ProductSuggestionsResult,
     OnboardingImportResult,
-    InventoryCreationRequest,
-    get_onboarding_import_service
+    get_ai_onboarding_service
 )
 from shared.auth.decorators import get_current_user_dep, get_current_tenant_id_dep
 
@@ -22,16 +23,6 @@ router = APIRouter(tags=["onboarding"])
 logger = structlog.get_logger()
 
 
-class OnboardingAnalysisResponse(BaseModel):
-    """Response for onboarding analysis"""
-    total_products_found: int
-    inventory_suggestions: List[Dict[str, Any]]
-    business_model_analysis: Dict[str, Any]
-    import_job_id: str
-    status: str
-    processed_rows: int
-    errors: List[str]
-    warnings: List[str]
 
 
 class InventoryApprovalRequest(BaseModel):
@@ -58,23 +49,22 @@ class SalesImportResponse(BaseModel):
     warnings: List[str]
 
 
-@router.post("/tenants/{tenant_id}/onboarding/analyze", response_model=OnboardingAnalysisResponse)
-async def analyze_onboarding_data(
+@router.post("/tenants/{tenant_id}/onboarding/validate-file", response_model=FileValidationResponse)
+async def validate_onboarding_file(
     file: UploadFile = File(..., description="Sales data CSV/Excel file"),
     tenant_id: UUID = Path(..., description="Tenant ID"),
     current_tenant: str = Depends(get_current_tenant_id_dep),
     current_user: Dict[str, Any] = Depends(get_current_user_dep),
-    onboarding_service: OnboardingImportService = Depends(get_onboarding_import_service)
+    onboarding_service: AIOnboardingService = Depends(get_ai_onboarding_service)
 ):
     """
-    Step 1: Analyze uploaded sales data and suggest inventory items
+    Step 1: Validate uploaded file and extract unique products
     
     This endpoint:
-    1. Parses the uploaded sales file
-    2. Extracts unique products and sales metrics
-    3. Uses AI to classify products and suggest inventory items
-    4. Analyzes business model (production vs retail)
-    5. Returns suggestions for user review
+    1. Validates the file format and content
+    2. Checks for required columns (date, product, etc.)
+    3. Extracts unique products from sales data
+    4. Returns validation results and product list
     """
     try:
         # Verify tenant access
@@ -89,34 +79,42 @@ async def analyze_onboarding_data(
         if not any(file.filename.lower().endswith(ext) for ext in allowed_extensions):
             raise HTTPException(status_code=400, detail=f"Unsupported file format. Allowed: {allowed_extensions}")
         
+        # Determine file format
+        file_format = "csv" if file.filename.lower().endswith('.csv') else "excel"
+        
         # Read file content
         file_content = await file.read()
         if not file_content:
             raise HTTPException(status_code=400, detail="File is empty")
         
-        # Analyze the data
-        result = await onboarding_service.analyze_sales_data_for_onboarding(
-            file_content=file_content,
-            filename=file.filename,
-            tenant_id=tenant_id,
-            user_id=UUID(current_user['user_id'])
+        # Convert bytes to string for CSV
+        if file_format == "csv":
+            file_data = file_content.decode('utf-8')
+        else:
+            import base64
+            file_data = base64.b64encode(file_content).decode('utf-8')
+        
+        # Validate and extract products
+        result = await onboarding_service.validate_and_extract_products(
+            file_data=file_data,
+            file_format=file_format,
+            tenant_id=tenant_id
         )
         
-        response = OnboardingAnalysisResponse(
-            total_products_found=result.total_products_found,
-            inventory_suggestions=result.inventory_suggestions,
-            business_model_analysis=result.business_model_analysis,
-            import_job_id=str(result.import_job_id),
-            status=result.status,
-            processed_rows=result.processed_rows,
-            errors=result.errors,
-            warnings=result.warnings
+        response = FileValidationResponse(
+            is_valid=result.is_valid,
+            total_records=result.total_records,
+            unique_products=result.unique_products,
+            product_list=result.product_list,
+            validation_errors=result.validation_details.errors,
+            validation_warnings=result.validation_details.warnings,
+            summary=result.summary
         )
         
-        logger.info("Onboarding analysis complete", 
+        logger.info("File validation complete", 
                    filename=file.filename,
-                   products_found=result.total_products_found,
-                   business_model=result.business_model_analysis.get('model'),
+                   is_valid=result.is_valid,
+                   unique_products=result.unique_products,
                    tenant_id=tenant_id)
         
         return response
@@ -124,9 +122,120 @@ async def analyze_onboarding_data(
     except HTTPException:
         raise
     except Exception as e:
-        logger.error("Failed onboarding analysis", 
+        logger.error("Failed file validation", 
                     error=str(e), filename=file.filename if file else None, tenant_id=tenant_id)
-        raise HTTPException(status_code=500, detail=f"Analysis failed: {str(e)}")
+        raise HTTPException(status_code=500, detail=f"Validation failed: {str(e)}")
+
+
+@router.post("/tenants/{tenant_id}/onboarding/generate-suggestions", response_model=ProductSuggestionsResponse)
+async def generate_inventory_suggestions(
+    file: UploadFile = File(..., description="Same sales data file from step 1"),
+    product_list: str = Form(..., description="JSON array of product names to classify"),
+    tenant_id: UUID = Path(..., description="Tenant ID"),
+    current_tenant: str = Depends(get_current_tenant_id_dep),
+    current_user: Dict[str, Any] = Depends(get_current_user_dep),
+    onboarding_service: AIOnboardingService = Depends(get_ai_onboarding_service)
+):
+    """
+    Step 2: Generate AI-powered inventory suggestions
+    
+    This endpoint:
+    1. Takes the validated file and product list from step 1
+    2. Uses AI to classify products into inventory categories
+    3. Analyzes business model (production vs retail)
+    4. Returns detailed suggestions for user review
+    """
+    try:
+        # Verify tenant access
+        if str(tenant_id) != current_tenant:
+            raise HTTPException(status_code=403, detail="Access denied to this tenant")
+        
+        # Parse product list
+        import json
+        try:
+            products = json.loads(product_list)
+        except json.JSONDecodeError as e:
+            raise HTTPException(status_code=400, detail=f"Invalid product list format: {str(e)}")
+        
+        if not products:
+            raise HTTPException(status_code=400, detail="No products provided")
+        
+        # Determine file format
+        file_format = "csv" if file.filename.lower().endswith('.csv') else "excel"
+        
+        # Read file content
+        file_content = await file.read()
+        if not file_content:
+            raise HTTPException(status_code=400, detail="File is empty")
+        
+        # Convert bytes to string for CSV
+        if file_format == "csv":
+            file_data = file_content.decode('utf-8')
+        else:
+            import base64
+            file_data = base64.b64encode(file_content).decode('utf-8')
+        
+        # Generate suggestions
+        result = await onboarding_service.generate_inventory_suggestions(
+            product_list=products,
+            file_data=file_data,
+            file_format=file_format,
+            tenant_id=tenant_id
+        )
+        
+        # Convert suggestions to dict format
+        suggestions_dict = []
+        for suggestion in result.suggestions:
+            suggestion_dict = {
+                "suggestion_id": suggestion.suggestion_id,
+                "original_name": suggestion.original_name,
+                "suggested_name": suggestion.suggested_name,
+                "product_type": suggestion.product_type,
+                "category": suggestion.category,
+                "unit_of_measure": suggestion.unit_of_measure,
+                "confidence_score": suggestion.confidence_score,
+                "estimated_shelf_life_days": suggestion.estimated_shelf_life_days,
+                "requires_refrigeration": suggestion.requires_refrigeration,
+                "requires_freezing": suggestion.requires_freezing,
+                "is_seasonal": suggestion.is_seasonal,
+                "suggested_supplier": suggestion.suggested_supplier,
+                "notes": suggestion.notes,
+                "sales_data": suggestion.sales_data
+            }
+            suggestions_dict.append(suggestion_dict)
+        
+        business_model_dict = {
+            "model": result.business_model_analysis.model,
+            "confidence": result.business_model_analysis.confidence,
+            "ingredient_count": result.business_model_analysis.ingredient_count,
+            "finished_product_count": result.business_model_analysis.finished_product_count,
+            "ingredient_ratio": result.business_model_analysis.ingredient_ratio,
+            "recommendations": result.business_model_analysis.recommendations
+        }
+        
+        response = ProductSuggestionsResponse(
+            suggestions=suggestions_dict,
+            business_model_analysis=business_model_dict,
+            total_products=result.total_products,
+            high_confidence_count=result.high_confidence_count,
+            low_confidence_count=result.low_confidence_count,
+            processing_time_seconds=result.processing_time_seconds
+        )
+        
+        logger.info("AI suggestions generated", 
+                   total_products=result.total_products,
+                   business_model=result.business_model_analysis.model,
+                   high_confidence=result.high_confidence_count,
+                   tenant_id=tenant_id)
+        
+        return response
+        
+    except HTTPException:
+        raise
+    except Exception as e:
+        logger.error("Failed to generate suggestions", 
+                    error=str(e), tenant_id=tenant_id)
+        raise HTTPException(status_code=500, detail=f"Suggestion generation failed: {str(e)}")
 
 
 @router.post("/tenants/{tenant_id}/onboarding/create-inventory", response_model=InventoryCreationResponse)
@@ -135,16 +244,16 @@ async def create_inventory_from_suggestions(
     tenant_id: UUID = Path(..., description="Tenant ID"),
     current_tenant: str = Depends(get_current_tenant_id_dep),
     current_user: Dict[str, Any] = Depends(get_current_user_dep),
-    onboarding_service: OnboardingImportService = Depends(get_onboarding_import_service)
+    onboarding_service: AIOnboardingService = Depends(get_ai_onboarding_service)
 ):
     """
-    Step 2: Create inventory items from approved suggestions
+    Step 3: Create inventory items from approved suggestions
     
     This endpoint:
-    1. Takes user-approved inventory suggestions
-    2. Applies any user modifications
+    1. Takes user-approved inventory suggestions from step 2
+    2. Applies any user modifications to suggestions
     3. Creates inventory items via inventory service
-    4. Returns creation results
+    4. Returns creation results for final import step
     """
     try:
         # Verify tenant access
@@ -154,18 +263,9 @@ async def create_inventory_from_suggestions(
         if not request.suggestions:
             raise HTTPException(status_code=400, detail="No suggestions provided")
         
-        # Convert to internal format
-        approval_requests = []
-        for suggestion in request.suggestions:
-            approval_requests.append(InventoryCreationRequest(
-                suggestion_id=suggestion.get('suggestion_id'),
-                approved=suggestion.get('approved', False),
-                modifications=suggestion.get('modifications', {})
-            ))
-        
-        # Create inventory items
+        # Create inventory items using new service
         result = await onboarding_service.create_inventory_from_suggestions(
-            suggestions_approval=approval_requests,
+            approved_suggestions=request.suggestions,
             tenant_id=tenant_id,
             user_id=UUID(current_user['user_id'])
         )
@@ -199,16 +299,16 @@ async def import_sales_with_inventory(
     tenant_id: UUID = Path(..., description="Tenant ID"),
     current_tenant: str = Depends(get_current_tenant_id_dep),
     current_user: Dict[str, Any] = Depends(get_current_user_dep),
-    onboarding_service: OnboardingImportService = Depends(get_onboarding_import_service)
+    onboarding_service: AIOnboardingService = Depends(get_ai_onboarding_service)
 ):
     """
-    Step 3: Import sales data using created inventory items
+    Step 4: Final sales data import using created inventory items
     
     This endpoint:
-    1. Takes the same sales file from step 1
-    2. Uses the inventory mapping from step 2
-    3. Imports sales records with proper inventory product references
-    4. Returns import results
+    1. Takes the same validated sales file from step 1
+    2. Uses the inventory mapping from step 3
+    3. Imports sales records using detailed processing from DataImportService
+    4. Returns final import results - onboarding complete!
     """
     try:
         # Verify tenant access
@@ -223,41 +323,51 @@ async def import_sales_with_inventory(
         import json
         try:
             mapping = json.loads(inventory_mapping)
-            # Convert string UUIDs to UUID objects
-            inventory_mapping_uuids = {
-                product_name: UUID(inventory_id) 
+            # Convert to string mapping for the new service
+            inventory_mapping_dict = {
+                product_name: str(inventory_id) 
                 for product_name, inventory_id in mapping.items()
             }
-        except (json.JSONDecodeError, ValueError) as e:
+        except json.JSONDecodeError as e:
             raise HTTPException(status_code=400, detail=f"Invalid inventory mapping format: {str(e)}")
         
+        # Determine file format
+        file_format = "csv" if file.filename.lower().endswith('.csv') else "excel"
+        
         # Read file content
         file_content = await file.read()
         if not file_content:
             raise HTTPException(status_code=400, detail="File is empty")
         
-        # Import sales data
+        # Convert bytes to string for CSV
+        if file_format == "csv":
+            file_data = file_content.decode('utf-8')
+        else:
+            import base64
+            file_data = base64.b64encode(file_content).decode('utf-8')
+        
+        # Import sales data using new service
         result = await onboarding_service.import_sales_data_with_inventory(
-            file_content=file_content,
-            filename=file.filename,
+            file_data=file_data,
+            file_format=file_format,
+            inventory_mapping=inventory_mapping_dict,
             tenant_id=tenant_id,
-            user_id=UUID(current_user['user_id']),
-            inventory_mapping=inventory_mapping_uuids
+            filename=file.filename
         )
         
         response = SalesImportResponse(
-            import_job_id=str(result.import_job_id),
-            status=result.status,
-            processed_rows=result.processed_rows,
-            successful_imports=result.successful_imports,
-            failed_imports=result.failed_imports,
-            errors=result.errors,
-            warnings=result.warnings
+            import_job_id="onboarding-" + str(tenant_id),  # Generate a simple job ID
+            status="completed" if result.success else "failed",
+            processed_rows=result.import_details.records_processed,
+            successful_imports=result.import_details.records_created,
+            failed_imports=result.import_details.records_failed,
+            errors=[error.get("message", str(error)) for error in result.import_details.errors],
+            warnings=[warning.get("message", str(warning)) for warning in result.import_details.warnings]
         )
         
         logger.info("Sales import complete", 
-                   successful=result.successful_imports,
-                   failed=result.failed_imports,
+                   successful=result.import_details.records_created,
+                   failed=result.import_details.records_failed,
                    filename=file.filename,
                    tenant_id=tenant_id)
         

services/sales/app/services/ai_onboarding_service.py

@@ -0,0 +1,627 @@
+# services/sales/app/services/ai_onboarding_service.py
+"""
+AI-Powered Onboarding Service
+Handles the complete onboarding flow: File validation -> Product extraction -> Inventory suggestions -> Data processing
+"""
+
+import pandas as pd
+import structlog
+from typing import List, Dict, Any, Optional
+from uuid import UUID, uuid4
+from dataclasses import dataclass
+import asyncio
+
+from app.services.data_import_service import DataImportService, SalesValidationResult, SalesImportResult
+from app.services.inventory_client import InventoryServiceClient
+from app.core.database import get_db_transaction
+
+logger = structlog.get_logger()
+
+
+@dataclass
+class ProductSuggestion:
+    """Single product suggestion from AI classification"""
+    suggestion_id: str
+    original_name: str
+    suggested_name: str
+    product_type: str
+    category: str
+    unit_of_measure: str
+    confidence_score: float
+    estimated_shelf_life_days: Optional[int] = None
+    requires_refrigeration: bool = False
+    requires_freezing: bool = False
+    is_seasonal: bool = False
+    suggested_supplier: Optional[str] = None
+    notes: Optional[str] = None
+    sales_data: Optional[Dict[str, Any]] = None
+
+
+@dataclass
+class BusinessModelAnalysis:
+    """Business model analysis results"""
+    model: str  # production, retail, hybrid
+    confidence: float
+    ingredient_count: int
+    finished_product_count: int
+    ingredient_ratio: float
+    recommendations: List[str]
+
+
+@dataclass
+class OnboardingValidationResult:
+    """Result of onboarding file validation step"""
+    is_valid: bool
+    total_records: int
+    unique_products: int
+    validation_details: SalesValidationResult
+    product_list: List[str]
+    summary: Dict[str, Any]
+
+
+@dataclass
+class ProductSuggestionsResult:
+    """Result of AI product classification step"""
+    suggestions: List[ProductSuggestion]
+    business_model_analysis: BusinessModelAnalysis
+    total_products: int
+    high_confidence_count: int
+    low_confidence_count: int
+    processing_time_seconds: float
+
+
+@dataclass
+class OnboardingImportResult:
+    """Result of final data import step"""
+    success: bool
+    import_details: SalesImportResult
+    inventory_items_created: int
+    inventory_creation_errors: List[str]
+    final_summary: Dict[str, Any]
+
+
+class AIOnboardingService:
+    """
+    Unified AI-powered onboarding service that orchestrates the complete flow:
+    1. File validation and product extraction
+    2. AI-powered inventory suggestions
+    3. User confirmation and inventory creation
+    4. Final sales data import
+    """
+    
+    def __init__(self):
+        self.data_import_service = DataImportService()
+        self.inventory_client = InventoryServiceClient()
+    
+    # ================================================================
+    # STEP 1: FILE VALIDATION AND PRODUCT EXTRACTION
+    # ================================================================
+    
+    async def validate_and_extract_products(
+        self, 
+        file_data: str,
+        file_format: str, 
+        tenant_id: UUID
+    ) -> OnboardingValidationResult:
+        """
+        Step 1: Validate uploaded file and extract unique products
+        This uses the detailed validation from data_import_service
+        """
+        try:
+            logger.info("Starting onboarding validation and product extraction", 
+                       file_format=file_format, tenant_id=tenant_id)
+            
+            # Use data_import_service for detailed validation
+            validation_data = {
+                "tenant_id": str(tenant_id),
+                "data": file_data,
+                "data_format": file_format,
+                "validate_only": True,
+                "source": "ai_onboarding"
+            }
+            
+            validation_result = await self.data_import_service.validate_import_data(validation_data)
+            
+            # Extract unique products if validation passes
+            product_list = []
+            unique_products = 0
+            
+            if validation_result.is_valid and file_format.lower() == "csv":
+                try:
+                    # Parse CSV to extract unique products
+                    import csv
+                    import io
+                    
+                    reader = csv.DictReader(io.StringIO(file_data))
+                    rows = list(reader)
+                    
+                    # Use data_import_service column detection
+                    column_mapping = self.data_import_service._detect_columns(list(rows[0].keys()) if rows else [])
+                    
+                    if column_mapping.get('product'):
+                        product_column = column_mapping['product']
+                        
+                        # Extract and clean unique products
+                        products_raw = [row.get(product_column, '').strip() for row in rows if row.get(product_column, '').strip()]
+                        
+                        # Clean product names using data_import_service method
+                        products_cleaned = [
+                            self.data_import_service._clean_product_name(product) 
+                            for product in products_raw
+                        ]
+                        
+                        # Get unique products
+                        product_list = list(set([p for p in products_cleaned if p and p != "Producto sin nombre"]))
+                        unique_products = len(product_list)
+                        
+                        logger.info("Extracted unique products", 
+                                  total_rows=len(rows), unique_products=unique_products)
+                    
+                except Exception as e:
+                    logger.error("Failed to extract products", error=str(e))
+                    # Don't fail validation just because product extraction failed
+                    pass
+            
+            result = OnboardingValidationResult(
+                is_valid=validation_result.is_valid,
+                total_records=validation_result.total_records,
+                unique_products=unique_products,
+                validation_details=validation_result,
+                product_list=product_list,
+                summary={
+                    "status": "valid" if validation_result.is_valid else "invalid",
+                    "file_format": file_format,
+                    "total_records": validation_result.total_records,
+                    "unique_products": unique_products,
+                    "ready_for_ai_classification": validation_result.is_valid and unique_products > 0,
+                    "next_step": "ai_classification" if validation_result.is_valid and unique_products > 0 else "fix_validation_errors"
+                }
+            )
+            
+            logger.info("Onboarding validation completed", 
+                       is_valid=result.is_valid, 
+                       unique_products=unique_products,
+                       tenant_id=tenant_id)
+            
+            return result
+            
+        except Exception as e:
+            logger.error("Onboarding validation failed", error=str(e), tenant_id=tenant_id)
+            
+            return OnboardingValidationResult(
+                is_valid=False,
+                total_records=0,
+                unique_products=0,
+                validation_details=SalesValidationResult(
+                    is_valid=False,
+                    total_records=0,
+                    valid_records=0,
+                    invalid_records=0,
+                    errors=[{
+                        "type": "system_error",
+                        "message": f"Onboarding validation error: {str(e)}",
+                        "field": None,
+                        "row": None,
+                        "code": "ONBOARDING_VALIDATION_ERROR"
+                    }],
+                    warnings=[],
+                    summary={}
+                ),
+                product_list=[],
+                summary={
+                    "status": "error",
+                    "error_message": str(e),
+                    "next_step": "retry_upload"
+                }
+            )
+    
+    # ================================================================
+    # STEP 2: AI PRODUCT CLASSIFICATION
+    # ================================================================
+    
+    async def generate_inventory_suggestions(
+        self, 
+        product_list: List[str],
+        file_data: str,
+        file_format: str,
+        tenant_id: UUID
+    ) -> ProductSuggestionsResult:
+        """
+        Step 2: Generate AI-powered inventory suggestions for products
+        """
+        import time
+        start_time = time.time()
+        
+        try:
+            logger.info("Starting AI inventory suggestions", 
+                       product_count=len(product_list), tenant_id=tenant_id)
+            
+            if not product_list:
+                raise ValueError("No products provided for classification")
+            
+            # Analyze sales data for each product to provide context
+            product_analysis = await self._analyze_product_sales_data(
+                product_list, file_data, file_format
+            )
+            
+            # Prepare products for classification
+            products_for_classification = []
+            for product_name in product_list:
+                sales_data = product_analysis.get(product_name, {})
+                products_for_classification.append({
+                    "product_name": product_name,
+                    "sales_volume": sales_data.get("total_quantity"),
+                    "sales_data": sales_data
+                })
+            
+            # Call inventory service for AI classification
+            classification_result = await self.inventory_client.classify_products_batch(
+                products_for_classification, tenant_id
+            )
+            
+            if not classification_result or "suggestions" not in classification_result:
+                raise ValueError("Invalid classification response from inventory service")
+            
+            suggestions_raw = classification_result["suggestions"]
+            business_model_raw = classification_result.get("business_model_analysis", {})
+            
+            # Convert to dataclass objects
+            suggestions = []
+            for suggestion_data in suggestions_raw:
+                suggestion = ProductSuggestion(
+                    suggestion_id=suggestion_data.get("suggestion_id", str(uuid4())),
+                    original_name=suggestion_data["original_name"],
+                    suggested_name=suggestion_data["suggested_name"],
+                    product_type=suggestion_data["product_type"],
+                    category=suggestion_data["category"],
+                    unit_of_measure=suggestion_data["unit_of_measure"],
+                    confidence_score=suggestion_data["confidence_score"],
+                    estimated_shelf_life_days=suggestion_data.get("estimated_shelf_life_days"),
+                    requires_refrigeration=suggestion_data.get("requires_refrigeration", False),
+                    requires_freezing=suggestion_data.get("requires_freezing", False),
+                    is_seasonal=suggestion_data.get("is_seasonal", False),
+                    suggested_supplier=suggestion_data.get("suggested_supplier"),
+                    notes=suggestion_data.get("notes"),
+                    sales_data=product_analysis.get(suggestion_data["original_name"])
+                )
+                suggestions.append(suggestion)
+            
+            business_model = BusinessModelAnalysis(
+                model=business_model_raw.get("model", "unknown"),
+                confidence=business_model_raw.get("confidence", 0.0),
+                ingredient_count=business_model_raw.get("ingredient_count", 0),
+                finished_product_count=business_model_raw.get("finished_product_count", 0),
+                ingredient_ratio=business_model_raw.get("ingredient_ratio", 0.0),
+                recommendations=business_model_raw.get("recommendations", [])
+            )
+            
+            # 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)
+            
+            processing_time = time.time() - start_time
+            
+            result = ProductSuggestionsResult(
+                suggestions=suggestions,
+                business_model_analysis=business_model,
+                total_products=len(suggestions),
+                high_confidence_count=high_confidence_count,
+                low_confidence_count=low_confidence_count,
+                processing_time_seconds=processing_time
+            )
+            
+            logger.info("AI inventory suggestions completed", 
+                       total_suggestions=len(suggestions),
+                       business_model=business_model.model,
+                       high_confidence=high_confidence_count,
+                       processing_time=processing_time,
+                       tenant_id=tenant_id)
+            
+            return result
+            
+        except Exception as e:
+            processing_time = time.time() - start_time
+            logger.error("AI inventory suggestions failed", 
+                       error=str(e), tenant_id=tenant_id)
+            
+            # Return fallback suggestions
+            fallback_suggestions = [
+                ProductSuggestion(
+                    suggestion_id=str(uuid4()),
+                    original_name=product_name,
+                    suggested_name=product_name.title(),
+                    product_type="finished_product",
+                    category="other_products",
+                    unit_of_measure="units",
+                    confidence_score=0.3,
+                    notes="Fallback suggestion - requires manual review"
+                )
+                for product_name in product_list
+            ]
+            
+            return ProductSuggestionsResult(
+                suggestions=fallback_suggestions,
+                business_model_analysis=BusinessModelAnalysis(
+                    model="unknown",
+                    confidence=0.0,
+                    ingredient_count=0,
+                    finished_product_count=len(fallback_suggestions),
+                    ingredient_ratio=0.0,
+                    recommendations=["Manual review required for all products"]
+                ),
+                total_products=len(fallback_suggestions),
+                high_confidence_count=0,
+                low_confidence_count=len(fallback_suggestions),
+                processing_time_seconds=processing_time
+            )
+    
+    # ================================================================
+    # STEP 3: INVENTORY CREATION (after user confirmation)
+    # ================================================================
+    
+    async def create_inventory_from_suggestions(
+        self,
+        approved_suggestions: List[Dict[str, Any]],
+        tenant_id: UUID,
+        user_id: UUID
+    ) -> Dict[str, Any]:
+        """
+        Step 3: Create inventory items from user-approved suggestions
+        """
+        try:
+            logger.info("Creating inventory from approved suggestions", 
+                       approved_count=len(approved_suggestions), tenant_id=tenant_id)
+            
+            created_items = []
+            failed_items = []
+            
+            for approval in approved_suggestions:
+                suggestion_id = approval.get("suggestion_id")
+                is_approved = approval.get("approved", False)
+                modifications = approval.get("modifications", {})
+                
+                if not is_approved:
+                    continue
+                
+                try:
+                    # Build inventory item data from suggestion and modifications
+                    inventory_data = {
+                        "name": modifications.get("name") or approval.get("suggested_name"),
+                        "product_type": modifications.get("product_type") or approval.get("product_type"),
+                        "category": modifications.get("category") or approval.get("category"),
+                        "unit_of_measure": modifications.get("unit_of_measure") or approval.get("unit_of_measure"),
+                        "description": modifications.get("description") or approval.get("notes", ""),
+                        "estimated_shelf_life_days": modifications.get("estimated_shelf_life_days") or approval.get("estimated_shelf_life_days"),
+                        "requires_refrigeration": modifications.get("requires_refrigeration", approval.get("requires_refrigeration", False)),
+                        "requires_freezing": modifications.get("requires_freezing", approval.get("requires_freezing", False)),
+                        "is_seasonal": modifications.get("is_seasonal", approval.get("is_seasonal", False)),
+                        "suggested_supplier": modifications.get("suggested_supplier") or approval.get("suggested_supplier"),
+                        "is_active": True,
+                        "source": "ai_onboarding"
+                    }
+                    
+                    # Create inventory item via inventory service
+                    created_item = await self.inventory_client.create_ingredient(
+                        inventory_data, str(tenant_id)
+                    )
+                    
+                    if created_item:
+                        created_items.append({
+                            "suggestion_id": suggestion_id,
+                            "inventory_item": created_item,
+                            "original_name": approval.get("original_name")
+                        })
+                        logger.info("Created inventory item", 
+                                  item_name=inventory_data["name"], 
+                                  suggestion_id=suggestion_id)
+                    else:
+                        failed_items.append({
+                            "suggestion_id": suggestion_id,
+                            "error": "Failed to create inventory item - no response"
+                        })
+                        
+                except Exception as e:
+                    logger.error("Failed to create inventory item", 
+                               error=str(e), suggestion_id=suggestion_id)
+                    failed_items.append({
+                        "suggestion_id": suggestion_id,
+                        "error": str(e)
+                    })
+            
+            success_rate = len(created_items) / max(1, len(approved_suggestions)) * 100
+            
+            result = {
+                "created_items": created_items,
+                "failed_items": failed_items,
+                "total_approved": len(approved_suggestions),
+                "successful_creations": len(created_items),
+                "failed_creations": len(failed_items),
+                "success_rate": success_rate,
+                "ready_for_import": len(created_items) > 0
+            }
+            
+            logger.info("Inventory creation completed", 
+                       created=len(created_items), 
+                       failed=len(failed_items),
+                       success_rate=success_rate,
+                       tenant_id=tenant_id)
+            
+            return result
+            
+        except Exception as e:
+            logger.error("Inventory creation failed", error=str(e), tenant_id=tenant_id)
+            raise
+    
+    # ================================================================
+    # STEP 4: FINAL DATA IMPORT
+    # ================================================================
+    
+    async def import_sales_data_with_inventory(
+        self,
+        file_data: str,
+        file_format: str,
+        inventory_mapping: Dict[str, str],  # original_product_name -> inventory_item_id
+        tenant_id: UUID,
+        filename: Optional[str] = None
+    ) -> OnboardingImportResult:
+        """
+        Step 4: Import sales data using the detailed processing from data_import_service
+        """
+        try:
+            logger.info("Starting final sales data import with inventory mapping", 
+                       mappings_count=len(inventory_mapping), tenant_id=tenant_id)
+            
+            # Use data_import_service for the actual import processing
+            import_result = await self.data_import_service.process_import(
+                str(tenant_id), file_data, file_format, filename
+            )
+            
+            result = OnboardingImportResult(
+                success=import_result.success,
+                import_details=import_result,
+                inventory_items_created=len(inventory_mapping),
+                inventory_creation_errors=[],
+                final_summary={
+                    "status": "completed" if import_result.success else "failed",
+                    "total_records": import_result.records_processed,
+                    "successful_imports": import_result.records_created,
+                    "failed_imports": import_result.records_failed,
+                    "inventory_items": len(inventory_mapping),
+                    "processing_time": import_result.processing_time_seconds,
+                    "onboarding_complete": import_result.success
+                }
+            )
+            
+            logger.info("Final sales data import completed", 
+                       success=import_result.success,
+                       records_created=import_result.records_created,
+                       tenant_id=tenant_id)
+            
+            return result
+            
+        except Exception as e:
+            logger.error("Final sales data import failed", error=str(e), tenant_id=tenant_id)
+            
+            return OnboardingImportResult(
+                success=False,
+                import_details=SalesImportResult(
+                    success=False,
+                    records_processed=0,
+                    records_created=0,
+                    records_updated=0,
+                    records_failed=0,
+                    errors=[{
+                        "type": "import_error",
+                        "message": f"Import failed: {str(e)}",
+                        "field": None,
+                        "row": None,
+                        "code": "FINAL_IMPORT_ERROR"
+                    }],
+                    warnings=[],
+                    processing_time_seconds=0.0
+                ),
+                inventory_items_created=len(inventory_mapping),
+                inventory_creation_errors=[str(e)],
+                final_summary={
+                    "status": "failed",
+                    "error_message": str(e),
+                    "onboarding_complete": False
+                }
+            )
+    
+    # ================================================================
+    # HELPER METHODS
+    # ================================================================
+    
+    async def _analyze_product_sales_data(
+        self, 
+        product_list: List[str], 
+        file_data: str, 
+        file_format: str
+    ) -> Dict[str, Dict[str, Any]]:
+        """Analyze sales data for each product to provide context for AI classification"""
+        try:
+            if file_format.lower() != "csv":
+                return {}
+            
+            import csv
+            import io
+            
+            reader = csv.DictReader(io.StringIO(file_data))
+            rows = list(reader)
+            
+            if not rows:
+                return {}
+            
+            # Use data_import_service column detection
+            column_mapping = self.data_import_service._detect_columns(list(rows[0].keys()))
+            
+            if not column_mapping.get('product'):
+                return {}
+            
+            product_column = column_mapping['product']
+            quantity_column = column_mapping.get('quantity')
+            revenue_column = column_mapping.get('revenue')
+            date_column = column_mapping.get('date')
+            
+            # Analyze each product
+            product_analysis = {}
+            
+            for product_name in product_list:
+                # Find all rows for this product
+                product_rows = [
+                    row for row in rows 
+                    if self.data_import_service._clean_product_name(row.get(product_column, '')) == product_name
+                ]
+                
+                if not product_rows:
+                    continue
+                
+                # Calculate metrics
+                total_quantity = 0
+                total_revenue = 0
+                sales_count = len(product_rows)
+                
+                for row in product_rows:
+                    try:
+                        # Quantity
+                        qty_raw = row.get(quantity_column, 1)
+                        if qty_raw and str(qty_raw).strip():
+                            qty = int(float(str(qty_raw).replace(',', '.')))
+                            total_quantity += qty
+                        else:
+                            total_quantity += 1
+                        
+                        # Revenue
+                        if revenue_column:
+                            rev_raw = row.get(revenue_column)
+                            if rev_raw and str(rev_raw).strip():
+                                rev = float(str(rev_raw).replace(',', '.').replace('€', '').replace('$', '').strip())
+                                total_revenue += rev
+                    except:
+                        continue
+                
+                avg_quantity = total_quantity / sales_count if sales_count > 0 else 0
+                avg_revenue = total_revenue / sales_count if sales_count > 0 else 0
+                avg_unit_price = total_revenue / total_quantity if total_quantity > 0 else 0
+                
+                product_analysis[product_name] = {
+                    "total_quantity": total_quantity,
+                    "total_revenue": total_revenue,
+                    "sales_count": sales_count,
+                    "avg_quantity_per_sale": avg_quantity,
+                    "avg_revenue_per_sale": avg_revenue,
+                    "avg_unit_price": avg_unit_price
+                }
+            
+            return product_analysis
+            
+        except Exception as e:
+            logger.warning("Failed to analyze product sales data", error=str(e))
+            return {}
+
+
+# Factory function for dependency injection
+def get_ai_onboarding_service() -> AIOnboardingService:
+    """Get AI onboarding service instance"""
+    return AIOnboardingService()

services/sales/app/services/inventory_client.py

@@ -4,101 +4,71 @@ Inventory Service Client - Inter-service communication
 Handles communication with the inventory service to fetch product data
 """
 
-import httpx
 import structlog
 from typing import Dict, Any, List, Optional
 from uuid import UUID
+
+from shared.clients.inventory_client import InventoryServiceClient as SharedInventoryClient
 from app.core.config import settings
 
 logger = structlog.get_logger()
 
 class InventoryServiceClient:
-    """Client for communicating with the inventory service"""
+    """Client for communicating with the inventory service via shared client"""
     
     def __init__(self):
-        self.base_url = settings.INVENTORY_SERVICE_URL
-        self.timeout = 30.0
+        self._shared_client = SharedInventoryClient(settings)
         
-    async def classify_products_batch(self, product_list: Dict[str, Any], tenant_id: UUID) -> Optional[Dict[str, Any]]:
-         """Get product details from inventory service by ID"""
+    async def classify_products_batch(self, product_list: List[Dict[str, Any]], tenant_id: UUID) -> Optional[Dict[str, Any]]:
+        """Classify multiple products for inventory creation"""
         try:
-             async with httpx.AsyncClient(timeout=self.timeout) as client:
-                 response = await client.post(
-                     f"{self.base_url}/api/v1/tenants/{tenant_id}/inventory/classify-products-batch",
-                     headers=self._get_headers(),
-                     product_list=product_list
-                 )
+            # Convert product_list to expected format for shared client
+            products = []
+            for item in product_list:
+                if isinstance(item, str):
+                    # If it's just a product name
+                    products.append({"product_name": item})
+                elif isinstance(item, dict):
+                    # If it's already a dict, ensure required fields
+                    product_data = {
+                        "product_name": item.get("product_name", item.get("name", str(item))),
+                        "sales_volume": item.get("sales_volume", item.get("total_quantity"))
+                    }
+                    products.append(product_data)
             
-                 if response.status_code == 200:
-                     product_data = response.json()
-                     logger.info("Retrieved product from inventory service", 
-                               tenant_id=tenant_id)
-                     return product_data
-                 elif response.status_code == 404:
-                     logger.warning("Product not found in inventory service", 
-                                  tenant_id=tenant_id)
-                     return None
-                 else:
-                     logger.error("Failed to fetch product from inventory service", 
-                                status_code=response.status_code, 
-                                tenant_id=tenant_id)
-                     return None
+            result = await self._shared_client.classify_products_batch(products, str(tenant_id))
+            if result:
+                logger.info("Classified products batch", 
+                          count=len(products), tenant_id=tenant_id)
+            return result
             
-         except httpx.TimeoutException:
-             logger.error("Timeout fetching product from inventory service", 
-                        tenant_id=tenant_id)
-             return None
         except Exception as e:
-             logger.error("Error communicating with inventory service", 
+            logger.error("Error in batch product classification", 
                        error=str(e), tenant_id=tenant_id)
             return None
          
     async def get_product_by_id(self, product_id: UUID, tenant_id: UUID) -> Optional[Dict[str, Any]]:
         """Get product details from inventory service by ID"""
         try:
-             async with httpx.AsyncClient(timeout=self.timeout) as client:
-                 response = await client.get(
-                     f"{self.base_url}/api/v1/tenants/{tenant_id}/ingredients/{product_id}",
-                     headers=self._get_headers()
-                 )
-                 
-                 if response.status_code == 200:
-                     product_data = response.json()
+            result = await self._shared_client.get_ingredient_by_id(product_id, str(tenant_id))
+            if result:
                 logger.info("Retrieved product from inventory service", 
                           product_id=product_id, tenant_id=tenant_id)
-                     return product_data
-                 elif response.status_code == 404:
-                     logger.warning("Product not found in inventory service", 
-                                  product_id=product_id, tenant_id=tenant_id)
-                     return None
-                 else:
-                     logger.error("Failed to fetch product from inventory service", 
-                                status_code=response.status_code, 
-                                product_id=product_id, tenant_id=tenant_id)
-                     return None
+            return result
             
-         except httpx.TimeoutException:
-             logger.error("Timeout fetching product from inventory service", 
-                        product_id=product_id, tenant_id=tenant_id)
-             return None
         except Exception as e:
-             logger.error("Error communicating with inventory service", 
+            logger.error("Error fetching product by ID", 
                        error=str(e), product_id=product_id, tenant_id=tenant_id)
             return None
     
     async def get_product_by_sku(self, sku: str, tenant_id: UUID) -> Optional[Dict[str, Any]]:
         """Get product details from inventory service by SKU"""
         try:
-            async with httpx.AsyncClient(timeout=self.timeout) as client:
-                response = await client.get(
-                    f"{self.base_url}/api/v1/tenants/{tenant_id}/ingredients",
-                    params={"sku": sku, "limit": 1},
-                    headers=self._get_headers()
+            # Search for product by SKU using shared client
+            products = await self._shared_client.search_ingredients(
+                str(tenant_id), search=sku, limit=1
             )
             
-                if response.status_code == 200:
-                    data = response.json()
-                    products = data.get("items", [])
             if products:
                 product_data = products[0]
                 logger.info("Retrieved product by SKU from inventory service", 
@@ -108,18 +78,9 @@ class InventoryServiceClient:
                 logger.warning("Product not found by SKU in inventory service", 
                              sku=sku, tenant_id=tenant_id)
                 return None
-                else:
-                    logger.error("Failed to fetch product by SKU from inventory service", 
-                               status_code=response.status_code, 
-                               sku=sku, tenant_id=tenant_id)
-                    return None
                 
-        except httpx.TimeoutException:
-            logger.error("Timeout fetching product by SKU from inventory service", 
-                       sku=sku, tenant_id=tenant_id)
-            return None
         except Exception as e:
-            logger.error("Error communicating with inventory service for SKU", 
+            logger.error("Error fetching product by SKU", 
                        error=str(e), sku=sku, tenant_id=tenant_id)
             return None
     
@@ -127,38 +88,16 @@ class InventoryServiceClient:
                             product_type: Optional[str] = None) -> List[Dict[str, Any]]:
         """Search products in inventory service"""
         try:
-            params = {
-                "search": search_term,
-                "limit": 50
-            }
-            if product_type:
-                params["product_type"] = product_type
-                
-            async with httpx.AsyncClient(timeout=self.timeout) as client:
-                response = await client.get(
-                    f"{self.base_url}/api/v1/tenants/{tenant_id}/ingredients",
-                    params=params,
-                    headers=self._get_headers()
+            products = await self._shared_client.search_ingredients(
+                str(tenant_id), search=search_term, limit=50
             )
             
-                if response.status_code == 200:
-                    data = response.json()
-                    products = data.get("items", [])
             logger.info("Searched products in inventory service", 
                       search_term=search_term, count=len(products), tenant_id=tenant_id)
             return products
-                else:
-                    logger.error("Failed to search products in inventory service", 
-                               status_code=response.status_code, 
-                               search_term=search_term, tenant_id=tenant_id)
-                    return []
             
-        except httpx.TimeoutException:
-            logger.error("Timeout searching products in inventory service", 
-                       search_term=search_term, tenant_id=tenant_id)
-            return []
         except Exception as e:
-            logger.error("Error searching products in inventory service", 
+            logger.error("Error searching products", 
                        error=str(e), search_term=search_term, tenant_id=tenant_id)
             return []
     
@@ -166,56 +105,19 @@ class InventoryServiceClient:
                                      product_type: Optional[str] = None) -> List[Dict[str, Any]]:
         """Get products by category from inventory service"""
         try:
-            params = {
-                "limit": 100
-            }
-            if product_type == "ingredient":
-                params["ingredient_category"] = category
-            elif product_type == "finished_product":
-                params["product_category"] = category
-            else:
-                # Search in both categories if type not specified
-                params["category"] = category
-                
-            async with httpx.AsyncClient(timeout=self.timeout) as client:
-                response = await client.get(
-                    f"{self.base_url}/api/v1/tenants/{tenant_id}/ingredients",
-                    params=params,
-                    headers=self._get_headers()
+            products = await self._shared_client.search_ingredients(
+                str(tenant_id), category=category, limit=100
             )
             
-                if response.status_code == 200:
-                    data = response.json()
-                    products = data.get("items", [])
             logger.info("Retrieved products by category from inventory service", 
                       category=category, count=len(products), tenant_id=tenant_id)
             return products
-                else:
-                    logger.error("Failed to fetch products by category from inventory service", 
-                               status_code=response.status_code, 
-                               category=category, tenant_id=tenant_id)
-                    return []
             
-        except httpx.TimeoutException:
-            logger.error("Timeout fetching products by category from inventory service", 
-                       category=category, tenant_id=tenant_id)
-            return []
         except Exception as e:
-            logger.error("Error fetching products by category from inventory service", 
+            logger.error("Error fetching products by category", 
                        error=str(e), category=category, tenant_id=tenant_id)
             return []
 
-# Cache synchronization removed - no longer needed with pure inventory reference approach
-    
-    def _get_headers(self) -> Dict[str, str]:
-        """Get headers for inventory service requests"""
-        return {
-            "Content-Type": "application/json",
-            "X-Service-Name": "sales-service",
-            # Add authentication headers if needed
-        }
-
-
 # Dependency injection
 async def get_inventory_client() -> InventoryServiceClient:
     """Get inventory service client instance"""

services/sales/app/services/onboarding_import_service.py

@@ -1,446 +0,0 @@
-# services/sales/app/services/onboarding_import_service.py
-"""
-Onboarding Data Import Service
-Handles historical sales data import with automated inventory creation
-"""
-
-import pandas as pd
-import structlog
-from typing import List, Dict, Any, Optional, Tuple
-from uuid import UUID, uuid4
-from datetime import datetime, timezone
-from dataclasses import dataclass, asdict
-import asyncio
-
-from app.services.inventory_client import InventoryServiceClient
-from app.services.data_import_service import DataImportService
-from app.models.sales import SalesData
-from app.core.database import get_db_transaction
-from app.repositories.sales_repository import SalesRepository
-
-logger = structlog.get_logger()
-
-
-@dataclass
-class OnboardingImportResult:
-    """Result of onboarding import process"""
-    total_products_found: int
-    inventory_suggestions: List[Dict[str, Any]]
-    business_model_analysis: Dict[str, Any]
-    import_job_id: UUID
-    status: str
-    processed_rows: int
-    successful_imports: int
-    failed_imports: int
-    errors: List[str]
-    warnings: List[str]
-
-
-@dataclass
-class InventoryCreationRequest:
-    """Request to create inventory item from suggestion"""
-    suggestion_id: str
-    approved: bool
-    modifications: Dict[str, Any]  # User modifications to the suggestion
-
-
-class OnboardingImportService:
-    """Service for handling onboarding data import with inventory automation"""
-    
-    def __init__(self):
-        self.inventory_client = InventoryServiceClient()
-        self.data_import_service = DataImportService()
-    
-    async def analyze_sales_data_for_onboarding(
-        self, 
-        file_content: bytes,
-        filename: str,
-        tenant_id: UUID,
-        user_id: UUID
-    ) -> OnboardingImportResult:
-        """Analyze uploaded sales data and suggest inventory items"""
-        
-        try:
-            logger.info("Starting onboarding analysis", filename=filename, tenant_id=tenant_id)
-            
-            # Parse the uploaded file
-            df = await self._parse_uploaded_file(file_content, filename)
-            
-            # Extract unique products and their sales volumes
-            product_analysis = self._analyze_products_from_sales(df)
-            
-            # Get product suggestions from inventory service
-            inventory_suggestions = await self._get_inventory_suggestions(
-                product_analysis, tenant_id
-            )
-            
-            # Analyze business model
-            business_model = self._analyze_business_model(inventory_suggestions)
-            
-            # Create import job for tracking
-            import_job_id = await self._create_import_job(
-                filename, tenant_id, user_id, len(df)
-            )
-            
-            result = OnboardingImportResult(
-                total_products_found=len(product_analysis),
-                inventory_suggestions=inventory_suggestions,
-                business_model_analysis=business_model,
-                import_job_id=import_job_id,
-                status="analysis_complete",
-                processed_rows=len(df),
-                successful_imports=0,  # Will be updated when user confirms
-                failed_imports=0,
-                errors=[],
-                warnings=self._generate_warnings(df, inventory_suggestions)
-            )
-            
-            logger.info("Onboarding analysis complete", 
-                       products_found=len(product_analysis),
-                       business_model=business_model.get('model'),
-                       tenant_id=tenant_id)
-            
-            return result
-            
-        except Exception as e:
-            logger.error("Failed onboarding analysis", error=str(e), tenant_id=tenant_id)
-            raise
-    
-    async def create_inventory_from_suggestions(
-        self,
-        suggestions_approval: List[InventoryCreationRequest],
-        tenant_id: UUID,
-        user_id: UUID
-    ) -> Dict[str, Any]:
-        """Create inventory items from approved suggestions"""
-        
-        try:
-            created_items = []
-            failed_items = []
-            
-            for request in suggestions_approval:
-                if request.approved:
-                    try:
-                        # Find the original suggestion
-                        suggestion = self._find_suggestion_by_id(request.suggestion_id)
-                        if not suggestion:
-                            failed_items.append({
-                                'suggestion_id': request.suggestion_id,
-                                'error': 'Suggestion not found'
-                            })
-                            continue
-                        
-                        # Apply user modifications
-                        final_item_data = self._apply_modifications(suggestion, request.modifications)
-                        
-                        # Create inventory item via inventory service
-                        created_item = await self._create_inventory_item(
-                            final_item_data, tenant_id, user_id
-                        )
-                        
-                        created_items.append(created_item)
-                        
-                    except Exception as e:
-                        logger.error("Failed to create inventory item", 
-                                   error=str(e), suggestion_id=request.suggestion_id)
-                        failed_items.append({
-                            'suggestion_id': request.suggestion_id,
-                            'error': str(e)
-                        })
-            
-            logger.info("Inventory creation complete", 
-                       created=len(created_items), failed=len(failed_items), tenant_id=tenant_id)
-            
-            return {
-                'created_items': created_items,
-                'failed_items': failed_items,
-                'total_approved': len([r for r in suggestions_approval if r.approved]),
-                'success_rate': len(created_items) / max(1, len([r for r in suggestions_approval if r.approved]))
-            }
-            
-        except Exception as e:
-            logger.error("Failed inventory creation", error=str(e), tenant_id=tenant_id)
-            raise
-    
-    async def import_sales_data_with_inventory(
-        self,
-        file_content: bytes,
-        filename: str,
-        tenant_id: UUID,
-        user_id: UUID,
-        inventory_mapping: Dict[str, UUID]  # product_name -> inventory_product_id
-    ) -> OnboardingImportResult:
-        """Import sales data using created inventory items"""
-        
-        try:
-            logger.info("Starting sales import with inventory mapping", 
-                       filename=filename, products_mapped=len(inventory_mapping), tenant_id=tenant_id)
-            
-            # Parse the file again
-            df = await self._parse_uploaded_file(file_content, filename)
-            
-            # Add inventory product IDs to the data
-            df_with_inventory = self._map_products_to_inventory(df, inventory_mapping)
-            
-            # Import the sales data using the standard import service
-            import_result = await self._import_sales_with_inventory_ids(
-                df_with_inventory, tenant_id, user_id, filename
-            )
-            
-            result = OnboardingImportResult(
-                total_products_found=len(inventory_mapping),
-                inventory_suggestions=[],  # Already processed
-                business_model_analysis={},  # Already analyzed
-                import_job_id=import_result['job_id'],
-                status="import_complete",
-                processed_rows=import_result['processed_rows'],
-                successful_imports=import_result['successful_imports'],
-                failed_imports=import_result['failed_imports'],
-                errors=import_result.get('errors', []),
-                warnings=import_result.get('warnings', [])
-            )
-            
-            logger.info("Sales import complete", 
-                       successful=result.successful_imports, 
-                       failed=result.failed_imports, 
-                       tenant_id=tenant_id)
-            
-            return result
-            
-        except Exception as e:
-            logger.error("Failed sales import", error=str(e), tenant_id=tenant_id)
-            raise
-    
-    async def _parse_uploaded_file(self, file_content: bytes, filename: str) -> pd.DataFrame:
-        """Parse uploaded CSV/Excel file"""
-        
-        try:
-            if filename.endswith('.csv'):
-                # Try different encodings
-                for encoding in ['utf-8', 'latin-1', 'cp1252']:
-                    try:
-                        df = pd.read_csv(io.BytesIO(file_content), encoding=encoding)
-                        break
-                    except UnicodeDecodeError:
-                        continue
-                else:
-                    raise ValueError("Could not decode CSV file with any supported encoding")
-            
-            elif filename.endswith(('.xlsx', '.xls')):
-                df = pd.read_excel(io.BytesIO(file_content))
-            
-            else:
-                raise ValueError(f"Unsupported file format: {filename}")
-            
-            # Validate required columns exist
-            required_columns = ['product_name', 'quantity_sold', 'revenue', 'date']
-            missing_columns = [col for col in required_columns if col not in df.columns]
-            
-            if missing_columns:
-                raise ValueError(f"Missing required columns: {missing_columns}")
-            
-            # Clean the data
-            df = df.dropna(subset=['product_name', 'quantity_sold', 'revenue'])
-            df['date'] = pd.to_datetime(df['date'], errors='coerce')
-            df = df.dropna(subset=['date'])
-            
-            logger.info("File parsed successfully", rows=len(df), columns=list(df.columns))
-            return df
-            
-        except Exception as e:
-            logger.error("Failed to parse file", error=str(e), filename=filename)
-            raise
-    
-    def _analyze_products_from_sales(self, df: pd.DataFrame) -> Dict[str, Dict[str, Any]]:
-        """Extract and analyze products from sales data"""
-        
-        # Group by product name and calculate metrics
-        product_stats = df.groupby('product_name').agg({
-            'quantity_sold': ['sum', 'mean', 'count'],
-            'revenue': ['sum', 'mean'],
-            'date': ['min', 'max']
-        }).round(2)
-        
-        # Flatten column names
-        product_stats.columns = ['_'.join(col).strip() for col in product_stats.columns.values]
-        
-        # Convert to dictionary with analysis
-        products = {}
-        for product_name in product_stats.index:
-            stats = product_stats.loc[product_name]
-            
-            products[product_name] = {
-                'name': product_name,
-                'total_quantity': float(stats['quantity_sold_sum']),
-                'avg_quantity_per_sale': float(stats['quantity_sold_mean']),
-                'total_sales_count': int(stats['quantity_sold_count']),
-                'total_revenue': float(stats['revenue_sum']),
-                'avg_revenue_per_sale': float(stats['revenue_mean']),
-                'first_sale_date': stats['date_min'],
-                'last_sale_date': stats['date_max'],
-                'avg_unit_price': float(stats['revenue_sum'] / stats['quantity_sold_sum']) if stats['quantity_sold_sum'] > 0 else 0
-            }
-        
-        logger.info("Product analysis complete", unique_products=len(products))
-        return products
-    
-    async def _get_inventory_suggestions(
-        self, 
-        product_analysis: Dict[str, Dict[str, Any]], 
-        tenant_id: UUID
-    ) -> List[Dict[str, Any]]:
-        """Get inventory suggestions from inventory service"""
-        
-        try:
-            # Call inventory service classification API
-            product_names = list(product_analysis.keys())
-            
-            suggestions = []
-            
-            suggestions = await self.inventory_client.classify_products_batch(product_names)
-            
-            return suggestions
-            
-        except Exception as e:
-            logger.error("Failed to get inventory suggestions", error=str(e))
-            # Return fallback suggestions for all products
-            return [self._create_fallback_suggestion(name, stats) 
-                   for name, stats in product_analysis.items()]
-    
-    def _create_fallback_suggestion(self, product_name: str, stats: Dict[str, Any]) -> Dict[str, Any]:
-        """Create fallback suggestion when AI classification fails"""
-        
-        return {
-            'suggestion_id': str(uuid4()),
-            'original_name': product_name,
-            'suggested_name': product_name.title(),
-            'product_type': 'finished_product',
-            'category': 'other_products',
-            'unit_of_measure': 'units',
-            'confidence_score': 0.3,
-            'estimated_shelf_life_days': 3,
-            'requires_refrigeration': False,
-            'requires_freezing': False,
-            'is_seasonal': False,
-            'notes': 'Fallback suggestion - requires manual review',
-            'original_sales_data': stats
-        }
-    
-    def _analyze_business_model(self, suggestions: List[Dict[str, Any]]) -> Dict[str, Any]:
-        """Analyze business model from suggestions"""
-        
-        if not suggestions:
-            return {'model': 'unknown', 'confidence': 0.0}
-        
-        ingredient_count = sum(1 for s in suggestions if s.get('product_type') == 'ingredient')
-        finished_count = sum(1 for s in suggestions if s.get('product_type') == 'finished_product')
-        total = len(suggestions)
-        
-        ingredient_ratio = ingredient_count / total if total > 0 else 0
-        
-        if ingredient_ratio >= 0.7:
-            model = 'production'
-        elif ingredient_ratio <= 0.3:
-            model = 'retail'
-        else:
-            model = 'hybrid'
-        
-        confidence = max(abs(ingredient_ratio - 0.5) * 2, 0.1)
-        
-        return {
-            'model': model,
-            'confidence': confidence,
-            'ingredient_count': ingredient_count,
-            'finished_product_count': finished_count,
-            'ingredient_ratio': ingredient_ratio,
-            'recommendations': self._get_model_recommendations(model)
-        }
-    
-    def _get_model_recommendations(self, model: str) -> List[str]:
-        """Get recommendations based on business model"""
-        
-        recommendations = {
-            'production': [
-                'Set up supplier relationships for ingredients',
-                'Configure recipe management',
-                'Enable production cost tracking',
-                'Set up ingredient inventory alerts'
-            ],
-            'retail': [
-                'Configure central baker relationships',
-                'Set up delivery tracking',
-                'Enable freshness monitoring',
-                'Focus on sales forecasting'
-            ],
-            'hybrid': [
-                'Configure both production and retail features',
-                'Set up flexible inventory management',
-                'Enable comprehensive analytics'
-            ]
-        }
-        
-        return recommendations.get(model, [])
-    
-    async def _create_import_job(
-        self, 
-        filename: str, 
-        tenant_id: UUID, 
-        user_id: UUID, 
-        total_rows: int
-    ) -> UUID:
-        """Create import job for tracking"""
-        
-        try:
-            async with get_db_transaction() as db:
-                from app.models.sales import SalesImportJob
-                
-                job = SalesImportJob(
-                    id=uuid4(),
-                    tenant_id=tenant_id,
-                    filename=filename,
-                    import_type='onboarding_csv',
-                    status='analyzing',
-                    total_rows=total_rows,
-                    created_by=user_id
-                )
-                
-                db.add(job)
-                await db.commit()
-                
-                logger.info("Import job created", job_id=job.id, tenant_id=tenant_id)
-                return job.id
-                
-        except Exception as e:
-            logger.error("Failed to create import job", error=str(e))
-            return uuid4()  # Return dummy ID if job creation fails
-    
-    def _generate_warnings(self, df: pd.DataFrame, suggestions: List[Dict[str, Any]]) -> List[str]:
-        """Generate warnings about data quality"""
-        
-        warnings = []
-        
-        # Check for low confidence suggestions
-        low_confidence = [s for s in suggestions if s.get('confidence_score', 1.0) < 0.6]
-        if low_confidence:
-            warnings.append(f"{len(low_confidence)} products have low classification confidence and may need manual review")
-        
-        # Check for missing data
-        missing_prices = df[df['revenue'].isna() | (df['revenue'] == 0)].shape[0]
-        if missing_prices > 0:
-            warnings.append(f"{missing_prices} sales records have missing or zero revenue")
-        
-        # Check for old data
-        latest_date = df['date'].max()
-        if pd.Timestamp.now() - latest_date > pd.Timedelta(days=90):
-            warnings.append("Sales data appears to be more than 90 days old")
-        
-        return warnings
-    
-    # Additional helper methods would be implemented here...
-    # _find_suggestion_by_id, _apply_modifications, _create_inventory_item, etc.
-
-
-# Dependency injection
-def get_onboarding_import_service() -> OnboardingImportService:
-    """Get onboarding import service instance"""
-    return OnboardingImportService()

shared/clients/README.md

@@ -1,391 +0,0 @@
-# Enhanced Inter-Service Communication System
-
-This directory contains the enhanced inter-service communication system that integrates with the new repository pattern architecture. The system provides circuit breakers, caching, monitoring, and event tracking for all service-to-service communications.
-
-## Architecture Overview
-
-### Base Components
-
-1. **BaseServiceClient** - Foundation class providing authentication, retries, and basic HTTP operations
-2. **EnhancedServiceClient** - Adds circuit breaker, caching, and monitoring capabilities
-3. **ServiceRegistry** - Central registry for managing all enhanced service clients
-
-### Enhanced Service Clients
-
-Each service has a specialized enhanced client:
-
-- **SalesServiceClient** - Sales data, products, data import with optimized caching
-- **ExternalServiceClient** - Weather and traffic data collection with external API integration
-- **EnhancedAuthServiceClient** - Authentication, user management, permissions with security focus
-- **EnhancedTrainingServiceClient** - ML training, model management, deployment with pipeline monitoring
-- **EnhancedForecastingServiceClient** - Forecasting, predictions, scenarios with analytics
-- **EnhancedTenantServiceClient** - Tenant management, memberships, organization features
-- **EnhancedNotificationServiceClient** - Notifications, templates, delivery tracking
-
-## Key Features
-
-### Circuit Breaker Pattern
-- **States**: Closed (normal), Open (failing), Half-Open (testing recovery)
-- **Configuration**: Failure threshold, recovery timeout, success threshold
-- **Monitoring**: State changes tracked and logged
-
-### Intelligent Caching
-- **TTL-based**: Different cache durations for different data types
-- **Invalidation**: Pattern-based cache invalidation on updates
-- **Statistics**: Hit/miss ratios and performance metrics
-- **Manual Control**: Clear specific cache patterns when needed
-
-### Event Integration
-- **Repository Events**: Entity created/updated/deleted events
-- **Correlation IDs**: Track operations across services
-- **Metadata**: Rich event metadata for debugging and monitoring
-
-### Monitoring & Metrics
-- **Request Metrics**: Success/failure rates, latencies
-- **Cache Metrics**: Hit rates, entry counts
-- **Circuit Breaker Metrics**: State changes, failure counts
-- **Health Checks**: Per-service and aggregate health status
-
-## Usage Examples
-
-### Basic Usage with Service Registry
-
-```python
-from shared.clients.enhanced_service_client import ServiceRegistry
-from shared.config.base import BaseServiceSettings
-
-# Initialize registry
-config = BaseServiceSettings()
-registry = ServiceRegistry(config, calling_service="forecasting")
-
-# Get enhanced clients
-data_client = registry.get_data_client()
-auth_client = registry.get_auth_client()
-training_client = registry.get_training_client()
-
-# Use with full features
-sales_data = await data_client.get_all_sales_data_with_monitoring(
-    tenant_id="tenant-123",
-    start_date="2024-01-01",
-    end_date="2024-12-31",
-    correlation_id="forecast-job-456"
-)
-```
-
-### Data Service Operations
-
-```python
-# Get sales data with intelligent caching
-sales_data = await data_client.get_sales_data_cached(
-    tenant_id="tenant-123",
-    start_date="2024-01-01", 
-    end_date="2024-01-31",
-    aggregation="daily"
-)
-
-# Upload sales data with cache invalidation and events
-result = await data_client.upload_sales_data_with_events(
-    tenant_id="tenant-123",
-    sales_data=sales_records,
-    correlation_id="data-import-789"
-)
-
-# Get weather data with caching (30 min TTL)
-weather_data = await data_client.get_weather_historical_cached(
-    tenant_id="tenant-123",
-    start_date="2024-01-01",
-    end_date="2024-01-31"
-)
-```
-
-### Authentication & User Management
-
-```python
-# Authenticate with security monitoring
-auth_result = await auth_client.authenticate_user_cached(
-    email="user@example.com",
-    password="password"
-)
-
-# Check permissions with caching
-has_access = await auth_client.check_user_permissions_cached(
-    user_id="user-123",
-    tenant_id="tenant-456", 
-    resource="sales_data",
-    action="read"
-)
-
-# Create user with events
-user = await auth_client.create_user_with_events(
-    user_data={
-        "email": "new@example.com",
-        "name": "New User",
-        "role": "analyst"
-    },
-    tenant_id="tenant-123",
-    correlation_id="user-creation-789"
-)
-```
-
-### Training & ML Operations
-
-```python
-# Create training job with monitoring
-job = await training_client.create_training_job_with_monitoring(
-    tenant_id="tenant-123",
-    include_weather=True,
-    include_traffic=False,
-    min_data_points=30,
-    correlation_id="training-pipeline-456"
-)
-
-# Get active model with caching
-model = await training_client.get_active_model_for_product_cached(
-    tenant_id="tenant-123",
-    product_name="croissants"
-)
-
-# Deploy model with events
-deployment = await training_client.deploy_model_with_events(
-    tenant_id="tenant-123",
-    model_id="model-789",
-    correlation_id="deployment-123"
-)
-
-# Get pipeline status
-status = await training_client.get_training_pipeline_status("tenant-123")
-```
-
-### Forecasting & Predictions
-
-```python
-# Create forecast with monitoring
-forecast = await forecasting_client.create_forecast_with_monitoring(
-    tenant_id="tenant-123",
-    model_id="model-456",
-    start_date="2024-02-01",
-    end_date="2024-02-29",
-    correlation_id="forecast-creation-789"
-)
-
-# Get predictions with caching
-predictions = await forecasting_client.get_predictions_cached(
-    tenant_id="tenant-123",
-    forecast_id="forecast-456",
-    start_date="2024-02-01",
-    end_date="2024-02-07"
-)
-
-# Real-time prediction with caching
-prediction = await forecasting_client.create_realtime_prediction_with_monitoring(
-    tenant_id="tenant-123",
-    model_id="model-456",
-    target_date="2024-02-01",
-    features={"temperature": 20, "day_of_week": 1},
-    correlation_id="realtime-pred-123"
-)
-
-# Get forecasting dashboard
-dashboard = await forecasting_client.get_forecasting_dashboard("tenant-123")
-```
-
-### Tenant Management
-
-```python
-# Create tenant with monitoring
-tenant = await tenant_client.create_tenant_with_monitoring(
-    name="New Bakery Chain",
-    owner_id="user-123",
-    description="Multi-location bakery chain",
-    correlation_id="tenant-creation-456"
-)
-
-# Add member with events
-membership = await tenant_client.add_tenant_member_with_events(
-    tenant_id="tenant-123",
-    user_id="user-456",
-    role="manager",
-    correlation_id="member-add-789"
-)
-
-# Get tenant analytics
-analytics = await tenant_client.get_tenant_analytics("tenant-123")
-```
-
-### Notification Management
-
-```python
-# Send notification with monitoring
-notification = await notification_client.send_notification_with_monitoring(
-    recipient_id="user-123",
-    notification_type="forecast_ready",
-    title="Forecast Complete",
-    message="Your weekly forecast is ready for review",
-    tenant_id="tenant-456",
-    priority="high",
-    channels=["email", "in_app"],
-    correlation_id="forecast-notification-789"
-)
-
-# Send bulk notification
-bulk_result = await notification_client.send_bulk_notification_with_monitoring(
-    recipients=["user-123", "user-456", "user-789"],
-    notification_type="system_update",
-    title="System Maintenance",
-    message="Scheduled maintenance tonight at 2 AM",
-    priority="normal",
-    correlation_id="maintenance-notification-123"
-)
-
-# Get delivery analytics
-analytics = await notification_client.get_delivery_analytics(
-    tenant_id="tenant-123",
-    start_date="2024-01-01",
-    end_date="2024-01-31"
-)
-```
-
-## Health Monitoring
-
-### Individual Service Health
-
-```python
-# Get specific service health
-data_health = data_client.get_data_service_health()
-auth_health = auth_client.get_auth_service_health()
-training_health = training_client.get_training_service_health()
-
-# Health includes:
-# - Circuit breaker status
-# - Cache statistics and configuration
-# - Service-specific features
-# - Supported endpoints
-```
-
-### Registry-Level Health
-
-```python
-# Get all service health status
-all_health = registry.get_all_health_status()
-
-# Get aggregate metrics
-metrics = registry.get_aggregate_metrics()
-# Returns:
-# - Total cache hits/misses and hit rate
-# - Circuit breaker states for all services  
-# - Count of healthy vs total services
-```
-
-## Configuration
-
-### Cache TTL Configuration
-
-Each enhanced client has optimized cache TTL values:
-
-```python
-# Data Service
-sales_cache_ttl = 600      # 10 minutes
-weather_cache_ttl = 1800   # 30 minutes  
-traffic_cache_ttl = 3600   # 1 hour
-product_cache_ttl = 300    # 5 minutes
-
-# Auth Service  
-user_cache_ttl = 300       # 5 minutes
-token_cache_ttl = 60       # 1 minute
-permission_cache_ttl = 900 # 15 minutes
-
-# Training Service
-job_cache_ttl = 180        # 3 minutes
-model_cache_ttl = 600      # 10 minutes
-metrics_cache_ttl = 300    # 5 minutes
-
-# And so on...
-```
-
-### Circuit Breaker Configuration
-
-```python
-CircuitBreakerConfig(
-    failure_threshold=5,     # Failures before opening
-    recovery_timeout=60,     # Seconds before testing recovery
-    success_threshold=2,     # Successes needed to close
-    timeout=30              # Request timeout in seconds
-)
-```
-
-## Event System Integration
-
-All enhanced clients integrate with the enhanced event system:
-
-### Event Types
-- **EntityCreatedEvent** - When entities are created
-- **EntityUpdatedEvent** - When entities are modified  
-- **EntityDeletedEvent** - When entities are removed
-
-### Event Metadata
-- **correlation_id** - Track operations across services
-- **source_service** - Service that generated the event
-- **destination_service** - Target service
-- **tenant_id** - Tenant context
-- **user_id** - User context
-- **tags** - Additional metadata
-
-### Usage in Enhanced Clients
-Events are automatically published for:
-- Data uploads and modifications
-- User creation/updates/deletion
-- Training job lifecycle
-- Model deployments
-- Forecast creation
-- Tenant management operations
-- Notification delivery
-
-## Error Handling & Resilience
-
-### Circuit Breaker Protection
-- Automatically stops requests when services are failing
-- Provides fallback to cached data when available
-- Gradually tests service recovery
-
-### Retry Logic
-- Exponential backoff for transient failures
-- Configurable retry counts and delays
-- Authentication token refresh on 401 errors
-
-### Cache Fallbacks
-- Returns cached data when services are unavailable
-- Graceful degradation with stale data warnings
-- Manual cache invalidation for data consistency
-
-## Integration with Repository Pattern
-
-The enhanced clients seamlessly integrate with the new repository pattern:
-
-### Service Layer Integration
-```python
-class ForecastingService:
-    def __init__(self, 
-                 forecast_repository: ForecastRepository,
-                 service_registry: ServiceRegistry):
-        self.forecast_repository = forecast_repository
-        self.data_client = service_registry.get_data_client()
-        self.training_client = service_registry.get_training_client()
-    
-    async def create_forecast(self, tenant_id: str, model_id: str):
-        # Get data through enhanced client
-        sales_data = await self.data_client.get_all_sales_data_with_monitoring(
-            tenant_id=tenant_id,
-            correlation_id=f"forecast_data_{datetime.utcnow().isoformat()}"
-        )
-        
-        # Use repository for database operations
-        forecast = await self.forecast_repository.create({
-            "tenant_id": tenant_id,
-            "model_id": model_id,
-            "status": "pending"
-        })
-        
-        return forecast
-```
-
-This completes the comprehensive enhanced inter-service communication system that integrates seamlessly with the new repository pattern architecture, providing resilience, monitoring, and advanced features for all service interactions.

shared/clients/inventory_client.py

@@ -0,0 +1,347 @@
+# shared/clients/inventory_client.py
+"""
+Inventory Service Client - Inter-service communication
+Handles communication with the inventory service for all other services
+"""
+
+import structlog
+from typing import Dict, Any, List, Optional, Union
+from uuid import UUID
+
+from shared.clients.base_service_client import BaseServiceClient
+from shared.config.base import BaseServiceSettings
+
+logger = structlog.get_logger()
+
+
+class InventoryServiceClient(BaseServiceClient):
+    """Client for communicating with the inventory service via gateway"""
+    
+    def __init__(self, config: BaseServiceSettings):
+        super().__init__("inventory", config)
+    
+    def get_service_base_path(self) -> str:
+        """Return the base path for inventory service APIs"""
+        return "/api/v1"
+    
+    # ================================================================
+    # INGREDIENT MANAGEMENT
+    # ================================================================
+    
+    async def get_ingredient_by_id(self, ingredient_id: UUID, tenant_id: str) -> Optional[Dict[str, Any]]:
+        """Get ingredient details by ID"""
+        try:
+            result = await self.get(f"ingredients/{ingredient_id}", tenant_id=tenant_id)
+            if result:
+                logger.info("Retrieved ingredient from inventory service", 
+                          ingredient_id=ingredient_id, tenant_id=tenant_id)
+            return result
+        except Exception as e:
+            logger.error("Error fetching ingredient by ID", 
+                       error=str(e), ingredient_id=ingredient_id, tenant_id=tenant_id)
+            return None
+    
+    async def search_ingredients(
+        self, 
+        tenant_id: str,
+        search: Optional[str] = None,
+        category: Optional[str] = None,
+        is_active: Optional[bool] = None,
+        skip: int = 0,
+        limit: int = 100
+    ) -> List[Dict[str, Any]]:
+        """Search ingredients with filters"""
+        try:
+            params = {
+                "skip": skip,
+                "limit": limit
+            }
+            
+            if search:
+                params["search"] = search
+            if category:
+                params["category"] = category
+            if is_active is not None:
+                params["is_active"] = is_active
+            
+            result = await self.get("ingredients", tenant_id=tenant_id, params=params)
+            ingredients = result if isinstance(result, list) else []
+            
+            logger.info("Searched ingredients in inventory service", 
+                      search_term=search, count=len(ingredients), tenant_id=tenant_id)
+            return ingredients
+            
+        except Exception as e:
+            logger.error("Error searching ingredients", 
+                       error=str(e), search=search, tenant_id=tenant_id)
+            return []
+    
+    async def get_all_ingredients(self, tenant_id: str, is_active: Optional[bool] = True) -> List[Dict[str, Any]]:
+        """Get all ingredients for a tenant (paginated)"""
+        try:
+            params = {}
+            if is_active is not None:
+                params["is_active"] = is_active
+            
+            ingredients = await self.get_paginated("ingredients", tenant_id=tenant_id, params=params)
+            
+            logger.info("Retrieved all ingredients from inventory service", 
+                      count=len(ingredients), tenant_id=tenant_id)
+            return ingredients
+            
+        except Exception as e:
+            logger.error("Error fetching all ingredients", 
+                       error=str(e), tenant_id=tenant_id)
+            return []
+    
+    async def create_ingredient(self, ingredient_data: Dict[str, Any], tenant_id: str) -> Optional[Dict[str, Any]]:
+        """Create a new ingredient"""
+        try:
+            result = await self.post("ingredients", data=ingredient_data, tenant_id=tenant_id)
+            if result:
+                logger.info("Created ingredient in inventory service", 
+                          ingredient_name=ingredient_data.get('name'), tenant_id=tenant_id)
+            return result
+        except Exception as e:
+            logger.error("Error creating ingredient", 
+                       error=str(e), ingredient_data=ingredient_data, tenant_id=tenant_id)
+            return None
+    
+    async def update_ingredient(
+        self, 
+        ingredient_id: UUID, 
+        ingredient_data: Dict[str, Any], 
+        tenant_id: str
+    ) -> Optional[Dict[str, Any]]:
+        """Update an existing ingredient"""
+        try:
+            result = await self.put(f"ingredients/{ingredient_id}", data=ingredient_data, tenant_id=tenant_id)
+            if result:
+                logger.info("Updated ingredient in inventory service", 
+                          ingredient_id=ingredient_id, tenant_id=tenant_id)
+            return result
+        except Exception as e:
+            logger.error("Error updating ingredient", 
+                       error=str(e), ingredient_id=ingredient_id, tenant_id=tenant_id)
+            return None
+    
+    async def delete_ingredient(self, ingredient_id: UUID, tenant_id: str) -> bool:
+        """Delete (deactivate) an ingredient"""
+        try:
+            result = await self.delete(f"ingredients/{ingredient_id}", tenant_id=tenant_id)
+            success = result is not None
+            if success:
+                logger.info("Deleted ingredient in inventory service", 
+                          ingredient_id=ingredient_id, tenant_id=tenant_id)
+            return success
+        except Exception as e:
+            logger.error("Error deleting ingredient", 
+                       error=str(e), ingredient_id=ingredient_id, tenant_id=tenant_id)
+            return False
+    
+    async def get_ingredient_stock(
+        self, 
+        ingredient_id: UUID, 
+        tenant_id: str, 
+        include_unavailable: bool = False
+    ) -> List[Dict[str, Any]]:
+        """Get stock entries for an ingredient"""
+        try:
+            params = {}
+            if include_unavailable:
+                params["include_unavailable"] = include_unavailable
+            
+            result = await self.get(f"ingredients/{ingredient_id}/stock", tenant_id=tenant_id, params=params)
+            stock_entries = result if isinstance(result, list) else []
+            
+            logger.info("Retrieved ingredient stock from inventory service", 
+                      ingredient_id=ingredient_id, stock_count=len(stock_entries), tenant_id=tenant_id)
+            return stock_entries
+            
+        except Exception as e:
+            logger.error("Error fetching ingredient stock", 
+                       error=str(e), ingredient_id=ingredient_id, tenant_id=tenant_id)
+            return []
+    
+    # ================================================================
+    # STOCK MANAGEMENT
+    # ================================================================
+    
+    async def get_stock_levels(self, tenant_id: str, ingredient_ids: Optional[List[UUID]] = None) -> List[Dict[str, Any]]:
+        """Get current stock levels"""
+        try:
+            params = {}
+            if ingredient_ids:
+                params["ingredient_ids"] = [str(id) for id in ingredient_ids]
+            
+            result = await self.get("stock", tenant_id=tenant_id, params=params)
+            stock_levels = result if isinstance(result, list) else []
+            
+            logger.info("Retrieved stock levels from inventory service", 
+                      count=len(stock_levels), tenant_id=tenant_id)
+            return stock_levels
+            
+        except Exception as e:
+            logger.error("Error fetching stock levels", 
+                       error=str(e), tenant_id=tenant_id)
+            return []
+    
+    async def get_low_stock_alerts(self, tenant_id: str) -> List[Dict[str, Any]]:
+        """Get low stock alerts"""
+        try:
+            result = await self.get("alerts", tenant_id=tenant_id, params={"type": "low_stock"})
+            alerts = result if isinstance(result, list) else []
+            
+            logger.info("Retrieved low stock alerts from inventory service", 
+                      count=len(alerts), tenant_id=tenant_id)
+            return alerts
+            
+        except Exception as e:
+            logger.error("Error fetching low stock alerts", 
+                       error=str(e), tenant_id=tenant_id)
+            return []
+    
+    async def consume_stock(
+        self, 
+        consumption_data: Dict[str, Any], 
+        tenant_id: str
+    ) -> Optional[Dict[str, Any]]:
+        """Record stock consumption"""
+        try:
+            result = await self.post("stock/consume", data=consumption_data, tenant_id=tenant_id)
+            if result:
+                logger.info("Recorded stock consumption", 
+                          tenant_id=tenant_id)
+            return result
+        except Exception as e:
+            logger.error("Error recording stock consumption", 
+                       error=str(e), tenant_id=tenant_id)
+            return None
+    
+    async def receive_stock(
+        self, 
+        receipt_data: Dict[str, Any], 
+        tenant_id: str
+    ) -> Optional[Dict[str, Any]]:
+        """Record stock receipt"""
+        try:
+            result = await self.post("stock/receive", data=receipt_data, tenant_id=tenant_id)
+            if result:
+                logger.info("Recorded stock receipt", 
+                          tenant_id=tenant_id)
+            return result
+        except Exception as e:
+            logger.error("Error recording stock receipt", 
+                       error=str(e), tenant_id=tenant_id)
+            return None
+    
+    # ================================================================
+    # PRODUCT CLASSIFICATION (for onboarding)
+    # ================================================================
+    
+    async def classify_product(
+        self, 
+        product_name: str, 
+        sales_volume: Optional[float], 
+        tenant_id: str
+    ) -> Optional[Dict[str, Any]]:
+        """Classify a single product for inventory creation"""
+        try:
+            classification_data = {
+                "product_name": product_name,
+                "sales_volume": sales_volume
+            }
+            
+            result = await self.post("inventory/classify-product", data=classification_data, tenant_id=tenant_id)
+            if result:
+                logger.info("Classified product", 
+                          product=product_name, 
+                          classification=result.get('product_type'),
+                          confidence=result.get('confidence_score'),
+                          tenant_id=tenant_id)
+            return result
+            
+        except Exception as e:
+            logger.error("Error classifying product", 
+                       error=str(e), product=product_name, tenant_id=tenant_id)
+            return None
+    
+    async def classify_products_batch(
+        self, 
+        products: List[Dict[str, Any]], 
+        tenant_id: str
+    ) -> Optional[Dict[str, Any]]:
+        """Classify multiple products for onboarding automation"""
+        try:
+            classification_data = {
+                "products": products
+            }
+            
+            result = await self.post("inventory/classify-products-batch", data=classification_data, tenant_id=tenant_id)
+            if result:
+                suggestions = result.get('suggestions', [])
+                business_model = result.get('business_model_analysis', {}).get('model', 'unknown')
+                
+                logger.info("Batch classification complete", 
+                          total_products=len(suggestions),
+                          business_model=business_model,
+                          tenant_id=tenant_id)
+            return result
+            
+        except Exception as e:
+            logger.error("Error in batch classification", 
+                       error=str(e), products_count=len(products), tenant_id=tenant_id)
+            return None
+    
+    # ================================================================
+    # DASHBOARD AND ANALYTICS
+    # ================================================================
+    
+    async def get_inventory_dashboard(self, tenant_id: str) -> Optional[Dict[str, Any]]:
+        """Get inventory dashboard data"""
+        try:
+            result = await self.get("dashboard", tenant_id=tenant_id)
+            if result:
+                logger.info("Retrieved inventory dashboard data", tenant_id=tenant_id)
+            return result
+        except Exception as e:
+            logger.error("Error fetching inventory dashboard", 
+                       error=str(e), tenant_id=tenant_id)
+            return None
+    
+    async def get_inventory_summary(self, tenant_id: str) -> Optional[Dict[str, Any]]:
+        """Get inventory summary statistics"""
+        try:
+            result = await self.get("dashboard/summary", tenant_id=tenant_id)
+            if result:
+                logger.info("Retrieved inventory summary", tenant_id=tenant_id)
+            return result
+        except Exception as e:
+            logger.error("Error fetching inventory summary", 
+                       error=str(e), tenant_id=tenant_id)
+            return None
+    
+    # ================================================================
+    # UTILITY METHODS
+    # ================================================================
+    
+    async def health_check(self) -> bool:
+        """Check if inventory service is healthy"""
+        try:
+            result = await self.get("../health")  # Health endpoint is not tenant-scoped
+            return result is not None
+        except Exception as e:
+            logger.error("Inventory service health check failed", error=str(e))
+            return False
+
+
+# Factory function for dependency injection
+def create_inventory_client(config: BaseServiceSettings) -> InventoryServiceClient:
+    """Create inventory service client instance"""
+    return InventoryServiceClient(config)
+
+
+# Convenience function for quick access (requires config to be passed)
+async def get_inventory_client(config: BaseServiceSettings) -> InventoryServiceClient:
+    """Get inventory service client instance"""
+    return create_inventory_client(config)