new

resultview.py

@@ -33,6 +33,7 @@ def tenant_visualization(similarity_matrix, requested_tenants):
 
 
 def tenant_inference(similarity_matrix, requested_tenants,dataframe):
+    print("Tenant inference check")
     #TODO THIS FUNCTION IS THE ONE USED DURING INFERENCE TIME THE MODEL WILL CALCULATE THE 4 TENANTS WITH THE HIGHER COMPATIBILITY
     try:
         similarity_matrix = similarity_matrix.astype(int)
@@ -55,6 +56,7 @@ def algo_start(id):
     #sm.data_checking(dataframe)
     similarity_matrix = sm.encoder_matrix(dataframe, min_range = 0, max_range=100)
     tenant_list = tenant_inference(similarity_matrix, id,original_dataframe)
+    print("Fin algo start check")
     return tenant_list
     #json_convert(tenant_list)
     #tenant_visualization(similarity_matrix, [20,40,50,18,15])

similarity.py

@@ -1,10 +1,9 @@
 import numpy as np
 from sklearn.preprocessing import OneHotEncoder
 import pandas as pd
-
+from sqlalchemy import URL, create_engine
 def data_preparing():
-    from sqlalchemy import URL, create_engine
-
+    print("Data preparing check")
     connection_string = URL.create(
         'postgresql',
         username='koyeb-adm',
@@ -26,6 +25,7 @@ def data_checking(dataframe):
             print(f"No missing values in column {col}")
 
 def encoder_matrix(dataframe, min_range, max_range):
+    print("Encoder matrix check")
     encoder = OneHotEncoder(sparse_output = False)
     data_encoded = encoder.fit_transform(dataframe)
     encoded_feature_names = encoder.get_feature_names_out()