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-rw-r--r--backend/api/api/Controllers/ModelController.cs38
-rw-r--r--backend/api/api/Models/Model.cs2
-rw-r--r--backend/api/api/Services/ChatHub.cs2
-rw-r--r--backend/api/api/Services/IMlConnectionService.cs2
-rw-r--r--backend/api/api/Services/MlConnectionService.cs10
-rw-r--r--backend/microservice/api/config.py2
-rw-r--r--backend/microservice/api/controller.py19
-rw-r--r--backend/microservice/api/ml_service.py2
-rw-r--r--backend/microservice/api/newmlservice.py28
-rw-r--r--backend/microservice/ml_socket.py (renamed from backend/microservice/api/ml_socket.py)3
10 files changed, 74 insertions, 34 deletions
diff --git a/backend/api/api/Controllers/ModelController.cs b/backend/api/api/Controllers/ModelController.cs
index 1f98ed26..5a4f8b65 100644
--- a/backend/api/api/Controllers/ModelController.cs
+++ b/backend/api/api/Controllers/ModelController.cs
@@ -3,6 +3,7 @@ using api.Services;
using Microsoft.AspNetCore.Authorization;
using Microsoft.AspNetCore.Http;
using Microsoft.AspNetCore.Mvc;
+using Microsoft.AspNetCore.SignalR;
using Microsoft.Net.Http.Headers;
using System.Net.Http.Headers;
@@ -20,10 +21,13 @@ namespace api.Controllers
private readonly IExperimentService _experimentService;
private IJwtToken jwtToken;
private readonly IMlConnectionService _mlConnectionService;
+ private readonly IUserService _userService;
+ private readonly IHubContext<ChatHub> _ichat;
- public ModelController(IMlConnectionService mlService, IModelService modelService, IMlConnectionService mlConnectionService, IDatasetService datasetService, IFileService fileService, IConfiguration configuration,IJwtToken token,IExperimentService experiment)
+ public ModelController(IMlConnectionService mlService, IModelService modelService, IMlConnectionService mlConnectionService, IDatasetService datasetService, IFileService fileService, IConfiguration configuration,IJwtToken token,IExperimentService experiment,IUserService user,IHubContext<ChatHub> ichat)
{
+
_mlService = mlService;
_modelService = modelService;
_datasetService = datasetService;
@@ -31,6 +35,8 @@ namespace api.Controllers
_experimentService = experiment;
jwtToken = token;
_mlConnectionService = mlConnectionService;
+ _userService= user;
+ _ichat= ichat;
}
public string getUserId()
@@ -70,7 +76,7 @@ namespace api.Controllers
[HttpPost("trainModel")]
[Authorize(Roles = "User,Guest")]
- public async Task<ActionResult<string>> Test([FromBody] TrainModelObject trainModelObject)
+ public async Task<ActionResult<string>> TrainModel([FromBody] TrainModelObject trainModelObject)
{
string experimentId = trainModelObject.ExperimentId;
string modelId = trainModelObject.ModelId;
@@ -84,10 +90,28 @@ namespace api.Controllers
var dataset = _datasetService.GetOneDataset(experiment.datasetId);
var filepath = _fileService.GetFilePath(dataset.fileId, uploaderId);
var model = _modelService.GetOneModel(modelId);
- _mlService.TrainModel(model,experiment,filepath);//To do Obavestiti korisnika kada se model istrenira
+ _mlService.TrainModel(model,experiment,filepath,dataset,uploaderId);//To do Obavestiti korisnika kada se model istrenira
return Ok();
}
+ [HttpPost("epoch")]
+ public async Task<ActionResult<string>> Epoch([FromBody] Epoch info)
+ {
+
+ var model=_modelService.GetOneModel(info.ModelId);
+ var user = _userService.GetUserByUsername(model.username);
+
+ if (ChatHub.CheckUser(user._id))
+ await _ichat.Clients.Client(ChatHub.Users[user._id]).SendAsync("NotifyEpoch",info.ModelId,info.Stat);
+
+ return Ok();
+ }
+
+
+
+
+
+
// GET: api/<ModelController>/mymodels
[HttpGet("mymodels")]
[Authorize(Roles = "User")]
@@ -166,8 +190,7 @@ namespace api.Controllers
if (existingModel != null && !overwrite)
return NotFound($"Model with name = {model.name} exisits");
else
- {
- model.isTrained = false;
+ {
//_modelService.Create(model);
//return Ok();
if (existingModel == null)
@@ -230,4 +253,9 @@ namespace api.Controllers
public string ExperimentId { get; set; }
}
+ public class Epoch
+ {
+ public string ModelId { get; set; }
+ public string Stat { get; set; }
+ }
}
diff --git a/backend/api/api/Models/Model.cs b/backend/api/api/Models/Model.cs
index 72f989a6..72ee093b 100644
--- a/backend/api/api/Models/Model.cs
+++ b/backend/api/api/Models/Model.cs
@@ -36,7 +36,7 @@ namespace api.Models
public string[] metrics { get; set; }
public int epochs { get; set; }
- public bool isTrained { get; set; }
+ //public bool isTrained { get; set; }
//public NullValues[] nullValues { get; set; }
}
}
diff --git a/backend/api/api/Services/ChatHub.cs b/backend/api/api/Services/ChatHub.cs
index efa13a3b..72febce1 100644
--- a/backend/api/api/Services/ChatHub.cs
+++ b/backend/api/api/Services/ChatHub.cs
@@ -20,6 +20,8 @@ namespace api.Services
public override async Task OnConnectedAsync()
{
string token=Context.GetHttpContext().Request.Query["access_token"];
+ if (token == null)
+ return;
string id=_tokenService.TokenToId(token);
Users.Add(id,Context.ConnectionId);
//await SendDirect(id, "poruka");
diff --git a/backend/api/api/Services/IMlConnectionService.cs b/backend/api/api/Services/IMlConnectionService.cs
index 9a9a81f4..d161bf49 100644
--- a/backend/api/api/Services/IMlConnectionService.cs
+++ b/backend/api/api/Services/IMlConnectionService.cs
@@ -7,7 +7,7 @@ namespace api.Services
{
Task<string> SendModelAsync(object model, object dataset);
Task PreProcess(Dataset dataset, string filePath,string id);
- Task TrainModel(Model model, Experiment experiment, string filePath);
+ Task TrainModel(Model model, Experiment experiment, string filePath, Dataset dataset, string id);
//Task<Dataset> PreProcess(Dataset dataset, byte[] file, string filename);
}
} \ No newline at end of file
diff --git a/backend/api/api/Services/MlConnectionService.cs b/backend/api/api/Services/MlConnectionService.cs
index d3a98042..bde9ce4c 100644
--- a/backend/api/api/Services/MlConnectionService.cs
+++ b/backend/api/api/Services/MlConnectionService.cs
@@ -32,19 +32,19 @@ namespace api.Services
var result = await this.client.ExecuteAsync(request);
return result.Content; //Response od ML microservisa
}
- public async Task TrainModel(Model model, Experiment experiment, string filePath)
+ public async Task TrainModel(Model model, Experiment experiment, string filePath,Dataset dataset,string id)
{
var request = new RestRequest("train", Method.Post);
request.AddParameter("model", JsonConvert.SerializeObject(model));
request.AddParameter("experiment", JsonConvert.SerializeObject(experiment));
+ request.AddParameter("dataset", JsonConvert.SerializeObject(dataset));
//request.AddFile("file", file,filename);
request.AddFile("file", filePath);
request.AddHeader("Content-Type", "multipart/form-data");
var result = await this.client.ExecuteAsync(request);
- Model newModel = JsonConvert.DeserializeObject<Model>(result.Content);
- newModel.isTrained = true;
- _modelService.Update(newModel._id, newModel);
+ if (ChatHub.CheckUser(id))
+ await _ichat.Clients.Client(ChatHub.Users[id]).SendAsync("NotifyModel",model.name,model._id);
return;
@@ -62,7 +62,7 @@ namespace api.Services
newDataset.isPreProcess = true;
_datasetService.Update(newDataset);
if(ChatHub.CheckUser(id))
- await _ichat.Clients.Client(ChatHub.Users[id]).SendAsync("Notify", "Preprocessed dataset with name "+newDataset.name);
+ await _ichat.Clients.Client(ChatHub.Users[id]).SendAsync("NotifyDataset",newDataset.name,newDataset._id);
return;
}
diff --git a/backend/microservice/api/config.py b/backend/microservice/api/config.py
new file mode 100644
index 00000000..2b6fbe89
--- /dev/null
+++ b/backend/microservice/api/config.py
@@ -0,0 +1,2 @@
+api_url = "http://localhost:5283/api"
+
diff --git a/backend/microservice/api/controller.py b/backend/microservice/api/controller.py
index 4d9f8f2a..08f953a6 100644
--- a/backend/microservice/api/controller.py
+++ b/backend/microservice/api/controller.py
@@ -1,10 +1,11 @@
import flask
from flask import request, jsonify
-import ml_socket
import newmlservice
import tensorflow as tf
import pandas as pd
import json
+import requests
+import config
app = flask.Flask(__name__)
app.config["DEBUG"] = True
@@ -17,16 +18,22 @@ class train_callback(tf.keras.callbacks.Callback):
#
def on_epoch_end(self, epoch, logs=None):
print(epoch)
- ml_socket.send(epoch)
+ #ml_socket.send(epoch)
+ #file = request.files.get("file")
+ url = config.api_url + "/Model/epoch"
+ requests.post(url, epoch).text
#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 = newmlservice.train(dataset, request.json["model"], train_callback)
+ f = request.files.get("file")
+ data = pd.read_csv(f)
+ paramsModel = json.loads(request.form["model"])
+ paramsExperiment = json.loads(request.form["experiment"])
+ paramsDataset = json.loads(request.form["dataset"])
+ #dataset, paramsModel, paramsExperiment, callback)
+ result = newmlservice.train(data, paramsModel, paramsExperiment,paramsDataset, train_callback)
print(result)
return jsonify(result)
diff --git a/backend/microservice/api/ml_service.py b/backend/microservice/api/ml_service.py
index 4d2212f7..16ee7cc6 100644
--- a/backend/microservice/api/ml_service.py
+++ b/backend/microservice/api/ml_service.py
@@ -101,7 +101,7 @@ class TrainingResultRegression:
class TrainingResult:
metrics: dict
'''
-def train(dataset, paramsModel, paramsExperiment, callback):
+def train(dataset, paramsModel, paramsExperiment, paramsDataset, callback):
problem_type = paramsModel["type"]
dataModel = pd.DataFrame()
dataExperiment = pd.DataFrame()
diff --git a/backend/microservice/api/newmlservice.py b/backend/microservice/api/newmlservice.py
index 02ce2250..d19a4e44 100644
--- a/backend/microservice/api/newmlservice.py
+++ b/backend/microservice/api/newmlservice.py
@@ -225,28 +225,28 @@ def train(dataset, params, callback):
#
###OPTIMIZATORI
- if(params['optimizer']=='Adam'):
+ if(params['optimizer']=='adam'):
opt=tf.keras.optimizers.Adam(learning_rate=params['learningRate'])
- elif(params['optimizer']=='Adadelta'):
+ elif(params['optimizer']=='adadelta'):
opt=tf.keras.optimizers.Adadelta(learning_rate=params['learningRate'])
- elif(params['optimizer']=='Adagrad'):
+ elif(params['optimizer']=='adagrad'):
opt=tf.keras.optimizers.Adagrad(learning_rate=params['learningRate'])
- elif(params['optimizer']=='Adamax'):
+ elif(params['optimizer']=='adamax'):
opt=tf.keras.optimizers.Adamax(learning_rate=params['learningRate'])
- elif(params['optimizer']=='Nadam'):
+ elif(params['optimizer']=='nadam'):
opt=tf.keras.optimizers.Nadam(learning_rate=params['learningRate'])
- elif(params['optimizer']=='SGD'):
+ elif(params['optimizer']=='sgd'):
opt=tf.keras.optimizers.SGD(learning_rate=params['learningRate'])
- elif(params['optimizer']=='Ftrl'):
+ elif(params['optimizer']=='ftrl'):
opt=tf.keras.optimizers.Ftrl(learning_rate=params['learningRate'])
- elif(params['optimizer']=='RMSprop'):
+ elif(params['optimizer']=='rmsprop'):
opt=tf.keras.optimizers.RMSprop(learning_rate=params['learningRate'])
###REGULARIZACIJA
@@ -282,10 +282,10 @@ def train(dataset, params, callback):
#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]),kernel_regularizer=kernelreg,bias_regularizer=biasreg,activity_regularizer=activityreg)#prvi skriveni + definisanje prethodnog-ulaznog
+ 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],kernel_regularizer=kernelreg,bias_regularizer=biasreg,activity_regularizer=activityreg))#i-ti skriveni sloj
+ 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
@@ -309,10 +309,10 @@ def train(dataset, params, callback):
#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],kernel_regularizer=kernelreg,bias_regularizer=biasreg,activity_regularizer=activityreg))#prvi skriveni + definisanje prethodnog-ulaznog
+ 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],kernel_regularizer=kernelreg,bias_regularizer=biasreg,activity_regularizer=activityreg))#i-ti skriveni sloj
+ 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'])
@@ -334,10 +334,10 @@ def train(dataset, params, callback):
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],kernel_regularizer=kernelreg,bias_regularizer=biasreg,activity_regularizer=activityreg))#prvi skriveni + definisanje prethodnog-ulaznog
+ 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],kernel_regularizer=kernelreg,bias_regularizer=biasreg,activity_regularizer=activityreg))#i-ti skriveni sloj
+ 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'])
diff --git a/backend/microservice/api/ml_socket.py b/backend/microservice/ml_socket.py
index cab157eb..c624ffaa 100644
--- a/backend/microservice/api/ml_socket.py
+++ b/backend/microservice/ml_socket.py
@@ -24,7 +24,8 @@ async def start():
get_or_create_eventloop().run_until_complete(start_server)
get_or_create_eventloop().run_forever()
+
async def send(msg):
print("WS sending message:")
print(msg)
- await websocket.send(msg) \ No newline at end of file
+ await websocket.send(msg)
generated by cgit v1.2.3 (git 2.39.1) at 2025-04-20 05:54:10 +0000