ids.alfacom.it/client/src/pages/Training.tsx
marco370 7b514f470f Implement form validation and improve error handling for ML features
Refactor Training and Whitelist pages to use react-hook-form and Zod for validation, and enhance ML backend API routes with timeouts, input validation, and better error reporting.

Replit-Commit-Author: Agent
Replit-Commit-Session-Id: 7a657272-55ba-4a79-9a2e-f1ed9bc7a528
Replit-Commit-Checkpoint-Type: intermediate_checkpoint
Replit-Commit-Event-Id: 95d9d0e3-3da7-43ff-b8d3-d9d5d8fd6f6f
Replit-Commit-Screenshot-Url: https://storage.googleapis.com/screenshot-production-us-central1/449cf7c4-c97a-45ae-8234-e5c5b8d6a84f/7a657272-55ba-4a79-9a2e-f1ed9bc7a528/Aqah4U9
2025-11-21 09:08:38 +00:00

464 lines
19 KiB
TypeScript

import { useQuery, useMutation } from "@tanstack/react-query";
import { queryClient, apiRequest } from "@/lib/queryClient";
import { Card, CardContent, CardHeader, CardTitle } from "@/components/ui/card";
import { Button } from "@/components/ui/button";
import { Badge } from "@/components/ui/badge";
import { Brain, Play, Search, CheckCircle2, XCircle, Clock, TrendingUp } from "lucide-react";
import { format } from "date-fns";
import type { TrainingHistory } from "@shared/schema";
import { useToast } from "@/hooks/use-toast";
import { useState } from "react";
import { useForm } from "react-hook-form";
import { zodResolver } from "@hookform/resolvers/zod";
import { z } from "zod";
import {
Dialog,
DialogContent,
DialogDescription,
DialogHeader,
DialogTitle,
DialogTrigger,
DialogFooter,
} from "@/components/ui/dialog";
import {
Form,
FormControl,
FormField,
FormItem,
FormLabel,
FormMessage,
FormDescription,
} from "@/components/ui/form";
import { Input } from "@/components/ui/input";
import { Checkbox } from "@/components/ui/checkbox";
interface MLStatsResponse {
logs?: { total: number; last_hour: number };
detections?: { total: number; blocked: number };
routers?: { active: number };
latest_training?: any;
}
const trainFormSchema = z.object({
max_records: z.coerce.number().min(1, "Minimo 1 record").max(1000000, "Massimo 1M record"),
hours_back: z.coerce.number().min(1, "Minimo 1 ora").max(720, "Massimo 720 ore (30 giorni)"),
});
const detectFormSchema = z.object({
max_records: z.coerce.number().min(1, "Minimo 1 record").max(1000000, "Massimo 1M record"),
hours_back: z.coerce.number().min(1, "Minimo 1 ora").max(720, "Massimo 720 ore"),
risk_threshold: z.coerce.number().min(0, "Minimo 0").max(100, "Massimo 100"),
auto_block: z.boolean().default(true),
});
export default function TrainingPage() {
const { toast } = useToast();
const [isTrainDialogOpen, setIsTrainDialogOpen] = useState(false);
const [isDetectDialogOpen, setIsDetectDialogOpen] = useState(false);
const trainForm = useForm<z.infer<typeof trainFormSchema>>({
resolver: zodResolver(trainFormSchema),
defaultValues: {
max_records: 100000,
hours_back: 24,
},
});
const detectForm = useForm<z.infer<typeof detectFormSchema>>({
resolver: zodResolver(detectFormSchema),
defaultValues: {
max_records: 50000,
hours_back: 1,
risk_threshold: 75,
auto_block: true,
},
});
const { data: history, isLoading } = useQuery<TrainingHistory[]>({
queryKey: ["/api/training-history"],
refetchInterval: 10000,
});
const { data: mlStats } = useQuery<MLStatsResponse>({
queryKey: ["/api/ml/stats"],
refetchInterval: 10000,
});
const trainMutation = useMutation({
mutationFn: async (params: z.infer<typeof trainFormSchema>) => {
return await apiRequest("POST", "/api/ml/train", params);
},
onSuccess: () => {
queryClient.invalidateQueries({ queryKey: ["/api/training-history"] });
queryClient.invalidateQueries({ queryKey: ["/api/ml/stats"] });
toast({
title: "Training avviato",
description: "Il modello ML è in addestramento. Controlla lo storico tra qualche minuto.",
});
setIsTrainDialogOpen(false);
trainForm.reset();
},
onError: (error: any) => {
toast({
title: "Errore",
description: error.message || "Impossibile avviare il training",
variant: "destructive",
});
},
});
const detectMutation = useMutation({
mutationFn: async (params: z.infer<typeof detectFormSchema>) => {
return await apiRequest("POST", "/api/ml/detect", params);
},
onSuccess: () => {
queryClient.invalidateQueries({ queryKey: ["/api/detections"] });
queryClient.invalidateQueries({ queryKey: ["/api/stats"] });
toast({
title: "Detection avviata",
description: "Analisi anomalie in corso. Controlla i rilevamenti tra qualche secondo.",
});
setIsDetectDialogOpen(false);
detectForm.reset();
},
onError: (error: any) => {
toast({
title: "Errore",
description: error.message || "Impossibile avviare la detection",
variant: "destructive",
});
},
});
const onTrainSubmit = (data: z.infer<typeof trainFormSchema>) => {
trainMutation.mutate(data);
};
const onDetectSubmit = (data: z.infer<typeof detectFormSchema>) => {
detectMutation.mutate(data);
};
return (
<div className="flex flex-col gap-6 p-6" data-testid="page-training">
<div>
<h1 className="text-3xl font-semibold" data-testid="text-page-title">Machine Learning</h1>
<p className="text-muted-foreground" data-testid="text-page-subtitle">
Training e detection del modello Isolation Forest
</p>
</div>
{/* ML Stats */}
{mlStats && (
<div className="grid grid-cols-1 md:grid-cols-3 gap-4">
<Card data-testid="card-ml-logs">
<CardHeader className="flex flex-row items-center justify-between gap-2 space-y-0 pb-2">
<CardTitle className="text-sm font-medium">Log Totali</CardTitle>
<Brain className="h-4 w-4 text-muted-foreground" />
</CardHeader>
<CardContent>
<div className="text-2xl font-semibold" data-testid="text-ml-logs-total">
{mlStats.logs?.total?.toLocaleString() || 0}
</div>
<p className="text-xs text-muted-foreground mt-1">
Ultima ora: {mlStats.logs?.last_hour?.toLocaleString() || 0}
</p>
</CardContent>
</Card>
<Card data-testid="card-ml-detections">
<CardHeader className="flex flex-row items-center justify-between gap-2 space-y-0 pb-2">
<CardTitle className="text-sm font-medium">Detection Totali</CardTitle>
<Search className="h-4 w-4 text-muted-foreground" />
</CardHeader>
<CardContent>
<div className="text-2xl font-semibold" data-testid="text-ml-detections-total">
{mlStats.detections?.total || 0}
</div>
<p className="text-xs text-muted-foreground mt-1">
Bloccati: {mlStats.detections?.blocked || 0}
</p>
</CardContent>
</Card>
<Card data-testid="card-ml-routers">
<CardHeader className="flex flex-row items-center justify-between gap-2 space-y-0 pb-2">
<CardTitle className="text-sm font-medium">Router Attivi</CardTitle>
<TrendingUp className="h-4 w-4 text-muted-foreground" />
</CardHeader>
<CardContent>
<div className="text-2xl font-semibold" data-testid="text-ml-routers-active">
{mlStats.routers?.active || 0}
</div>
</CardContent>
</Card>
</div>
)}
{/* Actions */}
<div className="grid grid-cols-1 md:grid-cols-2 gap-4">
<Card data-testid="card-train-action">
<CardHeader>
<CardTitle className="flex items-center gap-2">
<Brain className="h-5 w-5" />
Addestramento Modello
</CardTitle>
</CardHeader>
<CardContent className="space-y-4">
<p className="text-sm text-muted-foreground">
Addestra il modello Isolation Forest analizzando i log recenti per rilevare pattern di traffico normale.
</p>
<Dialog open={isTrainDialogOpen} onOpenChange={setIsTrainDialogOpen}>
<DialogTrigger asChild>
<Button className="w-full" data-testid="button-start-training">
<Play className="h-4 w-4 mr-2" />
Avvia Training
</Button>
</DialogTrigger>
<DialogContent data-testid="dialog-training">
<DialogHeader>
<DialogTitle>Avvia Training ML</DialogTitle>
<DialogDescription>
Configura i parametri per l'addestramento del modello
</DialogDescription>
</DialogHeader>
<Form {...trainForm}>
<form onSubmit={trainForm.handleSubmit(onTrainSubmit)} className="space-y-4 py-4">
<FormField
control={trainForm.control}
name="max_records"
render={({ field }) => (
<FormItem>
<FormLabel>Numero Record</FormLabel>
<FormControl>
<Input type="number" {...field} data-testid="input-train-records" />
</FormControl>
<FormDescription>Consigliato: 100000</FormDescription>
<FormMessage />
</FormItem>
)}
/>
<FormField
control={trainForm.control}
name="hours_back"
render={({ field }) => (
<FormItem>
<FormLabel>Ore Precedenti</FormLabel>
<FormControl>
<Input type="number" {...field} data-testid="input-train-hours" />
</FormControl>
<FormDescription>Consigliato: 24</FormDescription>
<FormMessage />
</FormItem>
)}
/>
<DialogFooter>
<Button
type="button"
variant="outline"
onClick={() => setIsTrainDialogOpen(false)}
data-testid="button-cancel-training"
>
Annulla
</Button>
<Button type="submit" disabled={trainMutation.isPending} data-testid="button-confirm-training">
{trainMutation.isPending ? "Avvio..." : "Avvia Training"}
</Button>
</DialogFooter>
</form>
</Form>
</DialogContent>
</Dialog>
</CardContent>
</Card>
<Card data-testid="card-detect-action">
<CardHeader>
<CardTitle className="flex items-center gap-2">
<Search className="h-5 w-5" />
Rilevamento Anomalie
</CardTitle>
</CardHeader>
<CardContent className="space-y-4">
<p className="text-sm text-muted-foreground">
Analizza i log recenti per rilevare anomalie e IP sospetti. Opzionalmente blocca automaticamente gli IP critici.
</p>
<Dialog open={isDetectDialogOpen} onOpenChange={setIsDetectDialogOpen}>
<DialogTrigger asChild>
<Button variant="secondary" className="w-full" data-testid="button-start-detection">
<Search className="h-4 w-4 mr-2" />
Avvia Detection
</Button>
</DialogTrigger>
<DialogContent data-testid="dialog-detection">
<DialogHeader>
<DialogTitle>Avvia Detection Anomalie</DialogTitle>
<DialogDescription>
Configura i parametri per il rilevamento anomalie
</DialogDescription>
</DialogHeader>
<Form {...detectForm}>
<form onSubmit={detectForm.handleSubmit(onDetectSubmit)} className="space-y-4 py-4">
<FormField
control={detectForm.control}
name="max_records"
render={({ field }) => (
<FormItem>
<FormLabel>Numero Record</FormLabel>
<FormControl>
<Input type="number" {...field} data-testid="input-detect-records" />
</FormControl>
<FormDescription>Consigliato: 50000</FormDescription>
<FormMessage />
</FormItem>
)}
/>
<FormField
control={detectForm.control}
name="hours_back"
render={({ field }) => (
<FormItem>
<FormLabel>Ore Precedenti</FormLabel>
<FormControl>
<Input type="number" {...field} data-testid="input-detect-hours" />
</FormControl>
<FormDescription>Consigliato: 1</FormDescription>
<FormMessage />
</FormItem>
)}
/>
<FormField
control={detectForm.control}
name="risk_threshold"
render={({ field }) => (
<FormItem>
<FormLabel>Soglia Rischio (%)</FormLabel>
<FormControl>
<Input type="number" min="0" max="100" {...field} data-testid="input-detect-threshold" />
</FormControl>
<FormDescription>Consigliato: 75</FormDescription>
<FormMessage />
</FormItem>
)}
/>
<FormField
control={detectForm.control}
name="auto_block"
render={({ field }) => (
<FormItem className="flex flex-row items-start space-x-3 space-y-0">
<FormControl>
<Checkbox
checked={field.value}
onCheckedChange={field.onChange}
data-testid="checkbox-auto-block"
/>
</FormControl>
<div className="space-y-1 leading-none">
<FormLabel>Blocco automatico IP critici (80)</FormLabel>
</div>
</FormItem>
)}
/>
<DialogFooter>
<Button
type="button"
variant="outline"
onClick={() => setIsDetectDialogOpen(false)}
data-testid="button-cancel-detection"
>
Annulla
</Button>
<Button type="submit" disabled={detectMutation.isPending} data-testid="button-confirm-detection">
{detectMutation.isPending ? "Avvio..." : "Avvia Detection"}
</Button>
</DialogFooter>
</form>
</Form>
</DialogContent>
</Dialog>
</CardContent>
</Card>
</div>
{/* Training History */}
<Card data-testid="card-training-history">
<CardHeader>
<CardTitle className="flex items-center gap-2">
<Clock className="h-5 w-5" />
Storico Training ({history?.length || 0})
</CardTitle>
</CardHeader>
<CardContent>
{isLoading ? (
<div className="text-center py-8 text-muted-foreground" data-testid="text-loading">
Caricamento...
</div>
) : history && history.length > 0 ? (
<div className="space-y-3">
{history.map((item) => (
<div
key={item.id}
className="p-4 rounded-lg border hover-elevate"
data-testid={`training-item-${item.id}`}
>
<div className="flex items-start justify-between gap-4">
<div className="flex-1 space-y-1">
<div className="flex items-center gap-2">
<p className="font-medium" data-testid={`text-version-${item.id}`}>
Versione {item.modelVersion}
</p>
{item.status === "success" ? (
<Badge variant="outline" className="bg-green-50" data-testid={`badge-status-${item.id}`}>
<CheckCircle2 className="h-3 w-3 mr-1" />
Successo
</Badge>
) : (
<Badge variant="destructive" data-testid={`badge-status-${item.id}`}>
<XCircle className="h-3 w-3 mr-1" />
Fallito
</Badge>
)}
</div>
<div className="grid grid-cols-2 md:grid-cols-4 gap-2 text-sm text-muted-foreground">
<div>
<span className="font-medium">Record:</span> {item.recordsProcessed.toLocaleString()}
</div>
<div>
<span className="font-medium">Feature:</span> {item.featuresCount}
</div>
{item.accuracy && (
<div>
<span className="font-medium">Accuracy:</span> {item.accuracy}%
</div>
)}
{item.trainingDuration && (
<div>
<span className="font-medium">Durata:</span> {item.trainingDuration}s
</div>
)}
</div>
<p className="text-xs text-muted-foreground" data-testid={`text-date-${item.id}`}>
{format(new Date(item.trainedAt), "dd/MM/yyyy HH:mm:ss")}
</p>
{item.notes && (
<p className="text-sm text-muted-foreground" data-testid={`text-notes-${item.id}`}>
{item.notes}
</p>
)}
</div>
</div>
</div>
))}
</div>
) : (
<div className="text-center py-12 text-muted-foreground" data-testid="text-empty">
<Brain className="h-12 w-12 mx-auto mb-4 opacity-50" />
<p className="font-medium">Nessun training eseguito</p>
<p className="text-sm mt-2">Avvia il primo training per addestrare il modello ML</p>
</div>
)}
</CardContent>
</Card>
</div>
);
}