Imporve the predicciones page
This commit is contained in:
Urtzi Alfaro
@@ -15,10 +15,11 @@ import {
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DeleteForecastResponse,
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GetForecastsParams,
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ForecastingHealthResponse,
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MultiDayForecastResponse,
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} from '../types/forecasting';
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export class ForecastingService {
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private readonly baseUrl = '/forecasts';
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private readonly baseUrl = '/tenants';
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/**
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* Generate a single product forecast
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@@ -29,7 +30,7 @@ export class ForecastingService {
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request: ForecastRequest
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): Promise<ForecastResponse> {
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return apiClient.post<ForecastResponse, ForecastRequest>(
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`/tenants/${tenantId}${this.baseUrl}/single`,
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`${this.baseUrl}/${tenantId}/forecasts/single`,
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request
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);
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}
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@@ -43,7 +44,7 @@ export class ForecastingService {
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request: BatchForecastRequest
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): Promise<BatchForecastResponse> {
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return apiClient.post<BatchForecastResponse, BatchForecastRequest>(
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`/tenants/${tenantId}${this.baseUrl}/batch`,
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`${this.baseUrl}/${tenantId}/forecasts/batch`,
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request
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);
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}
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@@ -75,8 +76,8 @@ export class ForecastingService {
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}
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const queryString = searchParams.toString();
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const url = `/tenants/${tenantId}${this.baseUrl}${queryString ? `?${queryString}` : ''}`;
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const url = `${this.baseUrl}/${tenantId}/forecasts${queryString ? `?${queryString}` : ''}`;
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return apiClient.get<ForecastListResponse>(url);
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}
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@@ -89,7 +90,7 @@ export class ForecastingService {
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forecastId: string
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): Promise<ForecastByIdResponse> {
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return apiClient.get<ForecastByIdResponse>(
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`/tenants/${tenantId}${this.baseUrl}/${forecastId}`
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`${this.baseUrl}/${tenantId}/forecasts/${forecastId}`
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);
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}
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@@ -102,7 +103,7 @@ export class ForecastingService {
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forecastId: string
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): Promise<DeleteForecastResponse> {
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return apiClient.delete<DeleteForecastResponse>(
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`/tenants/${tenantId}${this.baseUrl}/${forecastId}`
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`${this.baseUrl}/${tenantId}/forecasts/${forecastId}`
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);
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}
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@@ -114,7 +115,21 @@ export class ForecastingService {
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tenantId: string
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): Promise<ForecastStatistics> {
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return apiClient.get<ForecastStatistics>(
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`/tenants/${tenantId}${this.baseUrl}/statistics`
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`${this.baseUrl}/${tenantId}/forecasts/statistics`
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);
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}
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/**
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* Generate multi-day forecasts for a single product
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* POST /tenants/{tenant_id}/forecasts/multi-day
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*/
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async createMultiDayForecast(
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tenantId: string,
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request: ForecastRequest
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): Promise<MultiDayForecastResponse> {
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return apiClient.post<MultiDayForecastResponse, ForecastRequest>(
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`${this.baseUrl}/${tenantId}/forecasts/multi-day`,
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request
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);
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}
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@@ -83,7 +83,7 @@ class TrainingService {
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const queryString = params.toString() ? `?${params.toString()}` : '';
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return apiClient.get<PaginatedResponse<TrainedModelResponse>>(
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`${this.baseUrl}/${tenantId}/models${queryString}`
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`${this.baseUrl}/${tenantId}/models/${queryString}`
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);
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}
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@@ -157,4 +157,15 @@ export interface ForecastingHealthResponse {
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version: string;
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features: string[];
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timestamp: string;
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}
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export interface MultiDayForecastResponse {
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tenant_id: string;
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inventory_product_id: string;
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forecast_start_date: string; // ISO date string
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forecast_days: number;
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forecasts: ForecastResponse[];
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total_predicted_demand: number;
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average_confidence_level: number;
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processing_time_ms: number;
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}
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@@ -88,9 +88,15 @@ const DemandChart: React.FC<DemandChartProps> = ({
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// Process forecast data for chart
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const chartData = useMemo(() => {
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console.log('🔍 Processing forecast data for chart:', data);
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const processedData: ChartDataPoint[] = data.map(forecast => {
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// Convert forecast_date to a proper date format for the chart
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const forecastDate = new Date(forecast.forecast_date);
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const dateString = forecastDate.toISOString().split('T')[0];
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return {
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date: forecast.forecast_date,
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date: dateString,
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actualDemand: undefined, // Not available in current forecast response
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predictedDemand: forecast.predicted_demand,
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confidenceLower: forecast.confidence_lower,
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@@ -99,23 +105,32 @@ const DemandChart: React.FC<DemandChartProps> = ({
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};
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});
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console.log('📊 Processed chart data:', processedData);
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return processedData.sort((a, b) => new Date(a.date).getTime() - new Date(b.date).getTime());
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}, [data]);
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// Filter data based on selected period
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const filteredData = useMemo(() => {
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console.log('🔍 Filtering data - selected period:', selectedPeriod);
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console.log('🔍 Chart data before filtering:', chartData);
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if (!selectedPeriod.start || !selectedPeriod.end) {
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console.log('📊 No period filter, returning all chart data');
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return chartData;
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}
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return chartData.filter(point => {
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const filtered = chartData.filter(point => {
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const pointDate = new Date(point.date);
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return pointDate >= selectedPeriod.start! && pointDate <= selectedPeriod.end!;
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});
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console.log('📊 Filtered data:', filtered);
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return filtered;
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}, [chartData, selectedPeriod]);
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// Update zoomed data when filtered data changes
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useEffect(() => {
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console.log('🔍 Setting zoomed data from filtered data:', filteredData);
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setZoomedData(filteredData);
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}, [filteredData]);
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@@ -221,8 +236,11 @@ const DemandChart: React.FC<DemandChartProps> = ({
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);
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}
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// Empty state
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if (zoomedData.length === 0) {
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// Use filteredData if zoomedData is empty but we have data
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const displayData = zoomedData.length > 0 ? zoomedData : filteredData;
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// Empty state - only show if we truly have no data
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if (displayData.length === 0 && chartData.length === 0) {
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return (
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<Card className={className}>
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<CardHeader>
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@@ -286,7 +304,7 @@ const DemandChart: React.FC<DemandChartProps> = ({
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)}
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{/* Reset zoom */}
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{zoomedData.length !== filteredData.length && (
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{displayData.length !== filteredData.length && (
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<Button variant="ghost" size="sm" onClick={handleResetZoom}>
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Restablecer Zoom
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</Button>
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@@ -298,28 +316,57 @@ const DemandChart: React.FC<DemandChartProps> = ({
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<CardBody padding="lg">
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<div style={{ width: '100%', height }}>
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<ResponsiveContainer>
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<ComposedChart data={zoomedData} margin={{ top: 20, right: 30, left: 20, bottom: 5 }}>
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<CartesianGrid strokeDasharray="3 3" stroke="#e5e7eb" />
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<XAxis
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dataKey="date"
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<ComposedChart data={displayData} margin={{ top: 20, right: 30, left: 20, bottom: 60 }}>
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<defs>
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<linearGradient id="demandGradient" x1="0" y1="0" x2="0" y2="1">
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<stop offset="5%" stopColor="#10b981" stopOpacity={0.3}/>
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<stop offset="95%" stopColor="#10b981" stopOpacity={0.05}/>
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</linearGradient>
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<linearGradient id="confidenceGradient" x1="0" y1="0" x2="0" y2="1">
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<stop offset="5%" stopColor="#10b981" stopOpacity={0.1}/>
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<stop offset="95%" stopColor="#10b981" stopOpacity={0.02}/>
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</linearGradient>
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</defs>
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<CartesianGrid
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strokeDasharray="2 2"
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stroke="#e5e7eb"
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strokeOpacity={0.5}
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horizontal={true}
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vertical={false}
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/>
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<XAxis
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dataKey="date"
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stroke="#6b7280"
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fontSize={12}
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fontSize={11}
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tickMargin={8}
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angle={-45}
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textAnchor="end"
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height={80}
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interval={0}
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tickFormatter={(value) => {
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const date = new Date(value);
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return timeframe === 'weekly'
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? date.toLocaleDateString('es-ES', { month: 'short', day: 'numeric' })
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: timeframe === 'monthly'
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? date.toLocaleDateString('es-ES', { month: 'short', year: '2-digit' })
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: date.getFullYear().toString();
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return date.toLocaleDateString('es-ES', {
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month: 'short',
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day: 'numeric',
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weekday: displayData.length <= 7 ? 'short' : undefined
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});
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}}
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/>
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<YAxis
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stroke="#6b7280"
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fontSize={12}
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tickFormatter={(value) => `${value}`}
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<YAxis
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stroke="#6b7280"
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fontSize={11}
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width={60}
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tickFormatter={(value) => value.toFixed(0)}
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domain={['dataMin - 5', 'dataMax + 5']}
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/>
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<Tooltip
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content={<CustomTooltip />}
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cursor={{ stroke: '#10b981', strokeWidth: 1, strokeOpacity: 0.5 }}
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/>
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<Legend
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wrapperStyle={{ paddingTop: '20px' }}
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iconType="line"
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/>
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<Tooltip content={<CustomTooltip />} />
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<Legend />
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{/* Confidence interval area */}
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{showConfidenceInterval && (
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@@ -328,9 +375,9 @@ const DemandChart: React.FC<DemandChartProps> = ({
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dataKey="confidenceUpper"
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stackId={1}
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stroke="none"
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fill="#10b98120"
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fillOpacity={0.3}
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name="Intervalo de Confianza Superior"
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fill="url(#confidenceGradient)"
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fillOpacity={0.4}
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name="Límite Superior"
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/>
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)}
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{showConfidenceInterval && (
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@@ -340,20 +387,40 @@ const DemandChart: React.FC<DemandChartProps> = ({
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stackId={1}
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stroke="none"
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fill="#ffffff"
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name="Intervalo de Confianza Inferior"
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name="Límite Inferior"
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/>
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)}
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{/* Background area for main prediction */}
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<Area
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type="monotone"
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dataKey="predictedDemand"
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stroke="none"
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fill="url(#demandGradient)"
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fillOpacity={0.2}
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name="Área de Demanda"
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/>
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{/* Predicted demand line */}
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<Line
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type="monotone"
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dataKey="predictedDemand"
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stroke="#10b981"
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strokeWidth={3}
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dot={true}
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dotSize={6}
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activeDot={{ r: 8, stroke: '#10b981', strokeWidth: 2 }}
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dot={{
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fill: '#10b981',
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strokeWidth: 2,
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stroke: '#ffffff',
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r: 4
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}}
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activeDot={{
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r: 6,
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stroke: '#10b981',
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strokeWidth: 3,
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fill: '#ffffff'
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}}
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name="Demanda Predicha"
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connectNulls={false}
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/>
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</ComposedChart>
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</ResponsiveContainer>
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@@ -386,18 +453,46 @@ const DemandChart: React.FC<DemandChartProps> = ({
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</div>
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)}
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{/* Chart legend */}
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<div className="flex items-center justify-center gap-6 mt-4 text-sm">
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<div className="flex items-center gap-2">
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<div className="w-4 h-0.5 bg-green-500"></div>
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<span className="text-text-secondary">Demanda Predicha</span>
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</div>
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{showConfidenceInterval && (
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{/* Enhanced Chart legend and insights */}
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<div className="mt-6 space-y-4">
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<div className="flex items-center justify-center gap-6 text-sm">
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<div className="flex items-center gap-2">
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<div className="w-4 h-2 bg-green-500 bg-opacity-20"></div>
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<span className="text-text-secondary">Intervalo de Confianza</span>
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<div className="w-4 h-0.5 bg-green-500 rounded"></div>
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<span className="text-text-secondary">Demanda Predicha</span>
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</div>
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)}
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{showConfidenceInterval && (
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<div className="flex items-center gap-2">
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<div className="w-4 h-2 bg-green-500 bg-opacity-20 rounded"></div>
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<span className="text-text-secondary">Intervalo de Confianza</span>
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</div>
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)}
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<div className="flex items-center gap-2">
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<div className="w-3 h-3 bg-green-500 rounded-full"></div>
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<span className="text-text-secondary">Puntos de Datos</span>
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</div>
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</div>
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{/* Quick stats */}
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<div className="grid grid-cols-3 gap-4 p-4 bg-gray-50 rounded-lg">
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<div className="text-center">
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<div className="text-lg font-bold text-green-600">
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{Math.min(...displayData.map(d => d.predictedDemand || 0)).toFixed(1)}
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</div>
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<div className="text-xs text-gray-500">Mínimo</div>
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</div>
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<div className="text-center">
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<div className="text-lg font-bold text-blue-600">
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{(displayData.reduce((sum, d) => sum + (d.predictedDemand || 0), 0) / displayData.length).toFixed(1)}
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</div>
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<div className="text-xs text-gray-500">Promedio</div>
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</div>
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<div className="text-center">
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<div className="text-lg font-bold text-orange-600">
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{Math.max(...displayData.map(d => d.predictedDemand || 0)).toFixed(1)}
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</div>
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<div className="text-xs text-gray-500">Máximo</div>
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</div>
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</div>
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</div>
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</CardBody>
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</Card>
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File diff suppressed because it is too large
Load Diff
680
frontend/src/pages/app/database/models/ModelsConfigPage.tsx
Normal file
680
frontend/src/pages/app/database/models/ModelsConfigPage.tsx
Normal file
@@ -0,0 +1,680 @@
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import React, { useState, useMemo } from 'react';
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import { Brain, TrendingUp, AlertCircle, Play, RotateCcw, Eye, Loader, CheckCircle } from 'lucide-react';
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import { Button, Card, Badge, Modal, Table, Select, Input } from '../../../../components/ui';
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import { PageHeader } from '../../../../components/layout';
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import { useToast } from '../../../../hooks/ui/useToast';
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import { useCurrentTenant } from '../../../../stores/tenant.store';
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import { useIngredients } from '../../../../api/hooks/inventory';
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import {
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useModels,
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useActiveModel,
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useTrainSingleProduct,
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useModelMetrics,
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useModelPerformance,
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useTenantTrainingStatistics
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} from '../../../../api/hooks/training';
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import type { IngredientResponse } from '../../../../api/types/inventory';
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import type { TrainedModelResponse, SingleProductTrainingRequest } from '../../../../api/types/training';
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// Actual API response structure (different from expected paginated response)
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interface ModelsApiResponse {
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tenant_id: string;
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models: TrainedModelResponse[];
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total_returned: number;
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active_only: boolean;
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pagination: any;
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enhanced_features: boolean;
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repository_integration: boolean;
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}
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interface ModelStatus {
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ingredient: IngredientResponse;
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hasModel: boolean;
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model?: TrainedModelResponse;
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isTraining: boolean;
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trainingJobId?: string;
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lastTrainingDate?: string;
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accuracy?: number;
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status: 'no_model' | 'active' | 'training' | 'error';
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}
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const ModelsConfigPage: React.FC = () => {
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const { addToast } = useToast();
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const currentTenant = useCurrentTenant();
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const tenantId = currentTenant?.id || '';
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const [selectedIngredient, setSelectedIngredient] = useState<IngredientResponse | null>(null);
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const [showTrainingModal, setShowTrainingModal] = useState(false);
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const [showModelDetailsModal, setShowModelDetailsModal] = useState(false);
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const [trainingSettings, setTrainingSettings] = useState<Partial<SingleProductTrainingRequest>>({
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seasonality_mode: 'additive',
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daily_seasonality: true,
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weekly_seasonality: true,
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yearly_seasonality: false,
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});
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// API hooks
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const { data: ingredients = [], isLoading: ingredientsLoading } = useIngredients(tenantId);
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const { data: modelsData, isLoading: modelsLoading, error: modelsError } = useModels(tenantId);
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const { data: statistics, error: statsError } = useTenantTrainingStatistics(tenantId);
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const trainMutation = useTrainSingleProduct();
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// Debug: Log the models data structure
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React.useEffect(() => {
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console.log('Models data structure:', {
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modelsData,
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modelsLoading,
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modelsError,
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ingredients: ingredients.length
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||||
});
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}, [modelsData, modelsLoading, modelsError, ingredients]);
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// Build model status for each ingredient
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const modelStatuses = useMemo<ModelStatus[]>(() => {
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// Handle different possible data structures from the API response
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let models: TrainedModelResponse[] = [];
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|
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// The API actually returns { models: [...], tenant_id: ..., total_returned: ... }
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const apiResponse = modelsData as any as ModelsApiResponse;
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if (apiResponse?.models && Array.isArray(apiResponse.models)) {
|
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models = apiResponse.models;
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} else if (modelsData?.data && Array.isArray(modelsData.data)) {
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models = modelsData.data;
|
||||
} else if (modelsData && Array.isArray(modelsData)) {
|
||||
models = modelsData as any;
|
||||
}
|
||||
|
||||
console.log('Processing models:', {
|
||||
modelsData,
|
||||
extractedModels: models,
|
||||
modelsCount: models.length,
|
||||
ingredientsCount: ingredients.length
|
||||
});
|
||||
|
||||
return ingredients.map((ingredient: IngredientResponse) => {
|
||||
const model = models.find((m: any) => {
|
||||
// Focus only on ID-based matching
|
||||
return m.inventory_product_id === ingredient.id ||
|
||||
String(m.inventory_product_id) === String(ingredient.id);
|
||||
});
|
||||
|
||||
const isTraining = false; // We'll track this separately for active training jobs
|
||||
|
||||
console.log(`Ingredient ${ingredient.name} (${ingredient.id}):`, {
|
||||
hasModel: !!model,
|
||||
model: model ? {
|
||||
id: model.model_id,
|
||||
created: model.created_at,
|
||||
inventory_product_id: model.inventory_product_id
|
||||
} : null
|
||||
});
|
||||
|
||||
return {
|
||||
ingredient,
|
||||
hasModel: !!model,
|
||||
model,
|
||||
isTraining,
|
||||
lastTrainingDate: model?.created_at,
|
||||
accuracy: model?.training_metrics?.mape ? (100 - model.training_metrics.mape) : undefined,
|
||||
status: model
|
||||
? (isTraining ? 'training' : 'active')
|
||||
: 'no_model'
|
||||
};
|
||||
});
|
||||
}, [ingredients, modelsData]);
|
||||
|
||||
// Calculate orphaned models (models for ingredients that no longer exist)
|
||||
const orphanedModels = useMemo(() => {
|
||||
const apiResponse = modelsData as any as ModelsApiResponse;
|
||||
const models = apiResponse?.models || [];
|
||||
const ingredientIds = new Set(ingredients.map(ing => ing.id));
|
||||
|
||||
return models.filter((model: any) => !ingredientIds.has(model.inventory_product_id));
|
||||
}, [modelsData, ingredients]);
|
||||
|
||||
// Filter and search
|
||||
const [searchTerm, setSearchTerm] = useState('');
|
||||
const [statusFilter, setStatusFilter] = useState<string>('all');
|
||||
|
||||
const filteredStatuses = useMemo(() => {
|
||||
return modelStatuses.filter(status => {
|
||||
const matchesSearch = status.ingredient.name.toLowerCase().includes(searchTerm.toLowerCase());
|
||||
const matchesStatus = statusFilter === 'all' || status.status === statusFilter;
|
||||
return matchesSearch && matchesStatus;
|
||||
});
|
||||
}, [modelStatuses, searchTerm, statusFilter]);
|
||||
|
||||
const handleTrainModel = async () => {
|
||||
if (!selectedIngredient) return;
|
||||
|
||||
try {
|
||||
await trainMutation.mutateAsync({
|
||||
tenantId,
|
||||
inventoryProductId: selectedIngredient.id,
|
||||
request: trainingSettings
|
||||
});
|
||||
|
||||
addToast(`Entrenamiento iniciado para ${selectedIngredient.name}`, { type: 'success' });
|
||||
setShowTrainingModal(false);
|
||||
} catch (error) {
|
||||
addToast('Error al iniciar el entrenamiento', { type: 'error' });
|
||||
}
|
||||
};
|
||||
|
||||
const handleViewModelDetails = (ingredient: IngredientResponse) => {
|
||||
setSelectedIngredient(ingredient);
|
||||
setShowModelDetailsModal(true);
|
||||
};
|
||||
|
||||
const handleStartTraining = (ingredient: IngredientResponse) => {
|
||||
setSelectedIngredient(ingredient);
|
||||
setShowTrainingModal(true);
|
||||
};
|
||||
|
||||
const getStatusBadge = (status: ModelStatus['status']) => {
|
||||
switch (status) {
|
||||
case 'no_model':
|
||||
return <Badge variant="secondary">Sin modelo</Badge>;
|
||||
case 'active':
|
||||
return <Badge variant="success">Activo</Badge>;
|
||||
case 'training':
|
||||
return <Badge variant="warning">Entrenando</Badge>;
|
||||
case 'error':
|
||||
return <Badge variant="error">Error</Badge>;
|
||||
default:
|
||||
return <Badge variant="secondary">Desconocido</Badge>;
|
||||
}
|
||||
};
|
||||
|
||||
const getAccuracyBadge = (accuracy?: number) => {
|
||||
if (!accuracy) return null;
|
||||
|
||||
const variant = accuracy >= 90 ? 'success' : accuracy >= 75 ? 'warning' : 'error';
|
||||
return <Badge variant={variant} size="sm">{accuracy.toFixed(1)}%</Badge>;
|
||||
};
|
||||
|
||||
// Table columns configuration
|
||||
const tableColumns = [
|
||||
{
|
||||
key: 'ingredient',
|
||||
title: 'Ingrediente',
|
||||
render: (_: any, status: ModelStatus) => (
|
||||
<div className="flex items-center gap-3">
|
||||
<div className="w-10 h-10 bg-gradient-to-br from-[var(--color-primary)] to-[var(--color-primary-dark)] rounded-lg flex items-center justify-center text-white font-bold">
|
||||
{status.ingredient.name.charAt(0).toUpperCase()}
|
||||
</div>
|
||||
<div>
|
||||
<div className="font-medium text-[var(--text-primary)]">{status.ingredient.name}</div>
|
||||
<div className="text-sm text-[var(--text-secondary)]">{status.ingredient.category}</div>
|
||||
</div>
|
||||
</div>
|
||||
),
|
||||
},
|
||||
{
|
||||
key: 'status',
|
||||
title: 'Estado del Modelo',
|
||||
render: (_: any, status: ModelStatus) => (
|
||||
<div className="flex items-center gap-2">
|
||||
{getStatusBadge(status.status)}
|
||||
{status.accuracy && getAccuracyBadge(status.accuracy)}
|
||||
</div>
|
||||
),
|
||||
},
|
||||
{
|
||||
key: 'lastTrained',
|
||||
title: 'Último Entrenamiento',
|
||||
render: (_: any, status: ModelStatus) => (
|
||||
<div className="text-sm text-[var(--text-secondary)]">
|
||||
{status.lastTrainingDate
|
||||
? new Date(status.lastTrainingDate).toLocaleDateString('es-ES')
|
||||
: 'Nunca'
|
||||
}
|
||||
</div>
|
||||
),
|
||||
},
|
||||
{
|
||||
key: 'actions',
|
||||
title: 'Acciones',
|
||||
render: (_: any, status: ModelStatus) => (
|
||||
<div className="flex items-center gap-2">
|
||||
{status.hasModel && (
|
||||
<Button
|
||||
variant="ghost"
|
||||
size="sm"
|
||||
onClick={() => handleViewModelDetails(status.ingredient)}
|
||||
leftIcon={<Eye className="w-4 h-4" />}
|
||||
>
|
||||
Ver detalles
|
||||
</Button>
|
||||
)}
|
||||
<Button
|
||||
variant={status.hasModel ? "outline" : "primary"}
|
||||
size="sm"
|
||||
onClick={() => handleStartTraining(status.ingredient)}
|
||||
leftIcon={status.hasModel ? <RotateCcw className="w-4 h-4" /> : <Play className="w-4 h-4" />}
|
||||
disabled={status.isTraining}
|
||||
>
|
||||
{status.hasModel ? 'Reentrenar' : 'Entrenar'}
|
||||
</Button>
|
||||
</div>
|
||||
),
|
||||
},
|
||||
];
|
||||
|
||||
if (ingredientsLoading || modelsLoading) {
|
||||
return (
|
||||
<div className="p-6 space-y-6">
|
||||
<PageHeader
|
||||
title="Configuración de Modelos IA"
|
||||
description="Gestiona el entrenamiento y configuración de modelos de predicción para cada ingrediente"
|
||||
/>
|
||||
<div className="flex items-center justify-center h-64">
|
||||
<Loader className="w-8 h-8 animate-spin" />
|
||||
<span className="ml-2">
|
||||
{ingredientsLoading ? 'Cargando ingredientes...' : 'Cargando modelos...'}
|
||||
</span>
|
||||
</div>
|
||||
</div>
|
||||
);
|
||||
}
|
||||
|
||||
if (modelsError) {
|
||||
console.error('Error loading models:', modelsError);
|
||||
}
|
||||
|
||||
return (
|
||||
<div className="p-6 space-y-6">
|
||||
<PageHeader
|
||||
title="Configuración de Modelos IA"
|
||||
description="Gestiona el entrenamiento y configuración de modelos de predicción para cada ingrediente"
|
||||
/>
|
||||
|
||||
|
||||
{/* Statistics Cards */}
|
||||
<div className="grid grid-cols-1 md:grid-cols-4 gap-6">
|
||||
<Card className="p-6">
|
||||
<div className="flex items-center justify-between">
|
||||
<div>
|
||||
<div className="text-2xl font-bold text-[var(--text-primary)]">
|
||||
{modelStatuses.filter(s => s.hasModel).length}
|
||||
</div>
|
||||
<div className="text-sm text-[var(--text-secondary)]">Ingredientes con Modelo</div>
|
||||
</div>
|
||||
<Brain className="w-8 h-8 text-[var(--color-primary)]" />
|
||||
</div>
|
||||
</Card>
|
||||
|
||||
<Card className="p-6">
|
||||
<div className="flex items-center justify-between">
|
||||
<div>
|
||||
<div className="text-2xl font-bold text-[var(--text-primary)]">
|
||||
{modelStatuses.filter(s => s.status === 'no_model').length}
|
||||
</div>
|
||||
<div className="text-sm text-[var(--text-secondary)]">Sin Modelo</div>
|
||||
</div>
|
||||
<AlertCircle className="w-8 h-8 text-[var(--color-warning)]" />
|
||||
</div>
|
||||
</Card>
|
||||
|
||||
<Card className="p-6">
|
||||
<div className="flex items-center justify-between">
|
||||
<div>
|
||||
<div className="text-2xl font-bold text-[var(--text-primary)]">
|
||||
{orphanedModels.length}
|
||||
</div>
|
||||
<div className="text-sm text-[var(--text-secondary)]">Modelos Huérfanos</div>
|
||||
</div>
|
||||
<AlertCircle className="w-8 h-8 text-[var(--color-secondary)]" />
|
||||
</div>
|
||||
</Card>
|
||||
|
||||
<Card className="p-6">
|
||||
<div className="flex items-center justify-between">
|
||||
<div>
|
||||
<div className="text-2xl font-bold text-[var(--text-primary)]">
|
||||
{statsError ? 'N/A' : (statistics?.average_accuracy ? `${(100 - statistics.average_accuracy).toFixed(1)}%` : 'N/A')}
|
||||
</div>
|
||||
<div className="text-sm text-[var(--text-secondary)]">Precisión Promedio</div>
|
||||
</div>
|
||||
<TrendingUp className="w-8 h-8 text-[var(--color-primary)]" />
|
||||
</div>
|
||||
</Card>
|
||||
</div>
|
||||
|
||||
{/* Orphaned Models Warning */}
|
||||
{orphanedModels.length > 0 && (
|
||||
<Card className="p-4 bg-orange-50 border-orange-200">
|
||||
<div className="flex items-start gap-3">
|
||||
<AlertCircle className="w-5 h-5 text-orange-600 mt-0.5" />
|
||||
<div>
|
||||
<h4 className="font-medium text-orange-900 mb-1">
|
||||
Modelos Huérfanos Detectados
|
||||
</h4>
|
||||
<p className="text-sm text-orange-700">
|
||||
Se encontraron {orphanedModels.length} modelos entrenados para ingredientes que ya no existen en el inventario.
|
||||
Estos modelos pueden ser eliminados para optimizar el espacio de almacenamiento.
|
||||
</p>
|
||||
</div>
|
||||
</div>
|
||||
</Card>
|
||||
)}
|
||||
|
||||
{/* Filters */}
|
||||
<Card className="p-6">
|
||||
<div className="flex flex-col sm:flex-row gap-4">
|
||||
<div className="flex-1">
|
||||
<Input
|
||||
placeholder="Buscar ingrediente..."
|
||||
value={searchTerm}
|
||||
onChange={(e) => setSearchTerm(e.target.value)}
|
||||
/>
|
||||
</div>
|
||||
<div className="w-full sm:w-48">
|
||||
<Select
|
||||
value={statusFilter}
|
||||
onChange={(value) => setStatusFilter(value as string)}
|
||||
options={[
|
||||
{ value: 'all', label: 'Todos los estados' },
|
||||
{ value: 'no_model', label: 'Sin modelo' },
|
||||
{ value: 'active', label: 'Activo' },
|
||||
{ value: 'training', label: 'Entrenando' },
|
||||
{ value: 'error', label: 'Error' },
|
||||
]}
|
||||
/>
|
||||
</div>
|
||||
</div>
|
||||
</Card>
|
||||
|
||||
{/* Models Table */}
|
||||
<Card>
|
||||
{filteredStatuses.length === 0 ? (
|
||||
<div className="flex flex-col items-center justify-center py-12">
|
||||
<Brain className="w-12 h-12 text-[var(--color-secondary)] mb-4" />
|
||||
<h3 className="text-lg font-medium text-[var(--text-primary)] mb-2">
|
||||
No se encontraron ingredientes
|
||||
</h3>
|
||||
<p className="text-[var(--text-secondary)] text-center">
|
||||
No hay ingredientes que coincidan con los filtros aplicados.
|
||||
</p>
|
||||
</div>
|
||||
) : (
|
||||
<Table
|
||||
data={filteredStatuses}
|
||||
columns={tableColumns}
|
||||
/>
|
||||
)}
|
||||
</Card>
|
||||
|
||||
{/* Training Modal */}
|
||||
<Modal
|
||||
isOpen={showTrainingModal}
|
||||
onClose={() => setShowTrainingModal(false)}
|
||||
title={`Entrenar Modelo - ${selectedIngredient?.name}`}
|
||||
size="lg"
|
||||
>
|
||||
<div className="space-y-6">
|
||||
<div className="p-4 bg-blue-50 rounded-lg">
|
||||
<h4 className="font-medium text-blue-900 mb-2">Configuración de Entrenamiento</h4>
|
||||
<p className="text-sm text-blue-700">
|
||||
Configure los parámetros para el entrenamiento del modelo de predicción de demanda.
|
||||
</p>
|
||||
</div>
|
||||
|
||||
<div className="grid grid-cols-1 md:grid-cols-2 gap-6">
|
||||
<div>
|
||||
<label className="block text-sm font-medium mb-2">Modo de Estacionalidad</label>
|
||||
<Select
|
||||
value={trainingSettings.seasonality_mode || 'additive'}
|
||||
onChange={(value) => setTrainingSettings(prev => ({ ...prev, seasonality_mode: value as any }))}
|
||||
options={[
|
||||
{ value: 'additive', label: 'Aditivo' },
|
||||
{ value: 'multiplicative', label: 'Multiplicativo' }
|
||||
]}
|
||||
/>
|
||||
</div>
|
||||
|
||||
<div className="space-y-4">
|
||||
<h4 className="font-medium text-sm">Patrones Estacionales</h4>
|
||||
|
||||
<label className="flex items-center space-x-2">
|
||||
<input
|
||||
type="checkbox"
|
||||
checked={trainingSettings.daily_seasonality || false}
|
||||
onChange={(e) => setTrainingSettings(prev => ({ ...prev, daily_seasonality: e.target.checked }))}
|
||||
className="rounded border-[var(--border-primary)]"
|
||||
/>
|
||||
<span className="text-sm">Estacionalidad diaria</span>
|
||||
</label>
|
||||
|
||||
<label className="flex items-center space-x-2">
|
||||
<input
|
||||
type="checkbox"
|
||||
checked={trainingSettings.weekly_seasonality || false}
|
||||
onChange={(e) => setTrainingSettings(prev => ({ ...prev, weekly_seasonality: e.target.checked }))}
|
||||
className="rounded border-[var(--border-primary)]"
|
||||
/>
|
||||
<span className="text-sm">Estacionalidad semanal</span>
|
||||
</label>
|
||||
|
||||
<label className="flex items-center space-x-2">
|
||||
<input
|
||||
type="checkbox"
|
||||
checked={trainingSettings.yearly_seasonality || false}
|
||||
onChange={(e) => setTrainingSettings(prev => ({ ...prev, yearly_seasonality: e.target.checked }))}
|
||||
className="rounded border-[var(--border-primary)]"
|
||||
/>
|
||||
<span className="text-sm">Estacionalidad anual</span>
|
||||
</label>
|
||||
</div>
|
||||
</div>
|
||||
|
||||
<div className="flex justify-end space-x-3 pt-4 border-t">
|
||||
<Button variant="outline" onClick={() => setShowTrainingModal(false)}>
|
||||
Cancelar
|
||||
</Button>
|
||||
<Button
|
||||
onClick={handleTrainModel}
|
||||
isLoading={trainMutation.isPending}
|
||||
leftIcon={<Play className="w-4 h-4" />}
|
||||
>
|
||||
Iniciar Entrenamiento
|
||||
</Button>
|
||||
</div>
|
||||
</div>
|
||||
</Modal>
|
||||
|
||||
{/* Model Details Modal */}
|
||||
<Modal
|
||||
isOpen={showModelDetailsModal}
|
||||
onClose={() => setShowModelDetailsModal(false)}
|
||||
title={`Detalles del Modelo - ${selectedIngredient?.name}`}
|
||||
size="lg"
|
||||
>
|
||||
<div className="space-y-6">
|
||||
{selectedIngredient && (
|
||||
<ModelDetailsContent
|
||||
tenantId={tenantId}
|
||||
ingredientId={selectedIngredient.id}
|
||||
/>
|
||||
)}
|
||||
</div>
|
||||
</Modal>
|
||||
</div>
|
||||
);
|
||||
};
|
||||
|
||||
// Component for model details content
|
||||
const ModelDetailsContent: React.FC<{
|
||||
tenantId: string;
|
||||
ingredientId: string;
|
||||
}> = ({ tenantId, ingredientId }) => {
|
||||
const { data: activeModel } = useActiveModel(tenantId, ingredientId);
|
||||
|
||||
if (!activeModel) {
|
||||
return (
|
||||
<div className="text-center py-12">
|
||||
<AlertCircle className="w-16 h-16 text-[var(--color-warning)] mx-auto mb-4" />
|
||||
<h3 className="text-xl font-semibold mb-2 text-[var(--text-primary)]">No hay modelo disponible</h3>
|
||||
<p className="text-[var(--text-secondary)] max-w-md mx-auto">
|
||||
Este ingrediente no tiene un modelo entrenado disponible. Puedes entrenar uno nuevo usando el botón "Entrenar".
|
||||
</p>
|
||||
</div>
|
||||
);
|
||||
}
|
||||
|
||||
const precision = activeModel.training_metrics?.mape
|
||||
? (100 - activeModel.training_metrics.mape).toFixed(1)
|
||||
: 'N/A';
|
||||
|
||||
return (
|
||||
<div className="space-y-6">
|
||||
{/* Model Overview */}
|
||||
<div className="grid grid-cols-1 md:grid-cols-3 gap-4">
|
||||
<div className="bg-gradient-to-br from-green-50 to-green-100 p-6 rounded-xl border border-green-200">
|
||||
<div className="text-center">
|
||||
<div className="text-3xl font-bold text-green-700 mb-1">
|
||||
{precision}%
|
||||
</div>
|
||||
<div className="text-sm font-medium text-green-600">Precisión</div>
|
||||
</div>
|
||||
</div>
|
||||
|
||||
<div className="bg-gradient-to-br from-blue-50 to-blue-100 p-6 rounded-xl border border-blue-200">
|
||||
<div className="text-center">
|
||||
<div className="text-3xl font-bold text-blue-700 mb-1">
|
||||
{activeModel.training_metrics?.mae?.toFixed(2) || 'N/A'}
|
||||
</div>
|
||||
<div className="text-sm font-medium text-blue-600">MAE</div>
|
||||
</div>
|
||||
</div>
|
||||
|
||||
<div className="bg-gradient-to-br from-purple-50 to-purple-100 p-6 rounded-xl border border-purple-200">
|
||||
<div className="text-center">
|
||||
<div className="text-3xl font-bold text-purple-700 mb-1">
|
||||
{activeModel.training_metrics?.rmse?.toFixed(2) || 'N/A'}
|
||||
</div>
|
||||
<div className="text-sm font-medium text-purple-600">RMSE</div>
|
||||
</div>
|
||||
</div>
|
||||
</div>
|
||||
|
||||
{/* Model Information */}
|
||||
<Card className="p-6 bg-[var(--bg-primary)]">
|
||||
<h4 className="text-lg font-semibold mb-6 text-[var(--text-primary)] flex items-center">
|
||||
<Brain className="w-5 h-5 mr-2 text-[var(--color-primary)]" />
|
||||
Información del Modelo
|
||||
</h4>
|
||||
<div className="grid grid-cols-1 md:grid-cols-2 gap-6">
|
||||
<div className="space-y-4">
|
||||
<div className="flex flex-col">
|
||||
<span className="text-xs font-semibold text-[var(--text-tertiary)] uppercase tracking-wide mb-1">
|
||||
Creado
|
||||
</span>
|
||||
<span className="text-sm text-[var(--text-primary)]">
|
||||
{new Date(activeModel.created_at).toLocaleString('es-ES', {
|
||||
year: 'numeric',
|
||||
month: 'long',
|
||||
day: 'numeric',
|
||||
hour: '2-digit',
|
||||
minute: '2-digit'
|
||||
})}
|
||||
</span>
|
||||
</div>
|
||||
|
||||
<div className="flex flex-col">
|
||||
<span className="text-xs font-semibold text-[var(--text-tertiary)] uppercase tracking-wide mb-1">
|
||||
Características usadas
|
||||
</span>
|
||||
<span className="text-sm text-[var(--text-primary)]">
|
||||
{activeModel.features_used?.length || 0} variables
|
||||
</span>
|
||||
</div>
|
||||
</div>
|
||||
|
||||
<div className="space-y-4">
|
||||
<div className="flex flex-col">
|
||||
<span className="text-xs font-semibold text-[var(--text-tertiary)] uppercase tracking-wide mb-1">
|
||||
Período de entrenamiento
|
||||
</span>
|
||||
<span className="text-sm text-[var(--text-primary)]">
|
||||
{activeModel.training_period?.start_date && activeModel.training_period?.end_date
|
||||
? `${new Date(activeModel.training_period.start_date).toLocaleDateString('es-ES')} - ${new Date(activeModel.training_period.end_date).toLocaleDateString('es-ES')}`
|
||||
: 'No disponible'
|
||||
}
|
||||
</span>
|
||||
</div>
|
||||
|
||||
<div className="flex flex-col">
|
||||
<span className="text-xs font-semibold text-[var(--text-tertiary)] uppercase tracking-wide mb-1">
|
||||
Hiperparámetros
|
||||
</span>
|
||||
<span className="text-sm text-[var(--text-primary)]">
|
||||
{Object.keys(activeModel.hyperparameters || {}).length} configurados
|
||||
</span>
|
||||
</div>
|
||||
</div>
|
||||
</div>
|
||||
</Card>
|
||||
|
||||
{/* Features Used */}
|
||||
{activeModel.features_used && activeModel.features_used.length > 0 && (
|
||||
<Card className="p-6 bg-[var(--bg-primary)]">
|
||||
<h4 className="text-lg font-semibold mb-4 text-[var(--text-primary)] flex items-center">
|
||||
<TrendingUp className="w-5 h-5 mr-2 text-[var(--color-primary)]" />
|
||||
Características del Modelo
|
||||
</h4>
|
||||
<div className="grid grid-cols-2 md:grid-cols-3 lg:grid-cols-4 gap-3">
|
||||
{activeModel.features_used.map((feature: string, index: number) => (
|
||||
<div
|
||||
key={index}
|
||||
className="bg-[var(--bg-secondary)] border border-[var(--border-primary)] rounded-lg px-3 py-2 text-center"
|
||||
>
|
||||
<span className="text-sm font-medium text-[var(--text-primary)]">
|
||||
{feature.replace(/_/g, ' ').replace(/\b\w/g, l => l.toUpperCase())}
|
||||
</span>
|
||||
</div>
|
||||
))}
|
||||
</div>
|
||||
</Card>
|
||||
)}
|
||||
|
||||
{/* Training Performance */}
|
||||
{activeModel.training_metrics && (
|
||||
<Card className="p-6 bg-[var(--bg-primary)]">
|
||||
<h4 className="text-lg font-semibold mb-4 text-[var(--text-primary)] flex items-center">
|
||||
<CheckCircle className="w-5 h-5 mr-2 text-[var(--color-success)]" />
|
||||
Métricas de Rendimiento
|
||||
</h4>
|
||||
<div className="grid grid-cols-2 md:grid-cols-4 gap-4">
|
||||
<div className="text-center p-4 bg-[var(--bg-secondary)] rounded-lg border border-[var(--border-primary)]">
|
||||
<div className="text-2xl font-bold text-[var(--color-success)] mb-1">
|
||||
{precision}%
|
||||
</div>
|
||||
<div className="text-xs text-[var(--text-tertiary)] uppercase tracking-wide">Precisión</div>
|
||||
</div>
|
||||
<div className="text-center p-4 bg-[var(--bg-secondary)] rounded-lg border border-[var(--border-primary)]">
|
||||
<div className="text-2xl font-bold text-[var(--text-primary)] mb-1">
|
||||
{activeModel.training_metrics.mae?.toFixed(2) || 'N/A'}
|
||||
</div>
|
||||
<div className="text-xs text-[var(--text-tertiary)] uppercase tracking-wide">MAE</div>
|
||||
</div>
|
||||
<div className="text-center p-4 bg-[var(--bg-secondary)] rounded-lg border border-[var(--border-primary)]">
|
||||
<div className="text-2xl font-bold text-[var(--text-primary)] mb-1">
|
||||
{activeModel.training_metrics.rmse?.toFixed(2) || 'N/A'}
|
||||
</div>
|
||||
<div className="text-xs text-[var(--text-tertiary)] uppercase tracking-wide">RMSE</div>
|
||||
</div>
|
||||
<div className="text-center p-4 bg-[var(--bg-secondary)] rounded-lg border border-[var(--border-primary)]">
|
||||
<div className="text-2xl font-bold text-[var(--text-primary)] mb-1">
|
||||
{activeModel.training_metrics.r2_score?.toFixed(3) || 'N/A'}
|
||||
</div>
|
||||
<div className="text-xs text-[var(--text-tertiary)] uppercase tracking-wide">R²</div>
|
||||
</div>
|
||||
</div>
|
||||
</Card>
|
||||
)}
|
||||
</div>
|
||||
);
|
||||
};
|
||||
|
||||
export default ModelsConfigPage;
|
||||
1
frontend/src/pages/app/database/models/index.ts
Normal file
1
frontend/src/pages/app/database/models/index.ts
Normal file
@@ -0,0 +1 @@
|
||||
export { default as ModelsConfigPage } from './ModelsConfigPage';
|
||||
@@ -8,6 +8,7 @@ import {
|
||||
useProcurementPlans,
|
||||
useCurrentProcurementPlan,
|
||||
useCriticalRequirements,
|
||||
usePlanRequirements,
|
||||
useGenerateProcurementPlan,
|
||||
useUpdateProcurementPlanStatus,
|
||||
useTriggerDailyScheduler
|
||||
@@ -22,7 +23,9 @@ const ProcurementPage: React.FC = () => {
|
||||
const [selectedPlan, setSelectedPlan] = useState<any>(null);
|
||||
const [editingPlan, setEditingPlan] = useState<any>(null);
|
||||
const [editFormData, setEditFormData] = useState<any>({});
|
||||
|
||||
const [selectedPlanForRequirements, setSelectedPlanForRequirements] = useState<string | null>(null);
|
||||
const [showCriticalRequirements, setShowCriticalRequirements] = useState(false);
|
||||
|
||||
const { currentTenant } = useTenantStore();
|
||||
const tenantId = currentTenant?.id || '';
|
||||
|
||||
@@ -35,6 +38,15 @@ const ProcurementPage: React.FC = () => {
|
||||
});
|
||||
const { data: currentPlan, isLoading: isCurrentPlanLoading } = useCurrentProcurementPlan(tenantId);
|
||||
const { data: criticalRequirements, isLoading: isCriticalLoading } = useCriticalRequirements(tenantId);
|
||||
|
||||
// Get plan requirements for selected plan
|
||||
const { data: planRequirements, isLoading: isPlanRequirementsLoading } = usePlanRequirements({
|
||||
tenant_id: tenantId,
|
||||
plan_id: selectedPlanForRequirements || '',
|
||||
status: 'critical' // Only get critical requirements
|
||||
}, {
|
||||
enabled: !!selectedPlanForRequirements && !!tenantId
|
||||
});
|
||||
|
||||
const generatePlanMutation = useGenerateProcurementPlan();
|
||||
const updatePlanStatusMutation = useUpdateProcurementPlanStatus();
|
||||
@@ -107,6 +119,16 @@ const ProcurementPage: React.FC = () => {
|
||||
setEditFormData({});
|
||||
};
|
||||
|
||||
const handleShowCriticalRequirements = (planId: string) => {
|
||||
setSelectedPlanForRequirements(planId);
|
||||
setShowCriticalRequirements(true);
|
||||
};
|
||||
|
||||
const handleCloseCriticalRequirements = () => {
|
||||
setShowCriticalRequirements(false);
|
||||
setSelectedPlanForRequirements(null);
|
||||
};
|
||||
|
||||
if (!tenantId) {
|
||||
return (
|
||||
<div className="flex justify-center items-center h-64">
|
||||
@@ -391,6 +413,15 @@ const ProcurementPage: React.FC = () => {
|
||||
}
|
||||
});
|
||||
|
||||
// Show Critical Requirements button
|
||||
actions.push({
|
||||
label: 'Req. Críticos',
|
||||
icon: AlertCircle,
|
||||
variant: 'outline' as const,
|
||||
priority: 'secondary' as const,
|
||||
onClick: () => handleShowCriticalRequirements(plan.id)
|
||||
});
|
||||
|
||||
// Tertiary action: Cancel (least prominent, destructive)
|
||||
if (!['completed', 'cancelled'].includes(plan.status)) {
|
||||
actions.push({
|
||||
|
||||
@@ -33,6 +33,7 @@ const TeamPage = React.lazy(() => import('../pages/app/settings/team/TeamPage'))
|
||||
|
||||
// Database pages
|
||||
const DatabasePage = React.lazy(() => import('../pages/app/database/DatabasePage'));
|
||||
const ModelsConfigPage = React.lazy(() => import('../pages/app/database/models/ModelsConfigPage'));
|
||||
|
||||
// Data pages
|
||||
const WeatherPage = React.lazy(() => import('../pages/app/data/weather/WeatherPage'));
|
||||
@@ -176,6 +177,16 @@ export const AppRouter: React.FC = () => {
|
||||
</ProtectedRoute>
|
||||
}
|
||||
/>
|
||||
<Route
|
||||
path="/app/database/models"
|
||||
element={
|
||||
<ProtectedRoute>
|
||||
<AppShell>
|
||||
<ModelsConfigPage />
|
||||
</AppShell>
|
||||
</ProtectedRoute>
|
||||
}
|
||||
/>
|
||||
|
||||
{/* Analytics Routes */}
|
||||
<Route
|
||||
|
||||
@@ -330,6 +330,16 @@ export const routesConfig: RouteConfig[] = [
|
||||
showInNavigation: true,
|
||||
showInBreadcrumbs: true,
|
||||
},
|
||||
{
|
||||
path: '/app/database/models',
|
||||
name: 'ModelsConfig',
|
||||
component: 'ModelsConfigPage',
|
||||
title: 'Modelos IA',
|
||||
icon: 'training',
|
||||
requiresAuth: true,
|
||||
showInNavigation: true,
|
||||
showInBreadcrumbs: true,
|
||||
},
|
||||
],
|
||||
},
|
||||
|
||||
|
||||
@@ -113,6 +113,12 @@ async def proxy_tenant_models(request: Request, tenant_id: str = Path(...), path
|
||||
target_path = f"/api/v1/tenants/{tenant_id}/models/{path}".rstrip("/")
|
||||
return await _proxy_to_training_service(request, target_path, tenant_id=tenant_id)
|
||||
|
||||
@router.api_route("/{tenant_id}/statistics", methods=["GET", "OPTIONS"])
|
||||
async def proxy_tenant_statistics(request: Request, tenant_id: str = Path(...)):
|
||||
"""Proxy tenant statistics requests to training service"""
|
||||
target_path = f"/api/v1/tenants/{tenant_id}/statistics"
|
||||
return await _proxy_to_training_service(request, target_path, tenant_id=tenant_id)
|
||||
|
||||
# ================================================================
|
||||
# TENANT-SCOPED FORECASTING SERVICE ENDPOINTS
|
||||
# ================================================================
|
||||
|
||||
@@ -11,8 +11,8 @@ import uuid
|
||||
|
||||
from app.services.forecasting_service import EnhancedForecastingService
|
||||
from app.schemas.forecasts import (
|
||||
ForecastRequest, ForecastResponse, BatchForecastRequest,
|
||||
BatchForecastResponse
|
||||
ForecastRequest, ForecastResponse, BatchForecastRequest,
|
||||
BatchForecastResponse, MultiDayForecastResponse
|
||||
)
|
||||
from shared.auth.decorators import (
|
||||
get_current_user_dep,
|
||||
@@ -66,7 +66,7 @@ async def create_enhanced_single_forecast(
|
||||
forecast_id=forecast.id)
|
||||
|
||||
return forecast
|
||||
|
||||
|
||||
except ValueError as e:
|
||||
if metrics:
|
||||
metrics.increment_counter("enhanced_forecast_validation_errors_total")
|
||||
@@ -89,6 +89,70 @@ async def create_enhanced_single_forecast(
|
||||
)
|
||||
|
||||
|
||||
@router.post("/tenants/{tenant_id}/forecasts/multi-day", response_model=MultiDayForecastResponse)
|
||||
@track_execution_time("enhanced_multi_day_forecast_duration_seconds", "forecasting-service")
|
||||
async def create_enhanced_multi_day_forecast(
|
||||
request: ForecastRequest,
|
||||
tenant_id: str = Path(..., description="Tenant ID"),
|
||||
request_obj: Request = None,
|
||||
enhanced_forecasting_service: EnhancedForecastingService = Depends(get_enhanced_forecasting_service)
|
||||
):
|
||||
"""Generate multiple daily forecasts for the specified period using enhanced repository pattern"""
|
||||
metrics = get_metrics_collector(request_obj)
|
||||
|
||||
try:
|
||||
logger.info("Generating enhanced multi-day forecast",
|
||||
tenant_id=tenant_id,
|
||||
inventory_product_id=request.inventory_product_id,
|
||||
forecast_days=request.forecast_days,
|
||||
forecast_date=request.forecast_date.isoformat())
|
||||
|
||||
# Record metrics
|
||||
if metrics:
|
||||
metrics.increment_counter("enhanced_multi_day_forecasts_total")
|
||||
|
||||
# Validate forecast_days parameter
|
||||
if request.forecast_days <= 0 or request.forecast_days > 30:
|
||||
raise ValueError("forecast_days must be between 1 and 30")
|
||||
|
||||
# Generate multi-day forecast using enhanced service
|
||||
forecast_result = await enhanced_forecasting_service.generate_multi_day_forecast(
|
||||
tenant_id=tenant_id,
|
||||
request=request
|
||||
)
|
||||
|
||||
if metrics:
|
||||
metrics.increment_counter("enhanced_multi_day_forecasts_success_total")
|
||||
|
||||
logger.info("Enhanced multi-day forecast generated successfully",
|
||||
tenant_id=tenant_id,
|
||||
inventory_product_id=request.inventory_product_id,
|
||||
forecast_days=len(forecast_result.get("forecasts", [])))
|
||||
|
||||
return MultiDayForecastResponse(**forecast_result)
|
||||
|
||||
except ValueError as e:
|
||||
if metrics:
|
||||
metrics.increment_counter("enhanced_multi_day_forecast_validation_errors_total")
|
||||
logger.error("Enhanced multi-day forecast validation error",
|
||||
error=str(e),
|
||||
tenant_id=tenant_id)
|
||||
raise HTTPException(
|
||||
status_code=status.HTTP_400_BAD_REQUEST,
|
||||
detail=str(e)
|
||||
)
|
||||
except Exception as e:
|
||||
if metrics:
|
||||
metrics.increment_counter("enhanced_multi_day_forecasts_errors_total")
|
||||
logger.error("Enhanced multi-day forecast generation failed",
|
||||
error=str(e),
|
||||
tenant_id=tenant_id)
|
||||
raise HTTPException(
|
||||
status_code=status.HTTP_500_INTERNAL_SERVER_ERROR,
|
||||
detail="Enhanced multi-day forecast generation failed"
|
||||
)
|
||||
|
||||
|
||||
@router.post("/tenants/{tenant_id}/forecasts/batch", response_model=BatchForecastResponse)
|
||||
@track_execution_time("enhanced_batch_forecast_duration_seconds", "forecasting-service")
|
||||
async def create_enhanced_batch_forecast(
|
||||
|
||||
@@ -95,4 +95,15 @@ class BatchForecastResponse(BaseModel):
|
||||
forecasts: Optional[List[ForecastResponse]]
|
||||
error_message: Optional[str]
|
||||
|
||||
class MultiDayForecastResponse(BaseModel):
|
||||
"""Response schema for multi-day forecast results"""
|
||||
tenant_id: str = Field(..., description="Tenant ID")
|
||||
inventory_product_id: str = Field(..., description="Inventory product ID")
|
||||
forecast_start_date: date = Field(..., description="Start date of forecast period")
|
||||
forecast_days: int = Field(..., description="Number of forecasted days")
|
||||
forecasts: List[ForecastResponse] = Field(..., description="Daily forecasts")
|
||||
total_predicted_demand: float = Field(..., description="Total demand across all days")
|
||||
average_confidence_level: float = Field(..., description="Average confidence across all days")
|
||||
processing_time_ms: int = Field(..., description="Total processing time")
|
||||
|
||||
|
||||
|
||||
@@ -345,7 +345,101 @@ class EnhancedForecastingService:
|
||||
inventory_product_id=request.inventory_product_id,
|
||||
processing_time=processing_time)
|
||||
raise
|
||||
|
||||
|
||||
async def generate_multi_day_forecast(
|
||||
self,
|
||||
tenant_id: str,
|
||||
request: ForecastRequest
|
||||
) -> Dict[str, Any]:
|
||||
"""
|
||||
Generate multiple daily forecasts for the specified period.
|
||||
"""
|
||||
start_time = datetime.utcnow()
|
||||
forecasts = []
|
||||
|
||||
try:
|
||||
logger.info("Generating multi-day forecast",
|
||||
tenant_id=tenant_id,
|
||||
inventory_product_id=request.inventory_product_id,
|
||||
forecast_days=request.forecast_days,
|
||||
start_date=request.forecast_date.isoformat())
|
||||
|
||||
# Generate a forecast for each day
|
||||
for day_offset in range(request.forecast_days):
|
||||
# Calculate the forecast date for this day
|
||||
current_date = request.forecast_date
|
||||
if isinstance(current_date, str):
|
||||
from dateutil.parser import parse
|
||||
current_date = parse(current_date).date()
|
||||
|
||||
if day_offset > 0:
|
||||
from datetime import timedelta
|
||||
current_date = current_date + timedelta(days=day_offset)
|
||||
|
||||
# Create a new request for this specific day
|
||||
daily_request = ForecastRequest(
|
||||
inventory_product_id=request.inventory_product_id,
|
||||
forecast_date=current_date,
|
||||
forecast_days=1, # Single day for each iteration
|
||||
location=request.location,
|
||||
confidence_level=request.confidence_level
|
||||
)
|
||||
|
||||
# Generate forecast for this day
|
||||
daily_forecast = await self.generate_forecast(tenant_id, daily_request)
|
||||
forecasts.append(daily_forecast)
|
||||
|
||||
# Calculate summary statistics
|
||||
total_demand = sum(f.predicted_demand for f in forecasts)
|
||||
avg_confidence = sum(f.confidence_level for f in forecasts) / len(forecasts)
|
||||
processing_time = int((datetime.utcnow() - start_time).total_seconds() * 1000)
|
||||
|
||||
# Convert forecasts to dictionary format for the response
|
||||
forecast_dicts = []
|
||||
for forecast in forecasts:
|
||||
forecast_dicts.append({
|
||||
"id": forecast.id,
|
||||
"tenant_id": forecast.tenant_id,
|
||||
"inventory_product_id": forecast.inventory_product_id,
|
||||
"location": forecast.location,
|
||||
"forecast_date": forecast.forecast_date.isoformat() if hasattr(forecast.forecast_date, 'isoformat') else str(forecast.forecast_date),
|
||||
"predicted_demand": forecast.predicted_demand,
|
||||
"confidence_lower": forecast.confidence_lower,
|
||||
"confidence_upper": forecast.confidence_upper,
|
||||
"confidence_level": forecast.confidence_level,
|
||||
"model_id": forecast.model_id,
|
||||
"model_version": forecast.model_version,
|
||||
"algorithm": forecast.algorithm,
|
||||
"business_type": forecast.business_type,
|
||||
"is_holiday": forecast.is_holiday,
|
||||
"is_weekend": forecast.is_weekend,
|
||||
"day_of_week": forecast.day_of_week,
|
||||
"weather_temperature": forecast.weather_temperature,
|
||||
"weather_precipitation": forecast.weather_precipitation,
|
||||
"weather_description": forecast.weather_description,
|
||||
"traffic_volume": forecast.traffic_volume,
|
||||
"created_at": forecast.created_at.isoformat() if hasattr(forecast.created_at, 'isoformat') else str(forecast.created_at),
|
||||
"processing_time_ms": forecast.processing_time_ms,
|
||||
"features_used": forecast.features_used
|
||||
})
|
||||
|
||||
return {
|
||||
"tenant_id": tenant_id,
|
||||
"inventory_product_id": request.inventory_product_id,
|
||||
"forecast_start_date": request.forecast_date.isoformat() if hasattr(request.forecast_date, 'isoformat') else str(request.forecast_date),
|
||||
"forecast_days": request.forecast_days,
|
||||
"forecasts": forecast_dicts,
|
||||
"total_predicted_demand": total_demand,
|
||||
"average_confidence_level": avg_confidence,
|
||||
"processing_time_ms": processing_time
|
||||
}
|
||||
|
||||
except Exception as e:
|
||||
logger.error("Multi-day forecast generation failed",
|
||||
tenant_id=tenant_id,
|
||||
error=str(e))
|
||||
raise
|
||||
|
||||
async def get_forecast_history(
|
||||
self,
|
||||
tenant_id: str,
|
||||
|
||||
Reference in New Issue
Block a user