Ispravio mlkrontroler backend i frontend tako da je dataset sinhronizovan i osposobio preprocesiranje.

2 files changed, 21 insertions, 14 deletions

diff --git a/backend/microservice/api/controller.py b/backend/microservice/api/controller.py
index 1b17f727..ff803358 100644
--- a/backend/microservice/api/controller.py
+++ b/backend/microservice/api/controller.py
@@ -4,6 +4,7 @@ import ml_socket
 import newmlservice
 import tensorflow as tf
 import pandas as pd
+import json
 
 app = flask.Flask(__name__)
 app.config["DEBUG"] = True
@@ -41,14 +42,20 @@ def predict():
 
 @app.route('/preprocess',methods=['POST'])
 def returnColumnsInfo():
-    f=request.json['filepathcolinfo']
-    dataset=pd.read_csv(f)
-    
-    result=newmlservice.returnColumnsInfo(dataset)
-
-    return jsonify(result)
-    
-
+    print("********************************PREPROCESS*******************************")
+    dataset = json.loads(request.form["dataset"])
+    file = request.files.get("file")
+    data=pd.read_csv(file)
+    preprocess = newmlservice.returnColumnsInfo(data)
+    #samo 10 jedinstvenih posto ih ima previse, bilo bi dobro da promenimo ovo da to budu 10 najzastupljenijih vrednosti
+    for col in preprocess["columnInfo"]:
+        col["uniqueValues"] = col["uniqueValues"][0:10]
+    dataset["columnInfo"] = preprocess["columnInfo"]
+    dataset["nullCols"] = preprocess["allNullColl"]
+    dataset["nullRows"] = preprocess["allNullRows"]
+    dataset["isPreProcess"] = True
+    print(dataset)
+    return jsonify(dataset)
     
 print("App loaded.")
 ml_socket.start()
diff --git a/backend/microservice/api/newmlservice.py b/backend/microservice/api/newmlservice.py
index 2ea31702..02f2ad6d 100644
--- a/backend/microservice/api/newmlservice.py
+++ b/backend/microservice/api/newmlservice.py
@@ -21,7 +21,7 @@ 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;
+#from ann_visualizer.visualize import ann_viz;
 def returnColumnsInfo(dataset):
     dict=[]
     datafront=dataset.copy()
@@ -43,7 +43,7 @@ def returnColumnsInfo(dataset):
                         'uniqueValues':uniquevalues.tolist(),
                         'median':float(mean),
                         'mean':float(median),
-                        'numNulls':float(nullCount),
+                        'numNulls':int(nullCount),
                         'min':float(minimum),
                         'max':float(maximum)
             }
@@ -52,7 +52,7 @@ def returnColumnsInfo(dataset):
             minimum=min(datafront[kolona])
             maximum=max(datafront[kolona])
             mean=datafront[kolona].mean()
-            median=s.median(datafront[kolona])
+            median=s.median(datafront[kolona].copy().dropna())
             nullCount=datafront[kolona].isnull().sum()
             if(nullCount>0):
                 allNullCols=allNullCols+1
@@ -61,7 +61,7 @@ def returnColumnsInfo(dataset):
                         'uniqueValues':[],
                         'mean':float(mean),
                         'median':float(median),
-                        'numNulls':float(nullCount),
+                        'numNulls':int(nullCount),
                         'min':float(minimum),
                         'max':float(maximum)
             }
@@ -71,7 +71,7 @@ def returnColumnsInfo(dataset):
         #print(len(NullRows))
         allNullRows=len(NullRows)
 
-    return {'columnInfo':dict,'allNullColl':allNullCols,'allNullRows':allNullRows}
+    return {'columnInfo':dict,'allNullColl':int(allNullCols),'allNullRows':int(allNullRows)}
 
 @dataclass
 class TrainingResultClassification:
@@ -433,7 +433,7 @@ def manageH5(dataset,params,h5model):
     #print(x2)
     y2 = data[output_column].values
     h5model.summary()
-    ann_viz(h5model, title="My neural network")
+    #ann_viz(h5model, title="My neural network")
 
     h5model.compile(loss=params['lossFunction'], optimizer=params['optimizer'], metrics=params['metrics'])