Start integrating the onboarding flow with backend 4

frontend/src/components/domain/onboarding/steps/MLTrainingStep.tsx

@@ -11,6 +11,7 @@ import {
   TrainingCompletedMessage, 
   TrainingErrorMessage 
 } from '../../../../services/realtime/websocket.service';
+import { trainingService } from '../../../../services/api/training.service';
 
 interface TrainingMetrics {
   accuracy: number;
@@ -35,44 +36,7 @@ interface TrainingJob {
   metrics?: TrainingMetrics;
 }
 
-// Real training service using backend APIs
-class TrainingApiService {
-  private async apiCall(endpoint: string, options: RequestInit = {}) {
-    const response = await fetch(`/api${endpoint}`, {
-      ...options,
-      headers: {
-        'Content-Type': 'application/json',
-        ...options.headers,
-      },
-    });
-
-    if (!response.ok) {
-      throw new Error(`API call failed: ${response.statusText}`);
-    }
-
-    return response.json();
-  }
-
-  async startTrainingJob(tenantId: string, startDate?: string, endDate?: string): Promise<TrainingJob> {
-    return this.apiCall(`/tenants/${tenantId}/training/jobs`, {
-      method: 'POST',
-      body: JSON.stringify({
-        start_date: startDate,
-        end_date: endDate
-      }),
-    });
-  }
-
-  async getTrainingJob(tenantId: string, jobId: string): Promise<TrainingJob> {
-    return this.apiCall(`/tenants/${tenantId}/training/jobs/${jobId}`);
-  }
-
-  async getTrainingJobs(tenantId: string): Promise<TrainingJob[]> {
-    return this.apiCall(`/tenants/${tenantId}/training/jobs`);
-  }
-}
-
-const trainingService = new TrainingApiService();
+// Using the proper training service from services/api/training.service.ts
 
 export const MLTrainingStep: React.FC<OnboardingStepProps> = ({
   data,
@@ -114,13 +78,25 @@ export const MLTrainingStep: React.FC<OnboardingStepProps> = ({
     console.log('MLTrainingStep - dataProcessingData:', dataProcessingData);
     console.log('MLTrainingStep - reviewData:', reviewData);
     console.log('MLTrainingStep - inventoryData:', inventoryData);
+    console.log('MLTrainingStep - inventoryData.salesImportResult:', inventoryData?.salesImportResult);
 
     // Check if sales data was processed
     const hasProcessingResults = dataProcessingData?.processingResults && 
                                 dataProcessingData.processingResults.is_valid &&
                                 dataProcessingData.processingResults.total_records > 0;
     
+    // Check if sales data was imported (required for training)
+    const hasImportResults = inventoryData?.salesImportResult &&
+                            (inventoryData.salesImportResult.records_created > 0 || 
+                             inventoryData.salesImportResult.success === true ||
+                             inventoryData.salesImportResult.imported === true);
+    
     if (!hasProcessingResults) {
+      missingItems.push('Datos de ventas validados');
+    }
+    
+    // Sales data must be imported for ML training to work
+    if (!hasImportResults) {
       missingItems.push('Datos de ventas importados');
     }
 
@@ -152,6 +128,7 @@ export const MLTrainingStep: React.FC<OnboardingStepProps> = ({
       isValid: missingItems.length === 0,
       missingItems,
       hasProcessingResults,
+      hasImportResults,
       hasApprovedProducts,
       hasInventoryConfig
     });
@@ -205,7 +182,11 @@ export const MLTrainingStep: React.FC<OnboardingStepProps> = ({
     try {
       // Start training job
       addLog('Iniciando trabajo de entrenamiento ML...', 'info');
-      const job = await trainingService.startTrainingJob(tenantId);
+      const response = await trainingService.createTrainingJob({
+        start_date: undefined,
+        end_date: undefined
+      });
+      const job = response.data;
       
       setCurrentJob(job);
       setTrainingStatus('training');