diff options
21 files changed, 313 insertions, 86 deletions
@@ -4,4 +4,6 @@ sandbox/test-projekat-danijel/backend/.vs/ sandbox/testAppSonja/MiniApkSonja/MiniApkSonja/bin/ sandbox/testAppSonja/MiniApkSonja/MiniApkSonja/obj/ sandbox/TestTamara/TestTamara/TestTamara/obj/ -sandbox/TestTamara/TestTamara/TestTamara/bin/
\ No newline at end of file +sandbox/TestTamara/TestTamara/TestTamara/bin/ +backend/microservice/temp/ +backend/microservice/api/__pycache__/ diff --git a/backend/api/api/Controllers/ModelController.cs b/backend/api/api/Controllers/ModelController.cs index b997efa3..4bc094cd 100644 --- a/backend/api/api/Controllers/ModelController.cs +++ b/backend/api/api/Controllers/ModelController.cs @@ -14,22 +14,28 @@ namespace api.Controllers { private IMlConnectionService _mlService; + private readonly IDatasetService _datasetService; + private readonly IFileService _fileService; private readonly IModelService _modelService; private JwtToken jwtToken; - public ModelController(IMlConnectionService mlService, IModelService modelService, IConfiguration configuration) + public ModelController(IMlConnectionService mlService, IModelService modelService, IDatasetService datasetService, IFileService fileService, IConfiguration configuration) { _mlService = mlService; _modelService = modelService; + _datasetService = datasetService; + _fileService = fileService; jwtToken = new JwtToken(configuration); } [HttpPost("sendModel")] [Authorize(Roles = "User")] - public async Task<ActionResult<string>> Test([FromBody] object model) + public async Task<ActionResult<string>> Test([FromBody] Model model) { - var result = await _mlService.SendModelAsync(model); + var dataset = _datasetService.GetOneDataset(model.datasetId); + var filepath = _fileService.GetFilePath(dataset.fileId, dataset.username); + var result = await _mlService.SendModelAsync(model, filepath); return Ok(result); } diff --git a/backend/api/api/Models/Model.cs b/backend/api/api/Models/Model.cs index 5678daaf..2baab1c0 100644 --- a/backend/api/api/Models/Model.cs +++ b/backend/api/api/Models/Model.cs @@ -38,10 +38,13 @@ namespace api.Models public int batchSize { get; set; } // na izlazu je moguce da bude vise neurona (klasifikacioni problem sa vise od 2 klase) public int outputNeurons { get; set; } - public string inputLayerActivationFunction { get; set; } - public string hiddenLayerActivationFunction { get; set; } + public string[] hiddenLayerActivationFunctions { get; set; } public string outputLayerActivationFunction { get; set; } + public string[] metrics { get; set; } + public int epochs { get; set; } + public string nullValues { get; set; } + public string[] nullValuesReplacers { get; set; } } } diff --git a/backend/api/api/Services/DatasetService.cs b/backend/api/api/Services/DatasetService.cs index 5e708d11..45ed18a9 100644 --- a/backend/api/api/Services/DatasetService.cs +++ b/backend/api/api/Services/DatasetService.cs @@ -61,6 +61,11 @@ namespace api.Services } //odraditi za pretragu getOne + public Dataset GetOneDataset(string id) + { + return _dataset.Find(dataset => dataset._id == id).FirstOrDefault(); + } + //ako je potrebno da se zameni name ili ekstenzija public void Update(string username, string name, Dataset dataset) { diff --git a/backend/api/api/Services/IDatasetService.cs b/backend/api/api/Services/IDatasetService.cs index be56f5cb..dbe43321 100644 --- a/backend/api/api/Services/IDatasetService.cs +++ b/backend/api/api/Services/IDatasetService.cs @@ -6,6 +6,7 @@ namespace api.Services public interface IDatasetService { Dataset GetOneDataset(string username, string name); + Dataset GetOneDataset(string id); List<Dataset> SearchDatasets(string name, string username); List<Dataset> GetMyDatasets(string username); List<Dataset> SortDatasets(string username, bool ascdsc, int latest); diff --git a/backend/api/api/Services/IMlConnectionService.cs b/backend/api/api/Services/IMlConnectionService.cs index f38fb50a..ee839d28 100644 --- a/backend/api/api/Services/IMlConnectionService.cs +++ b/backend/api/api/Services/IMlConnectionService.cs @@ -3,6 +3,6 @@ namespace api.Services { public interface IMlConnectionService { - Task<string> SendModelAsync(object model); + Task<string> SendModelAsync(object model, object dataset); } }
\ No newline at end of file diff --git a/backend/api/api/Services/IModelService.cs b/backend/api/api/Services/IModelService.cs index ee5c279f..637d09a3 100644 --- a/backend/api/api/Services/IModelService.cs +++ b/backend/api/api/Services/IModelService.cs @@ -6,6 +6,7 @@ namespace api.Services public interface IModelService { Model GetOneModel(string username, string name); + Model GetOneModel(string id); List<Model> GetMyModels(string username); List<Model> GetLatestModels(string username); //List<Model> GetPublicModels(); diff --git a/backend/api/api/Services/MlConnectionService.cs b/backend/api/api/Services/MlConnectionService.cs index 9b167537..9c3b3fd8 100644 --- a/backend/api/api/Services/MlConnectionService.cs +++ b/backend/api/api/Services/MlConnectionService.cs @@ -6,13 +6,19 @@ namespace api.Services { public class MlConnectionService : IMlConnectionService { - public async Task<string> SendModelAsync(object model) + private RestClient client; + + public MlConnectionService() + { + this.client = new RestClient("http://127.0.0.1:5543"); + } + + public async Task<string> SendModelAsync(object model, object dataset) { - RestClient client = new RestClient("http://localhost:5000"); - var request = new RestRequest("data", Method.Post); - request.AddJsonBody(model); - var result = await client.ExecuteAsync(request); - return result.Content;//Response od ML microservisa + var request = new RestRequest("train", Method.Post); + request.AddJsonBody(new { model, dataset}); + var result = await this.client.ExecuteAsync(request); + return result.Content; //Response od ML microservisa } } } diff --git a/backend/api/api/Services/ModelService.cs b/backend/api/api/Services/ModelService.cs index f42219f5..eae8c78b 100644 --- a/backend/api/api/Services/ModelService.cs +++ b/backend/api/api/Services/ModelService.cs @@ -50,6 +50,11 @@ namespace api.Services return _model.Find(model => model.username == username && model.name == name).FirstOrDefault(); } + public Model GetOneModel(string id) + { + return _model.Find(model => model._id == id).FirstOrDefault(); + } + public void Update(string username, string name, Model model) { _model.ReplaceOne(model => model.username == username && model.name == name, model); diff --git a/backend/microservice/PythonServer/project/api/socket/client.py b/backend/microservice/PythonServer/project/socket_example/socket/client.py index d5740e25..d5740e25 100644 --- a/backend/microservice/PythonServer/project/api/socket/client.py +++ b/backend/microservice/PythonServer/project/socket_example/socket/client.py diff --git a/backend/microservice/PythonServer/project/api/socket/server.py b/backend/microservice/PythonServer/project/socket_example/socket/server.py index d6ff3f7c..d6ff3f7c 100644 --- a/backend/microservice/PythonServer/project/api/socket/server.py +++ b/backend/microservice/PythonServer/project/socket_example/socket/server.py diff --git a/backend/microservice/PythonServer/project/api/socket2/client.py b/backend/microservice/PythonServer/project/socket_example/socket2/client.py index 65e76b55..65e76b55 100644 --- a/backend/microservice/PythonServer/project/api/socket2/client.py +++ b/backend/microservice/PythonServer/project/socket_example/socket2/client.py diff --git a/backend/microservice/PythonServer/project/api/socket2/server.py b/backend/microservice/PythonServer/project/socket_example/socket2/server.py index c65dae78..c65dae78 100644 --- a/backend/microservice/PythonServer/project/api/socket2/server.py +++ b/backend/microservice/PythonServer/project/socket_example/socket2/server.py diff --git a/backend/microservice/api/controller.py b/backend/microservice/api/controller.py new file mode 100644 index 00000000..ceed02ad --- /dev/null +++ b/backend/microservice/api/controller.py @@ -0,0 +1,42 @@ +import flask +from flask import request, jsonify +import ml_socket +import ml_service +import tensorflow as tf +import pandas as pd + +app = flask.Flask(__name__) +app.config["DEBUG"] = True +app.config["SERVER_NAME"] = "127.0.0.1:5543" + +class train_callback(tf.keras.callbacks.Callback): + def __init__(self, x_test, y_test): + self.x_test = x_test + self.y_test = y_test + # + def on_epoch_end(self, epoch, logs=None): + print(epoch) + #print('Evaluation: ', self.model.evaluate(self.x_test,self.y_test),"\n") #broj parametara zavisi od izabranih metrika loss je default + +@app.route('/train', methods = ['POST']) +def train(): + print("******************************TRAIN*************************************************") + f = request.json["dataset"] + dataset = pd.read_csv(f) + # + result = ml_service.train(dataset, request.json["model"], train_callback) + print(result) + return jsonify(result) + +@app.route('/predict', methods = ['POST']) +def predict(): + f = request.json['filepath'] + dataset = pd.read_csv(f) + m = request.json['modelpath'] + #model = tf.keras.models.load_model(m) + # + #model.predict? + +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 new file mode 100644 index 00000000..efd24fdc --- /dev/null +++ b/backend/microservice/api/ml_service.py @@ -0,0 +1,155 @@ +import pandas as pd +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 +from sklearn.preprocessing import StandardScaler +from sklearn.model_selection import train_test_split +from dataclasses import dataclass + +@dataclass +class TrainingResult: + accuracy: float + precision: float + recall: float + tn: float + fp: float + fn: float + tp: float + specificity: float + f1: float + mse: float + mae: float + mape: float + rmse: float + fpr: float + tpr: float + +def train(dataset, params, callback): + 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 + # + 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) + # + # 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 + # + # Podela na test i trening skupove + # + test=params["randomTestSetDistribution"] + randomOrder = params["randomOrder"] + if(randomOrder): + random=50 + else: + random=0 + x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=test, random_state=random) + # + # 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"] + # + # Test + # + y_pred=classifier.predict(x_test) + y_pred=(y_pred>=0.5).astype('int') + #y_pred=(y_pred * 100).astype('int') + y_pred=y_pred.flatten() + result=pd.DataFrame({"Actual":y_test,"Predicted":y_pred}) + model_name = params['_id'] + classifier.save("temp/"+model_name, save_format='h5') + # + # 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) + # 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()) + + + + diff --git a/backend/microservice/ml_socket.py b/backend/microservice/api/ml_socket.py index 5489b787..65dd7321 100644 --- a/backend/microservice/ml_socket.py +++ b/backend/microservice/api/ml_socket.py @@ -14,12 +14,15 @@ def get_or_create_eventloop(): # create handler for each connection async def handler(websocket, path): #data = json.loads(await websocket.recv()) - #reply = f"Data recieved as: {data}!" #print(data['test']) msg = await websocket.recv() - await websocket.send("[" + msg + "]") + print(msg) -start_server = websockets.serve(handler, "localhost", 5027) +async def start(): + start_server = websockets.serve(handler, "localhost", 5027) + print('Websocket starting...') + get_or_create_eventloop().run_until_complete(start_server) + get_or_create_eventloop().run_forever() -get_or_create_eventloop().run_until_complete(start_server) -get_or_create_eventloop().run_forever()
\ No newline at end of file +async def send(msg): + await websocket.send(msg)
\ No newline at end of file diff --git a/frontend/src/app/_data/Model.ts b/frontend/src/app/_data/Model.ts index 48418d51..32247bbd 100644 --- a/frontend/src/app/_data/Model.ts +++ b/frontend/src/app/_data/Model.ts @@ -23,12 +23,14 @@ export default class Model { public hiddenLayerNeurons: number = 1, public hiddenLayers: number = 1, public batchSize: number = 5, - public hiddenLayerActivationFunction = [], + public hiddenLayerActivationFunctions: string[] = ['sigmoid'], //public inputLayerActivationFunction: ActivationFunction = ActivationFunction.Sigmoid, public outputLayerActivationFunction: ActivationFunction = ActivationFunction.Sigmoid, public username: string = '', public nullValues: NullValueOptions = NullValueOptions.DeleteRows, - public nullValuesReplacers = [] + public nullValuesReplacers = [], + public metrics: Metric[] = [], // TODO add to add-model form + public epochs: number = 5 // TODO add to add-model form ) { } } @@ -111,4 +113,11 @@ export enum ReplaceWith { None = 'Popuni...', Mean = 'Srednja vrednost', Median = 'Medijana' +} + +export enum Metric { + MSE = 'mse', + MAE = 'mae', + RMSE = 'rmse' + //... }
\ No newline at end of file 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 9dde9afe..38b3ed47 100644 --- a/frontend/src/app/_pages/add-model/add-model.component.html +++ b/frontend/src/app/_pages/add-model/add-model.component.html @@ -71,7 +71,8 @@ <br> <div *ngFor="let item of selectedDataset.header; let i = index"> <input class="form-check-input" type="checkbox" value="{{item}}" id="cb_{{item}}" - name="cbsNew" [checked] = "this.selectedOutputColumnVal != item" [disabled]="this.selectedOutputColumnVal == item"> + name="cbsNew" [checked]="this.selectedOutputColumnVal != item" + [disabled]="this.selectedOutputColumnVal == item"> <label class="form-check-label" for="cb_{{item}}"> {{item}} </label> @@ -163,7 +164,8 @@ </div> <div class="col-2"> <select id=typeOptions class="form-control" name="type" [(ngModel)]="newModel.type"> - <option *ngFor="let option of Object.keys(ProblemType); let optionName of Object.values(ProblemType)" + <option + *ngFor="let option of Object.keys(ProblemType); let optionName of Object.values(ProblemType)" [value]="option"> {{ optionName }} </option> @@ -176,7 +178,8 @@ </div> <div class="col-1"> <input type="number" min="1" class="form-control" name="hiddenLayers" - [(ngModel)]="newModel.hiddenLayers"> + [(ngModel)]="newModel.hiddenLayers" + (change)="newModel.hiddenLayerActivationFunctions = [].constructor(newModel.hiddenLayers).fill(newModel.hiddenLayerActivationFunctions[0])"> </div> </div> @@ -258,56 +261,36 @@ </div> <div class="row p-2"> - - <!-- <div class="col-1"> - </div> + <div class="col-3"></div> <div class="col-3"> - <label for="inputLayerActivationFunction" class="col-form-label">Funkcija aktivacije ulaznog - sloja:</label> + <label for="hiddenLayerActivationFunction" class="col-form-label">Funkcija aktivacije skrivenih + slojeva:</label> </div> - - <div class="col-2"> - <select id=inputLayerActivationFunctionOptions class="form-control" - name="inputLayerActivationFunction" [(ngModel)]="newModel.inputLayerActivationFunction"> - <option - *ngFor="let option of Object.keys(ActivationFunction); let optionName of Object.values(ActivationFunction)" - [value]="option"> - {{ optionName }} - </option> - </select> + <div class="col-3"> + <div *ngFor="let item of [].constructor(newModel.hiddenLayers); let i = index"> + <select [id]="'hiddenLayerActivationFunctionOption_'+i" class="form-control" + [(ngModel)]="newModel.hiddenLayerActivationFunctions[i]"> + <option + *ngFor="let option of Object.keys(ActivationFunction); let optionName of Object.values(ActivationFunction)" + [value]="option"> + {{ optionName }} + </option> + </select> + </div> </div> + <div class="col-3"></div> + </div> + + <div class="row p-2"> <div class="col-1"> </div> - --> - - <div class="col-5"> + <div class="col-3"> <label for="splitYesNo" class="form-check-label">Podela test skupa: <input id="splitYesNo" class="form-check-input" type="checkbox" [checked]="newModel.randomTestSet" (change)="newModel.randomTestSet = !newModel.randomTestSet"> </label> </div> - <div class="col"> - </div> - </div> - - <div class="row p-2"> - <div class="col-1"> - </div> - <div class="col-3"> - <label for="hiddenLayerActivationFunction" class="col-form-label">Funkcija aktivacije skrivenih - slojeva:</label> - </div> - <div class="col-2"> - <select id=hiddenLayerActivationFunctionOptions class="form-control" - name="hiddenLayerActivationFunction" [(ngModel)]="newModel.hiddenLayerActivationFunction"> - <option - *ngFor="let option of Object.keys(ActivationFunction); let optionName of Object.values(ActivationFunction)" - [value]="option"> - {{ optionName }} - </option> - </select> - </div> <div class="col-1"> </div> <div class="col-2"> @@ -357,7 +340,7 @@ <button class="btn btn-lg col-4" style="background-color:#003459; color:white;" (click)="addModel();">Sačuvaj model</button> <div class="col"></div> - <button class="btn btn-lg col-4 disabled" style="background-color:#003459; color:white;" + <button class="btn btn-lg col-4" style="background-color:#003459; color:white;" (click)="trainModel();">Treniraj model</button> <div class="col"></div> </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 37672d0a..4270a2db 100644 --- a/frontend/src/app/_pages/add-model/add-model.component.ts +++ b/frontend/src/app/_pages/add-model/add-model.component.ts @@ -76,19 +76,28 @@ export class AddModelComponent implements OnInit { addModel() { if (!this.showMyDatasets) - this.saveModelWithNewDataset(); + this.saveModelWithNewDataset(_ => { console.log('MODEL ADDED (with new dataset).') }); else - this.saveModelWithExistingDataset(); + this.saveModelWithExistingDataset(_ => { console.log('MODEL ADDED (with existing dataset).') }); } trainModel() { - this.saveModelWithNewDataset().subscribe((modelId: any) => { - if (modelId) - this.models.trainModel(modelId); - }); //privremeno cuvanje modela => vraca id sacuvanog modela koji cemo da treniramo sad + let saveFunc; + + if (!this.showMyDatasets) + saveFunc = (x: (arg0: any) => void) => { this.saveModelWithNewDataset(x) }; + else + saveFunc = (x: (arg0: any) => void) => { this.saveModelWithExistingDataset(x) }; + + saveFunc(((model: any) => { + console.log('Saved, training model...', model); + this.models.trainModel(model).subscribe(response => { + console.log('Train model complete!', response); + }); + })); //privremeno cuvanje modela => vraca id sacuvanog modela koji cemo da treniramo sad } - saveModelWithNewDataset(): any { + saveModelWithNewDataset(callback: ((arg0: any) => void)) { this.getCheckedInputCols(); this.getCheckedOutputCol(); @@ -117,7 +126,7 @@ export class AddModelComponent implements OnInit { this.newModel.username = shared.username; this.models.addModel(this.newModel).subscribe((response) => { - console.log('ADD MODEL: DONE! REPLY:\n', response); + callback(response); }, (error) => { alert("Model sa unetim nazivom već postoji u Vašoj kolekciji.\nPromenite naziv modela i nastavite sa kreiranim datasetom."); }); //kraj addModel subscribe @@ -133,8 +142,7 @@ export class AddModelComponent implements OnInit { } //kraj prvog ifa } - saveModelWithExistingDataset(): any { - + saveModelWithExistingDataset(callback: ((arg0: any) => void)): any { if (this.selectedDataset) { //dataset je izabran this.getCheckedInputCols(); this.getCheckedOutputCol(); @@ -147,7 +155,7 @@ export class AddModelComponent implements OnInit { this.newModel.username = shared.username; this.models.addModel(this.newModel).subscribe((response) => { - console.log('ADD MODEL: DONE! REPLY:\n', response); + callback(response); }, (error) => { alert("Model sa unetim nazivom već postoji u Vašoj kolekciji.\nPromenite naziv modela i nastavite sa kreiranim datasetom."); }); @@ -226,7 +234,7 @@ export class AddModelComponent implements OnInit { 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 + else break; //nema potrebe dalje } console.log(this.datasetFile); diff --git a/frontend/src/app/_services/models.service.ts b/frontend/src/app/_services/models.service.ts index 58ddb2e6..6ea4310c 100644 --- a/frontend/src/app/_services/models.service.ts +++ b/frontend/src/app/_services/models.service.ts @@ -35,25 +35,23 @@ export class ModelsService { addDataset(dataset: Dataset): Observable<any> { return this.http.post(`${API_SETTINGS.apiURL}/dataset/add`, dataset, { headers: this.authService.authHeader() }); } - trainModel(modelId: string): Observable<any> { - return this.http.post(`${API_SETTINGS.apiURL}/model/train`, modelId, { headers: this.authService.authHeader() }); + trainModel(model: Model): Observable<any> { + return this.http.post(`${API_SETTINGS.apiURL}/model/sendmodel`, model, { headers: this.authService.authHeader(), responseType: 'text' }); } getMyDatasets(): Observable<Dataset[]> { return this.http.get<Dataset[]>(`${API_SETTINGS.apiURL}/dataset/mydatasets`, { headers: this.authService.authHeader() }); } - + getMyModels(): Observable<Model[]> { return this.http.get<Model[]>(`${API_SETTINGS.apiURL}/model/mymodels`, { headers: this.authService.authHeader() }); } - editModel(model:Model) : Observable<Model> - { + editModel(model: Model): Observable<Model> { return this.http.put<Model>(`${API_SETTINGS.apiURL}/model/`, model, { headers: this.authService.authHeader() }); } - deleteModel(model:Model) : Observable<any> - { - return this.http.delete(`${API_SETTINGS.apiURL}/model/`+model.name, { headers: this.authService.authHeader() }); + deleteModel(model: Model): Observable<any> { + return this.http.delete(`${API_SETTINGS.apiURL}/model/` + model.name, { headers: this.authService.authHeader() }); } } diff --git a/frontend/src/app/_services/web-socket.service.ts b/frontend/src/app/_services/web-socket.service.ts index 890ada6b..1a7efa87 100644 --- a/frontend/src/app/_services/web-socket.service.ts +++ b/frontend/src/app/_services/web-socket.service.ts @@ -13,15 +13,15 @@ export class WebSocketService { constructor() { this.ws = new WebsocketBuilder(API_SETTINGS.apiWSUrl) - .withBackoff(new ConstantBackoff(30000)) - .onOpen((i, e) => { console.log('WS: Connected to ' + API_SETTINGS.apiWSUrl) }) + .withBackoff(new ConstantBackoff(120000)) + .onOpen((i, e) => { /*console.log('WS: Connected to ' + API_SETTINGS.apiWSUrl)*/ }) .onMessage((i, e) => { console.log('WS MESSAGE: ', e.data); this.handlers.forEach(handler => { handler(e.data); }) }) - .onClose((i, e) => { console.log('WS: Connection closed!') }) + .onClose((i, e) => { /*console.log('WS: Connection closed!')*/ }) .build(); } |