diff options
| -rw-r--r-- | backend/api/api/Controllers/DatasetController.cs | 7 | ||||
| -rw-r--r-- | backend/api/api/Models/Model.cs | 11 | ||||
| -rw-r--r-- | backend/api/api/Models/NullValues.cs | 7 | ||||
| -rw-r--r-- | backend/api/api/Services/FillAnEmptyDb.cs | 106 | ||||
| -rw-r--r-- | backend/api/api/Services/MlConnectionService.cs | 14 | ||||
| -rw-r--r-- | backend/microservice/api/newmlservice.py | 9 | ||||
| -rw-r--r-- | frontend/src/app/_elements/form-dataset/form-dataset.component.html | 9 |
7 files changed, 119 insertions, 44 deletions
diff --git a/backend/api/api/Controllers/DatasetController.cs b/backend/api/api/Controllers/DatasetController.cs index e1e36917..80c9f2a9 100644 --- a/backend/api/api/Controllers/DatasetController.cs +++ b/backend/api/api/Controllers/DatasetController.cs @@ -144,6 +144,7 @@ namespace api.Controllers [Authorize(Roles = "User,Guest")] public async Task<ActionResult<Dataset>> Post([FromBody] Dataset dataset) { + Console.WriteLine("PROBAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA"); string uploaderId = getUserId(); dataset.uploaderId = uploaderId; @@ -161,6 +162,8 @@ namespace api.Controllers dataset.isPreProcess = false; _datasetService.Create(dataset); _mlConnectionService.PreProcess(dataset, fileModel.path, uploaderId); + + return Ok(); } } @@ -227,9 +230,9 @@ namespace api.Controllers //nesto - dataset.isPreProcess = false; + dataset.isPreProcess = true; _datasetService.Create(dataset); - _mlConnectionService.PreProcess(dataset, fileModel.path, uploaderId); + //_mlConnectionService.PreProcess(dataset, fileModel.path, uploaderId); return Ok(); } } diff --git a/backend/api/api/Models/Model.cs b/backend/api/api/Models/Model.cs index f09940d0..a807316f 100644 --- a/backend/api/api/Models/Model.cs +++ b/backend/api/api/Models/Model.cs @@ -48,6 +48,17 @@ namespace api.Models public class Layer { + + + public Layer(int layerNumber, string activationFunction, int neurons, string regularisation, float regularisationRate) + { + this.layerNumber = layerNumber; + this.activationFunction = activationFunction; + this.neurons = neurons; + this.regularisation = regularisation; + this.regularisationRate = regularisationRate; + } + public int layerNumber { get; set; } public string activationFunction { get; set; } public int neurons { get; set; } diff --git a/backend/api/api/Models/NullValues.cs b/backend/api/api/Models/NullValues.cs index a8ce4f36..8e3415e7 100644 --- a/backend/api/api/Models/NullValues.cs +++ b/backend/api/api/Models/NullValues.cs @@ -2,6 +2,13 @@ { public class NullValues { + public NullValues(string column, string option, string value) + { + this.column = column; + this.option = option; + this.value = value; + } + public string column { get; set; } public string option { get; set; } public string value { get; set; } diff --git a/backend/api/api/Services/FillAnEmptyDb.cs b/backend/api/api/Services/FillAnEmptyDb.cs index 28f293ce..404d937a 100644 --- a/backend/api/api/Services/FillAnEmptyDb.cs +++ b/backend/api/api/Services/FillAnEmptyDb.cs @@ -1,6 +1,5 @@ using api.Interfaces; using api.Models; -using Microsoft.AspNetCore.SignalR; using MongoDB.Driver; namespace api.Services @@ -31,6 +30,7 @@ namespace api.Services public Task StartAsync(CancellationToken cancellationToken) { + if (_fileService.CheckDb()) { @@ -49,6 +49,7 @@ namespace api.Services _fileService.Create(file); + Dataset dataset = new Dataset(); dataset._id = ""; @@ -59,32 +60,42 @@ namespace api.Services dataset.extension = ".csv"; dataset.isPublic = true; dataset.accessibleByLink = true; - dataset.dateCreated = DateTime.Now; - dataset.lastUpdated = DateTime.Now; + dataset.dateCreated = DateTime.UtcNow; + dataset.lastUpdated = DateTime.UtcNow; dataset.delimiter = ","; - dataset.columnInfo = new ColumnInfo[] { }; dataset.columnInfo = new[] { - new ColumnInfo( "PassengerId", true, 0, 446, 1, 891, 446, new string[]{ }, new int[] {}, new float[] {}, 0.01f,0.1f ), - new ColumnInfo( "Survived", true, 0, 0.38383838534355164f, 0, 1, 0, new string[]{ }, new int[] {}, new float[] {}, 0.01f,0.1f ), - new ColumnInfo( "Pclass", true, 0, 2.3086419105529785f, 1, 3, 3, new string[]{ }, new int[] {}, new float[] {}, 0.01f,0.1f ), - new ColumnInfo( "Name", false, 0, 0, 0, 0, 0, new string[]{"Braund, Mr. Owen Harris", "Boulos, Mr. Hanna", "Frolicher-Stehli, Mr. Maxmillian", "Gilinski, Mr. Eliezer", "Murdlin, Mr. Joseph", "Rintamaki, Mr. Matti", "Stephenson, Mrs. Walter Bertram (Martha Eustis)", "Elsbury, Mr. William James", "Bourke, Miss. Mary", "Chapman, Mr. John Henry"}, new int[] {}, new float[] {}, 0.01f,0.1f ), - new ColumnInfo( "Sex", false, 0, 0, 0, 0, 0, new string[]{ "male", "female" }, new int[] {}, new float[] {}, 0.01f,0.1f ), - new ColumnInfo( "Age", true, 177, 29.69911766052246f, 0.41999998688697815f, 80, 28, new string[]{ }, new int[] {}, new float[] {}, 0.01f,0.1f ), - new ColumnInfo( "SibSp", true, 0, 0.523007869720459f, 0, 8, 0, new string[]{ }, new int[] {}, new float[] {}, 0.01f,0.1f ), - new ColumnInfo( "Parch", true, 0, 0.3815937042236328f, 0, 6, 0, new string[]{ }, new int[] {}, new float[] {}, 0.01f,0.1f ), - new ColumnInfo( "Ticket", false, 0, 0, 0, 0, 0, new string[]{ "347082", "CA. 2343", "1601", "3101295", "CA 2144", "347088", "S.O.C. 14879", "382652", "LINE", "PC 17757" }, new int[] {}, new float[] {}, 0.01f,0.1f ), - new ColumnInfo( "Fare", true, 0, 32.20420837402344f, 0, 512.3292236328125f, 14.45419979095459f, new string[]{ }, new int[] {}, new float[] {}, 0.01f,0.1f ), - new ColumnInfo( "Cabin", false, 687, 0, 0, 0, 0, new string[]{ "B96 B98", "G6", "C23 C25 C27", "C22 C26", "F33", "F2", "E101", "D", "C78", "C93" }, new int[] {}, new float[] {}, 0.01f,0.1f ), - new ColumnInfo( "Embarked", false, 2, 0.3815937042236328f, 0, 6, 0, new string[]{ "S", "C", "Q" }, new int[] {}, new float[] {}, 0.01f,0.1f ), + new ColumnInfo( "PassengerId", true, 0, 446, 1, 891, 446, new string[]{ "1","599","588", "589", "590", "591" }, new int[] { 1, 1, 1, 1, 1, 1}, new float[] { 0.0011223345063626766f, 0.0011223345063626766f, 0.0011223345063626766f, 0.0011223345063626766f, 0.0011223345063626766f, 0.0011223345063626766f }, 223.5f, 668.5f ), + new ColumnInfo( "Survived", true, 0, 0.38383838534355164f, 0, 1, 0, new string[]{ "0", "1" }, new int[] { 549, 342}, new float[] { 0.6161616444587708f, 0.38383838534355164f}, 0f, 1f ), + new ColumnInfo( "Pclass", true, 0, 2.3086419105529785f, 1, 3, 3, new string[]{ "3", "1", "2" }, new int[] {491, 216, 184}, new float[] {0.5510662198066711f, 0.24242424964904785f, 0.2065095454454422f }, 2f, 3f ), + new ColumnInfo( "Name", false, 0, 0, 0, 0, 0, new string[]{"Braund, Mr. Owen Harris", "Boulos, Mr. Hanna", "Frolicher-Stehli, Mr. Maxmillian", "Gilinski, Mr. Eliezer", "Murdlin, Mr. Joseph", "Rintamaki, Mr. Matti"}, new int[] {1,1,1,1,1,1}, new float[] {0.0011223345063626766f, 0.0011223345063626766f, 0.0011223345063626766f, 0.0011223345063626766f, 0.0011223345063626766f, 0.0011223345063626766f }, 0f, 0f ), + new ColumnInfo( "Sex", false, 0, 0, 0, 0, 0, new string[]{ "male", "female" }, new int[] {577,314}, new float[] {0.6475870013237f, 0.35241302847862244f}, 0f,0f ), + new ColumnInfo( "Age", true, 177, 29.69911766052246f, 0.41999998688697815f, 80, 28, new string[]{ "nan", "24.0", "22.0", "18.0", "28.0", "30.0" }, new int[] {177,30,27,26,25,25}, new float[] {0.1986531913280487f,0.033670034259557724f,0.03030303120613098f,0.029180696234107018f,0.028058361262083054f,0.028058361262083054f}, 20.125f,38f ), + new ColumnInfo( "SibSp", true, 0, 0.523007869720459f, 0, 8, 0, new string[]{ "0", "1", "2", "3", "4", "3", "8" }, new int[] {608, 209, 28, 18, 16, 7}, new float[] {0.6823793649673462f, 0.23456789553165436f, 0.031425364315509796f, 0.020202020183205605f, 0.017957352101802826f, 0.007856341078877449f }, 0f, 1f ), + new ColumnInfo( "Parch", true, 0, 0.3815937042236328f, 0, 6, 0, new string[]{ "0", "1", "2", "5", "3", "4" }, new int[] { 678, 118, 80, 5, 5, 4}, new float[] {0.7609427571296692f, 0.13243547081947327f, 0.08978675305843353f, 0.005611672066152096f, 0.005611672066152096f, 0.0044893380254507065f }, 0f,0f ), + new ColumnInfo( "Ticket", false, 0, 0, 0, 0, 0, new string[]{ "347082", "CA. 2343", "1601", "3101295", "CA 2144", "347088" }, new int[] {7, 7, 7, 6, 6, 6}, new float[] {0.007856341078877449f, 0.007856341078877449f, 0.007856341078877449f, 0.006734006572514772f, 0.006734006572514772f, 0.006734006572514772f}, 0f,0f ), + new ColumnInfo( "Fare", true, 0, 32.20420837402344f, 0, 512.3292236328125f, 14.45419979095459f, new string[]{ "8.05", "13.0", "7.8958", "7.75", "26.0", "10.5"}, new int[] {43, 42, 38, 34, 31, 24}, new float[] {0.04826038330793381f, 0.047138046473264694f, 0.04264871031045914f, 0.03815937042236328f, 0.03479236736893654f, 0.02693602629005909f }, 7.910399913787842f,31f ), + new ColumnInfo( "Cabin", false, 687, 0, 0, 0, 0, new string[]{ "B96 B98", "G6", "C23 C25 C27", "C22 C26", "F33", "F2" }, new int[] {4, 4, 4, 3, 3, 3}, new float[] {0.0044893380254507065f, 0.0044893380254507065f, 0.0044893380254507065f, 0.003367003286257386f, 0.003367003286257386f, 0.003367003286257386f }, 0f,0f ), + new ColumnInfo( "Embarked", false, 2, 0, 0, 0, 0, new string[]{ "S", "C", "Q" }, new int[] {644, 168, 77}, new float[] {0.7227833867073059f, 0.18855218589305878f, 0.08641975373029709f}, 0f,0f ), }; dataset.rowCount = 891; dataset.nullCols = 3; - dataset.nullRows = 708; + dataset.nullRows = 689; dataset.isPreProcess = true; - dataset.cMatrix = new float[11][]; - dataset.cMatrix[0] = new float[] { 1f, -0.005006660707294941f, -0.03514399379491806f, -0.03855886310338974f, 0.04293888062238693f, 0.03684719651937485f }; + dataset.cMatrix = new float[12][]; + dataset.cMatrix[0] = new float[] { 1, -0.005007f, -0.035144f, -0.038559f, 0.042939f, 0.036847f, -0.057527f, -0.001652f, -0.056554f, 0.012658f, -0.035077f, 0.013083f }; + dataset.cMatrix[1] = new float[] { -0.0050066607f, 1f, -0.33848104f, -0.057343315f, -0.54335135f, -0.077221096f, -0.0353225f, 0.08162941f, -0.16454913f, 0.25730652f, -0.25488788f, -0.16351666f }; + dataset.cMatrix[2] = new float[] { -0.035143994f, -0.33848104f, 1f, 0.052830875f, 0.13190049f, -0.369226f, 0.083081365f, 0.018442672f, 0.31986925f, -0.54949963f, 0.6841206f, 0.15711245f }; + dataset.cMatrix[3] = new float[] { -0.038558863f, -0.057343315f, 0.052830875f, 1f, 0.020313991f, 0.06258293f, -0.017230336f, -0.04910539f, 0.047348045f, -0.049172707f, 0.061959103f, -0.0045570857f }; + dataset.cMatrix[4] = new float[] { 0.04293888f, -0.54335135f, 0.13190049f, 0.020313991f, 1f, 0.093253575f, -0.11463081f, -0.24548896f, 0.059371985f, -0.18233283f, 0.09668117f, 0.104057096f }; + dataset.cMatrix[5] = new float[] { 0.036847197f, -0.077221096f, -0.369226f, 0.06258293f, 0.093253575f, 1f, -0.30824676f, -0.18911926f, -0.07593447f, 0.09606669f, -0.2523314f, -0.02525195f }; + dataset.cMatrix[6] = new float[] { -0.057526834f, -0.0353225f, 0.083081365f, -0.017230336f, -0.11463081f, -0.30824676f, 1f, 0.4148377f, 0.079461284f, 0.15965104f, 0.043592583f, 0.06665404f }; + dataset.cMatrix[7] = new float[] { -0.0016520123f, 0.08162941f, 0.018442672f, -0.04910539f, -0.24548896f, -0.18911926f, 0.4148377f, 1f, 0.020003473f, 0.21622494f, -0.02832425f, 0.038322248f }; + dataset.cMatrix[8] = new float[] { -0.056553647f, -0.16454913f, 0.31986925f, 0.047348045f, 0.059371985f, -0.07593447f, 0.079461284f, 0.020003473f, 1f, -0.013885464f, 0.24369627f, -0.0060414947f }; + dataset.cMatrix[9] = new float[] { 0.012658219f, 0.25730652f, -0.54949963f, -0.049172707f, -0.18233283f, 0.09606669f, 0.15965104f, 0.21622494f, -0.013885464f, 1f, -0.5033555f, -0.22122625f }; + dataset.cMatrix[10] = new float[] { -0.035077456f, -0.25488788f, 0.6841206f, 0.061959103f, 0.09668117f, -0.2523314f, 0.043592583f, -0.02832425f, 0.24369627f, -0.5033555f, 1f, 0.19320504f }; + dataset.cMatrix[11] = new float[] { 0.013083069f, -0.16351666f, 0.15711245f, -0.0045570857f, 0.104057096f, -0.02525195f, 0.06665404f, 0.038322248f, -0.0060414947f, -0.22122625f, 0.19320504f, 1f }; _datasetService.Create(dataset); @@ -93,19 +104,30 @@ namespace api.Services model._id = ""; model.uploaderId = "000000000000000000000000"; - model.name = "Model Titanik"; + model.name = "Titanik model"; model.description = "Model Titanik"; model.dateCreated = DateTime.Now; model.lastUpdated = DateTime.Now; model.type = "binarni-klasifikacioni"; model.optimizer = "Adam"; - model.lossFunction = "mean_squared_error"; - model.hiddenLayers = 5; - model.batchSize = "8"; + model.lossFunction = "binary_crossentropy"; + model.hiddenLayers = 4; + model.batchSize = "64"; + model.learningRate = "1"; model.outputNeurons = 0; + model.layers = new[] + { + new Layer ( 0,"sigmoid", 3,"l1", 1f ), + new Layer ( 1,"sigmoid", 3,"l1", 1f ), + new Layer ( 2,"sigmoid", 3,"l1", 1f ), + new Layer ( 3,"sigmoid", 3,"l1", 1f ), + }; model.outputLayerActivationFunction = "sigmoid"; model.metrics = new string[] { }; - model.epochs = 5; + model.epochs = 50; + model.randomOrder = true; + model.randomTestSet = true; + model.randomTestSetDistribution = 0.1f; model.isPublic = true; model.accessibleByLink = true; model.validationSize = 0.1f;//proveri @@ -116,23 +138,45 @@ namespace api.Services Experiment experiment = new Experiment(); experiment._id = ""; - experiment.name = "Eksperiment Titanik"; + experiment.name = "Titanik eksperiment (binarno-klasifikacioni)"; experiment.description = "Binarno klasifikacioni, label"; + experiment.type = "binarni-klasifikacioni"; experiment.ModelIds = new string[] { }.ToList(); experiment.datasetId = _datasetService.GetDatasetId(dataset.fileId); experiment.uploaderId = "000000000000000000000000"; - experiment.inputColumns = new string[] { "Embarked", "Survived" }; + experiment.inputColumns = new string[] { "Survived", "Pclass", "Sex", "Age", "SibSp", "Parch", "Ticket", "Fare", "Embarked" }; experiment.outputColumn = "Survived"; + experiment.nullValues = "delete_rows"; experiment.dateCreated = DateTime.Now; experiment.lastUpdated = DateTime.Now; - experiment.nullValues = "delete_rows"; - experiment.nullValuesReplacers = new NullValues[] { }; + experiment.nullValuesReplacers = new[] + { + new NullValues ("Survived", "delete_rows", ""), + new NullValues ("Pclass", "delete_rows", ""), + new NullValues ("Sex", "delete_rows", ""), + new NullValues ("Age", "delete_rows", ""), + new NullValues ("SibSp", "delete_rows", ""), + new NullValues ("Parch", "delete_rows", ""), + new NullValues ("Ticket", "delete_rows", ""), + new NullValues ("Fare", "delete_rows", ""), + new NullValues ("Embarked", "delete_rows", "") + }; experiment.encodings = new[] { + new ColumnEncoding( "PassengerId", "label" ), new ColumnEncoding( "Survived", "label" ), + new ColumnEncoding( "Pclass", "label" ), + new ColumnEncoding( "Name", "label" ), + new ColumnEncoding( "Sex", "label" ), + new ColumnEncoding( "Age", "label" ), + new ColumnEncoding( "SibSp", "label" ), + new ColumnEncoding( "Parch", "label" ), + new ColumnEncoding( "Ticket", "label" ), + new ColumnEncoding( "Fare", "label" ), + new ColumnEncoding( "Cabin", "label" ), new ColumnEncoding("Embarked", "label" ) }; - experiment.columnTypes = new string[] {"categorical"}; + experiment.columnTypes = new string[] { "numerical", "categorical", "categorical", "categorical", "categorical", "numerical", "categorical", "numerical", "categorical", "numerical", "categorical", "categorical" }; _experimentService.Create(experiment); @@ -183,8 +227,8 @@ namespace api.Services dataset.delimiter = ","; dataset.columnInfo = new[] { - new ColumnInfo( "Unnamed: 0", true, 0, 26969.5f, 0, 53939, 26969.5f, new string[]{ }, new int[] {}, new float[] {}, 0.01f,0.1f ), - new ColumnInfo( "carat", true, 0, 0.7979397773742676f, 0.20000000298023224f, 5.010000228881836f, 0.699999988079071f, new string[]{ }, new int[] {}, new float[] {}, 0.01f,0.1f ), + new ColumnInfo( "Unnamed: 0", true, 0, 26969.5f, 0, 53939, 26969.5f, new string[]{ "0", "35977", "35953", "35954", "35955", "35956" }, new int[] {1,1,1,1,1,1}, new float[] {0.000018539118173066527f, 0.000018539118173066527f, 0.000018539118173066527f, 0.000018539118173066527f, 0.000018539118173066527f, 0.000018539118173066527f}, 13484.75f,40454.25f ), + new ColumnInfo( "carat", true, 0, 0.7979397773742676f, 0.20000000298023224f, 5.010000228881836f, 0.699999988079071f, new string[]{ "0.3", "0.31", "1.01", "0.7", "0.32", "1.0" }, new int[] {2604, 2249, 2242, 1981, 1840, 1558}, new float[] {0.04827586188912392f, 0.04169447720050812f, 0.0415647029876709f, 0.03672599047422409f, 0.034111976623535156f, 0.02888394519686699f}, 0.4000000059604645f,1.0399999618530273f ), new ColumnInfo( "cut", false, 0, 0, 0, 0, 0, new string[]{ "Ideal", "Premium", "Very Good", "Good", "Fair" }, new int[] {}, new float[] {}, 0.01f,0.1f ), new ColumnInfo( "color", false, 0, 0, 0, 0, 0, new string[]{"G", "E", "F", "H", "D", "I", "I", "J"}, new int[] {}, new float[] {}, 0.01f,0.1f ), new ColumnInfo( "clarity", false, 0, 0, 0, 0, 0, new string[]{ "SI1", "VS2","SI2", "VS1", "VVS2", "VVS1", "IF", "I1" }, new int[] {}, new float[] {}, 0.01f,0.1f ), diff --git a/backend/api/api/Services/MlConnectionService.cs b/backend/api/api/Services/MlConnectionService.cs index b6d422ad..0ecdb1af 100644 --- a/backend/api/api/Services/MlConnectionService.cs +++ b/backend/api/api/Services/MlConnectionService.cs @@ -62,6 +62,20 @@ namespace api.Services if (ChatHub.CheckUser(id)) foreach (var connection in ChatHub.getAllConnectionsOfUser(id)) await _ichat.Clients.Client(connection).SendAsync("NotifyDataset",newDataset.name,newDataset._id); + + + string proba = ""; + + for (int i = 0; i < newDataset.cMatrix.Length; i++) + { + proba = i +" "; + for (int j = 0; j < newDataset.cMatrix[i].Length; j++) + proba += newDataset.cMatrix[i][j] + "f, "; + + Console.WriteLine(proba); + proba = ""; + } + return; } diff --git a/backend/microservice/api/newmlservice.py b/backend/microservice/api/newmlservice.py index 6a863013..fd21f8ce 100644 --- a/backend/microservice/api/newmlservice.py +++ b/backend/microservice/api/newmlservice.py @@ -291,11 +291,12 @@ def train(dataset, paramsModel,paramsExperiment,paramsDataset,callback): random=123 else: random=0 + + #x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=test, random_state=random) #print(x_train,x_test) x, x_test, y, y_test = train_test_split(x, y, test_size=test, random_state=random, shuffle=True) x_train, x_val, y_train, y_val = train_test_split(x, y, test_size=0.15, shuffle=True) - # # Treniranje modela # # @@ -369,7 +370,7 @@ def train(dataset, paramsModel,paramsExperiment,paramsDataset,callback): - classifier.compile(loss =paramsModel["lossFunction"] , optimizer =opt, metrics = ['accuracy','mae','mse']) + classifier.compile(loss =paramsModel["lossFunction"] , optimizer =opt, metrics = ['mae','mse']) history=classifier.fit( x=x_train, y=y_train, epochs = paramsModel['epochs'],batch_size=int(paramsModel['batchSize']),callbacks=callback(x_test, y_test,paramsModel['_id']),validation_data=(x_val, y_val)) @@ -507,9 +508,9 @@ def train(dataset, paramsModel,paramsExperiment,paramsDataset,callback): classifier.add(tf.keras.layers.Dense(units=paramsModel['layers'][i+1]['neurons'], activation=paramsModel['layers'][i+1]['activationFunction'],kernel_regularizer=kernelreg, bias_regularizer=biasreg, activity_regularizer=activityreg))#i-ti skriveni sloj - classifier.add(tf.keras.layers.Dense(units=1,activation=paramsModel['outputLayerActivationFunction'])) + classifier.add(tf.keras.layers.Dense(units=1)) - classifier.compile(loss =paramsModel["lossFunction"] , optimizer = opt , metrics = ['accuracy','mae','mse']) + classifier.compile(loss =paramsModel["lossFunction"] , optimizer = opt , metrics = ['mae','mse']) history=classifier.fit( x=x_train, y=y_train, epochs = paramsModel['epochs'],batch_size=int(paramsModel['batchSize']),callbacks=callback(x_test, y_test,paramsModel['_id']),validation_data=(x_val, y_val)) hist=history.history diff --git a/frontend/src/app/_elements/form-dataset/form-dataset.component.html b/frontend/src/app/_elements/form-dataset/form-dataset.component.html index 6736367e..7be838f1 100644 --- a/frontend/src/app/_elements/form-dataset/form-dataset.component.html +++ b/frontend/src/app/_elements/form-dataset/form-dataset.component.html @@ -3,8 +3,7 @@ <div class="topBar"> <div class="kolona mb-3"> <div class="fileButton"> - <button type="button" mat-raised-button (click)="fileInput.click()">Dodaj izvor podataka</button> - + <button type="button" mat-raised-button (click)="fileInput.click()"><span *ngIf="!firstInput">Dodaj izvor podataka</span><span *ngIf="firstInput">{{filename}}</span></button> </div> </div> @@ -32,11 +31,7 @@ </mat-form-field> </div> </div> - - <div class="row" *ngIf="firstInput"> - <label class="naslov mt-2">{{filename}}</label> - - </div> + <div class="row" style="margin-right: 0;"> <div class="file-container" [ngClass]="{'dottedClass': !tableData.hasInput}"> |