tree structure

app.py

@@ -15,11 +15,28 @@ def onehot(df, column):
 def dataframe(file_obj):
     df = pd.read_csv(file_obj.name)
     df = onehot(df, column='type')
-    df = df.drop(['nameOrig','nameDest'], axis=1)
+
+    #if the 'type' column doesn't have value 'CASH_OUT' then add a column 'type_CASH_OUT' with value 0
+    if 'type_CASH_OUT' not in df.columns:
+        df['type_CASH_OUT'] = 0
+    #if the 'type' column doesn't have value 'TRANSFER' then add a column 'type_TRANSFER' with value 0
+    if 'type_TRANSFER' not in df.columns:
+        df['type_TRANSFER'] = 0
+    #if the 'type' column doesn't have value 'PAYMENT' then add a column 'type_PAYMENT' with value 0
+    if 'type_PAYMENT' not in df.columns:
+        df['type_PAYMENT'] = 0
+    #if the 'type' column doesn't have value 'DEBIT' then add a column 'type_DEBIT' with value 0
+    if 'type_DEBIT' not in df.columns:
+        df['type_DEBIT'] = 0
+    #if the 'type' column doesn't have value 'PAYMENT' then add a column 'type_PAYMENT' with value 0
+    if 'type_PAYMENT' not in df.columns:
+        df['type_PAYMENT'] = 0
+    
+    df = df.drop(['nameOrig','nameDest','isFraud'], axis=1)
     print(df.shape)
     y_pred = model.predict(df)
     
-    pred_df = pd.DataFrame(y_pred, columns = ['isFraud'])
+    pred_df = pd.DataFrame(y_pred, columns = ['predictedFraud'])
     #append the predictions to the original dataframe
     pred_df = pd.concat([df, pred_df], axis=1)
     print(type(pred_df))
@@ -28,14 +45,15 @@ def dataframe(file_obj):
 #     return 'Classification Report:\n'+ clr
     return pred_df
 
-file = gr.inputs.File(file_count="single", type="file", label="CSV File for Predictions", optional=False)
-y_pred_df = gr.outputs.Dataframe(max_rows=20, max_cols=None, overflow_row_behaviour="paginate", type="pandas", label="Predictions of records in the file")
+file = gr.components.File(file_count="single", type="file", label="Fisierul CSV cu tranzactii", optional=False)
+y_pred_df = gr.components.Dataframe(max_rows=20, max_cols=5, overflow_row_behaviour="paginate", type="pandas", label="predictedFraud - Predictii bazate pe modelul de clasificare /n isFraud - Etichetele reale")
+tree_structure = gr.Image("https://imgur.com/a/dTj0c7X", label="Structura arborelui de decizie")
 interface_csv = gr.Interface(
                             fn=dataframe, 
                             inputs=file, 
                             outputs=y_pred_df,
                             title="Fraud Detection - EXPERT SYSTEM",
-                            theme="dark-peach"
+                            description="Sistem expert bazat pe un model de clasificare pentru detectarea fraudelor in tranzactii bancare",
                             )
 
 interface_csv.launch(inline=False)