Fix new Frontend 12

frontend/src/components/EnhancedTrainingProgress.tsx

@@ -0,0 +1,387 @@
+import React, { useState, useEffect } from 'react';
+import { 
+  Brain, Cpu, Database, TrendingUp, CheckCircle, AlertCircle, 
+  Clock, Zap, Target, BarChart3, Loader
+} from 'lucide-react';
+
+interface TrainingProgressProps {
+  progress: {
+    progress: number;
+    status: string;
+    currentStep: string;
+    productsCompleted: number;
+    productsTotal: number;
+    estimatedTimeRemaining: number;
+    error?: string;
+  };
+  onTimeout?: () => void;
+}
+
+// Map backend steps to user-friendly information
+const STEP_INFO_MAP = {
+  'data_validation': {
+    title: 'Validando tus datos',
+    description: 'Verificamos la calidad y completitud de tu información histórica',
+    tip: '💡 Datos más completos = predicciones más precisas',
+    icon: Database,
+    color: 'blue'
+  },
+  'feature_engineering': {
+    title: 'Creando características predictivas',
+    description: 'Identificamos patrones estacionales y tendencias en tus ventas',
+    tip: '💡 Tu modelo detectará automáticamente picos de demanda',
+    icon: TrendingUp,
+    color: 'indigo'
+  },
+  'model_training': {
+    title: 'Entrenando modelo de IA',
+    description: 'Creamos tu modelo personalizado usando algoritmos avanzados',
+    tip: '💡 Este proceso optimiza las predicciones para tu negocio específico',
+    icon: Brain,
+    color: 'purple'
+  },
+  'model_validation': {
+    title: 'Validando precisión',
+    description: 'Verificamos que el modelo genere predicciones confiables',
+    tip: '💡 Garantizamos que las predicciones sean útiles para tu toma de decisiones',
+    icon: Target,
+    color: 'green'
+  },
+  // Fallback for unknown steps
+  'default': {
+    title: 'Procesando...',
+    description: 'Procesando tus datos para crear el modelo de predicción',
+    tip: '💡 Cada paso nos acerca a predicciones más precisas',
+    icon: Cpu,
+    color: 'gray'
+  }
+};
+
+const EXPECTED_BENEFITS = [
+  {
+    icon: BarChart3,
+    title: 'Predicciones Precisas',
+    description: 'Conoce exactamente cuánto vender cada día'
+  },
+  {
+    icon: Zap,
+    title: 'Optimización Automática',
+    description: 'Reduce desperdicios y maximiza ganancias'
+  },
+  {
+    icon: TrendingUp,
+    title: 'Detección de Tendencias',
+    description: 'Identifica patrones estacionales y eventos especiales'
+  }
+];
+
+export default function EnhancedTrainingProgress({ progress, onTimeout }: TrainingProgressProps) {
+  const [showTimeoutWarning, setShowTimeoutWarning] = useState(false);
+  const [startTime] = useState(Date.now());
+
+  // Auto-show timeout warning after 8 minutes (480,000ms)
+  useEffect(() => {
+    const timeoutTimer = setTimeout(() => {
+      if (progress.status === 'running' && progress.progress < 100) {
+        setShowTimeoutWarning(true);
+      }
+    }, 480000); // 8 minutes
+
+    return () => clearTimeout(timeoutTimer);
+  }, [progress.status, progress.progress]);
+
+  const getCurrentStepInfo = () => {
+    // Try to match the current step from backend
+    const stepKey = progress.currentStep?.toLowerCase().replace(/\s+/g, '_');
+    return STEP_INFO_MAP[stepKey] || STEP_INFO_MAP['default'];
+  };
+
+  const formatTime = (seconds: number): string => {
+    if (!seconds || seconds <= 0) return '0m 0s';
+    const minutes = Math.floor(seconds / 60);
+    const remainingSeconds = seconds % 60;
+    return `${minutes}m ${remainingSeconds}s`;
+  };
+
+  const getProgressSteps = () => {
+    // Create progress steps based on current progress percentage
+    const steps = [
+      { id: 'data_validation', threshold: 25, name: 'Validación' },
+      { id: 'feature_engineering', threshold: 50, name: 'Características' },
+      { id: 'model_training', threshold: 80, name: 'Entrenamiento' },
+      { id: 'model_validation', threshold: 100, name: 'Validación' }
+    ];
+
+    return steps.map(step => ({
+      ...step,
+      completed: progress.progress >= step.threshold,
+      current: progress.progress >= (step.threshold - 25) && progress.progress < step.threshold
+    }));
+  };
+
+  const handleContinueToDashboard = () => {
+    setShowTimeoutWarning(false);
+    if (onTimeout) {
+      onTimeout();
+    }
+  };
+
+  const handleKeepWaiting = () => {
+    setShowTimeoutWarning(false);
+  };
+
+  const currentStepInfo = getCurrentStepInfo();
+  const progressSteps = getProgressSteps();
+
+  // Handle error state
+  if (progress.status === 'failed' || progress.error) {
+    return (
+      <div className="max-w-4xl mx-auto">
+        <div className="text-center mb-8">
+          <div className="inline-flex items-center justify-center w-20 h-20 bg-red-100 rounded-full mb-4">
+            <AlertCircle className="w-10 h-10 text-red-600" />
+          </div>
+          <h2 className="text-3xl font-bold text-gray-900 mb-2">
+            Error en el Entrenamiento
+          </h2>
+          <p className="text-lg text-gray-600 max-w-2xl mx-auto">
+            Ha ocurrido un problema durante el entrenamiento. Nuestro equipo ha sido notificado.
+          </p>
+        </div>
+
+        <div className="bg-white rounded-2xl shadow-xl p-8 mb-8">
+          <div className="bg-red-50 border border-red-200 rounded-xl p-6">
+            <div className="flex items-start space-x-4">
+              <AlertCircle className="w-6 h-6 text-red-600 flex-shrink-0 mt-1" />
+              <div>
+                <h3 className="text-lg font-semibold text-red-800 mb-2">
+                  Detalles del Error
+                </h3>
+                <p className="text-red-700">
+                  {progress.error || 'Error desconocido durante el entrenamiento'}
+                </p>
+                <div className="mt-4 text-sm text-red-600">
+                  <p>• Puedes intentar el entrenamiento nuevamente</p>
+                  <p>• Verifica que tus datos históricos estén completos</p>
+                  <p>• Contacta soporte si el problema persiste</p>
+                </div>
+              </div>
+            </div>
+          </div>
+
+          <div className="mt-6 text-center">
+            <button
+              onClick={() => window.location.reload()}
+              className="bg-blue-600 text-white px-6 py-3 rounded-lg font-medium hover:bg-blue-700 transition-colors"
+            >
+              Intentar Nuevamente
+            </button>
+          </div>
+        </div>
+      </div>
+    );
+  }
+
+  return (
+    <div className="max-w-4xl mx-auto">
+      {/* Header */}
+      <div className="text-center mb-8">
+        <div className="inline-flex items-center justify-center w-20 h-20 bg-blue-600 rounded-full mb-4">
+          <Brain className="w-10 h-10 text-white animate-pulse" />
+        </div>
+        <h2 className="text-3xl font-bold text-gray-900 mb-2">
+          🧠 Entrenando tu modelo de predicción
+        </h2>
+        <p className="text-lg text-gray-600 max-w-2xl mx-auto">
+          Estamos procesando tus datos históricos para crear predicciones personalizadas
+        </p>
+      </div>
+
+      {/* Main Progress Section */}
+      <div className="bg-white rounded-2xl shadow-xl p-8 mb-8">
+        {/* Overall Progress Bar */}
+        <div className="mb-8">
+          <div className="flex justify-between items-center mb-3">
+            <span className="text-sm font-medium text-gray-700">Progreso General</span>
+            <span className="text-sm font-bold text-blue-600">{progress.progress}%</span>
+          </div>
+          <div className="w-full bg-gray-200 rounded-full h-4 overflow-hidden">
+            <div 
+              className="bg-gradient-to-r from-blue-500 to-indigo-600 h-4 rounded-full transition-all duration-1000 ease-out relative"
+              style={{ width: `${progress.progress}%` }}
+            >
+              <div className="absolute inset-0 opacity-20 animate-pulse">
+                <div className="h-full bg-white rounded-full"></div>
+              </div>
+            </div>
+          </div>
+        </div>
+
+        {/* Current Step Info */}
+        <div className={`bg-${currentStepInfo.color}-50 border border-${currentStepInfo.color}-200 rounded-xl p-6 mb-6`}>
+          <div className="flex items-start space-x-4">
+            <div className="flex-shrink-0">
+              <div className={`w-12 h-12 bg-${currentStepInfo.color}-600 rounded-full flex items-center justify-center`}>
+                <currentStepInfo.icon className="w-6 h-6 text-white" />
+              </div>
+            </div>
+            <div className="flex-1">
+              <h3 className="text-xl font-semibold text-gray-900 mb-2">
+                {currentStepInfo.title}
+              </h3>
+              <p className="text-gray-700 mb-3">
+                {currentStepInfo.description}
+              </p>
+              <div className={`bg-${currentStepInfo.color}-100 border-l-4 border-${currentStepInfo.color}-500 p-3 rounded-r-lg`}>
+                <p className={`text-sm font-medium text-${currentStepInfo.color}-800`}>
+                  {currentStepInfo.tip}
+                </p>
+              </div>
+            </div>
+          </div>
+        </div>
+
+        {/* Step Progress Indicators */}
+        <div className="grid grid-cols-1 md:grid-cols-4 gap-4 mb-8">
+          {progressSteps.map((step, index) => (
+            <div 
+              key={step.id}
+              className={`p-4 rounded-lg border-2 transition-all duration-300 ${
+                step.completed 
+                  ? 'bg-green-50 border-green-200' 
+                  : step.current 
+                  ? 'bg-blue-50 border-blue-300 shadow-md' 
+                  : 'bg-gray-50 border-gray-200'
+              }`}
+            >
+              <div className="flex items-center mb-2">
+                {step.completed ? (
+                  <CheckCircle className="w-5 h-5 text-green-600 mr-2" />
+                ) : step.current ? (
+                  <div className="w-5 h-5 border-2 border-blue-600 border-t-transparent rounded-full animate-spin mr-2"></div>
+                ) : (
+                  <div className="w-5 h-5 border-2 border-gray-300 rounded-full mr-2"></div>
+                )}
+                <span className={`text-sm font-medium ${
+                  step.completed ? 'text-green-800' : step.current ? 'text-blue-800' : 'text-gray-600'
+                }`}>
+                  {step.name}
+                </span>
+              </div>
+            </div>
+          ))}
+        </div>
+
+        {/* Enhanced Stats Grid */}
+        <div className="grid grid-cols-1 md:grid-cols-3 gap-6">
+          <div className="text-center p-4 bg-gray-50 rounded-lg">
+            <div className="flex items-center justify-center mb-2">
+              <Cpu className="w-5 h-5 text-gray-600 mr-2" />
+              <span className="text-sm font-medium text-gray-700">Productos Procesados</span>
+            </div>
+            <div className="text-2xl font-bold text-gray-900">
+              {progress.productsCompleted}/{progress.productsTotal || 'N/A'}
+            </div>
+            {progress.productsTotal > 0 && (
+              <div className="w-full bg-gray-200 rounded-full h-2 mt-2">
+                <div 
+                  className="bg-blue-500 h-2 rounded-full transition-all duration-500"
+                  style={{ width: `${(progress.productsCompleted / progress.productsTotal) * 100}%` }}
+                ></div>
+              </div>
+            )}
+          </div>
+          
+          <div className="text-center p-4 bg-gray-50 rounded-lg">
+            <div className="flex items-center justify-center mb-2">
+              <Clock className="w-5 h-5 text-gray-600 mr-2" />
+              <span className="text-sm font-medium text-gray-700">Tiempo Restante</span>
+            </div>
+            <div className="text-2xl font-bold text-gray-900">
+              {progress.estimatedTimeRemaining 
+                ? formatTime(progress.estimatedTimeRemaining * 60) // Convert minutes to seconds
+                : 'Calculando...'
+              }
+            </div>
+          </div>
+
+          <div className="text-center p-4 bg-gray-50 rounded-lg">
+            <div className="flex items-center justify-center mb-2">
+              <Target className="w-5 h-5 text-gray-600 mr-2" />
+              <span className="text-sm font-medium text-gray-700">Precisión Esperada</span>
+            </div>
+            <div className="text-2xl font-bold text-green-600">
+              ~85%
+            </div>
+          </div>
+        </div>
+
+        {/* Status Indicator */}
+        <div className="mt-6 flex items-center justify-center">
+          <div className="flex items-center space-x-2 text-sm text-gray-600">
+            <Loader className="w-4 h-4 animate-spin" />
+            <span>Estado: {progress.status === 'running' ? 'Entrenando' : progress.status}</span>
+            <span>•</span>
+            <span>Paso actual: {progress.currentStep || 'Procesando...'}</span>
+          </div>
+        </div>
+      </div>
+
+      {/* Expected Benefits - Only show if progress < 80% to keep user engaged */}
+      {progress.progress < 80 && (
+        <div className="bg-white rounded-2xl shadow-xl p-8">
+          <h3 className="text-2xl font-bold text-gray-900 mb-6 text-center">
+            Lo que podrás hacer una vez completado
+          </h3>
+          <div className="grid grid-cols-1 md:grid-cols-3 gap-6">
+            {EXPECTED_BENEFITS.map((benefit, index) => (
+              <div key={index} className="text-center p-6 bg-gradient-to-br from-indigo-50 to-purple-50 rounded-xl">
+                <div className="inline-flex items-center justify-center w-12 h-12 bg-indigo-600 rounded-full mb-4">
+                  <benefit.icon className="w-6 h-6 text-white" />
+                </div>
+                <h4 className="text-lg font-semibold text-gray-900 mb-2">
+                  {benefit.title}
+                </h4>
+                <p className="text-gray-600">
+                  {benefit.description}
+                </p>
+              </div>
+            ))}
+          </div>
+        </div>
+      )}
+
+      {/* Timeout Warning Modal */}
+      {showTimeoutWarning && (
+        <div className="fixed inset-0 bg-black bg-opacity-50 flex items-center justify-center z-50">
+          <div className="bg-white rounded-2xl shadow-2xl p-8 max-w-md mx-4">
+            <div className="text-center">
+              <AlertCircle className="w-16 h-16 text-orange-500 mx-auto mb-4" />
+              <h3 className="text-xl font-bold text-gray-900 mb-4">
+                Entrenamiento tomando más tiempo
+              </h3>
+              <p className="text-gray-600 mb-6">
+                Puedes explorar el dashboard mientras terminamos el entrenamiento. 
+                Te notificaremos cuando esté listo.
+              </p>
+              <div className="flex flex-col sm:flex-row gap-3">
+                <button
+                  onClick={handleContinueToDashboard}
+                  className="flex-1 bg-blue-600 text-white px-6 py-3 rounded-lg font-medium hover:bg-blue-700 transition-colors"
+                >
+                  Continuar al Dashboard
+                </button>
+                <button
+                  onClick={handleKeepWaiting}
+                  className="flex-1 bg-gray-200 text-gray-800 px-6 py-3 rounded-lg font-medium hover:bg-gray-300 transition-colors"
+                >
+                  Seguir Esperando
+                </button>
+              </div>
+            </div>
+          </div>
+        </div>
+      )}
+    </div>
+  );
+}

frontend/src/pages/onboarding/OnboardingPage.tsx

@@ -2,6 +2,8 @@ import React, { useState, useEffect, useCallback } from 'react';
 import { ChevronLeft, ChevronRight, Upload, MapPin, Store, Factory, Check, Brain, Clock, CheckCircle, AlertTriangle, Loader } from 'lucide-react';
 import toast from 'react-hot-toast';
 
+import EnhancedTrainingProgress from '../../components/EnhancedTrainingProgress';
+
 import { 
   useTenant, 
   useTraining, 
@@ -352,11 +354,30 @@ const OnboardingPage: React.FC<OnboardingPageProps> = ({ user, onComplete }) =>
     setCurrentStep(5);
   };
 
-  const formatTimeRemaining = (seconds: number): string => {
+  const handleTrainingTimeout = () => {
-    const minutes = Math.floor(seconds / 60);
+  // Option 1: Navigate to dashboard with limited functionality
-    const secs = seconds % 60;
+  onComplete(); // This calls your existing completion handler
-    return `${minutes}:${secs.toString().padStart(2, '0')}`;
+  
-  };
+  // Option 2: Show a custom modal or message
+  // setShowLimitedAccessMessage(true);
+  
+  // Option 3: Set a flag to enable partial dashboard access
+  // setLimitedAccess(true);
+};
+
+// Then update the EnhancedTrainingProgress call:
+<EnhancedTrainingProgress 
+  progress={{
+    progress: trainingProgress.progress,
+    status: trainingProgress.status,
+    currentStep: trainingProgress.currentStep,
+    productsCompleted: trainingProgress.productsCompleted,
+    productsTotal: trainingProgress.productsTotal,
+    estimatedTimeRemaining: trainingProgress.estimatedTimeRemaining,
+    error: trainingProgress.error
+  }}
+  onTimeout={handleTrainingTimeout}
+/>
 
   const renderStep = () => {
     switch (currentStep) {
@@ -672,133 +693,24 @@ const OnboardingPage: React.FC<OnboardingPageProps> = ({ user, onComplete }) =>
         );
 
       case 4:
-        return (
+         return (
-          <div className="space-y-6">
+        <EnhancedTrainingProgress 
-            <div className="text-center">
+          progress={{
-              <div className="mx-auto w-16 h-16 bg-primary-100 rounded-full flex items-center justify-center mb-4">
+            progress: trainingProgress.progress,
-                <Brain className="h-8 w-8 text-primary-600" />
+            status: trainingProgress.status,
-              </div>
+            currentStep: trainingProgress.currentStep,
-              <h3 className="text-lg font-semibold text-gray-900 mb-2">
+            productsCompleted: trainingProgress.productsCompleted,
-                🧠 Entrenando tu modelo de predicción
+            productsTotal: trainingProgress.productsTotal,
-              </h3>
+            estimatedTimeRemaining: trainingProgress.estimatedTimeRemaining,
-              <p className="text-gray-600 mb-8">
+            error: trainingProgress.error
-                Estamos procesando tus datos históricos para crear predicciones personalizadas
+          }}
-              </p>
+          onTimeout={() => {
-            </div>
+            // Handle timeout - either navigate to dashboard or show limited access
-
+            console.log('Training timeout - user wants to continue to dashboard');
-            {/* WebSocket Connection Status */}
+            // You can add your custom timeout logic here
-            {tenantId && trainingJobId && (
+          }}
-              <div className="mb-4 text-xs text-gray-500 flex items-center">
+        />
-                <div className={`w-2 h-2 rounded-full mr-2 ${
+      );
-                  isConnected ? 'bg-green-500' : 'bg-red-500'
-                }`} />
-                {isConnected ? 'Conectado a actualizaciones en tiempo real' : 'Reconectando...'}
-              </div>
-            )}
-            <div className="space-y-4">
-              <div className="flex justify-between items-center text-sm">
-                <span className="text-gray-600">{trainingProgress.currentStep}</span>
-                <span className="text-gray-500">
-                  {trainingProgress.progress}% completado
-                </span>
-              </div>
-              
-              <div className="w-full bg-gray-200 rounded-full h-3">
-                <div
-                  className="bg-gradient-to-r from-primary-500 to-primary-600 h-3 rounded-full transition-all duration-1000 ease-out"
-                  style={{ width: `${trainingProgress.progress}%` }}
-                />
-              </div>
-
-              {trainingProgress.productsTotal > 0 && (
-                <div className="flex justify-between items-center text-sm text-gray-600">
-                  <span>
-                    📦 Productos: {trainingProgress.productsCompleted}/{trainingProgress.productsTotal}
-                  </span>
-                  {trainingProgress.estimatedTimeRemaining > 0 && (
-                    <span className="flex items-center">
-                      <Clock className="h-4 w-4 mr-1" />
-                      {formatTimeRemaining(trainingProgress.estimatedTimeRemaining)} restante
-                    </span>
-                  )}
-                </div>
-              )}
-            </div>
-
-            {/* Training Status */}
-            <div className="bg-gray-50 rounded-xl p-6">
-              {trainingProgress.status === 'running' && (
-                <div className="flex items-center text-blue-700">
-                  <Loader className="h-5 w-5 mr-3 animate-spin" />
-                  <div>
-                    <div className="font-medium">Entrenamiento en progreso</div>
-                    <div className="text-sm text-blue-600">
-                      Tu modelo está aprendiendo de los patrones históricos de ventas
-                    </div>
-                  </div>
-                </div>
-              )}
-
-              {trainingProgress.status === 'completed' && (
-                <div className="flex items-center text-green-700">
-                  <CheckCircle className="h-5 w-5 mr-3" />
-                  <div>
-                    <div className="font-medium">¡Entrenamiento completado!</div>
-                    <div className="text-sm text-green-600">
-                      Tu modelo está listo para generar predicciones precisas
-                    </div>
-                  </div>
-                </div>
-              )}
-
-              {trainingProgress.status === 'failed' && (
-                <div className="space-y-4">
-                  <div className="flex items-center text-red-700">
-                    <AlertTriangle className="h-5 w-5 mr-3" />
-                    <div>
-                      <div className="font-medium">Error en el entrenamiento</div>
-                      <div className="text-sm text-red-600">
-                        {trainingProgress.error || 'Ha ocurrido un error durante el entrenamiento'}
-                      </div>
-                    </div>
-                  </div>
-                  
-                  <div className="flex space-x-3">
-                    <button
-                      onClick={handleRetryTraining}
-                      className="flex-1 px-4 py-2 bg-primary-500 text-white rounded-lg hover:bg-primary-600 transition-colors"
-                    >
-                      Reintentar entrenamiento
-                    </button>
-                    <button
-                      onClick={handleSkipTraining}
-                      className="flex-1 px-4 py-2 border border-gray-300 text-gray-700 rounded-lg hover:bg-gray-50 transition-colors"
-                    >
-                      Continuar sin entrenar
-                    </button>
-                  </div>
-                </div>
-              )}
-            </div>
-
-            {/* Educational Content */}
-            <div className="grid grid-cols-1 md:grid-cols-2 gap-4 text-sm">
-              <div className="bg-blue-50 p-4 rounded-lg">
-                <div className="font-medium text-blue-900 mb-2">¿Qué está pasando?</div>
-                <div className="text-blue-700">
-                  Nuestro sistema está analizando patrones estacionales, tendencias de demanda y factores externos para crear un modelo personalizado para tu panadería.
-                </div>
-              </div>
-              
-              <div className="bg-green-50 p-4 rounded-lg">
-                <div className="font-medium text-green-900 mb-2">Beneficios esperados</div>
-                <div className="text-green-700">
-                  Predicciones de demanda precisas, reducción de desperdicio, optimización de stock y mejor planificación de producción.
-                </div>
-              </div>
-            </div>
-          </div>
-        );
 
       case 5:
         return (

services/training/app/api/training.py

@@ -80,28 +80,6 @@ async def start_training_job(
             requested_start=request.start_date,
             requested_end=request.end_date
         )
-        
-        training_config = {
-            "job_id": job_id,
-            "tenant_id": tenant_id,
-            "bakery_location": {
-                "latitude": 40.4168,
-                "longitude": -3.7038
-            },
-            "requested_start": request.start_date.isoformat() if request.start_date else None,
-            "requested_end": request.end_date.isoformat() if request.end_date else None,
-            "estimated_duration_minutes": 15,
-            "estimated_products": 10,
-            "background_execution": True,
-            "api_version": "v1"
-        }
-
-        # Publish immediate event (training started)
-        await publish_job_started(
-            job_id=job_id,
-            tenant_id=tenant_id,
-            config=training_config
-        )
 
         # Return immediate success response
         response_data = {
@@ -174,11 +152,30 @@ async def execute_training_job_background(
             
             status_manager = TrainingStatusManager(db_session=db_session)
             
-            # Publish progress event
-            await publish_job_progress(job_id, tenant_id, 5, "Initializing training pipeline")
-            
             try:
                 
+                training_config = {
+                    "job_id": job_id,
+                    "tenant_id": tenant_id,
+                    "bakery_location": {
+                        "latitude": 40.4168,
+                        "longitude": -3.7038
+                    },
+                    "requested_start": requested_start if requested_start else None,
+                    "requested_end": requested_end if requested_end else None,
+                    "estimated_duration_minutes": 15,
+                    "estimated_products": None,
+                    "background_execution": True,
+                    "api_version": "v1"
+                }
+                
+                # Publish immediate event (training started)
+                await publish_job_started(
+                    job_id=job_id,
+                    tenant_id=tenant_id,
+                    config=training_config
+                )
+                
                 await status_manager.update_job_status(
                     job_id=job_id,
                     status="running", 

services/training/app/ml/trainer.py

@@ -10,6 +10,8 @@ import numpy as np
 from datetime import datetime
 import logging
 import uuid
+import time
+from datetime import datetime
 
 from app.ml.data_processor import BakeryDataProcessor
 from app.ml.prophet_manager import BakeryProphetManager
@@ -75,6 +77,7 @@ class BakeryMLTrainer:
             processed_data = await self._process_all_products(
                 sales_df, weather_df, traffic_df, products
             )
+            await publish_job_progress(job_id, tenant_id, 20, "feature_engineering", estimated_time_remaining_minutes=7)
             
             # Train models for each processed product
             logger.info("Training models for all products...")
@@ -84,6 +87,7 @@ class BakeryMLTrainer:
             
             # Calculate overall training summary
             summary = self._calculate_training_summary(training_results)
+            await publish_job_progress(job_id, tenant_id, 90, "model_validation", estimated_time_remaining_minutes=1)
             
             result = {
                 "job_id": job_id,
@@ -354,6 +358,41 @@ class BakeryMLTrainer:
         
         return processed_data
     
+    def calculate_estimated_time_remaining(self, processing_times: List[float], completed: int, total: int) -> int:
+        """
+        Calculate estimated time remaining based on actual processing times
+        
+        Args:
+            processing_times: List of processing times for completed items (in seconds)
+            completed: Number of items completed so far
+            total: Total number of items to process
+        
+        Returns:
+            Estimated time remaining in minutes
+        """
+        if not processing_times or completed >= total:
+            return 0
+        
+        # Calculate average processing time
+        avg_time_per_item = sum(processing_times) / len(processing_times)
+        
+        # Use weighted average giving more weight to recent processing times
+        if len(processing_times) > 3:
+            # Use last 3 items for more accurate recent performance
+            recent_times = processing_times[-3:]
+            recent_avg = sum(recent_times) / len(recent_times)
+            # Weighted average: 70% recent, 30% overall
+            avg_time_per_item = (recent_avg * 0.7) + (avg_time_per_item * 0.3)
+        
+        # Calculate remaining items and estimated time
+        remaining_items = total - completed
+        estimated_seconds = remaining_items * avg_time_per_item
+        
+        # Convert to minutes and round up
+        estimated_minutes = max(1, int(estimated_seconds / 60) + (1 if estimated_seconds % 60 > 0 else 0))
+        
+        return estimated_minutes
+    
     async def _train_all_models(self,
                                tenant_id: str,
                                processed_data: Dict[str, pd.DataFrame],
@@ -361,7 +400,17 @@ class BakeryMLTrainer:
         """Train models for all processed products using Prophet manager"""
         training_results = {}
         
+        total_products = len(processed_data)
+        base_progress = 45
+        max_progress = 85  # or whatever your target end progress is
+        products_total = 0
+        i = 0
+        
+        start_time = time.time()
+        processing_times = []  # Store individual processing times
+
         for product_name, product_data in processed_data.items():
+            product_start_time = time.time()
             try:
                 logger.info(f"Training model for product: {product_name}")
                 
@@ -375,6 +424,7 @@ class BakeryMLTrainer:
                         'message': f'Need at least {settings.MIN_TRAINING_DATA_DAYS} data points, got {len(product_data)}'
                     }
                     logger.warning(f"Skipping {product_name}: insufficient data ({len(product_data)} < {settings.MIN_TRAINING_DATA_DAYS})")
+                    processing_times.append(time.time() - product_start_time)
                     continue
                 
                 # Train the model using Prophet manager
@@ -402,6 +452,29 @@ class BakeryMLTrainer:
                     'data_points': len(product_data) if product_data is not None else 0,
                     'failed_at': datetime.now().isoformat()
                 }
+            
+            # Record processing time for this product
+            product_processing_time = time.time() - product_start_time
+            processing_times.append(product_processing_time)
+            
+            i += 1
+            current_progress = base_progress + int((i / total_products) * (max_progress - base_progress))
+            
+            # Calculate estimated time remaining
+            estimated_time_remaining_minutes = self.calculate_estimated_time_remaining(
+                processing_times, i, total_products
+            )
+            
+            await publish_job_progress(
+                job_id, 
+                tenant_id, 
+                current_progress, 
+                "model_training", 
+                product_name, 
+                products_total, 
+                total_products, 
+                estimated_time_remaining_minutes=estimated_time_remaining_minutes
+            )
         
         return training_results
     

services/training/app/services/training_orchestrator.py

@@ -75,19 +75,13 @@ class TrainingDataOrchestrator:
         
         try:
             
-            await publish_job_progress(job_id, tenant_id, 5, "Extrayendo datos de ventas", 
-                          step_details="Conectando con servicio de datos")
             sales_data = await self.data_client.fetch_sales_data(tenant_id)
             
             # Step 1: Extract and validate sales data date range
-            await publish_job_progress(job_id, tenant_id, 10, "Validando fechas de datos de venta",
-                          step_details="Aplicando restricciones de fuentes de datos")
             sales_date_range = self._extract_sales_date_range(sales_data)
             logger.info(f"Sales data range detected: {sales_date_range.start} to {sales_date_range.end}")
             
             # Step 2: Apply date alignment across all data sources
-            await publish_job_progress(job_id, tenant_id, 15, "Alinear el rango de fechas",
-                          step_details="Aplicar la alineación de fechas en todas las fuentes de datos")
             aligned_range = self.date_alignment_service.validate_and_align_dates(
                 user_sales_range=sales_date_range,
                 requested_start=requested_start,
@@ -99,21 +93,15 @@ class TrainingDataOrchestrator:
                 logger.info(f"Applied constraints: {aligned_range.constraints}")
             
             # Step 3: Filter sales data to aligned date range
-            await publish_job_progress(job_id, tenant_id, 20, "Alinear el rango de las ventas",
-                          step_details="Aplicar la alineación de fechas de las ventas")
             filtered_sales = self._filter_sales_data(sales_data, aligned_range)
             
             # Step 4: Collect external data sources concurrently
             logger.info("Collecting external data sources...")
-            await publish_job_progress(job_id, tenant_id, 25, "Recopilación de fuentes de datos externas",
-                          step_details="Recopilación de fuentes de datos externas")
             weather_data, traffic_data = await self._collect_external_data(
                 aligned_range, bakery_location, tenant_id
             )
             
             # Step 5: Validate data quality
-            await publish_job_progress(job_id, tenant_id, 30, "Validando la calidad de los datos",
-                          step_details="Validando la calidad de los datos")
             data_quality_results = self._validate_data_sources(
                 filtered_sales, weather_data, traffic_data, aligned_range
             )
@@ -140,8 +128,6 @@ class TrainingDataOrchestrator:
             )
             
             # Step 7: Final validation
-            await publish_job_progress(job_id, tenant_id, 35, "Validancion final de los datos",
-                          step_details="Validancion final de los datos")
             final_validation = self.validate_training_data_quality(training_dataset)
             training_dataset.metadata["final_validation"] = final_validation
             

services/training/app/services/training_service.py

@@ -78,7 +78,6 @@ class TrainingService:
             
             # Step 1: Prepare training dataset with date alignment and orchestration
             logger.info("Step 1: Preparing and aligning training data")
-            await publish_job_progress(job_id, tenant_id, 0, "Extrayendo datos de ventas")
             training_dataset = await self.orchestrator.prepare_training_data(
                 tenant_id=tenant_id,
                 bakery_location=bakery_location,
@@ -86,10 +85,10 @@ class TrainingService:
                 requested_end=requested_end,
                 job_id=job_id
             )
+            await publish_job_progress(job_id, tenant_id, 10, "data_validation", estimated_time_remaining_minutes=8)
             
             # Step 2: Execute ML training pipeline
             logger.info("Step 2: Starting ML training pipeline")
-            await publish_job_progress(job_id, tenant_id, 35, "Starting ML training pipeline")
             training_results = await self.trainer.train_tenant_models(
                 tenant_id=tenant_id,
                 training_dataset=training_dataset,
@@ -117,7 +116,7 @@ class TrainingService:
             }
             
             logger.info(f"Training job {job_id} completed successfully")
-            await publish_job_completed(job_id, tenant_id, final_result);
+            await publish_job_completed(job_id, tenant_id, final_result)
             return TrainingService.create_detailed_training_response(final_result)
             
         except Exception as e: