Update app.py

app.py

@@ -1,222 +1,225 @@
-# Importo librerie utili
-import re
-import json
-import numpy as np
-import pandas as pd
-import streamlit as st
-from itertools import product
-from datetime import datetime
-import matplotlib.pyplot as plt
-from htbuilder.units import percent, px
-from sklearn.model_selection import train_test_split
-from htbuilder import HtmlElement, div, hr, a, p, img, styles
-from sklearn.preprocessing import MinMaxScaler, StandardScaler, LabelBinarizer, OneHotEncoder
-from sklearn.metrics import precision_score,recall_score,f1_score, accuracy_score, confusion_matrix, r2_score, precision_recall_curve, roc_auc_score, roc_curve
-
-# Importo i moduli custom presenti nella cartella "Functions" e il modello di rete neurale nella cartella "Model"
-# In alternativa, lasciarli nella stessa cartella di questo file .py e chiamarli con from name_module import * 
-from model import NeuralNetwork                                                                                        # Modello Neural Network
-from functions import remove_missing, feature_selection, imputation_process, missing_target, categoric_to_numeric      # Pulizia e trasformazione dataset
-from functions import custom_split, standardize_x_train, target_transformation, nn_builder, metrics_plot               # Training, Finalizzazione, e Pipeline
-from functions import model_finalization, test_pipeline                                                                # Finalizzazione e Pipeline
-from functions import footer                                                                                           # Footer
-
-##################################################################################################################################################################################################
-
-# Imposto la pagina
-st.set_page_config(
-    page_title = "Prediction with Neural Networks",
-    page_icon = "🧊",
-    layout = "wide",
-    menu_items = { 'About': "This is simple app to guide users and build a Neural Network model to make predictions." }
-)
-st.markdown('''<style> section.main > div {max-width:75rem} </style>''', unsafe_allow_html = True)
-
-# Titolo
-st.write("# Predictions with Neural Networks")
-st.write("")
-st.write("This is a simple app to guide you in the process of applying a custom Neural Network model to a dataset. Currently")
-st.write("- The app supports binary classification, multivariate classification, and regression analyses.")
-st.write("- The app cannot process dates in your dataset.")
-st.write("")
-st.write("### Upload Data")
-uploaded_file = st.file_uploader("Choose a CSV file for the analysis")        # Pulsante per upload dati (file CSV)
-st.write("Important: the delimiter in the csv file must be a semicolon!")
-
-if uploaded_file is not None:                                                 # Check se il file è stato caricato o meno - Tutto lo script si basa sul caricamento o meno di un file
-    st.write("File successfully uploaded!")                                   # Messaggio di caricamento     
-    dataframe = pd.read_csv(uploaded_file, delimiter = ';')
-    if st.checkbox('Show dataframe', key = 50):                
-        st.write(dataframe)
-
-    # Primo check sui valori mancanti
-    st.write("Number of missing values")
-    st.write(  pd.DataFrame( {'# Missing values': np.array(dataframe.isna().sum()), '% Missing values': np.array(100*dataframe.isna().sum()/dataframe.shape[0])}, index = dataframe.columns))
-    st.write("**Note**: Columns with more than 70\% of missing data will be removed from the dataset")
-    st.write("**Note**: Rows with more than 70\% of missing data will be removed from the dataset")
-
-    # Rimozione righe e colonne con più del 70% di valori mancanti    
-    dataframe, Selected_columns_start = remove_missing.remove_missing(dataframe)
-
-    # Type of analysis
-    Task1 = st.selectbox( 'What is the task of this analysis?', ['','Classification','Regression'] )
-    st.write("If the task is 'Classification', the final model will predict classes; if 'Regression', it will predict numbers.")
-
-    # L'app si avvia solo se è stata scelta la tipologia di analisi
-    if Task1 :
-        #-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-        # Selezione delle varie features (numeriche e categoriche) e della colonna target e correzione colonne numeriche del dataframe
-        st.text("")
-        st.text("")
-        st.text("")
-        st.write("### Select Target, Categorical, and Numerical features")
-        dataframe, Tar, Categ, Numer = feature_selection.feature_selection(dataframe, Task1)
-        st.write("Unselected columns will be excluded from the dataset.")
-        
-        #-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-        # Gestione dei valori mancanti nelle colonne categoriche e categoriche, e infine i valori mancanti nella colonna Target (che vengono eliminati)
-        st.text("")
-        st.text("")
-        st.text("")
-        st.write("### Deal with missing data")   
-        st.write("If the target features has missing values, they will be dropped from the dataset")
-        dataframe, categ_impute, numer_impute, Sub_categ_list, Sub_num_list, step_further, a, b = imputation_process.imputation(dataframe, Categ, Numer)
-        dataframe, step_further = missing_target.missing_target(dataframe, Categ, Numer, Tar, a, b, step_further)
-        st.write("Number of missing values: %d" % dataframe.isna().sum().sum())
-        if st.checkbox('Show dataframe', key = 53):                
-            st.write(dataframe)
-    
-        #-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-        # Conversione dei valori categorici in valori numerici    
-        # Check dello step, della presenza di colonne categoriche e dell'assenza di valori mancanti (altrimenti, lo script si interrompe)
-        if (step_further == 3) and (len(Categ) != 0 and Categ != ["None"]) and (dataframe.isna().sum().sum() == 0) :
-            dataframe, Tra_categ_list, step_further = categoric_to_numeric.categoric_to_numeric(dataframe, Categ, step_further)
-        elif (dataframe.isna().sum().sum() != 0) :
-            st.write("Something's wrong with the data. Missing values are still there...")
-        else :
-            step_further, Tra_categ_list = 4, [[], None, []]
-        
-        #-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-        # Creazione dei set di Training e Validation per la valutazione del modello
-        # Check iniziale per mostrare la sezione
-        if step_further == 4 and (Categ or Numer) and dataframe.isna().sum().sum() == 0:
-            X, y, X_train, X_test, y_train, y_test, step_further, final_columns = custom_split.train_test_customsplit(dataframe, Tar, step_further)
-            st.session_state["Final_columns"] = final_columns
-            
-        #-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-        # Standardizzazione del dataframe X per il Training
-        # Check avanzamento e valori nulli
-        if step_further == 5 and dataframe.isna().sum().sum() == 0:
-            X_train, X_test, Tra_num, step_further, flag_stand = standardize_x_train.standardize_x_train(dataframe, X_train, X_test, step_further)
-                
-        #-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-        # Trasformazione della colonna Target
-        if step_further == 6 :
-            y_train, y_test, Norm_tar_list, step_further = target_transformation.target_transformation(dataframe, Tar, y_train, y_test, step_further, Task1)
-            if st.checkbox('Show Target (training set)', key = 61):                
-                st.write(y_train)
-    
-        #-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-        # Costruzione della Rete Neurale
-        # Dopo il primo training, è possibile finalizzare il modello ed applicare un nuovo file di test
-        if step_further == 7 and Task1 :
-            # Scelta dei diversi parametri da parte dell'utente e costruzione dell'oggetto "Model"
-            Hidden_layers, Algo, Alpha, Regularization, Momentum, Early_stopping, Verbose, Max_iter, Function_, Batch, Decay, Lambda, Random_state, Patient, Final_metric = nn_builder.nn_builder(dataframe, Task1)
-            Model = NeuralNetwork.NeuralNet(task = Task1, function = Function_, Hidden_layers = Hidden_layers, algo = Algo, batch = Batch, alpha = float(Alpha), decay = float(Decay), 
-                            regularization = Regularization, Lambda = float(Lambda), Max_iter = int(Max_iter), momentum = float(Momentum), random_state = int(Random_state), verbose = Verbose,
-                            early_stopping = Early_stopping, patient = int(Patient), flag_plot = False, metric = Final_metric)
-            st.text("")
-            st.text("")
-            st.text("")
-            if st.button('Start the training!'):                                                                              # Pulsante per avviare il training
-                Model.Training(X_train, y_train, X_test, y_test)                                                              # Training NN
-                st.write('Training complete!')
-                metrics_plot.metrics_plot(Model, X_train, X_test, y_train, y_test, Task1, Norm_tar_list, Final_metric)        # Calcolo metriche finali (per Regressione) e plot
-
-            
-            #----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-            # Model Finalization
-            st.text("")
-            st.text("")
-            st.text("")
-            st.write("Now, if you want, you can finalize the model!")
-            st.write("The Training and the Test set will be used together to create the final model.")
-            st.write("The dataset will be standardized and the Target normalized using the methods defined above.") 
-            st.write(r"After the model finalization, you can download the JSON file with the model parameters and weights.")
-            
-            flag_finalization = 0                            # Flag finalizzazione
-            if "ButBut" not in st.session_state :            # Pulsante finalizzazione
-                st.session_state["ButBut"] = False
-            else :
-                st.session_state["ButBut"] = st.button("Finalization of the model")
-    
-            # Inizio processo di finalizzazione del modello: eseguo il training finale e salvo i parametri in un file JSON
-            if st.session_state["ButBut"] :
-                Final_model, Tra_num_list_final, flag_finalization, Norm_tar_list_final = model_finalization.finalization(X, y, Model, Task1, Final_metric, Tra_num, Norm_tar_list, flag_stand)
-    
-                # Salvo tutto nella sessione (per far in modo che, una volta caricato il file di test, tutti parametri rimangano salvati)
-                st.session_state["Final_model"] = Final_model
-                st.session_state["Tra_num_list_final"] = Tra_num_list_final
-                st.session_state["Norm_tar_list_final"] = Norm_tar_list_final
-                st.session_state["flag_finalization"] = flag_finalization
-    
-                # Salvataggio del modello finale nel file "Best_model_parameters.json"
-                file_name = "Best_model_parameters.json"
-                Final_model.Save_model(file_name)
-    
-            #----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-            # Model application on a new file
-            st.write("")
-            st.write("")
-            st.write("### Upload Data for testing the model")
-            st.write("If you finalized your model, you can upload a new file to apply the best model")
-            st.write("The file must have the same number of columns of the original file with the same names.")
-            uploaded_file_test = st.file_uploader("Choose a CSV file to apply the best model")                    # Upload a new file
-            if uploaded_file_test is not None and st.session_state["flag_finalization"] == 1:                     # Check se è stato caricato un file tramite pulsante   
-                # Trasformazione del dataset con pipeline
-                X_test_final, y_test_final, dataframe_test = test_pipeline.pipeline_nn(uploaded_file_test, Selected_columns_start, Numer, Categ, Tar, Sub_num_list, Sub_categ_list, 
-                                                Tra_categ_list, st.session_state["Final_columns"], st.session_state["Tra_num_list_final"], st.session_state["Norm_tar_list_final"], Task1)
-    
-                Predictions_test = st.session_state["Final_model"].Predict( X_test_final )                        # Applicazione del modello finale
-                if Task1 == "Classification" :                                                                    # Calcolo predizioni e probabilità (solo per classificazione)
-                    Predictions_prob_test = st.session_state["Final_model"].Predict_proba( X_test_final )
-                    dataframe_test["Probability"] = Predictions_prob_test
-                if Task1 == "Regression" :
-                    if st.session_state["Norm_tar_list_final"][0] == 3:
-                        Predictions_test = st.session_state["Norm_tar_list_final"][1].inverse_transform(Predictions_test)
-                    elif st.session_state["Norm_tar_list_final"][0] == 1:
-                        Predictions_test = 10**Predictions_test + 1
-                
-                dataframe_test["Predictions"] = Predictions_test
-                st.write("")
-                st.write("Uploaded table with predictions:")
-                st.write( dataframe_test )
-                
-                # Converto il dataframe e lo salvo in un file csv (se l'utente clicca un pulsante)
-                dataframe_test = dataframe_test.to_csv(index = False).encode('utf-8')
-                st.download_button( "Download the dataframe", dataframe_test, "Predictions.csv", "text/csv", key = 'download-csv' )
-    
-                st.write("")
-                st.write("If you want to make changes to the original model:")
-                st.write(" 1. Modify the model parameters accordingly")
-                st.write(" 2. Re-train and re-finalize the model")
-                st.write("After that, you will see the new predictions in the uploaded dataframe.")
-                st.write("")
-                st.write("-----------------------------")
-                st.write("")
-                st.write("")
-                html_str = f"""<style>p.a {{font: bold 23.5px Sans;}}</style><p class="a">Everything is done!</p>"""                
-                st.markdown(html_str, unsafe_allow_html = True)
-
-    # If a task has not been chosen
-    else :
-        st.write("")
-        st.write("")
-        st.write("Choose a task for this analysis!")
-
-# --------------------------------------------------------------------------------------------------------------------------------------------------------
-# Footer (le funzioni utilizzate sono in functions.py)
-if __name__ == "__main__":
-    myargs = [ "Made in ", footer.image_render('https://avatars3.githubusercontent.com/u/45109972?s=400&v=4', width = px(25), height = px(25)), 
-        " by ", footer.link_render("https://www.linkedin.com/in/samuele-campitiello-ph-d-913b90104/", "Samuele Campitiello") ]
-    footer.footer(*myargs)
+# Importo librerie utili
+import re
+import json
+import numpy as np
+import pandas as pd
+import streamlit as st
+from itertools import product
+from datetime import datetime
+import matplotlib.pyplot as plt
+from htbuilder.units import percent, px
+from sklearn.model_selection import train_test_split
+from htbuilder import HtmlElement, div, hr, a, p, img, styles
+from sklearn.preprocessing import MinMaxScaler, StandardScaler, LabelBinarizer, OneHotEncoder
+from sklearn.metrics import precision_score,recall_score,f1_score, accuracy_score, confusion_matrix, r2_score, precision_recall_curve, roc_auc_score, roc_curve
+import os
+os.mkdir("functions")
+os.mkdir("models")
+os.mkdir("test files")
+# Importo i moduli custom presenti nella cartella "Functions" e il modello di rete neurale nella cartella "Model"
+# In alternativa, lasciarli nella stessa cartella di questo file .py e chiamarli con from name_module import * 
+from model import NeuralNetwork                                                                                        # Modello Neural Network
+from functions import remove_missing, feature_selection, imputation_process, missing_target, categoric_to_numeric      # Pulizia e trasformazione dataset
+from functions import custom_split, standardize_x_train, target_transformation, nn_builder, metrics_plot               # Training, Finalizzazione, e Pipeline
+from functions import model_finalization, test_pipeline                                                                # Finalizzazione e Pipeline
+from functions import footer                                                                                           # Footer
+
+##################################################################################################################################################################################################
+
+# Imposto la pagina
+st.set_page_config(
+    page_title = "Prediction with Neural Networks",
+    page_icon = "🧊",
+    layout = "wide",
+    menu_items = { 'About': "This is simple app to guide users and build a Neural Network model to make predictions." }
+)
+st.markdown('''<style> section.main > div {max-width:75rem} </style>''', unsafe_allow_html = True)
+
+# Titolo
+st.write("# Predictions with Neural Networks")
+st.write("")
+st.write("This is a simple app to guide you in the process of applying a custom Neural Network model to a dataset. Currently")
+st.write("- The app supports binary classification, multivariate classification, and regression analyses.")
+st.write("- The app cannot process dates in your dataset.")
+st.write("")
+st.write("### Upload Data")
+uploaded_file = st.file_uploader("Choose a CSV file for the analysis")        # Pulsante per upload dati (file CSV)
+st.write("Important: the delimiter in the csv file must be a semicolon!")
+
+if uploaded_file is not None:                                                 # Check se il file è stato caricato o meno - Tutto lo script si basa sul caricamento o meno di un file
+    st.write("File successfully uploaded!")                                   # Messaggio di caricamento     
+    dataframe = pd.read_csv(uploaded_file, delimiter = ';')
+    if st.checkbox('Show dataframe', key = 50):                
+        st.write(dataframe)
+
+    # Primo check sui valori mancanti
+    st.write("Number of missing values")
+    st.write(  pd.DataFrame( {'# Missing values': np.array(dataframe.isna().sum()), '% Missing values': np.array(100*dataframe.isna().sum()/dataframe.shape[0])}, index = dataframe.columns))
+    st.write("**Note**: Columns with more than 70\% of missing data will be removed from the dataset")
+    st.write("**Note**: Rows with more than 70\% of missing data will be removed from the dataset")
+
+    # Rimozione righe e colonne con più del 70% di valori mancanti    
+    dataframe, Selected_columns_start = remove_missing.remove_missing(dataframe)
+
+    # Type of analysis
+    Task1 = st.selectbox( 'What is the task of this analysis?', ['','Classification','Regression'] )
+    st.write("If the task is 'Classification', the final model will predict classes; if 'Regression', it will predict numbers.")
+
+    # L'app si avvia solo se è stata scelta la tipologia di analisi
+    if Task1 :
+        #-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
+        # Selezione delle varie features (numeriche e categoriche) e della colonna target e correzione colonne numeriche del dataframe
+        st.text("")
+        st.text("")
+        st.text("")
+        st.write("### Select Target, Categorical, and Numerical features")
+        dataframe, Tar, Categ, Numer = feature_selection.feature_selection(dataframe, Task1)
+        st.write("Unselected columns will be excluded from the dataset.")
+        
+        #-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
+        # Gestione dei valori mancanti nelle colonne categoriche e categoriche, e infine i valori mancanti nella colonna Target (che vengono eliminati)
+        st.text("")
+        st.text("")
+        st.text("")
+        st.write("### Deal with missing data")   
+        st.write("If the target features has missing values, they will be dropped from the dataset")
+        dataframe, categ_impute, numer_impute, Sub_categ_list, Sub_num_list, step_further, a, b = imputation_process.imputation(dataframe, Categ, Numer)
+        dataframe, step_further = missing_target.missing_target(dataframe, Categ, Numer, Tar, a, b, step_further)
+        st.write("Number of missing values: %d" % dataframe.isna().sum().sum())
+        if st.checkbox('Show dataframe', key = 53):                
+            st.write(dataframe)
+    
+        #-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
+        # Conversione dei valori categorici in valori numerici    
+        # Check dello step, della presenza di colonne categoriche e dell'assenza di valori mancanti (altrimenti, lo script si interrompe)
+        if (step_further == 3) and (len(Categ) != 0 and Categ != ["None"]) and (dataframe.isna().sum().sum() == 0) :
+            dataframe, Tra_categ_list, step_further = categoric_to_numeric.categoric_to_numeric(dataframe, Categ, step_further)
+        elif (dataframe.isna().sum().sum() != 0) :
+            st.write("Something's wrong with the data. Missing values are still there...")
+        else :
+            step_further, Tra_categ_list = 4, [[], None, []]
+        
+        #-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
+        # Creazione dei set di Training e Validation per la valutazione del modello
+        # Check iniziale per mostrare la sezione
+        if step_further == 4 and (Categ or Numer) and dataframe.isna().sum().sum() == 0:
+            X, y, X_train, X_test, y_train, y_test, step_further, final_columns = custom_split.train_test_customsplit(dataframe, Tar, step_further)
+            st.session_state["Final_columns"] = final_columns
+            
+        #-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
+        # Standardizzazione del dataframe X per il Training
+        # Check avanzamento e valori nulli
+        if step_further == 5 and dataframe.isna().sum().sum() == 0:
+            X_train, X_test, Tra_num, step_further, flag_stand = standardize_x_train.standardize_x_train(dataframe, X_train, X_test, step_further)
+                
+        #-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
+        # Trasformazione della colonna Target
+        if step_further == 6 :
+            y_train, y_test, Norm_tar_list, step_further = target_transformation.target_transformation(dataframe, Tar, y_train, y_test, step_further, Task1)
+            if st.checkbox('Show Target (training set)', key = 61):                
+                st.write(y_train)
+    
+        #-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
+        # Costruzione della Rete Neurale
+        # Dopo il primo training, è possibile finalizzare il modello ed applicare un nuovo file di test
+        if step_further == 7 and Task1 :
+            # Scelta dei diversi parametri da parte dell'utente e costruzione dell'oggetto "Model"
+            Hidden_layers, Algo, Alpha, Regularization, Momentum, Early_stopping, Verbose, Max_iter, Function_, Batch, Decay, Lambda, Random_state, Patient, Final_metric = nn_builder.nn_builder(dataframe, Task1)
+            Model = NeuralNetwork.NeuralNet(task = Task1, function = Function_, Hidden_layers = Hidden_layers, algo = Algo, batch = Batch, alpha = float(Alpha), decay = float(Decay), 
+                            regularization = Regularization, Lambda = float(Lambda), Max_iter = int(Max_iter), momentum = float(Momentum), random_state = int(Random_state), verbose = Verbose,
+                            early_stopping = Early_stopping, patient = int(Patient), flag_plot = False, metric = Final_metric)
+            st.text("")
+            st.text("")
+            st.text("")
+            if st.button('Start the training!'):                                                                              # Pulsante per avviare il training
+                Model.Training(X_train, y_train, X_test, y_test)                                                              # Training NN
+                st.write('Training complete!')
+                metrics_plot.metrics_plot(Model, X_train, X_test, y_train, y_test, Task1, Norm_tar_list, Final_metric)        # Calcolo metriche finali (per Regressione) e plot
+
+            
+            #----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
+            # Model Finalization
+            st.text("")
+            st.text("")
+            st.text("")
+            st.write("Now, if you want, you can finalize the model!")
+            st.write("The Training and the Test set will be used together to create the final model.")
+            st.write("The dataset will be standardized and the Target normalized using the methods defined above.") 
+            st.write(r"After the model finalization, you can download the JSON file with the model parameters and weights.")
+            
+            flag_finalization = 0                            # Flag finalizzazione
+            if "ButBut" not in st.session_state :            # Pulsante finalizzazione
+                st.session_state["ButBut"] = False
+            else :
+                st.session_state["ButBut"] = st.button("Finalization of the model")
+    
+            # Inizio processo di finalizzazione del modello: eseguo il training finale e salvo i parametri in un file JSON
+            if st.session_state["ButBut"] :
+                Final_model, Tra_num_list_final, flag_finalization, Norm_tar_list_final = model_finalization.finalization(X, y, Model, Task1, Final_metric, Tra_num, Norm_tar_list, flag_stand)
+    
+                # Salvo tutto nella sessione (per far in modo che, una volta caricato il file di test, tutti parametri rimangano salvati)
+                st.session_state["Final_model"] = Final_model
+                st.session_state["Tra_num_list_final"] = Tra_num_list_final
+                st.session_state["Norm_tar_list_final"] = Norm_tar_list_final
+                st.session_state["flag_finalization"] = flag_finalization
+    
+                # Salvataggio del modello finale nel file "Best_model_parameters.json"
+                file_name = "Best_model_parameters.json"
+                Final_model.Save_model(file_name)
+    
+            #----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
+            # Model application on a new file
+            st.write("")
+            st.write("")
+            st.write("### Upload Data for testing the model")
+            st.write("If you finalized your model, you can upload a new file to apply the best model")
+            st.write("The file must have the same number of columns of the original file with the same names.")
+            uploaded_file_test = st.file_uploader("Choose a CSV file to apply the best model")                    # Upload a new file
+            if uploaded_file_test is not None and st.session_state["flag_finalization"] == 1:                     # Check se è stato caricato un file tramite pulsante   
+                # Trasformazione del dataset con pipeline
+                X_test_final, y_test_final, dataframe_test = test_pipeline.pipeline_nn(uploaded_file_test, Selected_columns_start, Numer, Categ, Tar, Sub_num_list, Sub_categ_list, 
+                                                Tra_categ_list, st.session_state["Final_columns"], st.session_state["Tra_num_list_final"], st.session_state["Norm_tar_list_final"], Task1)
+    
+                Predictions_test = st.session_state["Final_model"].Predict( X_test_final )                        # Applicazione del modello finale
+                if Task1 == "Classification" :                                                                    # Calcolo predizioni e probabilità (solo per classificazione)
+                    Predictions_prob_test = st.session_state["Final_model"].Predict_proba( X_test_final )
+                    dataframe_test["Probability"] = Predictions_prob_test
+                if Task1 == "Regression" :
+                    if st.session_state["Norm_tar_list_final"][0] == 3:
+                        Predictions_test = st.session_state["Norm_tar_list_final"][1].inverse_transform(Predictions_test)
+                    elif st.session_state["Norm_tar_list_final"][0] == 1:
+                        Predictions_test = 10**Predictions_test + 1
+                
+                dataframe_test["Predictions"] = Predictions_test
+                st.write("")
+                st.write("Uploaded table with predictions:")
+                st.write( dataframe_test )
+                
+                # Converto il dataframe e lo salvo in un file csv (se l'utente clicca un pulsante)
+                dataframe_test = dataframe_test.to_csv(index = False).encode('utf-8')
+                st.download_button( "Download the dataframe", dataframe_test, "Predictions.csv", "text/csv", key = 'download-csv' )
+    
+                st.write("")
+                st.write("If you want to make changes to the original model:")
+                st.write(" 1. Modify the model parameters accordingly")
+                st.write(" 2. Re-train and re-finalize the model")
+                st.write("After that, you will see the new predictions in the uploaded dataframe.")
+                st.write("")
+                st.write("-----------------------------")
+                st.write("")
+                st.write("")
+                html_str = f"""<style>p.a {{font: bold 23.5px Sans;}}</style><p class="a">Everything is done!</p>"""                
+                st.markdown(html_str, unsafe_allow_html = True)
+
+    # If a task has not been chosen
+    else :
+        st.write("")
+        st.write("")
+        st.write("Choose a task for this analysis!")
+
+# --------------------------------------------------------------------------------------------------------------------------------------------------------
+# Footer (le funzioni utilizzate sono in functions.py)
+if __name__ == "__main__":
+    myargs = [ "Made in ", footer.image_render('https://avatars3.githubusercontent.com/u/45109972?s=400&v=4', width = px(25), height = px(25)), 
+        " by ", footer.link_render("https://www.linkedin.com/in/samuele-campitiello-ph-d-913b90104/", "Samuele Campitiello") ]
+    footer.footer(*myargs)