DEEP-14 Interval import implemented. Training dialog added. Logging added

DeepTrace/Controls/Dialog.razor

@@ -3,18 +3,32 @@
         @Text
     </DialogContent>
     <DialogActions>
-        @if (AllowCancel)
+        @if( IsYesNoCancel )
         {
+            <MudButton Color="MudBlazor.Color.Primary" OnClick="Yes">Yes</MudButton>
+            <MudButton OnClick="No">No</MudButton>
             <MudButton OnClick="Cancel">Cancel</MudButton>
         }
-        <MudButton Color="MudBlazor.Color.Primary" OnClick="Submit">Ok</MudButton>
+        else 
+        {
+            if (AllowCancel)
+            {
+                <MudButton OnClick="Cancel">Cancel</MudButton>
+            }
+            <MudButton Color="MudBlazor.Color.Primary" OnClick="Submit">Ok</MudButton>
+        }
     </DialogActions>
 </MudDialog>
 @code {
     [CascadingParameter] MudDialogInstance? MudDialog { get; set; }
-    [Parameter] public bool AllowCancel { get; set; }
+    [Parameter] public bool AllowCancel               { get; set; }
-    [Parameter] public string Text { get; set; } = "";
+    [Parameter] public string Text                    { get; set; } = "";
+    [Parameter] public bool IsYesNoCancel             { get; set; } = false;
 
     void Submit() => MudDialog?.Close(DialogResult.Ok(true));
+    
     void Cancel() => MudDialog?.Cancel();
+    
+    void Yes()    => MudDialog?.Close(DialogResult.Ok(true));
+    void No()     => MudDialog?.Close(DialogResult.Ok(false));
 }

DeepTrace/Controls/TrainingDialog.razor

@@ -0,0 +1,51 @@
+@using DeepTrace.Data;
+@using DeepTrace.ML;
+
+<style>
+    .dialog-content{
+        min-width: 1000px;
+        width: fit-content;
+    }
+</style>
+
+<MudDialog Class="dialog-content">
+    <DialogContent>
+        
+        <h4>@Text</h4>
+        <MudTextField T="string" ReadOnly="true" Text="@_progressText"></MudTextField>
+        
+        
+    </DialogContent>
+    <DialogActions>
+        <MudButton Color="MudBlazor.Color.Primary" OnClick="Submit" Disabled="@_isTraining">Ok</MudButton>
+    </DialogActions>
+</MudDialog>
+@code {
+    [CascadingParameter] MudDialogInstance? MudDialog { get; set; }
+    [Parameter] public MLProcessor? Processor { get; set; }
+    [Parameter] public ModelDefinition? Model { get; set; }
+    [Parameter] public string Text { get; set; } = "";
+
+    private string _progressText = "";
+    private bool _isTraining = true;
+
+    void Submit() => MudDialog?.Close(DialogResult.Ok(true));
+
+    protected override async Task OnAfterRenderAsync(bool firstRender)
+    {
+        if (!firstRender || Processor==null || Model==null)
+        {
+            return;
+        }
+
+        await Processor.Train(Model, UpdateProgress);
+        _isTraining = false;
+        await InvokeAsync(StateHasChanged);
+    }
+
+    private async void UpdateProgress(string message)
+    {
+        _progressText = message;
+        await InvokeAsync(StateHasChanged);
+    }
+}

DeepTrace/Data/IntervalDefinition.cs

@@ -16,7 +16,7 @@
 
         public string Name { get; set; } = string.Empty;
 
-        public List<TimeSeriesDataSet> Data { get; set; }
+        public List<TimeSeriesDataSet> Data { get; set; } = new();
 
     }
 }

DeepTrace/Data/MLModel1.mbconfig

@@ -191,50 +191,50 @@
         "Version": 0,
         "Type": "Trial",
         "TrainerName": "SdcaMaximumEntropyMulti",
-        "Score": 0.87460317460317472,
+        "Score": 0.57954545454545447,
-        "RuntimeInSeconds": 5.47599983215332
+        "RuntimeInSeconds": 5.3579998016357422
-      },
-      {
-        "Version": 0,
-        "Type": "Trial",
-        "TrainerName": "FastForestOva",
-        "Score": 0.91460317460317475,
-        "RuntimeInSeconds": 3.6610000133514404
       },
       {
         "Version": 0,
         "Type": "Trial",
         "TrainerName": "FastTreeOva",
-        "Score": 0.91460317460317475,
+        "Score": 0.91090909090909089,
-        "RuntimeInSeconds": 3.2239999771118164
+        "RuntimeInSeconds": 3.3050000667572021
       },
       {
         "Version": 0,
         "Type": "Trial",
-        "TrainerName": "LbfgsLogisticRegressionOva",
+        "TrainerName": "FastTreeOva",
-        "Score": 0.96035353535353529,
+        "Score": 0.96460317460317468,
-        "RuntimeInSeconds": 2.812000036239624
+        "RuntimeInSeconds": 3.1909999847412109
-      },
-      {
-        "Version": 0,
-        "Type": "Trial",
-        "TrainerName": "SdcaLogisticRegressionOva",
-        "Score": 0.87460317460317472,
-        "RuntimeInSeconds": 7.8530001640319824
       },
       {
         "Version": 0,
         "Type": "Trial",
         "TrainerName": "LbfgsMaximumEntropyMulti",
-        "Score": 0.96035353535353529,
+        "Score": 0.95031746031746034,
-        "RuntimeInSeconds": 2.3250000476837158
+        "RuntimeInSeconds": 2.2969999313354492
       },
       {
         "Version": 0,
         "Type": "Trial",
-        "TrainerName": "LightGbmMulti",
+        "TrainerName": "FastForestOva",
-        "Score": 0.91460317460317475,
+        "Score": 0.91090909090909089,
-        "RuntimeInSeconds": 2.875
+        "RuntimeInSeconds": 3.9630000591278076
+      },
+      {
+        "Version": 0,
+        "Type": "Trial",
+        "TrainerName": "SdcaLogisticRegressionOva",
+        "Score": 0.57954545454545447,
+        "RuntimeInSeconds": 7.7239999771118164
+      },
+      {
+        "Version": 0,
+        "Type": "Trial",
+        "TrainerName": "LbfgsLogisticRegressionOva",
+        "Score": 0.95031746031746034,
+        "RuntimeInSeconds": 3.937000036239624
       }
     ],
     "Pipeline": {
@@ -311,20 +311,16 @@
           "OutputColumnName": "Q1mean"
         },
         "20": {
-          "OutputColumnNames": [
+          "NumberOfLeaves": 33,
-            "Features"
+          "MinimumExampleCountPerLeaf": 14,
-          ],
+          "NumberOfTrees": 4,
-          "InputColumnNames": [
+          "MaximumBinCountPerFeature": 1022,
-            "Features"
+          "FeatureFraction": 0.99999999,
-          ]
+          "LearningRate": 0.75792684413443268,
-        },
-        "21": {
-          "L1Regularization": 1.0,
-          "L2Regularization": 1.0,
           "LabelColumnName": "Name",
           "FeatureColumnName": "Features"
         },
-        "22": {
+        "21": {
           "OutputColumnName": "PredictedLabel",
           "InputColumnName": "PredictedLabel"
         },
@@ -378,8 +374,7 @@
         "FeaturizeText",
         "Concatenate",
         "MapValueToKey",
-        "NormalizeMinMax",
+        "FastTreeOva",
-        "LbfgsLogisticRegressionOva",
         "MapKeyToValue"
       ]
     },

DeepTrace/Data/MLModel1.training.cs

@@ -5,6 +5,7 @@ using System.Linq;
 using System.Text;
 using System.Threading.Tasks;
 using Microsoft.ML.Data;
+using Microsoft.ML.Trainers.FastTree;
 using Microsoft.ML.Trainers;
 using Microsoft.ML;
 
@@ -54,8 +55,7 @@ namespace DeepTrace
                                     .Append(mlContext.Transforms.Text.FeaturizeText(inputColumnName:@"Q5mean",outputColumnName:@"Q5mean"))      
                                     .Append(mlContext.Transforms.Concatenate(@"Features", new []{@"Q1max",@"Q1avg",@"Q1mean",@"Q2min",@"Q2max",@"Q2avg",@"Q2mean",@"Q3min",@"Q3max",@"Q3avg",@"Q3mean",@"Q4max",@"Q4avg",@"Q4mean",@"Q5min",@"Q5max",@"Q5avg",@"Q5mean"}))      
                                     .Append(mlContext.Transforms.Conversion.MapValueToKey(outputColumnName:@"Name",inputColumnName:@"Name"))      
-                                    .Append(mlContext.Transforms.NormalizeMinMax(@"Features", @"Features"))      
+                                    .Append(mlContext.MulticlassClassification.Trainers.OneVersusAll(binaryEstimator:mlContext.BinaryClassification.Trainers.FastTree(new FastTreeBinaryTrainer.Options(){NumberOfLeaves=33,MinimumExampleCountPerLeaf=14,NumberOfTrees=4,MaximumBinCountPerFeature=1022,FeatureFraction=0.99999999,LearningRate=0.757926844134433,LabelColumnName=@"Name",FeatureColumnName=@"Features"}),labelColumnName: @"Name"))      
-                                    .Append(mlContext.MulticlassClassification.Trainers.OneVersusAll(binaryEstimator: mlContext.BinaryClassification.Trainers.LbfgsLogisticRegression(new LbfgsLogisticRegressionBinaryTrainer.Options(){L1Regularization=1F,L2Regularization=1F,LabelColumnName=@"Name",FeatureColumnName=@"Features"}), labelColumnName:@"Name"))      
                                     .Append(mlContext.Transforms.Conversion.MapKeyToValue(outputColumnName:@"PredictedLabel",inputColumnName:@"PredictedLabel"));
 
             return pipeline;

DeepTrace/Data/TrainedModelDefinition.cs

@@ -7,7 +7,7 @@ namespace DeepTrace.Data
     {
         [BsonId]
         public ObjectId? Id { get; set; }
-        public string Name { get; set; }
+        public string Name { get; set; } = string.Empty;
-        public byte[] Value { get; set; } //base64
+        public byte[] Value { get; set; } = Array.Empty<byte>(); //base64
     }
 }

DeepTrace/DeepTrace.csproj

@@ -6,6 +6,8 @@
   </PropertyGroup>
   <ItemGroup>
     <PackageReference Include="Blazor-ApexCharts" Version="0.9.21-beta" />
+    <PackageReference Include="CsvHelper" Version="30.0.1" />
+    <PackageReference Include="Microsoft.Extensions.Logging.Log4Net.AspNetCore" Version="6.1.0" />
     <PackageReference Include="Microsoft.ML" Version="2.0.1" />
     <PackageReference Include="Microsoft.ML.FastTree" Version="1.7.1" />
     <PackageReference Include="Microsoft.ML.TimeSeries" Version="2.0.1" />

DeepTrace/ML/IMLProcessor.cs

@@ -5,7 +5,7 @@ namespace DeepTrace.ML;
 
 public interface IMLProcessor
 {
-    Task Train(ModelDefinition modelDef);
+    Task Train(ModelDefinition modelDef, Action<string> log);
     byte[] Export();
     void Import(byte[] data);
     string Predict(DataSourceDefinition dataSource);

DeepTrace/ML/MLProcessor.cs

@@ -12,13 +12,21 @@ namespace DeepTrace.ML
         private EstimatorBuilder _estimatorBuilder = new EstimatorBuilder();
         private DataViewSchema? _schema;
         private ITransformer? _transformer;
+        private static string _signature = "DeepTrace-Model-v1-" + typeof(MLProcessor).Name;
+        private readonly ILogger<MLProcessor> _logger;
+
+        public MLProcessor(ILogger<MLProcessor> logger)
+        {
+            _logger = logger;
+        }
 
         private string Name { get; set; } = "TestModel";
 
-        public async Task Train(ModelDefinition modelDef)
+        public async Task Train(ModelDefinition modelDef, Action<string> log)
         {
             var pipeline = _estimatorBuilder.BuildPipeline(_mlContext, modelDef);
             var (data, filename) = await MLHelpers.Convert(_mlContext, modelDef);
+            _mlContext.Log += (_,e) => LogEvents(log, e);
             try
             {
                 _schema = data.Schema;
@@ -31,7 +39,15 @@ namespace DeepTrace.ML
            
         }
 
-        private static string _signature = "DeepTrace-Model-v1-"+typeof(MLProcessor).Name;
+        private void LogEvents(Action<string> log, LoggingEventArgs e)
+        {
+            if(e.Kind.ToString() != "Trace")
+            {
+                _logger.LogDebug(e.Message);
+                log(e.Message);
+            }
+            
+        }
 
         public byte[] Export()
         {

DeepTrace/ML/MedianHelper.cs

@@ -13,7 +13,7 @@ public static class MedianHelper
     /// Pivot is selected ranodmly if random number generator is supplied else its selected as last element in the list.
     /// Reference: Introduction to Algorithms 3rd Edition, Corman et al, pp 171
     /// </summary>
-    private static int Partition<T>(this IList<T> list, int start, int end, Random rnd = null) where T : IComparable<T>
+    private static int Partition<T>(this IList<T> list, int start, int end, Random? rnd = null) where T : IComparable<T>
     {
         if (rnd != null)
             list.Swap(end, rnd.Next(start, end + 1));
@@ -34,11 +34,11 @@ public static class MedianHelper
     /// Note: specified list would be mutated in the process.
     /// Reference: Introduction to Algorithms 3rd Edition, Corman et al, pp 216
     /// </summary>
-    public static T NthOrderStatistic<T>(this IList<T> list, int n, Random rnd = null) where T : IComparable<T>
+    public static T NthOrderStatistic<T>(this IList<T> list, int n, Random? rnd = null) where T : IComparable<T>
     {
         return NthOrderStatistic(list, n, 0, list.Count - 1, rnd);
     }
-    private static T NthOrderStatistic<T>(this IList<T> list, int n, int start, int end, Random rnd) where T : IComparable<T>
+    private static T NthOrderStatistic<T>(this IList<T> list, int n, int start, int end, Random? rnd) where T : IComparable<T>
     {
         while (true)
         {

DeepTrace/Pages/DataSources.razor

@@ -372,6 +372,9 @@
 
     private void HandleTrain()
     {
+        if ( DisplayData == null )
+            return;
+
         var mlContext = new MLContext();
 
         var dataView = mlContext.Data.LoadFromEnumerable<MyTimeSeries>(DisplayData.Series[0].Data.Select(x => new MyTimeSeries(Time: x.TimeStamp, Value: x.Value)));
@@ -476,7 +479,7 @@
 
     // -------- Spike detection tutorial ---------
 
-    private static void DetectSpike(MLContext mLContext, IDataView dataView, TimeSeries[]? data)
+    private static void DetectSpike(MLContext mLContext, IDataView dataView, TimeSeries[] data)
     {
         string outputColumnName = nameof(IidSpikePrediction.Prediction);
         string inputColumnName = nameof(TimeSeriesDataTutorial.Value);
@@ -521,7 +524,7 @@
     class TimeSeriesDataTutorial
     {
         public float Value;
-    
+
         public TimeSeriesDataTutorial(float value)
         {
             Value = value;
@@ -531,7 +534,7 @@
     class IidSpikePrediction
     {
         [VectorType(3)]
-        public double[] Prediction { get; set; }
+        public double[] Prediction { get; set; } = Array.Empty<double>();
     }
 
 }

DeepTrace/Pages/Training.razor

@@ -1,4 +1,5 @@
 @page "/training"
+@using CsvHelper;
 @using DeepTrace.Data;
 @using DeepTrace.ML;
 @using DeepTrace.Services;
@@ -7,6 +8,7 @@
 @using Microsoft.ML;
 @using PrometheusAPI;
 @using System.Text;
+@using System.Globalization;
 
 @inject PrometheusClient Prometheus
 @inject IDialogService DialogService
@@ -16,6 +18,7 @@
 @inject IEstimatorBuilder EstimatorBuilder
 @inject NavigationManager NavManager
 @inject IJSRuntime Js
+@inject ILogger<MLProcessor> MLProcessorLogger
 
 
 <PageTitle>Training</PageTitle>
@@ -202,14 +205,14 @@
         }
     }
 
-    private ModelForm? _modelForm;
+    private ModelForm?              _modelForm;
-    private TimeSeriesData? DisplayData { get; set; }
     private List<DataSourceStorage> _dataSources = new();
-    private List<ModelDefinition> _modelDefinitions = new() {new()};
+    private List<ModelDefinition>   _modelDefinitions = new() {new()};
+    private DateTime?               _minDate;
+    private DateTime?               _maxDate;
 
-    private DateTime? _minDate;
+    private TimeSeriesData? DisplayData { get; set; }
-    private DateTime? _maxDate;
+    private bool IsAddDisabled => DisplayData == null;
-    private bool IsAddDisabled => DisplayData==null;
 
     private DateTime? MinDate
     {
@@ -245,9 +248,9 @@
 
         base.OnInitialized();
 
-        var sources = await StorageService.Load();
+        var sources               = await StorageService.Load();
-        var models = await ModelService.Load();
+        var models                = await ModelService.Load();
-        var trainedModels = await TrainedModelService.Load();
+        var trainedModels         = await TrainedModelService.Load();
         IList<IBrowserFile> files = new List<IBrowserFile>();
 
         if (sources.Count > 0)
@@ -256,8 +259,8 @@
             _modelDefinitions = models;
 
         _modelForm.CurrentModel = _modelDefinitions[0];
-        var source = _dataSources.FirstOrDefault(x => x.Name == _modelDefinitions[0].DataSource.Name);
+        var source              = _dataSources.FirstOrDefault(x => x.Name == _modelDefinitions[0].DataSource.Name);
-        _modelForm.DataSource = source ?? _dataSources[0];
+        _modelForm.DataSource   = source ?? _dataSources[0];
 
     }
 
@@ -293,7 +296,7 @@
         }
         catch (Exception e)
         {
-            ShowError(e.Message);
+            await ShowError(e.Message);
             return;
         }
 
@@ -303,20 +306,20 @@
         {
             if (res.Status != StatusType.Success)
             {
-                ShowError(res.Error ?? "Error");
+                await ShowError(res.Error ?? "Error");
                 return;
             }
 
             if (res.ResultType != ResultTypeType.Matrix)
             {
-                ShowError($"Got {res.ResultType}, but Matrix expected for {def.Query}");
+                await ShowError($"Got {res.ResultType}, but Matrix expected for {def.Query}");
                 return;
             }
 
             var m = res.AsMatrix().Result;
             if (m == null || m.Length != 1)
             {
-                ShowError($"No data returned for {def.Query}");
+                await ShowError($"No data returned for {def.Query}");
                 return;
             }
 
@@ -335,12 +338,15 @@
 
     private void HandleAddModel()
     {
+        if (_modelForm == null)
+            return;
+
         _modelDefinitions.Add(new());
         _modelForm.CurrentModel = _modelDefinitions[^1];
         StateHasChanged();
     }
 
-    private void HandleDeleteModel()
+    private async Task HandleDeleteModel()
     {
         if (_modelDefinitions.Count < 2)
         {
@@ -350,7 +356,7 @@
         var pos = _modelDefinitions.IndexOf(_modelForm!.CurrentModel);
         if (pos < 0)
         {
-            ShowError("Not found");
+            await ShowError("Not found");
             return;
         }
 
@@ -360,10 +366,10 @@
 
         if (toDelete.Id != null)
         {
-            ModelService.Delete(toDelete);
+            await ModelService.Delete(toDelete);
         }
 
-        StateHasChanged();
+        await InvokeAsync(StateHasChanged);
     }
 
     private async Task HandleAddTableContent()
@@ -399,11 +405,14 @@
 
     private void ItemHasBeenCommitted(object element)
     {
-        Task.Run(async ()=>ModelService.Store(_modelForm!.CurrentModel));
+        Task.Run(() => ModelService.Store(_modelForm!.CurrentModel));
     }
 
     private async Task HandleRefresh()
     {
+        if (DisplayData == null)
+            return;
+
         var previousIntervals = _modelForm!.CurrentModel.IntervalDefinitionList;
 
         foreach(var currentInterval in previousIntervals)
@@ -415,20 +424,72 @@
         // await InvokeAsync(StateHasChanged);
     }
 
-    //Doesn't work
+    private class ImportedCsv
+    {
+        public DateTime Start { get; set; }
+        public DateTime End   { get; set; }
+        public string Label   { get; set; } = "";
+    }
+
     private async Task HandleImport(IBrowserFile file)
     {
-        var result = new StringBuilder();
+        try
-        var reader = new StreamReader(file.OpenReadStream(file.Size));
+        {
+            await HandleImportInternal(file);
+        }
+        catch (Exception e)
+        {
+            await ShowError($"Can't import: {e.Message}");
+        }
+    }
 
-        while (reader.Peek() >= 0)
+    private async Task HandleImportInternal(IBrowserFile file)
-            result.AppendLine(await reader.ReadLineAsync());
+    {
-        
+        if ( _modelForm == null )
-        result.ToString();
+            return;
+
+        using var mem = new MemoryStream();
+        // https://stackoverflow.com/questions/67066860/blazorinputfile-synchronous-reads-are-not-supported
+        await file.OpenReadStream(file.Size).CopyToAsync(mem);
+        mem.Position = 0;
+
+        //        var text = Encoding.UTF8.GetString(mem.ToArray());
+
+        using (var reader = new StreamReader(mem))
+        using (var csv = new CsvReader(reader, CultureInfo.InvariantCulture))
+        {
+            var records = csv.GetRecords<ImportedCsv>();
+
+            var yes = await YesNo("If you want to replace all intervals click Yes. Click No to append.");
+            if (yes == null)
+                return;
+
+            if (yes == true)
+                _modelForm.CurrentModel.IntervalDefinitionList.Clear();
+
+            var normalWorkStart = DateTime.MinValue;
+            foreach( var rec in records)
+            {
+                if(normalWorkStart != DateTime.MinValue)
+                {
+                    var normalWorkEnd = rec.Start - TimeSpan.FromSeconds(1);
+                    if(normalWorkStart<normalWorkEnd)
+                        _modelForm.CurrentModel.IntervalDefinitionList.Add(new(normalWorkStart, normalWorkEnd, "Normal work"));
+                }
+                normalWorkStart = rec.End+TimeSpan.FromSeconds(1);
+                _modelForm.CurrentModel.IntervalDefinitionList.Add(new(rec.Start, rec.End, rec.Label));
+            }
+        }
+
+        await HandleRefresh();
+        await InvokeAsync(StateHasChanged);
     }
 
     private async Task HandleExport()
     {
+        if (_modelForm == null)
+            return;
+
         await Js.InvokeVoidAsync("open", $"{NavManager.BaseUri}api/download/mldata/{Uri.EscapeDataString(_modelForm.CurrentModel.Name)}", "_blank");
 
     }
@@ -440,7 +501,7 @@
         await InvokeAsync(StateHasChanged);
     }
 
-    private void ShowError(string text)
+    private async Task ShowError(string text)
     {
         var options = new DialogOptions
             {
@@ -449,19 +510,50 @@
         var parameters = new DialogParameters();
         parameters.Add("Text", text);
 
-        DialogService.Show<Controls.Dialog>("Error", parameters, options);
+        var d = DialogService.Show<Controls.Dialog>("Error", parameters, options);
+        await d.Result;
+    }
+
+    private async Task<bool?> YesNo(string text)
+    {
+        var options = new DialogOptions
+            {
+                CloseOnEscapeKey = true
+            };
+        var parameters = new DialogParameters();
+        parameters.Add(nameof(Controls.Dialog.Text), text);
+        parameters.Add(nameof(Controls.Dialog.IsYesNoCancel), true);
+
+        var d = DialogService.Show<Controls.Dialog>("Query", parameters, options);
+        var res = await d.Result;
+        return res?.Data as bool?;
     }
 
     private async Task HandleTrain()
     {
-        var mlProcessor = new MLProcessor();
+        var mlProcessor = new MLProcessor(MLProcessorLogger);
-        await mlProcessor.Train(_modelForm!.CurrentModel);
+        MLProcessorLogger.LogInformation("Training started");
+        
+        var options = new DialogOptions
+            {
+                CloseOnEscapeKey = true
+            };
+        var parameters = new DialogParameters();
+        parameters.Add(nameof(Controls.TrainingDialog.Text), _modelForm!.CurrentModel.Name);
+        parameters.Add(nameof(Controls.TrainingDialog.Processor), mlProcessor);
+        parameters.Add(nameof(Controls.TrainingDialog.Model), _modelForm.CurrentModel);
+
+        var d = DialogService.Show<Controls.TrainingDialog>("Training", parameters, options);
+        var res = await d.Result;
+
+        MLProcessorLogger.LogInformation("Training finished");
         var bytes = mlProcessor.Export();
         
         //save to Mongo
         var trainedModel = new TrainedModelDefinition
         {
-            Name = "TrainedModel",
+            Id = _modelForm!.CurrentModel.Id,
+            Name = _modelForm!.CurrentModel.Name,
             Value = bytes
         };
         await TrainedModelService.Store(trainedModel);

DeepTrace/Program.cs

@@ -3,9 +3,19 @@ using PrometheusAPI;
 using MongoDB.Driver;
 using DeepTrace.Services;
 using DeepTrace.ML;
+using ApexCharts;
+using log4net;
 
 var builder = WebApplication.CreateBuilder(args);
 
+builder.Services.AddLogging(logging =>
+{
+    logging.ClearProviders();
+    GlobalContext.Properties["LOGS_ROOT"] = Environment.GetEnvironmentVariable("LOGS_ROOT") ?? "";
+    System.Text.Encoding.RegisterProvider(System.Text.CodePagesEncodingProvider.Instance);
+    logging.AddLog4Net("log4net.config");
+});
+
 // Add services to the container.
 builder.Services.AddRazorPages();
 builder.Services.AddServerSideBlazor();

DeepTrace/appsettings.Development.json

@@ -2,8 +2,8 @@
   "DetailedErrors": true,
   "Logging": {
     "LogLevel": {
-      "Default": "Information",
+      "Default": "Trace",
-      "Microsoft.AspNetCore": "Warning"
+      "Microsoft.AspNetCore": "Information"
     }
   }
 }

DeepTrace/appsettings.json

@@ -1,12 +1,12 @@
 {
   "Logging": {
     "LogLevel": {
-      "Default": "Information",
+      "Default": "Trace",
-      "Microsoft.AspNetCore": "Warning"
+      "Microsoft.AspNetCore": "Information"
     }
   },
   "AllowedHosts": "*",
-  
+
   "Connections": {
     "Prometheus": "http://localhost:9090",
     "MongoDb": "mongodb://localhost:27017"

DeepTrace/log4net.config

@@ -0,0 +1,63 @@
+<?xml version="1.0" encoding="utf-8" ?>
+<log4net>
+	<appender name="DebugAppender" type="log4net.Appender.DebugAppender" >
+		<layout type="log4net.Layout.PatternLayout">
+			<conversionPattern value="%date [%-2thread] %-5level %logger - %message%newline" />
+		</layout>
+	</appender>
+
+	<!--
+	<appender name="Console" type="log4net.Appender.ConsoleAppender">
+		<layout type="log4net.Layout.PatternLayout">
+			<conversionPattern value="%date [%-2thread] %-5level %-20logger - %message%newline" />
+		</layout>
+	</appender>
+-->
+
+	<!--Console appender-->
+	<appender name="Console" type="log4net.Appender.ManagedColoredConsoleAppender">
+		<mapping>
+			<level value="INFO" />
+			<forecolor value="Green" />
+		</mapping>
+		<mapping>
+			<level value="WARN" />
+			<forecolor value="Yellow" />
+		</mapping>
+		<mapping>
+			<level value="ERROR" />
+			<forecolor value="Red" />
+		</mapping>
+		<mapping>
+			<level value="DEBUG" />
+			<forecolor value="Blue" />
+		</mapping>
+		<layout type="log4net.Layout.PatternLayout">
+			<conversionPattern value="%date{HH:mm:ss,fff} [%3thread] %-5level %-15logger{1} %message%newline" />
+		</layout>
+	</appender>
+
+	<appender name="RollingFile" type="log4net.Appender.RollingFileAppender">
+		<file value="log-file.txt" />
+		<appendToFile value="false" />
+		<layout type="log4net.Layout.PatternLayout">
+			<conversionPattern value="%date [%3thread] %-5level %logger [%property{NDC}] - %message%newline" />
+		</layout>
+	</appender>
+
+	<appender name="BufferingForwardingAppender" type="log4net.Appender.BufferingForwardingAppender" >
+		<bufferSize value="1"/>
+		<appender-ref ref="DebugAppender" />
+		<appender-ref ref="Console" />
+		<appender-ref ref="RollingFile" />
+	</appender>
+
+	<logger name="System.Net.Http">
+		<level value="ERROR"/>
+	</logger>
+
+	<root>
+		<level value="ALL"/>
+		<appender-ref ref="BufferingForwardingAppender" />
+	</root>
+</log4net>