56 files changed, 1687 insertions, 313 deletions

diff --git a/backend/api/api/Controllers/FileController.cs b/backend/api/api/Controllers/FileController.cs
index 0fe8415b..d29c5676 100644
--- a/backend/api/api/Controllers/FileController.cs
+++ b/backend/api/api/Controllers/FileController.cs
@@ -4,6 +4,7 @@ using api.Services;
 using Microsoft.AspNetCore.Authorization;
 using Microsoft.AspNetCore.Mvc;
 using Microsoft.Net.Http.Headers;
+
 namespace api.Controllers
 {
     [Route("api/[controller]")]
@@ -11,6 +12,7 @@ namespace api.Controllers
     public class FileController : ControllerBase
     {
         private string[] permittedExtensions = { ".csv" };
+        private string[] permittedExtensionsH5 = { ".h5" };//niz da bi dodali h4 itd
         private readonly IConfiguration _configuration;
         private IJwtToken _token;
         private IFileService _fileservice;
@@ -22,6 +24,77 @@ namespace api.Controllers
 
         }
 
+        [HttpPost("h5")]
+        [Authorize(Roles = "User,Guest")]
+        public async Task<ActionResult<string>> H5Upload([FromForm] IFormFile file)
+        {
+
+            //get username from jwtToken
+            string uploaderId;
+            string folderName;
+            var header = Request.Headers[HeaderNames.Authorization];
+            if (AuthenticationHeaderValue.TryParse(header, out var headerValue))
+            {
+
+                var scheme = headerValue.Scheme;
+                var parameter = headerValue.Parameter;
+                uploaderId = _token.TokenToId(parameter);
+                if (uploaderId == null)
+                    return null;
+            }
+            else
+                return BadRequest();
+            if (uploaderId == "")
+            {
+                folderName = "TempFiles";
+            }
+            else
+            {
+                folderName = "UploadedFiles";
+            }
+
+
+            //Check filetype
+            var filename = file.FileName;
+            var ext = Path.GetExtension(filename).ToLowerInvariant();
+            var name = Path.GetFileNameWithoutExtension(filename).ToLowerInvariant();
+            if (string.IsNullOrEmpty(ext) || !permittedExtensionsH5.Contains(ext))
+            {
+                return BadRequest("Wrong file type");
+            }
+            var folderPath = Path.Combine(Directory.GetCurrentDirectory(), folderName, uploaderId);
+            //Check Directory
+            if (!Directory.Exists(folderPath))
+            {
+                Directory.CreateDirectory(folderPath);
+            }
+            //Index file if same filename
+            var fullPath = Path.Combine(folderPath, filename);
+            int i = 0;
+
+            while (System.IO.File.Exists(fullPath))
+            {
+                i++;
+                fullPath = Path.Combine(folderPath, name + i.ToString() + ext);
+            }
+
+
+            //Write file
+            using (var stream = new FileStream(fullPath, FileMode.Create))
+            {
+                await file.CopyToAsync(stream);
+            }
+            FileModel fileModel = new FileModel();
+            fileModel.type = "h5";
+            fileModel.path = fullPath;
+            fileModel.uploaderId = uploaderId;
+            fileModel.date = DateTime.Now.ToUniversalTime();
+            fileModel = _fileservice.Create(fileModel);
+
+
+            return Ok(fileModel);
+        }
+
 
         [HttpPost("Csv")]
         [Authorize(Roles = "User,Guest")]
@@ -81,6 +154,7 @@ namespace api.Controllers
                 await file.CopyToAsync(stream);
             }
             FileModel fileModel= new FileModel();
+            fileModel.type = "csv";
             fileModel.path=fullPath;
             fileModel.uploaderId= uploaderId;
             fileModel.date = DateTime.Now.ToUniversalTime();
@@ -90,6 +164,35 @@ namespace api.Controllers
             return Ok(fileModel);
         }
 
+
+        //msm generalno moze da se koristi Download samo
+        [HttpGet("downloadh5")]
+        [Authorize(Roles = "User,Guest")]
+        public async Task<ActionResult> DownloadH5(string id)
+        {
+            //Get Username
+            string uploaderId;
+            var header = Request.Headers[HeaderNames.Authorization];
+            if (AuthenticationHeaderValue.TryParse(header, out var headerValue))
+            {
+
+                var scheme = headerValue.Scheme;
+                var parameter = headerValue.Parameter;
+                uploaderId = _token.TokenToId(parameter);
+                if (uploaderId == null)
+                    return null;
+            }
+            else
+                return BadRequest();
+
+            string filePath = _fileservice.GetFilePath(id, uploaderId);
+            if (filePath == null)
+                return BadRequest();
+
+            return File(System.IO.File.ReadAllBytes(filePath), "application/octet-stream", Path.GetFileName(filePath));
+
+        }
+
         [HttpGet("Download")]
         [Authorize(Roles = "User,Guest")]
         public async Task<ActionResult> DownloadFile(string id)
diff --git a/backend/api/api/Controllers/PredictorController.cs b/backend/api/api/Controllers/PredictorController.cs
index cdc14632..161271e2 100644
--- a/backend/api/api/Controllers/PredictorController.cs
+++ b/backend/api/api/Controllers/PredictorController.cs
@@ -77,7 +77,7 @@ namespace api.Controllers
 
         // GET api/<PredictorController>/getpredictor/{name}
         [HttpGet("getpredictor/{id}")]
-        [Authorize(Roles = "User")]
+        [Authorize(Roles = "User,Guest")]
         public ActionResult<Predictor> GetPredictor(string id)
         {
             string username;
@@ -188,8 +188,8 @@ namespace api.Controllers
 
         // POST api/<PredictorController>/usepredictor {predictor,inputs}
         [HttpPost("usepredictor/{id}")]
-        [Authorize(Roles = "User")]
-        public ActionResult UsePredictor(String id, [FromBody] String[] inputs)
+        [Authorize(Roles = "User,Guest")]
+        public ActionResult UsePredictor(String id, [FromBody] PredictorColumns[] inputs)
         {
 
             string username;
@@ -207,8 +207,8 @@ namespace api.Controllers
 
             Predictor predictor = _predictorService.GetPredictor(username, id);
             
-            foreach(String i in inputs)
-                Debug.WriteLine(i); 
+            foreach(PredictorColumns i in inputs)
+                Debug.WriteLine(i.value.ToString()); 
             return NoContent();
         }
 
diff --git a/backend/api/api/Models/FileModel.cs b/backend/api/api/Models/FileModel.cs
index 1043309d..47b12110 100644
--- a/backend/api/api/Models/FileModel.cs
+++ b/backend/api/api/Models/FileModel.cs
@@ -8,6 +8,7 @@ namespace api.Models
         [BsonId]
         [BsonRepresentation(BsonType.ObjectId)]
         public string _id { get; set; }
+        public string type { get; set; }
         public string uploaderId { get; set; }
         public string path { get; set; }
         [BsonDateTimeOptions(Kind = DateTimeKind.Utc)]
diff --git a/backend/api/api/Models/PredictorColumns.cs b/backend/api/api/Models/PredictorColumns.cs
new file mode 100644
index 00000000..82f3e979
--- /dev/null
+++ b/backend/api/api/Models/PredictorColumns.cs
@@ -0,0 +1,8 @@
+namespace api.Models
+{
+    public class PredictorColumns
+    {
+        public String name { get; set; }
+        public String value { get; set; }
+	}
+}
diff --git a/backend/api/api/Services/PredictorService.cs b/backend/api/api/Services/PredictorService.cs
index 01bc8359..b15255ac 100644
--- a/backend/api/api/Services/PredictorService.cs
+++ b/backend/api/api/Services/PredictorService.cs
@@ -42,7 +42,7 @@ namespace api.Services
         }
         public Predictor GetPredictor(string username, string id)
         {
-            return _predictor.Find(predictor => predictor.username == username && predictor._id == id).FirstOrDefault();
+            return _predictor.Find(predictor => predictor._id == id && (predictor.username == username || predictor.isPublic == true)).FirstOrDefault();
 
         }
         //last private models
diff --git a/backend/api/api/Services/UserService.cs b/backend/api/api/Services/UserService.cs
index 7ec6f4b2..7fc4bdb1 100644
--- a/backend/api/api/Services/UserService.cs
+++ b/backend/api/api/Services/UserService.cs
@@ -50,7 +50,7 @@ namespace api.Services
             //username koji postoji u bazi
             using (var session =  _client.StartSession())
             {
-                
+                if(username!=user.Username)
                 if(_users.Find(u => u.Username == user.Username).FirstOrDefault()!=null)
                 {
                     return false;
diff --git a/backend/microservice/__pycache__/mlservice.cpython-310.pyc b/backend/microservice/__pycache__/mlservice.cpython-310.pyc
index c079459a..ac93f3db 100644
--- a/backend/microservice/__pycache__/mlservice.cpython-310.pyc
+++ b/backend/microservice/__pycache__/mlservice.cpython-310.pyc
diff --git a/backend/microservice/api.py b/backend/microservice/api.py
index 4768f34c..9a28b159 100644
--- a/backend/microservice/api.py
+++ b/backend/microservice/api.py
@@ -9,7 +9,7 @@ import csv
 import json
 import mlservice
 import h5py
-from mlservice2 import unositok
+from mlservice import unositok
 
 
 app = flask.Flask(__name__)
diff --git a/backend/microservice/api/controller.py b/backend/microservice/api/controller.py
index 059af317..1b17f727 100644
--- a/backend/microservice/api/controller.py
+++ b/backend/microservice/api/controller.py
@@ -1,7 +1,7 @@
 import flask
 from flask import request, jsonify
 import ml_socket
-import ml_service
+import newmlservice
 import tensorflow as tf
 import pandas as pd
 
@@ -25,7 +25,7 @@ def train():
     f = request.json["dataset"]
     dataset = pd.read_csv(f)
     #
-    result = ml_service.train(dataset, request.json["model"], train_callback)
+    result = newmlservice.train(dataset, request.json["model"], train_callback)
     print(result)
     return jsonify(result)
 
@@ -34,10 +34,22 @@ def predict():
     f = request.json['filepath']
     dataset = pd.read_csv(f)
     m = request.json['modelpath']
-    #model = tf.keras.models.load_model(m)
-    #
-    #model.predict?
+    model = tf.keras.models.load_model(m)
+    print("********************************model loaded*******************************")
+    newmlservice.manageH5(dataset,request.json['model'],model)
+    return "done"
+
+@app.route('/preprocess',methods=['POST'])
+def returnColumnsInfo():
+    f=request.json['filepathcolinfo']
+    dataset=pd.read_csv(f)
+    
+    result=newmlservice.returnColumnsInfo(dataset)
+
+    return jsonify(result)
+    
 
+    
 print("App loaded.")
 ml_socket.start()
 app.run()
\ No newline at end of file
diff --git a/backend/microservice/api/ml_service.py b/backend/microservice/api/ml_service.py
index ea562212..0aed3dc9 100644
--- a/backend/microservice/api/ml_service.py
+++ b/backend/microservice/api/ml_service.py
@@ -1,4 +1,8 @@
+from cmath import nan
+from enum import unique
+from itertools import count
 import pandas as pd
+from sklearn import datasets
 import tensorflow as tf
 import keras
 import numpy as np
@@ -11,12 +15,67 @@ from typing_extensions import Self
 from copyreg import constructor
 from flask import request, jsonify, render_template
 from sklearn.preprocessing import LabelEncoder
+from sklearn.preprocessing import OrdinalEncoder
+import category_encoders as ce
 from sklearn.preprocessing import StandardScaler
 from sklearn.model_selection import train_test_split
 from dataclasses import dataclass
+import statistics as s
+from sklearn.metrics import roc_auc_score
+
+def returnColumnsInfo(dataset):
+    dict=[]
+    datafront=dataset.copy()
+    svekolone=datafront.columns
+    kategorijskekolone=datafront.select_dtypes(include=['object']).columns
+    allNullCols=0
+    for kolona in svekolone:
+        if(kolona in kategorijskekolone):
+            uniquevalues=datafront[kolona].unique()
+            mean=0
+            median=0
+            min=0
+            max=0
+            nullCount=datafront[kolona].isnull().sum()
+            if(nullCount>0):
+                allNullCols=allNullCols+1
+            frontreturn={'columnName':kolona,
+                        'isNumber':False,
+                        'uniqueValues':uniquevalues.tolist(),
+                        'mean':float(mean),
+                        'median':float(median),
+                        'numNulls':float(nullCount),
+                        'min':min,
+                        'max':max
+            }
+            dict.append(frontreturn)
+        else:
+            mean=datafront[kolona].mean()
+            median=s.median(datafront[kolona])
+            nullCount=datafront[kolona].isnull().sum()
+            min=min(datafront[kolona])
+            max=max(datafront[kolona])
+            if(nullCount>0):
+                allNullCols=allNullCols+1
+            frontreturn={'columnName':kolona,
+                        'isNumber':1,
+                        'uniqueValues':[],
+                        'mean':float(mean),
+                        'median':float(median),
+                        'numNulls':float(nullCount),
+                        'min':min,
+                        'max':max
+            }
+            dict.append(frontreturn)
+        NullRows = datafront[datafront.isnull().any(axis=1)]
+        #print(NullRows)
+        #print(len(NullRows))
+        allNullRows=len(NullRows)
+
+    return {'columnInfo':dict,'allNullColl':allNullCols,'allNullRows':allNullRows}
 
 @dataclass
-class TrainingResult:
+class TrainingResultClassification:
     accuracy: float
     precision: float
     recall: float
@@ -26,18 +85,29 @@ class TrainingResult:
     tp: float
     specificity: float
     f1: float
+    logloss: float
+    fpr: float
+    tpr: float
+    metrics: dict
+'''
+@datasets
+class TrainingResultRegression:
     mse: float
     mae: float
     mape: float
     rmse: float
-    fpr: float
-    tpr: float
 
+@dataclass
+class TrainingResult:
+    metrics: dict
+'''
 def train(dataset, params, callback):
     problem_type = params["type"]
     data = pd.DataFrame()
     for col in params["inputColumns"]:
         data[col]=dataset[col]
+
+    print(data.head())
     output_column = params["columnToPredict"]
     data[output_column] = dataset[output_column]
     #
@@ -66,6 +136,7 @@ def train(dataset, params, callback):
             data.pop(col)
     #
     # Enkodiranje
+    # https://www.analyticsvidhya.com/blog/2020/08/types-of-categorical-data-encoding/
     #
     encoding=params["encoding"]
     if(encoding=='label'):
@@ -79,6 +150,34 @@ def train(dataset, params, callback):
             if(data[col].dtype==np.object_):
                 category_columns.append(col)
         data=pd.get_dummies(data, columns=category_columns, prefix=category_columns)
+    elif(encoding=='ordinal'):
+        encoder = OrdinalEncoder()
+        for col in data.columns:
+            if(data[col].dtype==np.object_):
+                data[col]=encoder.fit_transform(data[col])
+ 
+    elif(encoding=='hashing'):
+        category_columns=[]
+        for col in data.columns:
+            if(data[col].dtype==np.object_):
+                category_columns.append(col)
+        encoder=ce.HashingEncoder(cols=category_columns, n_components=len(category_columns))
+        encoder.fit_transform(data)
+    elif(encoding=='binary'):
+        category_columns=[]
+        for col in data.columns:
+            if(data[col].dtype==np.object_):
+                category_columns.append(col)
+        encoder=ce.BinaryEncoder(cols=category_columns, return_df=True)
+        encoder.fit_transform(data)
+       
+    elif(encoding=='baseN'):
+        category_columns=[]
+        for col in data.columns:
+            if(data[col].dtype==np.object_):
+                category_columns.append(col)
+        encoder=ce.BaseNEncoder(cols=category_columns, return_df=True, base=5)
+        encoder.fit_transform(data)
     #
     # Input - output
     #
@@ -88,71 +187,271 @@ def train(dataset, params, callback):
             x_columns.append(col)
     x = data[x_columns].values
     y = data[output_column].values
+    print(x_columns)
+    print(x)
     #
     # Podela na test i trening skupove
     #
     test=params["randomTestSetDistribution"]
     randomOrder = params["randomOrder"]
     if(randomOrder):
-        random=50
+        random=123
     else:
         random=0
-    x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=test, random_state=random)
+    x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.5,random_state=0)
     #
     # Skaliranje vrednosti
     #
+    '''
     scaler=StandardScaler()
     scaler.fit(x_train)
     x_test=scaler.transform(x_test)
     x_train=scaler.transform(x_train)
+    '''
+    
     #
     # Treniranje modela
     #
-    classifier=tf.keras.Sequential()
+    #  
     hidden_layer_neurons = params["hiddenLayerNeurons"]
-    for func in params["hiddenLayerActivationFunctions"]:
-        classifier.add(tf.keras.layers.Dense(units=hidden_layer_neurons,activation=func))
-    output_func = params["outputLayerActivationFunction"]
-    classifier.add(tf.keras.layers.Dense(units=1,activation=output_func))
-    optimizer = params["optimizer"]
-    metrics=params['metrics']
-    loss_func=params["lossFunction"]
-    classifier.compile(optimizer=optimizer, loss=loss_func,metrics=metrics)
-    batch_size = params["batchSize"]
-    epochs = params["epochs"]
-    history=classifier.fit(x_train, y_train, batch_size=batch_size, epochs=epochs, callbacks=callback(x_test, y_test), validation_split=0.2) # TODO params["validationSplit"]
+
+    if(problem_type=='multi-klasifikacioni'):
+        func=params['hiddenLayerActivationFunctions']
+        output_func = params["outputLayerActivationFunction"]
+        optimizer = params["optimizer"]
+        metrics=params['metrics']
+        loss_func=params["lossFunction"]
+        batch_size = params["batchSize"]
+        epochs = params["epochs"]
+        inputDim = len(data.columns) - 1
+        '''
+        classifier=tf.keras.Sequential()
+        
+        classifier.add(tf.keras.layers.Dense(units=len(data.columns),input_dim=inputDim))#input layer
+        
+        for f in func:#hidden layers
+            classifier.add(tf.keras.layers.Dense(hidden_layer_neurons,activation=f))
+        
+        numberofclasses=len(output_column.unique())
+        classifier.add(tf.keras.layers.Dense(numberofclasses,activation=output_func))#output layer
+        '''
+        model=tf.keras.Sequential()
+        model.add(tf.keras.layers.Dense(1,input_dim=x_train.shape[1]))#input layer
+        model.add(tf.keras.layers.Dense(1, activation='sigmoid'))
+        model.add(tf.keras.layers.Dense(len(output_column.unique())+1, activation='softmax'))
+        classifier.compile(optimizer=optimizer, loss=loss_func,metrics=metrics)
+        
+        history=classifier.fit(x_train, y_train, batch_size=batch_size, epochs=epochs, callbacks=callback(x_test, y_test))
+    else:    
+        classifier=tf.keras.Sequential()
+        
+        for func in params["hiddenLayerActivationFunctions"]:
+            classifier.add(tf.keras.layers.Dense(units=hidden_layer_neurons,activation=func))
+        output_func = params["outputLayerActivationFunction"]
+
+        if(problem_type!="regresioni"):
+            classifier.add(tf.keras.layers.Dense(units=1,activation=output_func))
+        else:
+            classifier.add(tf.keras.layers.Dense(units=1))
+
+        optimizer = params["optimizer"]
+        metrics=params['metrics']
+        loss_func=params["lossFunction"]
+        classifier.compile(optimizer=optimizer, loss=loss_func,metrics=metrics)
+        batch_size = params["batchSize"]
+        epochs = params["epochs"]
+        history=classifier.fit(x_train, y_train, batch_size=batch_size, epochs=epochs, callbacks=callback(x_test, y_test), validation_split=0.2) # TODO params["validationSplit"]
     #
     # Test
     #
     model_name = params['_id']
-    y_pred=classifier.predict(x_test)
+    #y_pred=classifier.predict(x_test)
     if(problem_type == "regresioni"):
-        classifier.evaluate(x_test, y_test)
-    elif(problem_type == "binarni-klasifikacioni"):
+        y_pred=classifier.predict(x_test)
+        print(classifier.evaluate(x_test, y_test))
+    elif(problem_type == "binarni-klasifikacioni"):    
+        y_pred=classifier.predict(x_test)
         y_pred=(y_pred>=0.5).astype('int')
+    elif(problem_type=='multi-klasifikacioni'):
+        y_pred=classifier.predict(x_test)
+        y_pred=np.argmax(y_pred,axis=1)
+
     y_pred=y_pred.flatten()
     result=pd.DataFrame({"Actual":y_test,"Predicted":y_pred})
     classifier.save("temp/"+model_name, save_format='h5')
+    # ROC multi-klasifikacioni
+    def roc_auc_score_multiclass(actual_class, pred_class, average = "macro"):
+    
+        #creating a set of all the unique classes using the actual class list
+        unique_class = set(actual_class)
+        roc_auc_dict = {}
+        for per_class in unique_class:
+            
+            #creating a list of all the classes except the current class 
+            other_class = [x for x in unique_class if x != per_class]
+
+            #marking the current class as 1 and all other classes as 0
+            new_actual_class = [0 if x in other_class else 1 for x in actual_class]
+            new_pred_class = [0 if x in other_class else 1 for x in pred_class]
+
+            #using the sklearn metrics method to calculate the roc_auc_score
+            roc_auc = roc_auc_score(new_actual_class, new_pred_class, average = average)
+            roc_auc_dict[per_class] = roc_auc
+
+        return roc_auc_dict
     #
     # Metrike
     #
     print("HELLO???")
     print(result)
     print("HELLO???")
-    accuracy = float(sm.accuracy_score(y_test,y_pred))
-    precision = float(sm.precision_score(y_test,y_pred))
-    recall = float(sm.recall_score(y_test,y_pred))
-    tn, fp, fn, tp = sm.confusion_matrix(y_test,y_pred).ravel()
-    specificity = float(tn / (tn+fp))
-    f1 = float(sm.f1_score(y_test,y_pred))
-    mse = float(sm.mean_squared_error(y_test,y_pred))
-    mae = float(sm.mean_absolute_error(y_test,y_pred))
-    mape = float(sm.mean_absolute_percentage_error(y_test,y_pred))
-    rmse = float(np.sqrt(sm.mean_squared_error(y_test,y_pred)))
-    fpr, tpr, _ = sm.roc_curve(y_test,y_pred)
+    if(problem_type=="binarni-klasifikacioni"):
+        accuracy = float(sm.accuracy_score(y_test,y_pred))
+        precision = float(sm.precision_score(y_test,y_pred))
+        recall = float(sm.recall_score(y_test,y_pred))
+        tn, fp, fn, tp = sm.confusion_matrix(y_test,y_pred).ravel()
+        specificity = float(tn / (tn+fp))
+        f1 = float(sm.f1_score(y_test,y_pred))
+        fpr, tpr, _ = sm.roc_curve(y_test,y_pred)
+        logloss = float(sm.log_loss(y_test, y_pred))
+        metrics= {"accuracy" : accuracy,
+            "precision" : precision,
+            "recall" : recall,
+            "specificity" : specificity,
+            "f1" : f1,
+            "tn" : float(tn),
+            "fp" : float(fp),
+            "fn" : float(fn),
+            "tp" : float(tp),
+            "fpr" : fpr.tolist(),
+            "tpr" : tpr.tolist(),
+            "logloss" : logloss
+            }
+    elif(problem_type=="regresioni"):
+        # https://www.analyticsvidhya.com/blog/2021/05/know-the-best-evaluation-metrics-for-your-regression-model/
+        mse = float(sm.mean_squared_error(y_test,y_pred))
+        mae = float(sm.mean_absolute_error(y_test,y_pred))
+        mape = float(sm.mean_absolute_percentage_error(y_test,y_pred))
+        rmse = float(np.sqrt(sm.mean_squared_error(y_test,y_pred)))
+        rmsle = float(np.sqrt(sm.mean_squared_error(y_test, y_pred)))
+        r2 = float(sm.r2_score(y_test, y_pred))
+        # n - num of observations
+        # k - num of independent variables
+        n = 40
+        k = 2
+        adj_r2 = float(1 - ((1-r2)*(n-1)/(n-k-1)))
+        metrics= {"mse" : mse,
+            "mae" : mae,
+            "mape" : mape,
+            "rmse" : rmse,
+            "rmsle" : rmsle,
+            "r2" : r2,
+            "adj_r2" : adj_r2
+            }
+    elif(problem_type=="multi-klasifikacioni"):
+        
+        cr=sm.classification_report(y_test, y_pred)
+        cm=sm.confusion_matrix(y_test,y_pred)
+        # https://www.kaggle.com/code/nkitgupta/evaluation-metrics-for-multi-class-classification/notebook
+        accuracy=metrics.accuracy_score(y_test, y_pred)
+        macro_averaged_precision=metrics.precision_score(y_test, y_pred, average = 'macro')
+        micro_averaged_precision=metrics.precision_score(y_test, y_pred, average = 'micro')
+        macro_averaged_recall=metrics.recall_score(y_test, y_pred, average = 'macro')
+        micro_averaged_recall=metrics.recall_score(y_test, y_pred, average = 'micro')
+        macro_averaged_f1=metrics.f1_score(y_test, y_pred, average = 'macro')
+        micro_averaged_f1=metrics.f1_score(y_test, y_pred, average = 'micro')
+        roc_auc_dict=roc_auc_score_multiclass(y_test, y_pred)
+
+
     # TODO upload trenirani model nazad na backend
-    return TrainingResult(accuracy, precision, recall, float(tn), float(fp), float(fn), float(tp), specificity, f1, mse, mae, mape, rmse, fpr.tolist(), tpr.tolist())
+    #return TrainingResult(metrics)
 
 
+def manageH5(datain,params,h5model):
+    dataset=datain.copy()
+    problem_type = params["type"]
+    data = pd.DataFrame()
+    for col in params["inputColumns"]:
+        data[col]=dataset[col]
+    output_column = params["columnToPredict"]
+    data[output_column] = dataset[output_column]
+    #
+    # Brisanje null kolona / redova / zamena
+    #nullreplace=[
+    #    {"column":"Embarked","value":"C","deleteRow":false,"deleteCol":true},
+    #    {"column": "Cabin","value":"C123","deleteRow":"0","deleteCol":"0"}]
+        
+    null_value_options = params["nullValues"]
+    null_values_replacers = params["nullValuesReplacers"]
+    
+    if(null_value_options=='replace'):
+        print("replace null") # TODO
+    elif(null_value_options=='delete_rows'):
+        data=data.dropna()
+    elif(null_value_options=='delete_columns'):
+        data=data.dropna()
+    #
+    #print(data.isnull().any())
+    #
+    # Brisanje kolona koje ne uticu na rezultat
+    #
+    num_rows=data.shape[0]
+    for col in data.columns:
+        if((data[col].nunique()==(num_rows)) and (data[col].dtype==np.object_)):
+            data.pop(col)
+    #
+    # Enkodiranje
+    # https://www.analyticsvidhya.com/blog/2020/08/types-of-categorical-data-encoding/
+    #
+    encoding=params["encoding"]
+    if(encoding=='label'):
+        encoder=LabelEncoder()
+        for col in data.columns:
+            if(data[col].dtype==np.object_):
+                data[col]=encoder.fit_transform(data[col])
+    elif(encoding=='onehot'):
+        category_columns=[]
+        for col in data.columns:
+            if(data[col].dtype==np.object_):
+                category_columns.append(col)
+        data=pd.get_dummies(data, columns=category_columns, prefix=category_columns)
+    elif(encoding=='ordinal'):
+        encoder = OrdinalEncoder()
+        for col in data.columns:
+            if(data[col].dtype==np.object_):
+                data[col]=encoder.fit_transform(data[col])
 
+    elif(encoding=='hashing'):
+        category_columns=[]
+        for col in data.columns:
+            if(data[col].dtype==np.object_):
+                category_columns.append(col)
+        encoder=ce.HashingEncoder(cols=category_columns, n_components=len(category_columns))
+        encoder.fit_transform(data)
+    elif(encoding=='binary'):
+        category_columns=[]
+        for col in data.columns:
+            if(data[col].dtype==np.object_):
+                category_columns.append(col)
+        encoder=ce.BinaryEncoder(cols=category_columns, return_df=True)
+        encoder.fit_transform(data)
     
+    elif(encoding=='baseN'):
+        category_columns=[]
+        for col in data.columns:
+            if(data[col].dtype==np.object_):
+                category_columns.append(col)
+        encoder=ce.BaseNEncoder(cols=category_columns, return_df=True, base=5)
+        encoder.fit_transform(data)
+    #
+    # Input - output
+    #
+    x_columns = []
+    for col in data.columns:
+        if(col!=output_column):
+            x_columns.append(col)
+    x = data[x_columns].values
+    y = data[output_column].values
+
+
+    y_pred=h5model.predict_classes(x)
\ No newline at end of file
diff --git a/backend/microservice/api/newmlservice.py b/backend/microservice/api/newmlservice.py
new file mode 100644
index 00000000..50af15f8
--- /dev/null
+++ b/backend/microservice/api/newmlservice.py
@@ -0,0 +1,424 @@
+from enum import unique
+from itertools import count
+import pandas as pd
+from sklearn import datasets, multiclass
+import tensorflow as tf
+import keras
+import numpy as np
+import csv
+import json
+import h5py
+import sklearn.metrics as sm
+from statistics import mode
+from typing_extensions import Self
+from copyreg import constructor
+from flask import request, jsonify, render_template
+from sklearn.preprocessing import LabelEncoder, MinMaxScaler
+from sklearn.preprocessing import OrdinalEncoder
+import category_encoders as ce
+from sklearn.preprocessing import StandardScaler
+from sklearn.model_selection import train_test_split
+from dataclasses import dataclass
+import statistics as s
+from sklearn.metrics import roc_auc_score
+from ann_visualizer.visualize import ann_viz;
+def returnColumnsInfo(dataset):
+    dict=[]
+    datafront=dataset.copy()
+    svekolone=datafront.columns
+    kategorijskekolone=datafront.select_dtypes(include=['object']).columns
+    allNullCols=0
+    for kolona in svekolone:
+        if(kolona in kategorijskekolone):
+            uniquevalues=datafront[kolona].unique()
+            mean=0
+            median=0
+            nullCount=datafront[kolona].isnull().sum()
+            if(nullCount>0):
+                allNullCols=allNullCols+1
+            frontreturn={'columnName':kolona,
+                        'isNumber':False,
+                        'uniqueValues':uniquevalues.tolist(),
+                        'median':float(mean),
+                        'mean':float(median),
+                        'numNulls':float(nullCount)
+            }
+            dict.append(frontreturn)
+        else:
+            mean=datafront[kolona].mean()
+            median=s.median(datafront[kolona])
+            nullCount=datafront[kolona].isnull().sum()
+            if(nullCount>0):
+                allNullCols=allNullCols+1
+            frontreturn={'columnName':kolona,
+                        'isNumber':1,
+                        'uniqueValues':[],
+                        'mean':float(mean),
+                        'median':float(median),
+                        'numNulls':float(nullCount)
+            }
+            dict.append(frontreturn)
+        NullRows = datafront[datafront.isnull().any(axis=1)]
+        #print(NullRows)
+        #print(len(NullRows))
+        allNullRows=len(NullRows)
+
+    return {'columnInfo':dict,'allNullColl':allNullCols,'allNullRows':allNullRows}
+
+@dataclass
+class TrainingResultClassification:
+    accuracy: float
+    precision: float
+    recall: float
+    tn: float
+    fp: float
+    fn: float
+    tp: float
+    specificity: float
+    f1: float
+    logloss: float
+    fpr: float
+    tpr: float
+    metrics: dict
+'''
+@datasets
+class TrainingResultRegression:
+    mse: float
+    mae: float
+    mape: float
+    rmse: float
+
+@dataclass
+class TrainingResult:
+    metrics: dict
+'''
+
+def train(dataset, params, callback):
+    problem_type = params["type"]
+    print(problem_type)
+    data = pd.DataFrame()
+    print(data)
+    for col in params["inputColumns"]:
+        print(col)
+        data[col]=dataset[col]
+    output_column = params["columnToPredict"]
+    data[output_column] = dataset[output_column]
+    print(data)
+
+    ###NULL
+    null_value_options = params["nullValues"]
+    null_values_replacers = params["nullValuesReplacers"]
+    
+    if(null_value_options=='replace'):
+        print("replace null") # TODO
+    elif(null_value_options=='delete_rows'):
+        data=data.dropna()
+    elif(null_value_options=='delete_columns'):
+        data=data.dropna()
+    print(data.shape)
+    
+    #
+    # Brisanje kolona koje ne uticu na rezultat
+    #
+    num_rows=data.shape[0]
+    for col in data.columns:
+        if((data[col].nunique()==(num_rows)) and (data[col].dtype==np.object_)):
+            data.pop(col)
+    #
+    ### Enkodiranje
+    encoding=params["encoding"]
+    if(encoding=='label'):
+        encoder=LabelEncoder()
+        for col in data.columns:
+            if(data[col].dtype==np.object_):
+                data[col]=encoder.fit_transform(data[col])
+    
+    
+    elif(encoding=='onehot'):
+        category_columns=[]
+        for col in data.columns:
+            if(data[col].dtype==np.object_):
+                category_columns.append(col)
+        data=pd.get_dummies(data, columns=category_columns, prefix=category_columns)
+
+    elif(encoding=='ordinal'):
+        encoder = OrdinalEncoder()
+        for col in data.columns:
+            if(data[col].dtype==np.object_):
+                data[col]=encoder.fit_transform(data[col])
+ 
+    elif(encoding=='hashing'):
+        category_columns=[]
+        for col in data.columns:
+            if(data[col].dtype==np.object_):
+                category_columns.append(col)
+        encoder=ce.HashingEncoder(cols=category_columns, n_components=len(category_columns))
+        encoder.fit_transform(data)
+    elif(encoding=='binary'):
+        category_columns=[]
+        for col in data.columns:
+            if(data[col].dtype==np.object_):
+                category_columns.append(col)
+        encoder=ce.BinaryEncoder(cols=category_columns, return_df=True)
+        encoder.fit_transform(data)
+       
+    elif(encoding=='baseN'):
+        category_columns=[]
+        for col in data.columns:
+            if(data[col].dtype==np.object_):
+                category_columns.append(col)
+        encoder=ce.BaseNEncoder(cols=category_columns, return_df=True, base=5)
+        encoder.fit_transform(data)
+    #
+    # Input - output
+    #
+    x_columns = []
+    for col in data.columns:
+        if(col!=output_column):
+            x_columns.append(col)
+    print(x_columns)
+    x = data[x_columns].values
+    y = data[output_column].values
+
+    #
+    # Podela na test i trening skupove
+    #
+    test=params["randomTestSetDistribution"]
+    randomOrder = params["randomOrder"]
+    if(randomOrder):
+        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)
+
+    #
+    # Treniranje modela
+    #
+    #  
+    if(problem_type=='multi-klasifikacioni'):
+        #print('multi')
+        classifier=tf.keras.Sequential()
+    
+        classifier.add(tf.keras.layers.Dense(units=params['hiddenLayerNeurons'], activation=params['hiddenLayerActivationFunctions'][0],input_dim=x_train.shape[1]))#prvi skriveni + definisanje prethodnog-ulaznog
+        for i in range(params['hiddenLayers']-1):#ako postoji vise od jednog skrivenog sloja
+            #print(i)
+            classifier.add(tf.keras.layers.Dense(units=params['hiddenLayerNeurons'], activation=params['hiddenLayerActivationFunctions'][i+1]))#i-ti skriveni sloj
+        classifier.add(tf.keras.layers.Dense(units=5, activation=params['outputLayerActivationFunction']))#izlazni sloj
+
+        classifier.compile(loss =params["lossFunction"] , optimizer = params['optimizer'] , metrics =params['metrics'])
+
+        history=classifier.fit(x_train, y_train, epochs = params['epochs'],batch_size=params['batchSize'])
+
+        y_pred=classifier.predict(x_test)
+        y_pred=np.argmax(y_pred,axis=1)
+        #print(y_pred.flatten())
+        #print(y_test)
+        scores = classifier.evaluate(x_test, y_test)
+        print("\n%s: %.2f%%" % (classifier.metrics_names[1], scores[1]*100))
+        classifier.save("temp/"+params['name'], save_format='h5')
+        #vizuelizacija u python-u
+        #from ann_visualizer.visualize import ann_viz;
+        #ann_viz(classifier, title="My neural network")
+
+    elif(problem_type=='binarni-klasifikacioni'):
+        #print('*************************************************************************binarni')
+        classifier=tf.keras.Sequential()
+    
+        classifier.add(tf.keras.layers.Dense(units=params['hiddenLayerNeurons'], activation=params['hiddenLayerActivationFunctions'][0],input_dim=x_train.shape[1]))#prvi skriveni + definisanje prethodnog-ulaznog
+        for i in range(params['hiddenLayers']-1):#ako postoji vise od jednog skrivenog sloja
+            #print(i)
+            classifier.add(tf.keras.layers.Dense(units=params['hiddenLayerNeurons'], activation=params['hiddenLayerActivationFunctions'][i+1]))#i-ti skriveni sloj
+        classifier.add(tf.keras.layers.Dense(units=1, activation=params['outputLayerActivationFunction']))#izlazni sloj
+
+        classifier.compile(loss =params["lossFunction"] , optimizer = params['optimizer'] , metrics =params['metrics'])
+
+        history=classifier.fit(x_train, y_train, epochs = params['epochs'],batch_size=params['batchSize'])
+
+        y_pred=classifier.predict(x_test)
+        y_pred=(y_pred>=0.5).astype('int')
+
+        print(y_pred.flatten())
+        print(y_test)
+        
+        scores = classifier.evaluate(x_test, y_test)
+        print("\n%s: %.2f%%" % (classifier.metrics_names[1], scores[1]*100))
+        #ann_viz(classifier, title="My neural network")
+        
+        classifier.save("temp/"+params['name'], save_format='h5')
+
+    elif(problem_type=='regresioni'):
+        classifier=tf.keras.Sequential()
+    
+        classifier.add(tf.keras.layers.Dense(units=params['hiddenLayerNeurons'], activation=params['hiddenLayerActivationFunctions'][0],input_dim=x_train.shape[1]))#prvi skriveni + definisanje prethodnog-ulaznog
+        for i in range(params['hiddenLayers']-1):#ako postoji vise od jednog skrivenog sloja
+            #print(i)
+            classifier.add(tf.keras.layers.Dense(units=params['hiddenLayerNeurons'], activation=params['hiddenLayerActivationFunctions'][i+1]))#i-ti skriveni sloj
+        classifier.add(tf.keras.layers.Dense(units=1))
+
+        classifier.compile(loss =params["lossFunction"] , optimizer = params['optimizer'] , metrics =params['metrics'])
+
+        history=classifier.fit(x_train, y_train, epochs = params['epochs'],batch_size=params['batchSize'])
+        y_pred=classifier.predict(x_test)
+        print(classifier.evaluate(x_test, y_test))
+
+    def roc_auc_score_multiclass(actual_class, pred_class, average = "macro"):
+    
+        #creating a set of all the unique classes using the actual class list
+        unique_class = set(actual_class)
+        roc_auc_dict = {}
+        for per_class in unique_class:
+            
+            #creating a list of all the classes except the current class 
+            other_class = [x for x in unique_class if x != per_class]
+
+            #marking the current class as 1 and all other classes as 0
+            new_actual_class = [0 if x in other_class else 1 for x in actual_class]
+            new_pred_class = [0 if x in other_class else 1 for x in pred_class]
+
+            #using the sklearn metrics method to calculate the roc_auc_score
+            roc_auc = roc_auc_score(new_actual_class, new_pred_class, average = average)
+            roc_auc_dict[per_class] = roc_auc
+
+        return roc_auc_dict
+    #
+    # Metrike
+    #
+    
+    if(problem_type=="binarni-klasifikacioni"):
+        accuracy = float(sm.accuracy_score(y_test,y_pred))
+        precision = float(sm.precision_score(y_test,y_pred))
+        recall = float(sm.recall_score(y_test,y_pred))
+        tn, fp, fn, tp = sm.confusion_matrix(y_test,y_pred).ravel()
+        specificity = float(tn / (tn+fp))
+        f1 = float(sm.f1_score(y_test,y_pred))
+        fpr, tpr, _ = sm.roc_curve(y_test,y_pred)
+        logloss = float(sm.log_loss(y_test, y_pred))
+        metrics= {"accuracy" : accuracy,
+            "precision" : precision,
+            "recall" : recall,
+            "specificity" : specificity,
+            "f1" : f1,
+            "tn" : float(tn),
+            "fp" : float(fp),
+            "fn" : float(fn),
+            "tp" : float(tp),
+            "fpr" : fpr.tolist(),
+            "tpr" : tpr.tolist(),
+            "logloss" : logloss
+            }
+    elif(problem_type=="regresioni"):
+        # https://www.analyticsvidhya.com/blog/2021/05/know-the-best-evaluation-metrics-for-your-regression-model/
+        mse = float(sm.mean_squared_error(y_test,y_pred))
+        mae = float(sm.mean_absolute_error(y_test,y_pred))
+        mape = float(sm.mean_absolute_percentage_error(y_test,y_pred))
+        rmse = float(np.sqrt(sm.mean_squared_error(y_test,y_pred)))
+        rmsle = float(np.sqrt(sm.mean_squared_error(y_test, y_pred)))
+        r2 = float(sm.r2_score(y_test, y_pred))
+        # n - num of observations
+        # k - num of independent variables
+        n = 40
+        k = 2
+        adj_r2 = float(1 - ((1-r2)*(n-1)/(n-k-1)))
+        metrics= {"mse" : mse,
+            "mae" : mae,
+            "mape" : mape,
+            "rmse" : rmse,
+            "rmsle" : rmsle,
+            "r2" : r2,
+            "adj_r2" : adj_r2
+            }
+    '''
+    elif(problem_type=="multi-klasifikacioni"):
+        
+        cr=sm.classification_report(y_test, y_pred)
+        cm=sm.confusion_matrix(y_test,y_pred)
+        # https://www.kaggle.com/code/nkitgupta/evaluation-metrics-for-multi-class-classification/notebook
+        accuracy=metrics.accuracy_score(y_test, y_pred)
+        macro_averaged_precision=metrics.precision_score(y_test, y_pred, average = 'macro')
+        micro_averaged_precision=metrics.precision_score(y_test, y_pred, average = 'micro')
+        macro_averaged_recall=metrics.recall_score(y_test, y_pred, average = 'macro')
+        micro_averaged_recall=metrics.recall_score(y_test, y_pred, average = 'micro')
+        macro_averaged_f1=metrics.f1_score(y_test, y_pred, average = 'macro')
+        micro_averaged_f1=metrics.f1_score(y_test, y_pred, average = 'micro')
+        roc_auc_dict=roc_auc_score_multiclass(y_test, y_pred)
+    '''
+
+def manageH5(dataset,params,h5model):
+    problem_type = params["type"]
+    print(problem_type)
+    data = pd.DataFrame()
+    #print(data)
+    for col in params["inputColumns"]:
+        print(col)
+        data[col]=dataset[col]
+    output_column = params["columnToPredict"]
+    data[output_column] = dataset[output_column]
+    #print(data)
+
+    ###NULL
+    null_value_options = params["nullValues"]
+    null_values_replacers = params["nullValuesReplacers"]
+    
+    if(null_value_options=='replace'):
+        print("replace null") # TODO
+    elif(null_value_options=='delete_rows'):
+        data=data.dropna()
+    elif(null_value_options=='delete_columns'):
+        data=data.dropna()
+    print(data.shape)
+    
+    #
+    # Brisanje kolona koje ne uticu na rezultat
+    #
+    num_rows=data.shape[0]
+    for col in data.columns:
+        if((data[col].nunique()==(num_rows)) and (data[col].dtype==np.object_)):
+            data.pop(col)
+    #
+    ### Enkodiranje
+    encoding=params["encoding"]
+    if(encoding=='label'):
+        encoder=LabelEncoder()
+        for col in data.columns:
+            if(data[col].dtype==np.object_):
+                data[col]=encoder.fit_transform(data[col])
+    
+    
+    elif(encoding=='onehot'):
+        category_columns=[]
+        for col in data.columns:
+            if(data[col].dtype==np.object_):
+                category_columns.append(col)
+        data=pd.get_dummies(data, columns=category_columns, prefix=category_columns)
+    #print(data)
+
+    #
+    # Input - output
+    #
+    x_columns = []
+    for col in data.columns:
+        if(col!=output_column):
+            x_columns.append(col)
+    #print(x_columns)
+    x2 = data[x_columns]
+    print(x2)
+    print(x2.values)
+    x2 = data[x_columns].values
+    print(x2)
+    y2 = data[output_column].values
+    h5model.summary()
+    ann_viz(h5model, title="My neural network")
+
+    h5model.compile(loss=params['lossFunction'], optimizer=params['optimizer'], metrics=params['metrics'])
+
+    history=h5model.fit(x2, y2, epochs = params['epochs'],batch_size=params['batchSize'])
+    
+    y_pred2=h5model.predict(x2)
+     
+    y_pred2=np.argmax(y_pred2,axis=1)
+    #y_pred=h5model.predict_classes(x)
+    score = h5model.evaluate(x2,y_pred2, verbose=0)
+    print("%s: %.2f%%" % (h5model.metrics_names[1], score[1]*100))
+    print(y_pred2)
+    print( 'done')
\ No newline at end of file
diff --git a/backend/microservice/mlservice.py b/backend/microservice/mlservice.py
index b2eafe9a..8f56fc3f 100644
--- a/backend/microservice/mlservice.py
+++ b/backend/microservice/mlservice.py
@@ -54,6 +54,38 @@ def obuka(dataunos,params,modelunos,dataunosdrugog):
         data[zeljenekolone[i]]=dataunos[zeljenekolone[i]]
     #print(data.head(10))
 
+    ### 0.1) Povratne vrednosti statistike za front (za popunjavanje null vrednosti izabranih kolona) PART4
+    datafront=data.copy()
+    svekolone=datafront.columns
+    kategorijskekolone=datafront.select_dtypes(include=['object']).columns
+    #print(kategorijskekolone )
+    #kategorijskekolone=datacategorical.columns
+    #print(svekolone)
+    for i in range(len(svekolone)):
+        nazivkolone=svekolone[i]
+        if(nazivkolone in kategorijskekolone):
+            svekategorije=datafront[nazivkolone].unique()
+            medijana=None
+            srednjavrednost=None
+            frontreturn={'colName':nazivkolone,
+                        'colType':'categorical',
+                        'categoricalValues':svekategorije,
+                        'mean':medijana,
+                        'average':srednjavrednost
+            }
+        else:
+            svekategorije=None
+            medijana=datafront[nazivkolone].mean()
+            srednjavrednost=sum(datafront[nazivkolone])/len(datafront[nazivkolone])
+            frontreturn={'colName':nazivkolone,
+                        'colType':'noncategorical',
+                        'categoricalValues':svekategorije,
+                        'mean':medijana,
+                        'average':srednjavrednost
+            }
+
+        print(frontreturn)
+
    
     #predvidetikol=input("UNETI NAZIV KOLONE ČIJU VREDNOST TREBA PREDVIDETI ")
     ###sta se cuva od promenjivih broj kolone ili naziv kolone???
diff --git a/frontend/angular.json b/frontend/angular.json
index b1aaac3f..f9825281 100644
--- a/frontend/angular.json
+++ b/frontend/angular.json
@@ -33,7 +33,10 @@
               "./node_modules/@angular/material/prebuilt-themes/indigo-pink.css"
             ],
             "scripts": [
-              "node_modules/bootstrap/dist/js/bootstrap.bundle.min.js"
+              "node_modules/bootstrap/dist/js/bootstrap.bundle.min.js",
+              "node_modules/jquery/dist/jquery.min.js",
+              "node_modules/popper.js/dist/popper.min.js",
+              "node_modules/bootstrap/dist/js/bootstrap.min.js"
             ]
           },
           "configurations": {
@@ -41,13 +44,13 @@
               "budgets": [
                 {
                   "type": "initial",
-                  "maximumWarning": "500kb",
-                  "maximumError": "1mb"
+                  "maximumWarning": "2mb",
+                  "maximumError": "4mb"
                 },
                 {
                   "type": "anyComponentStyle",
-                  "maximumWarning": "2kb",
-                  "maximumError": "4kb"
+                  "maximumWarning": "10kb",
+                  "maximumError": "15kb"
                 }
               ],
               "fileReplacements": [
diff --git a/frontend/package-lock.json b/frontend/package-lock.json
index 962905b7..c79f4ea9 100644
--- a/frontend/package-lock.json
+++ b/frontend/package-lock.json
@@ -27,6 +27,7 @@
         "chart.js": "^3.7.1",
         "csv-parser": "^3.0.0",
         "d3-graphviz": "^2.6.1",
+        "jquery": "^3.6.0",
         "mdb-angular-ui-kit": "^2.0.0",
         "ng-multiselect-dropdown": "^0.3.8",
         "ng-uikit-pro-standard": "^1.0.0",
@@ -34,6 +35,7 @@
         "ng2-search-filter": "^0.5.1",
         "ngx-cookie-service": "^13.1.2",
         "ngx-csv-parser": "^0.0.7",
+        "popper.js": "^1.16.1",
         "rxjs": "~7.5.0",
         "tslib": "^2.3.1",
         "websocket-ts": "^1.1.1",
@@ -7082,6 +7084,11 @@
         "url": "https://github.com/chalk/supports-color?sponsor=1"
       }
     },
+    "node_modules/jquery": {
+      "version": "3.6.0",
+      "resolved": "https://registry.npmjs.org/jquery/-/jquery-3.6.0.tgz",
+      "integrity": "sha512-JVzAR/AjBvVt2BmYhxRCSYysDsPcssdmTFnzyLEts9qNwmjmu4JTAMYubEfwVOSwpQ1I1sKKFcxhZCI2buerfw=="
+    },
     "node_modules/js-tokens": {
       "version": "4.0.0",
       "resolved": "https://registry.npmjs.org/js-tokens/-/js-tokens-4.0.0.tgz",
@@ -9018,6 +9025,16 @@
         "node": ">=8"
       }
     },
+    "node_modules/popper.js": {
+      "version": "1.16.1",
+      "resolved": "https://registry.npmjs.org/popper.js/-/popper.js-1.16.1.tgz",
+      "integrity": "sha512-Wb4p1J4zyFTbM+u6WuO4XstYx4Ky9Cewe4DWrel7B0w6VVICvPwdOpotjzcf6eD8TsckVnIMNONQyPIUFOUbCQ==",
+      "deprecated": "You can find the new Popper v2 at @popperjs/core, this package is dedicated to the legacy v1",
+      "funding": {
+        "type": "opencollective",
+        "url": "https://opencollective.com/popperjs"
+      }
+    },
     "node_modules/portfinder": {
       "version": "1.0.28",
       "resolved": "https://registry.npmjs.org/portfinder/-/portfinder-1.0.28.tgz",
@@ -16882,6 +16899,11 @@
         }
       }
     },
+    "jquery": {
+      "version": "3.6.0",
+      "resolved": "https://registry.npmjs.org/jquery/-/jquery-3.6.0.tgz",
+      "integrity": "sha512-JVzAR/AjBvVt2BmYhxRCSYysDsPcssdmTFnzyLEts9qNwmjmu4JTAMYubEfwVOSwpQ1I1sKKFcxhZCI2buerfw=="
+    },
     "js-tokens": {
       "version": "4.0.0",
       "resolved": "https://registry.npmjs.org/js-tokens/-/js-tokens-4.0.0.tgz",
@@ -18345,6 +18367,11 @@
         "find-up": "^4.0.0"
       }
     },
+    "popper.js": {
+      "version": "1.16.1",
+      "resolved": "https://registry.npmjs.org/popper.js/-/popper.js-1.16.1.tgz",
+      "integrity": "sha512-Wb4p1J4zyFTbM+u6WuO4XstYx4Ky9Cewe4DWrel7B0w6VVICvPwdOpotjzcf6eD8TsckVnIMNONQyPIUFOUbCQ=="
+    },
     "portfinder": {
       "version": "1.0.28",
       "resolved": "https://registry.npmjs.org/portfinder/-/portfinder-1.0.28.tgz",
diff --git a/frontend/package.json b/frontend/package.json
index 9df48b0c..ad68e176 100644
--- a/frontend/package.json
+++ b/frontend/package.json
@@ -30,6 +30,7 @@
     "chart.js": "^3.7.1",
     "csv-parser": "^3.0.0",
     "d3-graphviz": "^2.6.1",
+    "jquery": "^3.6.0",
     "mdb-angular-ui-kit": "^2.0.0",
     "ng-multiselect-dropdown": "^0.3.8",
     "ng-uikit-pro-standard": "^1.0.0",
@@ -37,6 +38,7 @@
     "ng2-search-filter": "^0.5.1",
     "ngx-cookie-service": "^13.1.2",
     "ngx-csv-parser": "^0.0.7",
+    "popper.js": "^1.16.1",
     "rxjs": "~7.5.0",
     "tslib": "^2.3.1",
     "websocket-ts": "^1.1.1",
diff --git a/frontend/src/app/Shared.ts b/frontend/src/app/Shared.ts
index 31afb1a6..86e26687 100644
--- a/frontend/src/app/Shared.ts
+++ b/frontend/src/app/Shared.ts
@@ -1,9 +1,32 @@
+import { ElementRef } from "@angular/core";
+import { NgbModal } from "@ng-bootstrap/ng-bootstrap";
+import { MatDialog, MatDialogRef, MAT_DIALOG_DATA } from '@angular/material/dialog';
+import { AlertDialogComponent } from './_modals/alert-dialog/alert-dialog.component';
+
 class Shared {
     constructor(
         public loggedIn: boolean,
         public username: string = '',
-        public photoId: string = '1'
+        public photoId: string = '1',
+        public dialog?: MatDialog
+        //public alertDialog?: ElementRef
     ) { }
+
+
+    openDialog(title: string, message: string): void {
+        console.log("USAO U OPEN DIALOG 1");
+
+        if (this.dialog) {
+            console.log("USAO U OPEN DIALOG 2");
+            const dialogRef = this.dialog.open(AlertDialogComponent, {
+                //width: '250px',
+                data: { title: title, message: message }
+            });
+            dialogRef.afterClosed().subscribe(res => {
+                //nesto
+            });
+        }
+    }
 }
 
 export default new Shared(false);
\ No newline at end of file
diff --git a/frontend/src/app/_data/Model.ts b/frontend/src/app/_data/Model.ts
index 1ad4fc6d..85b6db2b 100644
--- a/frontend/src/app/_data/Model.ts
+++ b/frontend/src/app/_data/Model.ts
@@ -45,22 +45,22 @@ export enum ProblemType {
 export enum Encoding {
     Label = 'label',
     OneHot = 'onehot',
+    Ordinal = 'ordinal',
+    Hashing = 'hashing',
+    Binary = 'binary',
+    BaseN = 'baseN'
     /*
     BackwardDifference = 'backward difference',
-    BaseN = 'baseN',
-    Binary = 'binary',
     CatBoost = 'cat boost',
     Count = 'count',
     GLMM = 'glmm',
-    Hashing = 'hashing',
+    Target = 'target',
     Helmert = 'helmert',
     JamesStein = 'james stein',
     LeaveOneOut = 'leave one out',
     MEstimate = 'MEstimate',
-    Ordinal = 'ordinal',
     Sum = 'sum',
     Polynomial = 'polynomial',
-    Target = 'target',
     WOE = 'woe',
     Quantile = 'quantile'
     */
@@ -104,7 +104,7 @@ export enum LossFunction {
     BinaryCrossEntropy = 'binary_crossentropy',
     SquaredHingeLoss = 'squared_hinge_loss',
     HingeLoss = 'hinge_loss',
-    // multi-class classiication loss functions
+    // multi-class classification loss functions
     CategoricalCrossEntropy = 'categorical_crossentropy',
     SparseCategoricalCrossEntropy = 'sparse_categorical_crosentropy',
     KLDivergence = 'kullback_leibler_divergence',
diff --git a/frontend/src/app/_elements/add-new-dataset/add-new-dataset.component.css b/frontend/src/app/_elements/add-new-dataset/add-new-dataset.component.css
new file mode 100644
index 00000000..e69de29b
--- /dev/null
+++ b/frontend/src/app/_elements/add-new-dataset/add-new-dataset.component.css
diff --git a/frontend/src/app/_elements/add-new-dataset/add-new-dataset.component.html b/frontend/src/app/_elements/add-new-dataset/add-new-dataset.component.html
new file mode 100644
index 00000000..dfeb4f62
--- /dev/null
+++ b/frontend/src/app/_elements/add-new-dataset/add-new-dataset.component.html
@@ -0,0 +1,41 @@
+<div class="row mb-4">
+    <div class="col-2">
+    </div>
+    <div class="col-3">
+      <label for="name" class="col-form-label">Naziv dataseta:</label>
+      <input type="text" class="form-control mb-1" name="name" placeholder="Naziv..." [(ngModel)]="dataset.name">
+
+      <label for="desc" class="col-sm-2 col-form-label">Opis:</label>
+      <div>
+        <textarea class="form-control" name="desc" rows="3" [(ngModel)]="dataset.description"></textarea>
+      </div>
+
+      <label for="checkboxIsPublic" class="form-check-label mt-3 mb-1">Želite li da dataset bude javan?
+        <input class="mx-3 form-check-input" type="checkbox" [(ngModel)]="dataset.isPublic" (change)="checkAccessible()" type="checkbox"
+          value="" id="checkboxIsPublic">
+      </label>    
+      
+      <label for="checkboxAccessibleByLink" class="form-check-label">Želite li da bude deljiv linkom? &nbsp;
+        <input class="mx-3 form-check-input" type="checkbox" [(ngModel)]="dataset.accessibleByLink" type="checkbox"
+          value="" id="checkboxAccessibleByLink">
+      </label>
+    </div>
+    <div class="col-1">
+    </div>
+    <div class="col-4 mt-4">
+
+      <input list=delimiterOptions placeholder="Izaberite ili ukucajte delimiter za .csv fajl" class="form-control mt-2"
+        [(ngModel)]="dataset.delimiter" (input)="update()">
+      <datalist id=delimiterOptions>
+        <option *ngFor="let option of delimiterOptions">{{option}}</option>
+      </datalist>
+
+      <label for="type" class="form-check-label my-5">Da li .csv ima header?
+        <input class="mx-3 form-check-input" type="checkbox" (input)="update()" [(ngModel)]="dataset.hasHeader" type="checkbox"
+          value="" id="checkboxHeader" checked>
+      </label>
+      <br>
+      <input id="fileInput" class="form-control" type="file" class="upload" (change)="changeListener($event)"
+        accept=".csv">
+    </div>
+  </div>
\ No newline at end of file
diff --git a/frontend/src/app/_elements/add-new-dataset/add-new-dataset.component.spec.ts b/frontend/src/app/_elements/add-new-dataset/add-new-dataset.component.spec.ts
new file mode 100644
index 00000000..a9ea25b4
--- /dev/null
+++ b/frontend/src/app/_elements/add-new-dataset/add-new-dataset.component.spec.ts
@@ -0,0 +1,25 @@
+import { ComponentFixture, TestBed } from '@angular/core/testing';
+
+import { AddNewDatasetComponent } from './add-new-dataset.component';
+
+describe('AddNewDatasetComponent', () => {
+  let component: AddNewDatasetComponent;
+  let fixture: ComponentFixture<AddNewDatasetComponent>;
+
+  beforeEach(async () => {
+    await TestBed.configureTestingModule({
+      declarations: [ AddNewDatasetComponent ]
+    })
+    .compileComponents();
+  });
+
+  beforeEach(() => {
+    fixture = TestBed.createComponent(AddNewDatasetComponent);
+    component = fixture.componentInstance;
+    fixture.detectChanges();
+  });
+
+  it('should create', () => {
+    expect(component).toBeTruthy();
+  });
+});
diff --git a/frontend/src/app/_elements/add-new-dataset/add-new-dataset.component.ts b/frontend/src/app/_elements/add-new-dataset/add-new-dataset.component.ts
new file mode 100644
index 00000000..fceb53cf
--- /dev/null
+++ b/frontend/src/app/_elements/add-new-dataset/add-new-dataset.component.ts
@@ -0,0 +1,78 @@
+import { Component, EventEmitter, Output, ViewChild } from '@angular/core';
+import { NgxCsvParser, NgxCSVParserError } from 'ngx-csv-parser';
+import Dataset from 'src/app/_data/Dataset';
+
+@Component({
+  selector: 'app-add-new-dataset',
+  templateUrl: './add-new-dataset.component.html',
+  styleUrls: ['./add-new-dataset.component.css']
+})
+export class AddNewDatasetComponent {
+
+  @Output() loaded = new EventEmitter<string>();
+
+  delimiterOptions: Array<string> = [",", ";", "\t", "razmak", "|"]; //podrazumevano ","
+
+  //hasHeader: boolean = true;
+  hasInput: boolean = false;
+
+  csvRecords: any[] = [];
+  files: File[] = [];
+  rowsNumber: number = 0;
+  colsNumber: number = 0;
+
+  dataset: Dataset; //dodaj ! potencijalno
+
+  constructor(private ngxCsvParser: NgxCsvParser) {
+    this.dataset = new Dataset();
+  }
+
+  //@ViewChild('fileImportInput', { static: false }) fileImportInput: any; cemu je ovo sluzilo?
+
+  changeListener($event: any): void {
+    this.files = $event.srcElement.files;
+    if (this.files.length == 0 || this.files[0] == null) {
+      //console.log("NEMA FAJLA");
+      //this.loaded.emit("not loaded");
+      this.hasInput = false;
+      return;
+    }
+    else
+      this.hasInput = true;
+
+    this.update();
+  }
+
+  update() {
+
+    if (this.files.length < 1)
+      return;
+
+    this.ngxCsvParser.parse(this.files[0], { header: false, delimiter: (this.dataset.delimiter == "razmak") ? " " : (this.dataset.delimiter == "") ? "," : this.dataset.delimiter })
+      .pipe().subscribe((result) => {
+
+        console.log('Result', result);
+        if (result.constructor === Array) {
+          this.csvRecords = result;
+          if (this.dataset.hasHeader)
+            this.rowsNumber = this.csvRecords.length - 1;
+          else
+            this.rowsNumber = this.csvRecords.length;
+          this.colsNumber = this.csvRecords[0].length;
+
+          if (this.dataset.hasHeader) //kasnije dodati opciju kada nema header da korisnik rucno unosi header-e
+            this.dataset.header = this.csvRecords[0];
+
+          this.loaded.emit("loaded");
+        }
+      }, (error: NgxCSVParserError) => {
+        console.log('Error', error);
+      });
+  }
+
+  checkAccessible() {
+    if (this.dataset.isPublic)
+      this.dataset.accessibleByLink = true;
+  }
+
+}
diff --git a/frontend/src/app/_elements/annvisual/annvisual.component.html b/frontend/src/app/_elements/annvisual/annvisual.component.html
index f23022de..09251398 100644
--- a/frontend/src/app/_elements/annvisual/annvisual.component.html
+++ b/frontend/src/app/_elements/annvisual/annvisual.component.html
@@ -1,5 +1,5 @@
-<div style="text-align: center; width: 100%;" >
+<div style="text-align: center; " >
     <button (click)="d3()" mat-raised-button color="primary">Prikaz veštačke neuronske mreže</button>
-    <div id="graph" align-items-center ></div>
+    <div id="graph" align-items-center style="width: 12rem;"></div>
   </div>
 
diff --git a/frontend/src/app/_elements/dataset-load/dataset-load.component.css b/frontend/src/app/_elements/dataset-load/dataset-load.component.css
index 05819702..54e0738e 100644
--- a/frontend/src/app/_elements/dataset-load/dataset-load.component.css
+++ b/frontend/src/app/_elements/dataset-load/dataset-load.component.css
@@ -1,6 +1,13 @@
-#divInputs {
-    margin-left: 20px;
+.btnType1 {
+    background-color: #003459;
+    color: white;
 }
-#divOutputs {
-    margin-left: 20px;
+.btnType2 {
+    background-color: white;
+    color: #003459;
+    border-color: #003459;
+}
+.selectedDatasetClass {
+    /*border-color: 2px solid #003459;*/
+    background-color: lightblue;
 }
\ No newline at end of file
diff --git a/frontend/src/app/_elements/dataset-load/dataset-load.component.html b/frontend/src/app/_elements/dataset-load/dataset-load.component.html
index 76e46092..674e5990 100644
--- a/frontend/src/app/_elements/dataset-load/dataset-load.component.html
+++ b/frontend/src/app/_elements/dataset-load/dataset-load.component.html
@@ -1,44 +1,42 @@
 <div>
 
-  <div class="row mb-4">
-    <div class="col-2">
-    </div>
-    <div class="col-3">
-      <label for="name" class="col-form-label">Naziv dataseta:</label>
-      <input type="text" class="form-control mb-1" name="name" placeholder="Naziv..." [(ngModel)]="dataset.name">
-
-      <label for="desc" class="col-sm-2 col-form-label">Opis:</label>
-      <div>
-        <textarea class="form-control" name="desc" rows="3" [(ngModel)]="dataset.description"></textarea>
-      </div>
+  <!--Sklonjeno ucitavanje novog dataseta i sve opcije u vezi sa tim, premesteno u add-new-dataset-->
 
-      <label for="checkboxIsPublic" class="form-check-label mt-3 mb-1">Želite li da dataset bude javan?
-        <input class="mx-3 form-check-input" type="checkbox" [(ngModel)]="dataset.isPublic" (change)="checkAccessible()" type="checkbox"
-          value="" id="checkboxIsPublic">
-      </label>    
-      
-      <label for="checkboxAccessibleByLink" class="form-check-label">Želite li da bude deljiv linkom? &nbsp;
-        <input class="mx-3 form-check-input" type="checkbox" [(ngModel)]="dataset.accessibleByLink" type="checkbox"
-          value="" id="checkboxAccessibleByLink">
-      </label>
-    </div>
+  <div class="col-12 d-flex my-5">
+    <h2 class="">Izvor podataka:</h2>
     <div class="col-1">
     </div>
-    <div class="col-4 mt-4">
-
-      <input list=delimiterOptions placeholder="Izaberite ili ukucajte delimiter za .csv fajl" class="form-control mt-2"
-        [(ngModel)]="dataset.delimiter" (input)="update()">
-      <datalist id=delimiterOptions>
-        <option *ngFor="let option of delimiterOptions">{{option}}</option>
-      </datalist>
+    <button type="button" id="btnMyDataset" class="btn" (click)="viewMyDatasetsForm()"
+        [ngClass]="{'btnType1': showMyDatasets, 'btnType2': !showMyDatasets}">
+        Izaberite dataset iz kolekcije
+    </button>
+    <h3 class="mt-3 mx-3">ili</h3>
+    <button type="button" id="btnNewDataset" class="btn" (click)="viewNewDatasetForm()"
+        [ngClass]="{'btnType1': !showMyDatasets, 'btnType2': showMyDatasets}">
+        Dodajte novi dataset
+    </button>
+  </div>
+  <div class="px-5 my-2">
+      <input *ngIf="showMyDatasets" type="text" class="form-control" placeholder="Pretraga"
+          [(ngModel)]="term">
+  </div>
+  <div class="px-5">
+      <div *ngIf="showMyDatasets" class="overflow-auto" style="max-height: 500px;">
+          <ul class="list-group">
+              <li class="list-group-item p-3" *ngFor="let dataset of myDatasets|filter:term"
+                  [ngClass]="{'selectedDatasetClass': this.selectedDataset == dataset}">
+                  <app-item-dataset name="usersDataset" [dataset]="dataset"
+                      (click)="selectThisDataset(dataset);"></app-item-dataset>
+              </li>
+          </ul>
+      </div>
+  </div>
 
-      <label for="type" class="form-check-label my-5">Da li .csv ima header?
-        <input class="mx-3 form-check-input" type="checkbox" (input)="update()" [(ngModel)]="dataset.hasHeader" type="checkbox"
-          value="" id="checkboxHeader" checked>
-      </label>
-      <br>
-      <input id="fileInput" class="form-control" type="file" class="upload" (change)="changeListener($event)"
-        accept=".csv">
-    </div>
+  <app-add-new-dataset [style]="(showMyDatasets)?'display:none;visibility:hidden;':''" id="dataset"
+      (loaded)="datasetLoaded = true; selectedDataset = addNewDatasetComponent?.dataset; datasetFile = addNewDatasetComponent?.csvRecords; datasetHasHeader = addNewDatasetComponent?.dataset!.hasHeader">
+  </app-add-new-dataset>
+  <div class="px-5 mt-5">
+      <app-datatable [data]="datasetFile" [hasHeader]="datasetHasHeader"></app-datatable>
   </div>
+
 </div>
\ No newline at end of file
diff --git a/frontend/src/app/_elements/dataset-load/dataset-load.component.ts b/frontend/src/app/_elements/dataset-load/dataset-load.component.ts
index f9343117..ed71dc3c 100644
--- a/frontend/src/app/_elements/dataset-load/dataset-load.component.ts
+++ b/frontend/src/app/_elements/dataset-load/dataset-load.component.ts
@@ -1,6 +1,12 @@
-import { Component, EventEmitter, Output, ViewChild } from '@angular/core';
-import { NgxCsvParser, NgxCSVParserError } from 'ngx-csv-parser';
+import { Component, OnInit, ViewChild } from '@angular/core';
+import { AddNewDatasetComponent } from '../add-new-dataset/add-new-dataset.component';
+import { ModelsService } from 'src/app/_services/models.service';
+import shared from 'src/app/Shared';
 import Dataset from 'src/app/_data/Dataset';
+import { DatatableComponent } from 'src/app/_elements/datatable/datatable.component';
+import { DatasetsService } from 'src/app/_services/datasets.service';
+import { CsvParseService } from 'src/app/_services/csv-parse.service';
+import { Output, EventEmitter } from '@angular/core';
 
 @Component({
   selector: 'app-dataset-load',
@@ -9,70 +15,77 @@ import Dataset from 'src/app/_data/Dataset';
 })
 export class DatasetLoadComponent {
 
-  @Output() loaded = new EventEmitter<string>();
+  @Output() selectedDatasetChangeEvent = new EventEmitter<Dataset>();
 
-  delimiterOptions: Array<string> = [",", ";", "\t", "razmak", "|"]; //podrazumevano ","
+  @ViewChild(AddNewDatasetComponent) addNewDatasetComponent?: AddNewDatasetComponent;
+  @ViewChild(AddNewDatasetComponent) datatable?: DatatableComponent;
+  datasetLoaded: boolean = false;
+  selectedDatasetLoaded: boolean = false;
 
-  //hasHeader: boolean = true;
-  hasInput: boolean = false;
+  showMyDatasets: boolean = true;
+  myDatasets?: Dataset[];
+  existingDatasetSelected: boolean = false;
+  selectedDataset?: Dataset;
+  otherDataset?: Dataset;
+  otherDatasetFile?: any[];
+  datasetFile?: any[];
+  datasetHasHeader?: boolean = true;
 
-  csvRecords: any[] = [];
-  files: File[] = [];
-  rowsNumber: number = 0;
-  colsNumber: number = 0;
+  term: string = "";
 
-  dataset: Dataset; //dodaj ! potencijalno
-
-  constructor(private ngxCsvParser: NgxCsvParser) {
-    this.dataset = new Dataset();
+  constructor(private models: ModelsService, private datasets: DatasetsService, private csv: CsvParseService) {
+    this.datasets.getMyDatasets().subscribe((datasets) => {
+      this.myDatasets = datasets;
+    });
   }
 
-  @ViewChild('fileImportInput', { static: false }) fileImportInput: any;
-
-  changeListener($event: any): void {
-    this.files = $event.srcElement.files;
-    if (this.files.length == 0 || this.files[0] == null) {
-      //console.log("NEMA FAJLA");
-      //this.loaded.emit("not loaded");
-      this.hasInput = false;
-      return;
-    }
-    else
-      this.hasInput = true;
-
-    this.update();
+  viewMyDatasetsForm() {
+    this.showMyDatasets = true;
+    this.resetSelectedDataset();
+    //this.resetCbsAndRbs();        //TREBA DA SE DESI
+  }
+  viewNewDatasetForm() {
+    this.showMyDatasets = false;
+    this.resetSelectedDataset();
+    //this.resetCbsAndRbs();        //TREBA DA SE DESI
   }
 
-  update() {
-
-    if (this.files.length < 1)
-      return;
-
-    this.ngxCsvParser.parse(this.files[0], { header: false, delimiter: (this.dataset.delimiter == "razmak") ? " " : (this.dataset.delimiter == "") ? "," : this.dataset.delimiter })
-      .pipe().subscribe((result) => {
-
-        console.log('Result', result);
-        if (result.constructor === Array) {
-          this.csvRecords = result;
-          if (this.dataset.hasHeader)
-            this.rowsNumber = this.csvRecords.length - 1;
+  selectThisDataset(dataset: Dataset) {
+    this.selectedDataset = dataset;
+    this.selectedDatasetLoaded = false;
+    this.existingDatasetSelected = true;
+    this.datasetHasHeader = this.selectedDataset.hasHeader;
+
+    this.datasets.getDatasetFile(dataset.fileId).subscribe((file: string | undefined) => {
+      if (file) {
+        this.datasetFile = this.csv.csvToArray(file, (dataset.delimiter == "razmak") ? " " : (dataset.delimiter == "") ? "," : dataset.delimiter);
+        /*for (let i = this.datasetFile.length - 1; i >= 0; i--) {  //moguce da je vise redova na kraju fajla prazno i sl.
+          if (this.datasetFile[i].length != this.datasetFile[0].length)
+            this.datasetFile[i].pop();
           else
-            this.rowsNumber = this.csvRecords.length;
-          this.colsNumber = this.csvRecords[0].length;
+            break; //nema potrebe dalje
+        }*/
+        //console.log(this.datasetFile);
+        //this.resetCbsAndRbs();                        //TREBA DA SE DESI
+        //this.refreshThreeNullValueRadioOptions();       //TREBA DA SE DESI
+        this.selectedDatasetLoaded = true;
+        //this.scrollToNextForm();
+      }
+    });
+  }
 
-          if (this.dataset.hasHeader) //kasnije dodati opciju kada nema header da korisnik rucno unosi header-e
-            this.dataset.header = this.csvRecords[0];
+  resetSelectedDataset(): boolean {
+    const temp = this.selectedDataset;
+    this.selectedDataset = this.otherDataset;
+    this.otherDataset = temp;
+    const tempFile = this.datasetFile;
+    this.datasetFile = this.otherDatasetFile;
+    this.otherDatasetFile = tempFile;
 
-          this.loaded.emit("loaded");
-        }
-      }, (error: NgxCSVParserError) => {
-        console.log('Error', error);
-      });
-  }
+    this.selectedDatasetChangeEvent.emit(this.selectedDataset);
 
-  checkAccessible() {
-    if (this.dataset.isPublic)
-      this.dataset.accessibleByLink = true;
+    return true;
   }
 
+
 }
diff --git a/frontend/src/app/_elements/item-model/item-model.component.html b/frontend/src/app/_elements/item-model/item-model.component.html
index 9466da01..c8c1a36d 100644
--- a/frontend/src/app/_elements/item-model/item-model.component.html
+++ b/frontend/src/app/_elements/item-model/item-model.component.html
@@ -6,9 +6,9 @@
     <div class="card-body overflow-hidden">
         <p class="card-text">
             {{"Opis: "+ model.description}}<br>
-            {{"Datum kreiranja:" + model.dateCreated}}<br>
-            {{"Poslednje ažuriranje:" + model.lastUpdated}}<br>
+            {{"Datum kreiranja: " + model.dateCreated}}<br>
+            {{"Poslednje ažuriranje: " + model.lastUpdated}}<br>
         </p>
-        
+        <app-annvisual class="align-items-center"  [model]="model" style="width: 12rem"></app-annvisual>
     </div>
 </div>
\ No newline at end of file
diff --git a/frontend/src/app/_elements/item-predictor/item-predictor.component.ts b/frontend/src/app/_elements/item-predictor/item-predictor.component.ts
index b6b5c9db..246032e0 100644
--- a/frontend/src/app/_elements/item-predictor/item-predictor.component.ts
+++ b/frontend/src/app/_elements/item-predictor/item-predictor.component.ts
@@ -17,8 +17,7 @@ export class ItemPredictorComponent implements OnInit {
   }
 
   openPredictor() {
-    this.router.navigate(['predict/'+ '6244958a26cf2385bc29ba2c']);
-    //this.router.navigate(['predict'+this.predictor._id]);
+    this.router.navigate(['predict/'+ this.predictor._id]);
     }
 
 }
diff --git a/frontend/src/app/_elements/navbar/navbar.component.ts b/frontend/src/app/_elements/navbar/navbar.component.ts
index 2e4bde91..368508ed 100644
--- a/frontend/src/app/_elements/navbar/navbar.component.ts
+++ b/frontend/src/app/_elements/navbar/navbar.component.ts
@@ -3,6 +3,7 @@ import { Location } from '@angular/common';
 import { AuthService } from '../../_services/auth.service';
 import shared from 'src/app/Shared';
 import { UserInfoService } from 'src/app/_services/user-info.service';
+import { MatDialog } from '@angular/material/dialog';
 
 @Component({
   selector: 'app-navbar',
@@ -14,7 +15,8 @@ export class NavbarComponent implements OnInit {
   currentUrl: string;
   shared = shared;
 
-  constructor(public location: Location, private auth: AuthService, private userInfoService: UserInfoService) {
+  constructor(public location: Location, private auth: AuthService, private userInfoService: UserInfoService, private matDialog: MatDialog) {
+    shared.dialog = matDialog;
     this.currentUrl = this.location.path();
     this.location.onUrlChange(() => {
       this.currentUrl = this.location.path();
diff --git a/frontend/src/app/_modals/alert-dialog/alert-dialog.component.css b/frontend/src/app/_modals/alert-dialog/alert-dialog.component.css
new file mode 100644
index 00000000..e69de29b
--- /dev/null
+++ b/frontend/src/app/_modals/alert-dialog/alert-dialog.component.css
diff --git a/frontend/src/app/_modals/alert-dialog/alert-dialog.component.html b/frontend/src/app/_modals/alert-dialog/alert-dialog.component.html
new file mode 100644
index 00000000..82365193
--- /dev/null
+++ b/frontend/src/app/_modals/alert-dialog/alert-dialog.component.html
@@ -0,0 +1,7 @@
+<h2 mat-dialog-title class="text-muted">{{data.title}}</h2>
+<div mat-dialog-content class="mt-4" style="color: rgb(81, 76, 76);">
+  {{data.message}}
+</div>
+<div mat-dialog-actions class="d-flex justify-content-center mt-4">
+  <button mat-button cdkFocusInitial (click)="onOkClick()" style="background-color: lightgray;">OK</button>
+</div>
\ No newline at end of file
diff --git a/frontend/src/app/_modals/alert-dialog/alert-dialog.component.spec.ts b/frontend/src/app/_modals/alert-dialog/alert-dialog.component.spec.ts
new file mode 100644
index 00000000..a93fc493
--- /dev/null
+++ b/frontend/src/app/_modals/alert-dialog/alert-dialog.component.spec.ts
@@ -0,0 +1,25 @@
+import { ComponentFixture, TestBed } from '@angular/core/testing';
+
+import { AlertDialogComponent } from './alert-dialog.component';
+
+describe('AlertDialogComponent', () => {
+  let component: AlertDialogComponent;
+  let fixture: ComponentFixture<AlertDialogComponent>;
+
+  beforeEach(async () => {
+    await TestBed.configureTestingModule({
+      declarations: [ AlertDialogComponent ]
+    })
+    .compileComponents();
+  });
+
+  beforeEach(() => {
+    fixture = TestBed.createComponent(AlertDialogComponent);
+    component = fixture.componentInstance;
+    fixture.detectChanges();
+  });
+
+  it('should create', () => {
+    expect(component).toBeTruthy();
+  });
+});
diff --git a/frontend/src/app/_modals/alert-dialog/alert-dialog.component.ts b/frontend/src/app/_modals/alert-dialog/alert-dialog.component.ts
new file mode 100644
index 00000000..e15f3c6f
--- /dev/null
+++ b/frontend/src/app/_modals/alert-dialog/alert-dialog.component.ts
@@ -0,0 +1,28 @@
+import { Component, OnInit } from '@angular/core';
+import { Inject} from '@angular/core';
+import { MatDialog, MatDialogRef, MAT_DIALOG_DATA} from '@angular/material/dialog';
+
+interface DialogData {
+  title: string;
+  message: string;
+}
+
+@Component({
+  selector: 'app-alert-dialog',
+  templateUrl: './alert-dialog.component.html',
+  styleUrls: ['./alert-dialog.component.css']
+})
+export class AlertDialogComponent {
+
+  constructor(
+    public dialogRef: MatDialogRef<AlertDialogComponent>,
+    @Inject(MAT_DIALOG_DATA) public data: DialogData,
+    //public dialog: MatDialog
+  ) {}
+
+  onOkClick(): void {
+    this.dialogRef.close();
+  }
+
+
+}
diff --git a/frontend/src/app/_modals/login-modal/login-modal.component.html b/frontend/src/app/_modals/login-modal/login-modal.component.html
index d7836848..03048155 100644
--- a/frontend/src/app/_modals/login-modal/login-modal.component.html
+++ b/frontend/src/app/_modals/login-modal/login-modal.component.html
@@ -3,7 +3,7 @@
   <div class="modal-dialog modal-dialog-centered">
     <div class="modal-content">
       <div class="modal-header" style="background-color: #003459;">
-        <button id="closeButton" type="button" class="btn-close" style="background-color:white;" data-bs-dismiss="modal" aria-label="Close" (click)="resetData()"></button>
+        <button #closeButton type="button" class="btn-close" style="background-color:white;" data-bs-dismiss="modal" aria-label="Close" (click)="resetData()"></button>
       </div>
       <div class="modal-body px-5" style="color:#003459">
           <h1 class="text-center mt-2 mb-4">Prijavite se</h1>
diff --git a/frontend/src/app/_modals/login-modal/login-modal.component.ts b/frontend/src/app/_modals/login-modal/login-modal.component.ts
index c86c269a..e1535a25 100644
--- a/frontend/src/app/_modals/login-modal/login-modal.component.ts
+++ b/frontend/src/app/_modals/login-modal/login-modal.component.ts
@@ -4,6 +4,7 @@ import { CookieService } from 'ngx-cookie-service';
 import { AuthService } from 'src/app/_services/auth.service';
 import { UserInfoService } from 'src/app/_services/user-info.service';
 import shared from '../../Shared';
+import {AfterViewInit, ElementRef} from '@angular/core';
 
 @Component({
   selector: 'app-login-modal',
@@ -12,6 +13,8 @@ import shared from '../../Shared';
 })
 export class LoginModalComponent implements OnInit {
 
+  @ViewChild('closeButton') closeButton?: ElementRef;
+
   username: string = '';
   password: string = '';
 
@@ -38,7 +41,7 @@ export class LoginModalComponent implements OnInit {
         }
         else {
           this.authService.authenticate(response);
-          (<HTMLSelectElement>document.getElementById('closeButton')).click();
+          (<HTMLSelectElement>this.closeButton?.nativeElement).click();
           this.userInfoService.getUserInfo().subscribe((response) => {
             shared.photoId = response.photoId;
           });
diff --git a/frontend/src/app/_modals/register-modal/register-modal.component.ts b/frontend/src/app/_modals/register-modal/register-modal.component.ts
index 13ef7eba..05888589 100644
--- a/frontend/src/app/_modals/register-modal/register-modal.component.ts
+++ b/frontend/src/app/_modals/register-modal/register-modal.component.ts
@@ -1,6 +1,9 @@
 import { Component, OnInit } from '@angular/core';
 import { AuthService } from 'src/app/_services/auth.service';
 import User from 'src/app/_data/User';
+import { DOCUMENT } from '@angular/common';
+import { Inject }  from '@angular/core';
+import shared from 'src/app/Shared';
 
 @Component({
   selector: 'app-register-modal',
@@ -29,8 +32,11 @@ export class RegisterModalComponent implements OnInit {
   pattEmail: RegExp = /^[a-zA-Z0-9]+([\.\-\+][a-zA-Z0-9]+)*\@([a-zA-Z\-0-9]+\.)+[a-zA-Z]{2,}$/;
   pattPassword: RegExp = /.{6,30}$/;
 
+  shared = shared;
+
   constructor(
-    private authService: AuthService
+    private authService: AuthService,
+    @Inject(DOCUMENT) document: Document
   ) { }
 
   ngOnInit(): void {
@@ -149,11 +155,11 @@ export class RegisterModalComponent implements OnInit {
               }, (error) => console.warn(error));             
             }
             else if (response == 'Email Already Exists') {
-              alert('Nalog sa unetim email-om već postoji!');
+              shared.openDialog("Greška!", "Nalog sa unetim email-om već postoji!");
               (<HTMLSelectElement>document.getElementById('email')).focus();
             }
             else if (response == 'Username Already Exists') {
-              alert('Nalog sa unetim korisničkim imenom već postoji!');
+              shared.openDialog("Greška!", "Nalog sa unetim korisničkim imenom već postoji!");
               (<HTMLSelectElement>document.getElementById('username-register')).focus();
             }
           }
diff --git a/frontend/src/app/_pages/add-model/add-model.component.css b/frontend/src/app/_pages/add-model/add-model.component.css
index 6d961287..7f05af0f 100644
--- a/frontend/src/app/_pages/add-model/add-model.component.css
+++ b/frontend/src/app/_pages/add-model/add-model.component.css
@@ -32,4 +32,11 @@
 }
 ul li:hover {
     background-color: lightblue;
-}
\ No newline at end of file
+}
+
+#divInputs {
+    margin-left: 20px;
+}
+#divOutputs {
+    margin-left: 20px;
+}
diff --git a/frontend/src/app/_pages/add-model/add-model.component.html b/frontend/src/app/_pages/add-model/add-model.component.html
index 3d5fd7b1..179e9aea 100644
--- a/frontend/src/app/_pages/add-model/add-model.component.html
+++ b/frontend/src/app/_pages/add-model/add-model.component.html
@@ -25,42 +25,8 @@
 
         <div class="py-3 pr-5 justify-content-center">
 
-            <div class="col-12 d-flex my-5">
-                <h2 class="">Izvor podataka:</h2>
-                <div class="col-1">
-                </div>
-                <button type="button" id="btnMyDataset" class="btn" (click)="viewMyDatasetsForm()"
-                    [ngClass]="{'btnType1': showMyDatasets, 'btnType2': !showMyDatasets}">
-                    Izaberite dataset iz kolekcije
-                </button>
-                <h3 class="mt-3 mx-3">ili</h3>
-                <button type="button" id="btnNewDataset" class="btn" (click)="viewNewDatasetForm()"
-                    [ngClass]="{'btnType1': !showMyDatasets, 'btnType2': showMyDatasets}">
-                    Dodajte novi dataset
-                </button>
-            </div>
-            <div class="px-5 my-2">
-                <input *ngIf="showMyDatasets" type="text" class="form-control" placeholder="Pretraga"
-                    [(ngModel)]="term">
-            </div>
-            <div class="px-5">
-                <div *ngIf="showMyDatasets" class="overflow-auto" style="max-height: 500px;">
-                    <ul class="list-group">
-                        <li class="list-group-item p-3" *ngFor="let dataset of myDatasets|filter:term"
-                            [ngClass]="{'selectedDatasetClass': this.selectedDataset == dataset}">
-                            <app-item-dataset name="usersDataset" [dataset]="dataset"
-                                (click)="selectThisDataset(dataset);"></app-item-dataset>
-                        </li>
-                    </ul>
-                </div>
-            </div>
+            <app-dataset-load (selectedDatasetChangeEvent)="datasetHasChanged($event)"></app-dataset-load>
 
-            <app-dataset-load [style]="(showMyDatasets)?'display:none;visibility:hidden;':''" id="dataset"
-                (loaded)="scrollToNextForm(); datasetLoaded = true; selectedDataset = datasetLoadComponent?.dataset; datasetFile = datasetLoadComponent?.csvRecords; datasetHasHeader = datasetLoadComponent?.dataset!.hasHeader">
-            </app-dataset-load>
-            <div class="px-5 mt-5">
-                <app-datatable [data]="datasetFile" [hasHeader]="datasetHasHeader"></app-datatable>
-            </div>
         </div>
         <span id="selectInAndOuts"></span>
         <div
diff --git a/frontend/src/app/_pages/add-model/add-model.component.ts b/frontend/src/app/_pages/add-model/add-model.component.ts
index d47b24e6..ba8f7d01 100644
--- a/frontend/src/app/_pages/add-model/add-model.component.ts
+++ b/frontend/src/app/_pages/add-model/add-model.component.ts
@@ -5,9 +5,7 @@ import { DatasetLoadComponent } from 'src/app/_elements/dataset-load/dataset-loa
 import { ModelsService } from 'src/app/_services/models.service';
 import shared from 'src/app/Shared';
 import Dataset from 'src/app/_data/Dataset';
-import { DatatableComponent } from 'src/app/_elements/datatable/datatable.component';
 import { DatasetsService } from 'src/app/_services/datasets.service';
-import { NgxCsvParser } from 'ngx-csv-parser';
 import { CsvParseService } from 'src/app/_services/csv-parse.service';
 
 
@@ -18,11 +16,6 @@ import { CsvParseService } from 'src/app/_services/csv-parse.service';
 })
 export class AddModelComponent implements OnInit {
 
-  @ViewChild(DatasetLoadComponent) datasetLoadComponent?: DatasetLoadComponent;
-  @ViewChild(DatatableComponent) datatable?: DatatableComponent;
-  datasetLoaded: boolean = false;
-  selectedDatasetLoaded: boolean = false;
-
   newModel: Model;
 
   ProblemType = ProblemType;
@@ -71,8 +64,13 @@ export class AddModelComponent implements OnInit {
     (<HTMLInputElement>document.getElementById("btnMyDataset")).focus();
   }
 
+  datasetHasChanged(selectedDataset: Dataset) {
+    this.selectedDataset = selectedDataset;
+    this.resetCbsAndRbs();
+    this.refreshThreeNullValueRadioOptions();
+  }
 
-  viewMyDatasetsForm() {
+  /*viewMyDatasetsForm() {
     this.showMyDatasets = true;
     this.resetSelectedDataset();
     //this.datasetLoaded = false;
@@ -82,7 +80,7 @@ export class AddModelComponent implements OnInit {
     this.showMyDatasets = false;
     this.resetSelectedDataset();
     this.resetCbsAndRbs();
-  }
+  }*/
 
   addModel() {
     if (!this.showMyDatasets)
@@ -174,7 +172,7 @@ export class AddModelComponent implements OnInit {
         this.models.addModel(this.newModel).subscribe((response) => {
           callback(response);
         }, (error) => {
-          alert("Model sa unetim nazivom već postoji u Vašoj kolekciji.\nPromenite naziv modela i nastavite sa kreiranim datasetom.");
+          shared.openDialog("Neuspeo pokušaj!", "Model sa unetim nazivom već postoji u Vašoj kolekciji. Promenite naziv modela i nastavite sa kreiranim datasetom.");
         });
       }
     }
@@ -217,47 +215,47 @@ export class AddModelComponent implements OnInit {
       return false;
     }
     else if (this.newModel.inputColumns.length == 0) {
-      alert("Molimo Vas da izaberete ulaznu kolonu/kolone za mrežu.");
+      shared.openDialog("Neuspeo pokušaj!", "Molimo Vas da izaberete ulaznu kolonu/kolone za mrežu.");
       return false;
     }
     else if (this.newModel.columnToPredict == '') {
-      alert("Molimo Vas da izaberete izlaznu kolonu za mrežu.");
+      shared.openDialog("Neuspeo pokušaj!", "Molimo Vas da izaberete izlaznu kolonu za mrežu.");
       return false;
     }
     for (let i = 0; i < this.newModel.inputColumns.length; i++) {
       if (this.newModel.inputColumns[i] == this.newModel.columnToPredict) {
         let colName = this.newModel.columnToPredict;
-        alert("Izabrali ste istu kolonu (" + colName + ") kao ulaznu i izlaznu iz mreže. Korigujte izbor.");
+        shared.openDialog("Neuspeo pokušaj!", "Izabrali ste istu kolonu (" + colName + ") kao ulaznu i izlaznu iz mreže. Korigujte izbor.");
         return false;
       }
     }
     return true;*/
   }
 
-  selectThisDataset(dataset: Dataset) {
+  /*selectThisDataset(dataset: Dataset) {
     this.selectedDataset = dataset;
-    this.selectedDatasetLoaded = false;
+    //this.selectedDatasetLoaded = false;
     this.existingDatasetSelected = true;
     this.datasetHasHeader = this.selectedDataset.hasHeader;
 
     this.datasets.getDatasetFile(dataset.fileId).subscribe((file: string | undefined) => {
       if (file) {
         this.datasetFile = this.csv.csvToArray(file, (dataset.delimiter == "razmak") ? " " : (dataset.delimiter == "") ? "," : dataset.delimiter);
-        /*for (let i = this.datasetFile.length - 1; i >= 0; i--) {  //moguce da je vise redova na kraju fajla prazno i sl.
-          if (this.datasetFile[i].length != this.datasetFile[0].length)
-            this.datasetFile[i].pop();
-          else
-            break; //nema potrebe dalje
-        }*/
+        //for (let i = this.datasetFile.length - 1; i >= 0; i--) {  //moguce da je vise redova na kraju fajla prazno i sl.
+          //if (this.datasetFile[i].length != this.datasetFile[0].length)
+            //this.datasetFile[i].pop();
+          //else
+          //  break; //nema potrebe dalje
+        //}
         //console.log(this.datasetFile);
         this.resetCbsAndRbs();
         this.refreshThreeNullValueRadioOptions();
-        this.selectedDatasetLoaded = true;
+        //this.selectedDatasetLoaded = true;
         this.scrollToNextForm();
       }
     });
     //this.datasetHasHeader = false;
-  }
+  }*/
 
   scrollToNextForm() {
     (<HTMLSelectElement>document.getElementById("selectInAndOuts")).scrollIntoView({
@@ -267,7 +265,7 @@ export class AddModelComponent implements OnInit {
     });
   }
 
-  resetSelectedDataset(): boolean {
+  /*resetSelectedDataset(): boolean {
     const temp = this.selectedDataset;
     this.selectedDataset = this.otherDataset;
     this.otherDataset = temp;
@@ -275,7 +273,7 @@ export class AddModelComponent implements OnInit {
     this.datasetFile = this.otherDatasetFile;
     this.otherDatasetFile = tempFile;
     return true;
-  }
+  }*/
   resetCbsAndRbs(): boolean {
     this.uncheckRbs();
     this.checkAllCbs();
@@ -345,7 +343,7 @@ export class AddModelComponent implements OnInit {
       let colIndex = this.findColIndexByName(colName);
       let sumOfNulls = 0;
 
-      let startValue = (this.datasetLoadComponent?.dataset.hasHeader) ? 1 : 0;
+      let startValue = (this.selectedDataset!.hasHeader) ? 1 : 0;
       for (let i = startValue; i < this.datasetFile.length; i++) {
         if (this.datasetFile[i][colIndex] == "" || this.datasetFile[i][colIndex] == undefined)
           ++sumOfNulls;
@@ -360,7 +358,7 @@ export class AddModelComponent implements OnInit {
       let sum = 0;
       let n = 0;
 
-      let startValue = (this.datasetLoadComponent?.dataset.hasHeader) ? 1 : 0;
+      let startValue = (this.selectedDataset!.hasHeader) ? 1 : 0;
       for (let i = startValue; i < this.datasetFile.length; i++)
         if (this.datasetFile[i][colIndex] != '') {
           sum += Number(this.datasetFile[i][colIndex]);
@@ -467,9 +465,13 @@ export class AddModelComponent implements OnInit {
 
   arrayColumn = (arr: any[][], n: number) => [...this.dropEmptyString(new Set(arr.map(x => x[n])))];
 
-  dropEmptyString(set: Set<string>): Set<string> {
+  dropEmptyString(set: Set<any>): Set<string> {
     if (set.has(""))
       set.delete("");
+    if (set.has(null))
+      set.delete(null);
+    if (set.has(undefined))
+      set.delete(undefined);
     return set;
   }
 
diff --git a/frontend/src/app/_pages/browse-predictors/browse-predictors.component.html b/frontend/src/app/_pages/browse-predictors/browse-predictors.component.html
index a4ab6e2c..27e06884 100644
--- a/frontend/src/app/_pages/browse-predictors/browse-predictors.component.html
+++ b/frontend/src/app/_pages/browse-predictors/browse-predictors.component.html
@@ -5,7 +5,9 @@
         <div class="row mt-3 mb-2 d-flex justify-content-center">
     
     <div class="col-sm-6" style="margin-bottom: 10px;">
+      <p class="glyphicon glyphicon-search"></p>
     <input type="text" class="form-control" placeholder="Pretraga" [(ngModel)]="term">
+    
     </div>
 
             <div class="row">
@@ -14,7 +16,7 @@
                     <div class="card-body">
                       <h3 class="card-title"><b>{{predictor.name}}</b></h3>
                       <p class="card-text">{{predictor.description}}</p>
-                      <a  class="btn btn-primary" (click)="openPredictor(predictor._id)">Otvori</a>
+                      <a  class="btn btn-primary" (click)="openPredictor(predictor._id)">Iskoristi</a>
                     </div>
                     <div class="card-footer text-muted">
                         Kreirao: {{predictor.username}} <br>
diff --git a/frontend/src/app/_pages/browse-predictors/browse-predictors.component.ts b/frontend/src/app/_pages/browse-predictors/browse-predictors.component.ts
index 4f96fc36..891b3cab 100644
--- a/frontend/src/app/_pages/browse-predictors/browse-predictors.component.ts
+++ b/frontend/src/app/_pages/browse-predictors/browse-predictors.component.ts
@@ -20,7 +20,7 @@ export class BrowsePredictorsComponent implements OnInit {
   ngOnInit(): void {
   }
   openPredictor(id:string):void{
-    this.router.navigateByUrl('/predict?id='+id);
+    this.router.navigate(['predict/'+id]);
   };
 
 }
diff --git a/frontend/src/app/_pages/filter-datasets/filter-datasets.component.ts b/frontend/src/app/_pages/filter-datasets/filter-datasets.component.ts
index b75decf2..fc146046 100644
--- a/frontend/src/app/_pages/filter-datasets/filter-datasets.component.ts
+++ b/frontend/src/app/_pages/filter-datasets/filter-datasets.component.ts
@@ -4,6 +4,7 @@ import Dataset from 'src/app/_data/Dataset';
 import {Router} from '@angular/router'
 import { JwtHelperService } from '@auth0/angular-jwt';
 import { CookieService } from 'ngx-cookie-service';
+import shared from 'src/app/Shared';
 
 @Component({
   selector: 'app-filter-datasets',
@@ -12,6 +13,7 @@ import { CookieService } from 'ngx-cookie-service';
 })
 export class FilterDatasetsComponent implements OnInit {
 
+  shared = shared;
   publicDatasets?: Dataset[];
   term: string = "";
   constructor(private datasets: DatasetsService,private router:Router, private cookie: CookieService) {
@@ -37,11 +39,9 @@ export class FilterDatasetsComponent implements OnInit {
     if(name!=null && name!="")
     this.datasets.addDataset(newDataset).subscribe((response:string)=>{
       console.log(response);
-      alert("Uspenso ste dodali dataset sa imenom "+newDataset.name);
+      shared.openDialog("Obaveštenje", "Uspešno ste dodali dataset sa nazivom " + newDataset.name);
     },(error)=>{
-      alert("Vec imate dataset sa istim imenom molim vas unesite drugo ime");
-      
-
+      shared.openDialog("Obaveštenje", "U svojoj kolekciji već imate dataset sa ovim imenom. Molimo Vas da unesete drugo ime.");
     });
   
   };
diff --git a/frontend/src/app/_pages/my-datasets/my-datasets.component.html b/frontend/src/app/_pages/my-datasets/my-datasets.component.html
index 623b9ac8..2e17201d 100644
--- a/frontend/src/app/_pages/my-datasets/my-datasets.component.html
+++ b/frontend/src/app/_pages/my-datasets/my-datasets.component.html
@@ -1,5 +1,37 @@
-<ul class="list-group my-2">
-    <li class="list-group-item" *ngFor="let dataset of myDatasets">
-        <app-item-dataset [dataset]="dataset"></app-item-dataset>
-    </li>
-</ul>
\ No newline at end of file
+<div id="wrapper">
+    <div id="container" class="container p-5" style="background-color: white; min-height: 100%;">
+        <div class="row mt-3 mb-2 d-flex justify-content-center">
+        
+        <div class="col-sm-6" style="margin-bottom: 10px;">
+        </div>
+        
+            <div class="row">
+                <div class="col-sm-4" style="margin-bottom: 10px;" *ngFor="let dataset of myDatasets">
+                <app-item-dataset [dataset]="dataset"></app-item-dataset>
+                
+                <div class="panel-footer row"><!-- panel-footer -->
+                    <div class="col-xs-6 text-center">
+                        <div>
+                          <button type="button" class="btn btn-default btn-lg" mat-raised-button color="primary" (click)="deleteThisDataset(dataset)">Obriši
+                            <span class="glyphicon glyphicon-chevron-right"></span>
+                        </button>
+                        </div>
+                    </div>
+                </div><!-- end panel-footer -->
+
+                
+                
+                </div>
+            </div>
+        <div class="text-center" *ngIf="this.myDatasets.length == 0" >
+        <h2>Nema rezultata</h2>
+        </div>
+        </div>
+    
+    </div>
+    
+    
+    
+    
+    
+    </div>
diff --git a/frontend/src/app/_pages/my-datasets/my-datasets.component.ts b/frontend/src/app/_pages/my-datasets/my-datasets.component.ts
index 13b0c47b..eb5e32f8 100644
--- a/frontend/src/app/_pages/my-datasets/my-datasets.component.ts
+++ b/frontend/src/app/_pages/my-datasets/my-datasets.component.ts
@@ -1,5 +1,9 @@
 import { Component, OnInit } from '@angular/core';
+import {Router} from '@angular/router';
+import { DatasetsService } from 'src/app/_services/datasets.service';
 import Dataset from 'src/app/_data/Dataset';
+import { JwtHelperService } from '@auth0/angular-jwt';
+import { CookieService } from 'ngx-cookie-service';
 
 @Component({
   selector: 'app-my-datasets',
@@ -7,18 +11,47 @@ import Dataset from 'src/app/_data/Dataset';
   styleUrls: ['./my-datasets.component.css']
 })
 export class MyDatasetsComponent implements OnInit {
+  myDatasets: Dataset[] = [];
 
-  myDatasets?: Dataset[];
+  constructor(private datasetsS : DatasetsService) {
 
-  constructor() {
-    this.myDatasets = [
-      new Dataset('Titanik', 'Opis titanik', ['K1', 'K2', 'K3', 'Ime', 'Preziveli']),
-      new Dataset('Neki drugi set', 'opis', ['a', 'b', 'c']),
-      new Dataset('Treci set', 'opis', ['a', 'b', 'c'])
-    ];
-  }
+    
+
+     }
 
   ngOnInit(): void {
+    this.datasetsS.getMyDatasets();
+
+  }
+/*
+  editModel(): void{
+    this.modelsS.editModel().subscribe(m => {
+      this.myModel = m;
+
+    })
+  }
+*/
+
+deleteThisDataset(dataset: Dataset): void{
+  console.log("OK");
+  this.datasetsS.deleteDataset(dataset).subscribe((response) => {
+    console.log("OBRISANO JE", response);
+    //na kraju uspesnog
+    this.getAllMyDatasets();
+  }, (error) =>{
+      if (error.error == "Dataset with name = {name} deleted") {
+        alert("Greška pri brisanju dataseta!");
+      }
+    });
+
+}
+
+  getAllMyDatasets(): void{
+    this.datasetsS.getMyDatasets().subscribe(m => {
+      
+      this.myDatasets = m;
+      console.log(this.myDatasets);
+    });
   }
 
 }
diff --git a/frontend/src/app/_pages/my-models/my-models.component.html b/frontend/src/app/_pages/my-models/my-models.component.html
index e2533d89..4aebc1f2 100644
--- a/frontend/src/app/_pages/my-models/my-models.component.html
+++ b/frontend/src/app/_pages/my-models/my-models.component.html
@@ -8,12 +8,22 @@
             <div class="row">
                 <div class="col-sm-4" style="margin-bottom: 10px;" *ngFor="let model of myModels">
                 <app-item-model [model]="model"></app-item-model>
-                <app-annvisual align-items-center [model]="model" style="width: 100%;"></app-annvisual>
-                <div style="width: 25%; margin: auto;">
-                    <button mat-raised-button color="primary" (click)="deleteThisModel(model)" style="margin-top: 3px; width: 100%;">Obriši</button>
-                    
-                    <button mat-raised-button color="primary" (click)="deleteThisModel(model)" style="margin-top: 3px; width: 100%;">Koristi</button>
-                </div>
+                
+                <div class="panel-footer row"><!-- panel-footer -->
+                    <div class="col-xs-6 text-center">
+                        <div>
+                          <button type="button" class="btn btn-default btn-lg" (click)="deleteThisModel(model)" mat-raised-button color="primary">Koristi
+                              <span class="glyphicon glyphicon-search"></span>
+                          </button>
+                          <button type="button" class="btn btn-default btn-lg" mat-raised-button color="primary" (click)="deleteThisModel(model)">Obriši
+                            <span class="glyphicon glyphicon-chevron-right"></span>
+                        </button>
+                        </div>
+                    </div>
+                </div><!-- end panel-footer -->
+
+                
+                
                 </div>
             </div>
         <div class="text-center" *ngIf="this.myModels.length == 0" >
diff --git a/frontend/src/app/_pages/my-models/my-models.component.ts b/frontend/src/app/_pages/my-models/my-models.component.ts
index bd6b0a2b..6086b1b1 100644
--- a/frontend/src/app/_pages/my-models/my-models.component.ts
+++ b/frontend/src/app/_pages/my-models/my-models.component.ts
@@ -1,4 +1,5 @@
 import { Component, OnInit } from '@angular/core';
+import shared from 'src/app/Shared';
 import Model from 'src/app/_data/Model';
 import { ModelsService } from 'src/app/_services/models.service';
 
@@ -38,7 +39,7 @@ deleteThisModel(model: Model): void{
     this.getAllMyModels();
   }, (error) =>{
       if (error.error == "Model with name = {name} deleted") {
-        alert("Greška pri brisanju modela!");
+        shared.openDialog("Obaveštenje", "Greška prilikom brisanja modela.");
       }
     });
 
diff --git a/frontend/src/app/_pages/my-predictors/my-predictors.component.html b/frontend/src/app/_pages/my-predictors/my-predictors.component.html
index d739f561..3746d35e 100644
--- a/frontend/src/app/_pages/my-predictors/my-predictors.component.html
+++ b/frontend/src/app/_pages/my-predictors/my-predictors.component.html
@@ -7,7 +7,7 @@
         <app-item-predictor [predictor]="predictor"></app-item-predictor>
     </div>
     <div>
-        <button (click)="delete()" mat-raised-button color="warn" style="min-width: 15rem;float: right" ><mat-icon>delete</mat-icon></button>
+        <button (click)="delete(predictor)" mat-raised-button color="warn" style="min-width: 15rem;float: right" ><mat-icon>delete</mat-icon></button>
     </div>
     
 </div>
diff --git a/frontend/src/app/_pages/my-predictors/my-predictors.component.ts b/frontend/src/app/_pages/my-predictors/my-predictors.component.ts
index 58daa44f..13cfdab2 100644
--- a/frontend/src/app/_pages/my-predictors/my-predictors.component.ts
+++ b/frontend/src/app/_pages/my-predictors/my-predictors.component.ts
@@ -1,5 +1,6 @@
 import { Component, OnInit } from '@angular/core';
 import Predictor from 'src/app/_data/Predictor';
+import { PredictorsService } from 'src/app/_services/predictors.service';
 
 @Component({
   selector: 'app-my-predictors',
@@ -7,22 +8,38 @@ import Predictor from 'src/app/_data/Predictor';
   styleUrls: ['./my-predictors.component.css']
 })
 export class MyPredictorsComponent implements OnInit {
-  predictors: Predictor[];
-  constructor() { 
-  this.predictors = [
-    new Predictor('Titanik', 'Opis titanik', ['K1', 'K2', 'K3', 'Ime', 'Preziveli'],'Preziveli'),
-    new Predictor('Neki drugi set', 'opis', ['a', 'b', 'c'],'c'),
-    new Predictor('Preživeli', 'Za uneto ime osobe, predvidja da li je ta osoba preživela ili ne.', ['Ime'], 'OsobaJePreživela'),
-    new Predictor('Drugi model', 'Lorem ipsum dolor sir amet', ['kruska'], 'jagoda')];
+  predictors: Predictor[] = [];
+  constructor(private predictorsS : PredictorsService) {
   }
   ngOnInit(): void {
+    this.getAllMyPredictors();
+
   }
 
-  delete(){
-    confirm("IZABRANI MODEL ĆE BITI IZBRISAN")
+  delete(predictor: Predictor){
+    if(window.confirm("IZABRANI MODEL ĆE BITI IZBRISAN"))
+      {
+      this.predictorsS.deletePredictor(predictor).subscribe((response) => {
+        console.log("OBRISANOOO JEE", response);
+        //na kraju uspesnog
+        this.getAllMyPredictors();
+      }, (error) =>{
+          if (error.error == "Predictor with name = {name} deleted") {
+            alert("Greška pri brisanju modela!");
+          }
+        });
+    }
+    
     
   }
-  
+
+  getAllMyPredictors(): void{
+    this.predictorsS.getMyPredictors().subscribe(m => {
+      
+      this.predictors = m;
+      console.log(this.predictors);
+    });
+  }
 
 
 }
diff --git a/frontend/src/app/_pages/predict/predict.component.css b/frontend/src/app/_pages/predict/predict.component.css
index e69de29b..dab059a5 100644
--- a/frontend/src/app/_pages/predict/predict.component.css
+++ b/frontend/src/app/_pages/predict/predict.component.css
@@ -0,0 +1,3 @@
+#wrapper {
+    color: #003459;
+}
\ No newline at end of file
diff --git a/frontend/src/app/_pages/predict/predict.component.html b/frontend/src/app/_pages/predict/predict.component.html
index fe17c96d..13afa8e4 100644
--- a/frontend/src/app/_pages/predict/predict.component.html
+++ b/frontend/src/app/_pages/predict/predict.component.html
@@ -18,8 +18,25 @@
             <div>
                 <label for="output" class="col-sm-5 col-form-label">Opis prediktora: <b>{{predictor.description}}</b></label>
             </div>
+
+            
+        </div>
+    <br>
+        <label for="type" class="form-check-label" ><b>Informacije o prediktoru</b></label>
+        <div class="col-5 mt-2">
+            <label for="type" class="form-check-label"  >Prediktor {{predictor.isPublic?"je":"nije"}} javni.</label>
+        </div>
+        <div class="col-5 mt-2">
+            <label for="type" class="form-check-label" >Prediktor {{predictor.accessibleByLink?"je":"nije"}} dostupan za deljenje.</label>
+        </div>
+        <br>
+        <div class="col-2">
+            <label for="dateCreated" class="col-form-label">Datum:</label> 
+            <input type="text" class="form-control-plaintext" id="dateCreated" placeholder="--/--/--"
+                value="{{predictor.dateCreated | date: 'dd/MM/yyyy'}}" readonly>
         </div>
 
+
         <br>
         <div >
             <!--input -->
@@ -28,44 +45,20 @@
                 <div *ngIf="predictor" class="form-group row mt-3 mb-2 d-flex justify-content-left">
                     <div *ngFor="let input of predictor.inputs; let i = index">
                         <label for="{{input}}" class="col-sm-2 col-form-label"><b>{{input}}</b></label>
-                        <input name="{{input}}"  type="text"  [(ngModel)]="inputs[i]" >
+                        <input name="{{input}}"  type="text"  [(ngModel)]="inputs[i].value" >
 
                     </div>
                     
                 </div>
             </div>
            
-            
+            <br>    
 
         </div>
         <div>
             <label for="output" class="col-sm-2 col-form-label">Izlaz: <b>{{predictor.output}}</b></label>
         </div>
-        <br>
-        <br>
-        <br>
-        <br>
-        <br>
-
-        <div class="col-5 mt-2">
-            <label for="type" class="form-check-label">Da li je prediktor javan?</label>
-            <input class="mx-3 form-check-input" type="checkbox" [(ngModel)]="predictor.isPublic"
-                type="checkbox" value="" >
-        </div>
-        <div class="col-5 mt-2">
-            <label for="type" class="form-check-label">Da li je dostupan za deljenje?</label>
-            <input class="mx-3 form-check-input" type="checkbox" [(ngModel)]="predictor.accessibleByLink"
-                type="checkbox" value="" >
-        </div>
-        <br>
-        <div class="col-2">
-            <label for="dateCreated" class="col-form-label">Datum:</label> 
-            <input type="text" class="form-control-plaintext" id="dateCreated" placeholder="--/--/--"
-                value="{{predictor.dateCreated | date: 'dd/MM/yyyy'}}" readonly>
-        </div>
-
         
-        <br><br>
         <div class="form-group row mt-5 mb-3">
             <div class="col"></div>
             <button class="btn btn-lg col-4" style="background-color:#003459; color:white;"
diff --git a/frontend/src/app/_pages/predict/predict.component.ts b/frontend/src/app/_pages/predict/predict.component.ts
index 3f431fff..1c1c7425 100644
--- a/frontend/src/app/_pages/predict/predict.component.ts
+++ b/frontend/src/app/_pages/predict/predict.component.ts
@@ -2,6 +2,7 @@ import { Component, OnInit } from '@angular/core';
 import { ActivatedRoute } from '@angular/router';
 import Predictor from 'src/app/_data/Predictor';
 import { PredictorsService } from 'src/app/_services/predictors.service';
+import shared from 'src/app/Shared';
 
 @Component({
   selector: 'app-predict',
@@ -10,7 +11,8 @@ import { PredictorsService } from 'src/app/_services/predictors.service';
 })
 export class PredictComponent implements OnInit {
 
-  inputs : String[] = [];
+  inputs : Column[] = [];
+
 
   predictor:Predictor;
   constructor(private predictS : PredictorsService, private route: ActivatedRoute) {
@@ -22,6 +24,7 @@ export class PredictComponent implements OnInit {
       this.predictS.getPredictor(url["id"]).subscribe(p => {
       
         this.predictor = p;
+        this.predictor.inputs.forEach((p,index)=> this.inputs[index] = new Column(p, ""));
         console.log(this.predictor);
       })
     });
@@ -29,9 +32,16 @@ export class PredictComponent implements OnInit {
 
   usePredictor(): void{ 
     this.predictS.usePredictor(this.predictor, this.inputs).subscribe(p => {
-      
-      alert("Uspesno ste poslali preditor!");
+      shared.openDialog("Obaveštenje", "Prediktor je uspešno poslat na probu."); //pisalo je "na treniranje" ??
     })
     console.log(this.inputs);
   }
 }
+
+
+export class Column {
+  constructor(
+    public name : string, 
+    public value : (number | string)){
+  }
+}
\ No newline at end of file
diff --git a/frontend/src/app/_pages/profile/profile.component.html b/frontend/src/app/_pages/profile/profile.component.html
index d082a003..557d69fd 100644
--- a/frontend/src/app/_pages/profile/profile.component.html
+++ b/frontend/src/app/_pages/profile/profile.component.html
@@ -30,12 +30,14 @@
                                     <label class="small mb-1" for="inputPassword">Važeća lozinka</label>
                                     <input class="form-control" id="inputPassword" name="inputPassword" type="password" [(ngModel)]="this.oldPass" placeholder="Trenutna lozinka">
                                     <small *ngIf="wrongPassBool" class="form-text text-danger">Neispravna lozinka.</small>
+                                    <small *ngIf="wrongOldPassBool" class="form-text text-danger">Pogrešan format.</small>
                                 </div>
                                 <!-- Form Group (new password)-->
                                 <div class="col-md-6">
                                     <label class="small mb-1" for="inputNewPassword">Nova lozinka</label>
                                     <input class="form-control" id="inputNewPassword" name="inputNewPassword" type="password" [(ngModel)]="this.newPass1" placeholder="Ukucaj novu lozinku">
                                     <small *ngIf="wrongNewPassBool" class="form-text text-danger">Lozinke se ne podudaraju.</small>
+                                    <small *ngIf="wrongNewPass1Bool" class="form-text text-danger">Pogrešan format.</small>
                                 </div>
                             </div>
     
@@ -52,6 +54,7 @@
                                     <label class="small mb-1" for="inputNewPasswordAgain">Ponovo nova lozinka</label>
                                     <input class="form-control" id="inputNewPasswordAgain" name="inputNewPasswordAgain" type="password" [(ngModel)]="this.newPass2" placeholder="Ukucaj novu lozinku">
                                     <small *ngIf="wrongNewPassBool" class="form-text text-danger">Lozinke se ne podudaraju.</small>
+                                    <small *ngIf="wrongNewPass2Bool" class="form-text text-danger">Pogrešan format.</small>
                                 </div>
                             </div>
                         </div>
@@ -73,11 +76,13 @@
                             <div class="col-md-6">
                                 <label class="small mb-1" for="inputUsername">Korisničko ime (kako će ostali korisnici videti tvoje ime)</label>
                                 <input class="form-control" id="inputUsername" name="inputUsername" type="text" [(ngModel)]="this.username">
+                                <small *ngIf="wrongUsernameBool" class="form-text text-danger">Pogrešan format.</small>
                             </div>
                             <!-- Form Group (email address)-->
                             <div class="col-md-6">
                                 <label class="small mb-1" for="inputEmailAddress">Email adresa</label>
                                 <input class="form-control" id="inputEmailAddress" name="inputEmailAddress" type="email" [(ngModel)]="this.email">
+                                <small *ngIf="wrongEmailBool" class="form-text text-danger">Pogrešan format.</small>
                             </div>
                         </div>
 
@@ -87,11 +92,13 @@
                             <div class="col-md-6">
                                 <label class="small mb-1" for="inputFirstName">Ime</label>
                                 <input class="form-control" id="inputFirstName" name="inputFirstName" type="text" [(ngModel)]="this.firstName">
+                                <small *ngIf="wrongFirstNameBool" class="form-text text-danger">Pogrešan format.</small>
                             </div>
                             <!-- Form Group (last name)-->
                             <div class="col-md-6">
                                 <label class="small mb-1" for="inputLastName">Prezime</label>
                                 <input class="form-control" id="inputLastName" name="inputLastName" type="text" [(ngModel)]="this.lastName">
+                                <small *ngIf="wrongLastNameBool" class="form-text text-danger">Pogrešan format.</small>
                             </div>
                         </div>
 
diff --git a/frontend/src/app/_pages/profile/profile.component.ts b/frontend/src/app/_pages/profile/profile.component.ts
index 3e9a0d11..d055fad3 100644
--- a/frontend/src/app/_pages/profile/profile.component.ts
+++ b/frontend/src/app/_pages/profile/profile.component.ts
@@ -6,6 +6,7 @@ import { Router } from '@angular/router';
 import { PICTURES } from 'src/app/_data/ProfilePictures';
 import { Picture } from 'src/app/_data/ProfilePictures';
 import shared from '../../Shared';
+import { share } from 'rxjs';
 
 
 @Component({
@@ -71,6 +72,9 @@ export class ProfileComponent implements OnInit {
   }
 
   saveInfoChanges() {
+    if (!(this.checkInfoChanges())) //nije prosao regex
+      return;
+
     let editedUser: User = {
       _id: this.user._id,
       username: this.username,
@@ -84,18 +88,20 @@ export class ProfileComponent implements OnInit {
     this.userInfoService.changeUserInfo(editedUser).subscribe((response: any) =>{
       if (this.user.username != editedUser.username) { //promenio username, ide logout
         this.user = editedUser;
-        alert("Nakon promene korisničkog imena, moraćete ponovo da se ulogujete.");
+        this.resetInfo();
+        shared.openDialog("Obaveštenje", "Nakon promene korisničkog imena, moraćete ponovo da se ulogujete.");
         this.authService.logOut();
         this.router.navigate(['']);
         return;
       }
+      shared.openDialog("Obaveštenje", "Podaci su uspešno promenjeni.");
       this.user = editedUser;
       console.log(this.user);
       this.resetInfo();
     }, (error: any) =>{
       if (error.error == "Username already exists!") {
-        alert("Ukucano korisničko ime je već zauzeto!\nIzaberite neko drugo.");
-        (<HTMLSelectElement>document.getElementById("inputUsername")).focus();
+        shared.openDialog("Obaveštenje", "Ukucano korisničko ime je već zauzeto! Izaberite neko drugo.");
+        //(<HTMLSelectElement>document.getElementById("inputUsername")).focus();
         //poruka obavestenja ispod inputa
         this.resetInfo();
       }
@@ -103,38 +109,41 @@ export class ProfileComponent implements OnInit {
   }
 
   savePasswordChanges() {
+    this.wrongPassBool = false;
+    this.wrongNewPassBool = false;
+
+    if (!(this.checkPasswordChanges())) //nije prosao regex
+      return;
+
     if (this.newPass1 == '' && this.newPass2 == '') //ne zeli da promeni lozinku
       return;
-    console.log("zeli da promeni lozinku");
+    //console.log("zeli da promeni lozinku");
 
     if (this.newPass1 != this.newPass2) { //netacno ponovio novu lozinku
       this.wrongNewPassBool = true;
       this.resetNewPassInputs();
-      console.log("Netacno ponovljena lozinka");
+      //console.log("Netacno ponovljena lozinka");
       return;
     }
 
-    this.wrongPassBool = false;
-    this.wrongNewPassBool = false;
-
     let passwordArray: string[] = [this.oldPass, this.newPass1];
     this.userInfoService.changeUserPassword(passwordArray).subscribe((response: any) => {
-      console.log("PROMENIO LOZINKU");
+      //console.log("PROMENIO LOZINKU");
       this.resetNewPassInputs();
-      alert("Nakon promene lozinke, moraćete ponovo da se ulogujete.");
+      shared.openDialog("Obaveštenje", "Nakon promene lozinke, moraćete ponovo da se ulogujete.");
       this.authService.logOut();
       this.router.navigate(['']);
     }, (error: any) => {
       console.log("error poruka: ", error.error);
       if (error.error == 'Wrong old password!') {
         this.wrongPassBool = true;
-        (<HTMLSelectElement>document.getElementById("inputPassword")).focus();
+        //(<HTMLSelectElement>document.getElementById("inputPassword")).focus();
         return;
       }
       else if (error.error == 'Identical password!') {
-        alert("Stara i nova lozinka su identične.");
+        shared.openDialog("Obaveštenje", "Stara i nova lozinka su identične."); 
         this.resetNewPassInputs();
-        (<HTMLSelectElement>document.getElementById("inputNewPassword")).focus();
+        //(<HTMLSelectElement>document.getElementById("inputNewPassword")).focus();
         return;
       }
     });
@@ -161,5 +170,95 @@ export class ProfileComponent implements OnInit {
     shared.photoId = this.photoId;
   }
   
+  checkPasswordChanges() : boolean {
+    this.passwordValidation();
+
+    if (!(this.wrongOldPassBool || this.wrongNewPass1Bool || this.wrongNewPass2Bool))
+      return true;
+    return false;
+  }
+  checkInfoChanges() : boolean {
+    this.firstName = this.firstName.trim();
+    this.lastName = this.lastName.trim();
+    this.username = this.username.trim();
+    this.email = this.email.trim();
+
+    this.firstNameValidation();
+    this.lastNameValidation();
+    this.usernameValidation();
+    this.emailValidation();
+
+    if (!(this.wrongUsernameBool || this.wrongEmailBool || this.wrongFirstNameBool || this.wrongLastNameBool))
+      return true;
+    return false;
+  }
+
+  isCorrectName(element: string): boolean {
+    if (this.pattName.test(element) && !(this.pattTwoSpaces.test(element)) && (element.length >= 1 && element.length <= 30))
+      return true;
+    return false;
+  }
+  isCorrectUsername(element: string): boolean {
+    if (this.pattUsername.test(element) && !(this.pattTwoSpaces.test(element)) && (element.length >= 1 && element.length <= 30))
+      return true;
+    return false;
+  }
+  isCorrectEmail(element: string): boolean {
+    if (this.pattEmail.test(element.toLowerCase()) && element.length <= 320)
+      return true;
+    return false;
+  }
+  isCorrectPassword(element: string): boolean {
+    if (this.pattPassword.test(element))
+      return true;
+    return false;
+  }
+  firstNameValidation() {
+    if (this.isCorrectName(this.firstName) == true) {
+      this.wrongFirstNameBool = false;
+      return;
+    }
+    //(<HTMLSelectElement>document.getElementById('firstName')).focus();
+    this.wrongFirstNameBool = true;
+  }
+  lastNameValidation() {
+    if (this.isCorrectName(this.lastName) == true) {
+      this.wrongLastNameBool = false;
+      return;
+    }
+    //(<HTMLSelectElement>document.getElementById('lastName')).focus();
+    this.wrongLastNameBool = true;
+  }
+  usernameValidation() {
+    if (this.isCorrectUsername(this.username) == true) {
+      this.wrongUsernameBool = false;
+      return;
+    }
+    //(<HTMLSelectElement>document.getElementById('username-register')).focus();
+    this.wrongUsernameBool = true;
+  }
+  emailValidation() {
+    if (this.isCorrectEmail(this.email) == true) {
+      this.wrongEmailBool = false;
+      return;
+    }
+    //(<HTMLSelectElement>document.getElementById('email')).focus();
+    this.wrongEmailBool = true;
+  }
+  passwordValidation() {
+    if (this.isCorrectPassword(this.oldPass) && this.isCorrectPassword(this.newPass1) && this.newPass1 == this.newPass2) {
+      this.wrongOldPassBool = false;
+      this.wrongNewPass1Bool = false;
+      this.wrongNewPass2Bool = false;
+      return;
+    }
+    this.oldPass = '';
+    this.newPass1 = '';
+    this.newPass2 = '';
+    //(<HTMLSelectElement>document.getElementById('pass1')).focus();
+    this.wrongOldPassBool = true;
+    this.wrongNewPass1Bool = true;
+    this.wrongNewPass2Bool = true;
+  }
 
 }
diff --git a/frontend/src/app/_services/datasets.service.ts b/frontend/src/app/_services/datasets.service.ts
index 0ff63828..cd901481 100644
--- a/frontend/src/app/_services/datasets.service.ts
+++ b/frontend/src/app/_services/datasets.service.ts
@@ -27,4 +27,12 @@ export class DatasetsService {
   getDatasetFile(fileId: any): any {
     return this.http.get(`${API_SETTINGS.apiURL}/file/download?id=${fileId}`, { headers: this.authService.authHeader(), responseType: 'text' });
   }
+
+  editDataset(dataset: Dataset): Observable<Dataset> {
+    return this.http.put<Dataset>(`${API_SETTINGS.apiURL}/dataset/`, dataset, { headers: this.authService.authHeader() });
+  }
+
+  deleteDataset(dataset: Dataset) {
+    return this.http.delete(`${API_SETTINGS.apiURL}/dataset/` + dataset.name, { headers: this.authService.authHeader(), responseType: "text" });
+  }
 }
diff --git a/frontend/src/app/_services/predictors.service.ts b/frontend/src/app/_services/predictors.service.ts
index 54ae5694..844cd706 100644
--- a/frontend/src/app/_services/predictors.service.ts
+++ b/frontend/src/app/_services/predictors.service.ts
@@ -3,6 +3,7 @@ import { Injectable } from '@angular/core';
 import { Observable } from 'rxjs';
 import { API_SETTINGS } from 'src/config';
 import Predictor from '../_data/Predictor';
+import { Column } from '../_pages/predict/predict.component';
 import { AuthService } from './auth.service';
 
 @Injectable({
@@ -21,7 +22,15 @@ export class PredictorsService {
     return this.http.get<Predictor>(`${API_SETTINGS.apiURL}/predictor/getpredictor/`+ id, { headers: this.authService.authHeader() });
   }
 
-  usePredictor(predictor: Predictor, inputs : String[]) {
+  usePredictor(predictor: Predictor, inputs : Column[]) {
     return this.http.post(`${API_SETTINGS.apiURL}/predictor/usepredictor/` + predictor._id, inputs, { headers: this.authService.authHeader() });
   }
+
+  deletePredictor(predictor: Predictor) {
+    return this.http.delete(`${API_SETTINGS.apiURL}/predictor/` + predictor.name, { headers: this.authService.authHeader(), responseType: "text" });
+  }
+
+  getMyPredictors(): Observable<Predictor[]> {
+    return this.http.get<Predictor[]>(`${API_SETTINGS.apiURL}/predictor/mypredictors`, { headers: this.authService.authHeader() });
+  }
 }
diff --git a/frontend/src/app/app.module.ts b/frontend/src/app/app.module.ts
index d701f9d7..04523989 100644
--- a/frontend/src/app/app.module.ts
+++ b/frontend/src/app/app.module.ts
@@ -1,4 +1,4 @@
-import { NgModule } from '@angular/core';
+import { NgModule, CUSTOM_ELEMENTS_SCHEMA } from '@angular/core';
 import { BrowserModule } from '@angular/platform-browser';
 import { FormsModule } from '@angular/forms';
 import { AppRoutingModule } from './app-routing.module';
@@ -42,6 +42,8 @@ import { AnnvisualComponent } from './_elements/annvisual/annvisual.component';
 import { ExperimentComponent } from './experiment/experiment.component';
 import { LoadingComponent } from './_elements/loading/loading.component';
 import { ModelLoadComponent } from './_elements/model-load/model-load.component';
+import { AlertDialogComponent } from './_modals/alert-dialog/alert-dialog.component';
+import { AddNewDatasetComponent } from './_elements/add-new-dataset/add-new-dataset.component';
 
 @NgModule({
   declarations: [
@@ -73,7 +75,9 @@ import { ModelLoadComponent } from './_elements/model-load/model-load.component'
     AnnvisualComponent,
     ExperimentComponent,
     LoadingComponent,
-    ModelLoadComponent
+    ModelLoadComponent,
+    AlertDialogComponent,
+    AddNewDatasetComponent
   ],
   imports: [
     BrowserModule,
@@ -90,6 +94,8 @@ import { ModelLoadComponent } from './_elements/model-load/model-load.component'
     Ng2SearchPipeModule,
   ],
   providers: [],
-  bootstrap: [AppComponent]
+  bootstrap: [AppComponent],
+  schemas: [CUSTOM_ELEMENTS_SCHEMA],
+  entryComponents: [AlertDialogComponent]
 })
 export class AppModule { }
diff --git a/frontend/src/app/material.module.ts b/frontend/src/app/material.module.ts
index e85419ee..d16cef3d 100644
--- a/frontend/src/app/material.module.ts
+++ b/frontend/src/app/material.module.ts
@@ -1,18 +1,21 @@
 import { NgModule } from '@angular/core';
-
+import { CommonModule } from '@angular/common';
 import { MatDialogModule } from '@angular/material/dialog';
 import { MatButtonModule } from '@angular/material/button';
 import { MatFormFieldModule } from '@angular/material/form-field';
 import { MatInputModule } from '@angular/material/input';
 import { MatCheckboxModule } from '@angular/material/checkbox';
+import { MatIconModule } from '@angular/material/icon';
 
 @NgModule({
   exports: [
+    CommonModule,
     MatDialogModule,
     MatButtonModule,
     MatFormFieldModule,
     MatInputModule,
-    MatCheckboxModule
+    MatCheckboxModule,
+    MatIconModule
   ]
 })
 export class MaterialModule {}
\ No newline at end of file