1 files changed, 13 insertions, 6 deletions
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)