Merge branch 'dev' into 'master'

Merge master

See merge request igrannonica/neuronstellar!27

1 files changed, 76 insertions, 18 deletions

diff --git a/backend/microservice/api/controller.py b/backend/microservice/api/controller.py
index 8e12c41d..f870b2b1 100644
--- a/backend/microservice/api/controller.py
+++ b/backend/microservice/api/controller.py
@@ -1,3 +1,7 @@
+from cmath import log
+from dataclasses import dataclass
+from distutils.command.upload import upload
+from gc import callbacks
 import flask
 from flask import request, jsonify
 import newmlservice
@@ -9,47 +13,101 @@ import config
 
 app = flask.Flask(__name__)
 app.config["DEBUG"] = True
-app.config["SERVER_NAME"] = "127.0.0.1:5543"
-  
+app.config["SERVER_NAME"] = config.hostIP
+
+#@dataclass
+#class Predictor:
+#    _id : str
+    # username: str
+    # inputs : list
+    # output : str
+    # isPublic: bool
+    # accessibleByLink: bool
+    # dateCreated: DateTime
+    # experimentId: str
+    # modelId: str
+    # h5FileId: str
+    # metrics: list
+
+
 class train_callback(tf.keras.callbacks.Callback):
-    def __init__(self, x_test, y_test):
+    def __init__(self, x_test, y_test,modelId):
         self.x_test = x_test
         self.y_test = y_test
+        self.modelId=modelId
     #
     def on_epoch_end(self, epoch, logs=None):
-        print(epoch)
+        #print('Evaluation: ', self.model.evaluate(self.x_test,self.y_test),"\n")
+       
+        #print(epoch)
+        
+        #print(logs)
+        
         #ml_socket.send(epoch)
         #file = request.files.get("file")
         url = config.api_url + "/Model/epoch"
-        requests.post(url, epoch).text
+        r=requests.post(url, json={"Stat":str(logs),"ModelId":str(self.modelId),"EpochNum":epoch}).text
+        
+        #print(r)
         #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*************************************************")
+    #print("******************************TRAIN*************************************************")
+    
     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)
+    filepath,result = newmlservice.train(data, paramsModel, paramsExperiment,paramsDataset, train_callback)
+    """
+    f = request.json['filepath']
+    dataset = pd.read_csv(f)
+    filepath,result=newmlservice.train(dataset,request.json['model'],train_callback)
     print(result)
-    return jsonify(result)
+    """
+
+    url = config.api_url + "/file/h5"
+    files = {'file': open(filepath, 'rb')}
+    r=requests.post(url, files=files,data={"uploaderId":paramsExperiment['uploaderId']})
+    fileId=r.text
+    m = []
+    for attribute, value in result.items():
+        m.append({"Name" : attribute, "JsonValue" : value})
+    predictor = {
+        "_id" : "",
+        "uploaderId" : paramsModel["uploaderId"],
+        "inputs" : paramsExperiment["inputColumns"],
+        "output" : paramsExperiment["outputColumn"],
+        "isPublic" : False,
+        "accessibleByLink" : False,
+        "experimentId" : paramsExperiment["_id"],
+        "modelId" : paramsModel["_id"],
+        "h5FileId" : fileId,
+        "metrics" : m
+    }
+    #print(predictor)
+    #print('\n')
+    url = config.api_url + "/Predictor/add"
+    r = requests.post(url, json=predictor).text
+    #print(r)
+    return r
 
 @app.route('/predict', methods = ['POST'])
 def predict():
-    f = request.json['filepath']
-    dataset = pd.read_csv(f)
-    m = request.json['modelpath']
-    model = tf.keras.models.load_model(m)
-    print("********************************model loaded*******************************")
-    newmlservice.manageH5(dataset,request.json['model'],model)
-    return "done"
+    h5 = request.files.get("h5file")
+    model = tf.keras.models.load_model(h5)
+    paramsExperiment = json.loads(request.form["experiment"])
+    paramsPredictor = json.loads(request.form["predictor"])
+    #print("********************************model loaded*******************************")
+    result = newmlservice.predict(paramsExperiment, paramsPredictor, model)
+    return result
 
 @app.route('/preprocess',methods=['POST'])
 def returnColumnsInfo():
-    print("********************************PREPROCESS*******************************")
+    #print("********************************PREPROCESS*******************************")
     dataset = json.loads(request.form["dataset"])
     file = request.files.get("file")
     data=pd.read_csv(file)
@@ -69,8 +127,8 @@ def returnColumnsInfo():
     dataset["colCount"] = preprocess["colCount"]
     dataset["rowCount"] = preprocess["rowCount"]
     dataset["isPreProcess"] = True
-    print(dataset)
+    #print(dataset)
     return jsonify(dataset)
     
-print("App loaded.")
+#print("App loaded.")
 app.run()
\ No newline at end of file