From b573b0069512f4444aefdac0a24eb99c8fcb6586 Mon Sep 17 00:00:00 2001
From: Danijel Andjelkovic <adanijel99@gmail.com>
Date: Thu, 31 Mar 2022 13:48:13 +0200
Subject: Malo sredio add-model stranicu, popravio bug sa prikazom javnih
 datasetova na naslovnoj strani. ML socket salje poruke za epohe backendu.

---
 backend/microservice/api/controller.py |  1 +
 backend/microservice/api/ml_service.py | 12 +++++-------
 backend/microservice/api/ml_socket.py  |  2 ++
 3 files changed, 8 insertions(+), 7 deletions(-)

(limited to 'backend/microservice/api')

diff --git a/backend/microservice/api/controller.py b/backend/microservice/api/controller.py
index ceed02ad..059af317 100644
--- a/backend/microservice/api/controller.py
+++ b/backend/microservice/api/controller.py
@@ -16,6 +16,7 @@ class train_callback(tf.keras.callbacks.Callback):
     #
     def on_epoch_end(self, epoch, logs=None):
         print(epoch)
+        ml_socket.send(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'])
diff --git a/backend/microservice/api/ml_service.py b/backend/microservice/api/ml_service.py
index 68595a89..ea562212 100644
--- a/backend/microservice/api/ml_service.py
+++ b/backend/microservice/api/ml_service.py
@@ -124,17 +124,15 @@ def train(dataset, params, callback):
     #
     # Test
     #
+    model_name = params['_id']
+    y_pred=classifier.predict(x_test)
     if(problem_type == "regresioni"):
         classifier.evaluate(x_test, y_test)
-        classifier.save("temp/"+model_name, save_format='h5')
     elif(problem_type == "binarni-klasifikacioni"):
-        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')
+    y_pred=y_pred.flatten()
+    result=pd.DataFrame({"Actual":y_test,"Predicted":y_pred})
+    classifier.save("temp/"+model_name, save_format='h5')
     #
     # Metrike
     #
diff --git a/backend/microservice/api/ml_socket.py b/backend/microservice/api/ml_socket.py
index 65dd7321..cab157eb 100644
--- a/backend/microservice/api/ml_socket.py
+++ b/backend/microservice/api/ml_socket.py
@@ -25,4 +25,6 @@ async def start():
     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
-- 
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