Update app.py

app.py

@@ -1,9 +1,10 @@
 import gradio as gr
 import pandas as pd
 import pickle
+from tensorflow import keras
 
 treemodel = pickle.load(open('decision_tree.pkl', 'rb'))
-#nnmodel = pickle.load(open('neural_network.pkl', 'rb'))
+nnmodel = keras.models.load_model("nnmodel.h5")
 
 def onehot(df, column):
     df = df.copy()
@@ -45,15 +46,15 @@ def tree(file_obj):
     pred_df = pd.concat([df_original, pred_df], axis=1)
     return pred_df
 
-#def nn(file_obj):
-#    nn_df = pd.read_csv(file_obj.name)
-#    nn_df = dataframe(nn_df)
-#    y_prednn = nnmodel.predict(nn_df)
-#    pred_dfnn = pd.DataFrame(y_prednn, columns = ['predictedFraud'])
-#    #append the predictions to the original dataframe
-#    df_originalnn = pd.read_csv(file_obj.name)
-#    pred_dfnn = pd.concat([df_originalnn, pred_dfnn], axis=1)
-#    return pred_dfnn
+def nn(file_obj):
+    nn_df = pd.read_csv(file_obj.name)
+    nn_df = dataframe(nn_df)
+    y_prednn = nnmodel.predict(nn_df)
+    pred_dfnn = pd.DataFrame(y_prednn, columns = ['predictedFraud'])
+    #append the predictions to the original dataframe
+    df_originalnn = pd.read_csv(file_obj.name)
+    pred_dfnn = pd.concat([df_originalnn, pred_dfnn], axis=1)
+    return pred_dfnn
 
 file = gr.components.File(file_count="single", type="file", label="Fisierul CSV cu tranzactii", optional=False)
 tree_output = gr.components.Dataframe(max_rows=20, max_cols=None, overflow_row_behaviour="paginate", type="pandas", label="predictedFraud - Predictii bazate pe modelul de clasificare isFraud - Etichetele reale")
@@ -66,12 +67,13 @@ tree_interface = gr.Interface(
                             description='<h2>Sistem expert bazat pe un model de clasificare pentru detectarea fraudelor in tranzactii bancare.<h2><h3>predictedFraud - Predictii bazate pe modelul de clasificare. isFraud - Etichetele reale<h3>'
                             )
 
-#nn_interface = gr.Interface(
-#                            fn=nn, 
-#                            inputs=file, 
-#                            outputs=nn_output,
-#                            title="Fraud Detection - NEURAL NETWORK EXPERT SYSTEM",
-#                            description='<h2>Sistem expert bazat pe un model de clasificare pentru detectarea fraudelor in tranzactii bancare.<h2><h3>predictedFraud - Predictii bazate pe modelul de clasificare. isFraud - Etichetele reale<h3>'
-#                            )
-tree_interface.launch(inline=True)
-#gr.Parallel(tree_interface, nn_interface).launch()
+nn_interface = gr.Interface(
+                            fn=nn, 
+                            inputs=file, 
+                            outputs=nn_output,
+                            title="Fraud Detection - NEURAL NETWORK EXPERT SYSTEM",
+                            description='<h2>Sistem expert bazat pe un model de clasificare pentru detectarea fraudelor in tranzactii bancare.<h2><h3>predictedFraud - Predictii bazate pe modelul de clasificare. isFraud - Etichetele reale<h3>'
+
+                            )
+#tree_interface.launch(inline=True)
+gr.Parallel(tree_interface, nn_interface).launch()