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-rw-r--r--backend/api/api/Controllers/FileController.cs103
-rw-r--r--backend/api/api/Controllers/PredictorController.cs10
-rw-r--r--backend/api/api/Models/FileModel.cs1
-rw-r--r--backend/api/api/Models/PredictorColumns.cs8
-rw-r--r--backend/api/api/Services/PredictorService.cs2
-rw-r--r--backend/microservice/api/ml_service.py33
6 files changed, 141 insertions, 16 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/microservice/api/ml_service.py b/backend/microservice/api/ml_service.py
index b264b428..73b191da 100644
--- a/backend/microservice/api/ml_service.py
+++ b/backend/microservice/api/ml_service.py
@@ -205,21 +205,26 @@ def train(dataset, params, callback):
if(problem_type=='multi-klasifikacioni'):
func=params['hiddenLayerActivationFunctions']
- funcFirst=func.pop(0)
- inputDim = len(data.columns) - 1
- classifier=tf.keras.Sequential(units=hidden_layer_neurons,input_dim=inputDim,activation=funcFirst)
- for f in func:
- classifier.add(tf.keras.layers.Dense(units=hidden_layer_neurons,activation=func))
output_func = params["outputLayerActivationFunction"]
- numberofclasses=len(output_column.unique())
- classifier.add(tf.keras.layers.Dense(units=numberofclasses,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"]
+ 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(units=hidden_layer_neurons,activation=f))
+
+ numberofclasses=len(output_column.unique())
+ classifier.add(tf.keras.layers.Dense(units=numberofclasses,activation=output_func))#output layer
+
+ 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()
@@ -227,10 +232,12 @@ def train(dataset, params, callback):
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"]
@@ -249,7 +256,10 @@ def train(dataset, params, callback):
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')
@@ -323,6 +333,9 @@ def train(dataset, params, callback):
"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')
generated by cgit v1.2.3 (git 2.39.1) at 2025-04-20 01:57:08 +0000