pre forward files fix

src/participation-hub/federated-learning-microservice/app/routes/developers.py

@@ -5,6 +5,8 @@ import requests
 import pandas as pd
 import sys
 import network_constants
+import time
+from os.path import dirname, abspath
 
 modules_path = './'
 if os.path.exists(modules_path):
@@ -13,7 +15,7 @@ if os.path.exists(modules_path):
 def last(use_case: str):    
     #FORWARD TO GPU SERVER WITH IP AND PORT
 
-    url = f'https://{network_constants.FEDERATED_TRAINING_HOSTNAME}:{network_constants.FEDERATED_TRAINING_REST_PORT}/api/Developers/use_case/{use_case}/last_train'
+    url = f'http://{network_constants.FEDERATED_TRAINING_HOSTNAME}:{network_constants.FEDERATED_TRAINING_REST_PORT}/api/Developers/use_case/{use_case}/last_train'
     
     response = requests.get(
             url,
@@ -30,17 +32,37 @@ def upload_and_train(use_case: str, developer_id: int):
     #data = {'use_case' : use_case,
 #           'developer_id' : developer_id}
 
-    url = f'https://{network_constants.FEDERATED_TRAINING_HOSTNAME}:{network_constants.FEDERATED_TRAINING_REST_PORT}/api/Developers/use_cases/{use_case}/developer_id/{developer_id}/upload_and_train'
+    
     #url= 'gpu3.itec.aau.at/home/itec/bogdan/Articonf/smart/tools/federated-training/app/routes/developers'
-    response = requests.post(
-            url,
-            verify = False, 
-            proxies = { "http":None, "https":None },
-            files= request.files,
-            #data = data
-    )
+    try:
+        use_case_path = 'processing/'+use_case+'/'
+        app_path = dirname(dirname(abspath(__file__)))
+        file_dict = request.files
+        db_File_True = file_dict["dataset_file1"]
+        db_File_Fake = file_dict["dataset_file2"]
+        true_csv_path = os.path.join(app_path+"/"+use_case_path+"db/", "True.csv")
+        fake_csv_path = os.path.join(app_path+"/"+use_case_path+"db/", "Fake.csv")
+        db_File_True.save(true_csv_path)
+        db_File_Fake.save(fake_csv_path)
+        time.sleep(2) #wait for hte files to be copied
+        forwarded_files = {
+                    "dataset_file1": open(true_csv_path,"rb"),
+                    "dataset_file2": open(fake_csv_path,"rb")} 
+    
+    
+        url = f'http://{network_constants.FEDERATED_TRAINING_HOSTNAME}:{network_constants.FEDERATED_TRAINING_REST_PORT}/api/Developers/use_cases/{use_case}/developer_id/{developer_id}/upload_and_train'
+        response = requests.post(
+                url,
+                verify = False, 
+                proxies = { "http":None, "https":None },
+                files= forwarded_files
+                #data = data
+        )
 
-    return json.loads(response.text)
+        return json.loads(response.text)
+
+    except Exception as e:
+        return json.loads(str(e))
 
 
 #upload_and_train("text_processing",1)

tools/federated-training/app/processing/text_processing/ledger.csv

@@ -12,3 +12,4 @@ Trainer_id,Model_id,Dataset_id,Accuracy,Loss
 1,1624021190,1624021190,0.75,nan
 1,1624284673,1624284673,0.5,nan
 0,1624550528,1624550528,0.75,nan
+2,1624872086,1624872086,0.5,nan

tools/federated-training/app/routes/developers.py

 import json
 import os
 from flask import Response, request
+import requests
 import pandas as pd
 import sys
 from os.path import dirname, abspath
+import time
 modules_path = './'
 if os.path.exists(modules_path):
     sys.path.insert(1, modules_path)
@@ -34,11 +36,12 @@ def upload_and_train(use_case: str, developer_id: int):
     app_path = dirname(dirname(abspath(__file__)))
     file_dict = request.files
     db_File_True = file_dict["dataset_file1"]
-    db_File_False = file_dict["dataset_file2"]
+    db_File_Fake = file_dict["dataset_file2"]
     true_csv_path = os.path.join(app_path+"/"+use_case_path+"db/", "True.csv")
-    false_csv_path = os.path.join(app_path+"/"+use_case_path+"db/", "False.csv")
+    fake_csv_path = os.path.join(app_path+"/"+use_case_path+"db/", "Fake.csv")
     db_File_True.save(true_csv_path)
-    db_File_False.save(false_csv_path)
+    db_File_Fake.save(fake_csv_path)
+    time.sleep(2) #wait for hte files to be copied
     #THEN start processing
     last_train_metrics = main_proc.start_processing(use_case,developer_id)
     print("## Last train metrics")