Update app.py

app.py

@@ -3,7 +3,7 @@ import pandas as pd
 import pickle
 
 treemodel = pickle.load(open('decision_tree.pkl', 'rb'))
-nnmodel = pickle.load(open('neural_network.pkl', 'rb'))
+#nnmodel = pickle.load(open('neural_network.pkl', 'rb'))
 
 def onehot(df, column):
     df = df.copy()
@@ -45,15 +45,15 @@ 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 +66,12 @@ 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>'
-                            )
-
-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()