Fix: Fed training 'No Context' TFF Error

tools/federated-training/app/processing/text_processing/federated_algorithm.py

@@ -3,12 +3,12 @@ import global_hyperparams as globals
 from model import get_simple_LSTM_model
 
 import pickle
-import tensorflow as tf
-import tensorflow_federated as tff
 
 
 
 def model_fn():
+    import tensorflow as tf
+    import tensorflow_federated as tff
     keras_model = get_simple_LSTM_model()
 
     return tff.learning.from_keras_model(
@@ -18,6 +18,8 @@ def model_fn():
         metrics=[tf.keras.metrics.SparseCategoricalAccuracy()])
 
 def federated_computation_new(train_dataset,test_dataset):
+    import tensorflow as tf
+    import tensorflow_federated as tff
 
     if(globals.INPUT_SPEC == None):
         #should never reach this place because INPUT_SPEC is instantiated inside get_preprocessed_train_test_data.

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

@@ -21,3 +21,6 @@ Trainer_id,Model_id,Dataset_id,Accuracy,Loss
 3,1633518451,1633518451,0.25,nan
 4,1633525163,1633525163,0.75,nan
 4,1633528455,1633528455,0.5,nan
+3,1634034576,1634034576,0.75,nan
+0,1634036764,1634036764,0.75,nan
+0,1634553320,1634553320,0.5,nan

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

@@ -41,7 +41,7 @@ def upload_and_train(use_case: str, developer_id: int):
     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(5) #wait for the files to be copied before proceeding with the processing (they are copied in a separate thread, i think?)
+    time.sleep(10) #wait for the files to be copied before proceeding with the processing (they are copied in a separate thread, i think?)
     #THEN start processing
     last_train_metrics = main_proc.start_processing(use_case,developer_id)
     print("## Last train metrics")
@@ -62,6 +62,6 @@ def trainMetricsToJSON(last_train_metrics : list):
     metricsDict["Loss"] = last_train_metrics[4]
     return json.dumps(metricsDict)
 
-#upload_and_train("text_processing",1)
+#upload_and_train("text_processing",3)
 #last("text_processing")