Doradjen training zahtev.

4 files changed, 13 insertions, 36 deletions

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..937e643b 100644
--- a/backend/microservice/api/controller.py
+++ b/backend/microservice/api/controller.py
@@ -5,6 +5,8 @@ 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 +19,19 @@ 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)
+    #dataset, paramsModel, paramsExperiment, callback)
+    result = newmlservice.train(data, request.json["model"], request.json["experiment"], request.json["dataset"], 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/ml_socket.py b/backend/microservice/api/ml_socket.py
deleted file mode 100644
index cab157eb..00000000
--- a/backend/microservice/api/ml_socket.py
+++ /dev/null
@@ -1,30 +0,0 @@
-import asyncio
-import websockets
-import json
-
-def get_or_create_eventloop():
-    try:
-        return asyncio.get_event_loop()
-    except RuntimeError as ex:
-        if "There is no current event loop in thread" in str(ex):
-            loop = asyncio.new_event_loop()
-            asyncio.set_event_loop(loop)
-            return asyncio.get_event_loop()
-
-# create handler for each connection
-async def handler(websocket, path):
-    #data = json.loads(await websocket.recv())
-    #print(data['test'])
-    msg = await websocket.recv()
-    print(msg)
-
-async def start():
-    start_server = websockets.serve(handler, "localhost", 5027)
-    print('Websocket starting...')
-    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