added classification report

app.py

@@ -1,6 +1,7 @@
 import gradio as gr
 import pandas as pd
 import pickle
+import numpy as np
 from tensorflow import keras
 
 treemodel = pickle.load(open('decision_tree.pkl', 'rb'))
@@ -50,30 +51,51 @@ 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'])
+    pred_proc = pd.DataFrame(y_prednn, columns = ['predictedFraudProbability'])
+    pred=np.where(y_prednn<0.44,0,1)
+    pred_dfnn = pd.DataFrame(pred, 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)
+    pred_dfnn = pd.concat([df_originalnn, pred_proc, 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")
-nn_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")
+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 DECISION TREE")
+nn_output = gr.components.Dataframe(max_rows=20, max_cols=None, overflow_row_behaviour="paginate", type="pandas", label="predictedFraud - Predictii bazate pe modelul de clasificare NEURAL NETWORK")
 tree_interface = gr.Interface(
-                            fn=tree, 
-                            inputs=file, 
-                            outputs=tree_output,
-                            title="Fraud Detection - DECISION TREE 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>'
-                            )
+    fn=tree, 
+    inputs=file, 
+    outputs=tree_output,
+)
 
 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>'
+    fn=nn, 
+    inputs=file, 
+    outputs=nn_output,
+)
+
+def report_tree():
+    creport_tree = pd.read_csv('report_tree.csv')
+    return creport_tree
+
+def report_nn():
+    creport_nn = pd.read_csv('report_nn.csv')
+    return creport_nn
+
+cr_tree = gr.components.DataFrame(type="pandas", label="Classification Report (Decision Tree)")
+cr_nn = gr.components.DataFrame(type="pandas", label="Classification Report (Neural Network)")
+
+r_tree = gr.Interface(
+    fn=report_tree,
+    inputs = None,
+    outputs = cr_tree,
+)
+
+r_nn = gr.Interface(
+    fn=report_nn,
+    inputs = None,
+    outputs = cr_nn,
+)
 
-                            )
 #tree_interface.launch(inline=True)
-gr.Parallel(tree_interface, nn_interface).launch()
+gr.Parallel(tree_interface, nn_interface, r_tree, r_nn).launch()

report_nn.csv

@@ -0,0 +1,6 @@
+metric,precision,recall,f1-score,support
+not_fraud,1.00,1.00,1.00,1906322
+fraud,0.64,0.86,0.73,2464
+accuracy,,,1.00,1908786
+macro avg,0.82,0.93,0.87,1908786
+weighted avg,1.00,1.00,1.00,1908786

report_tree.csv

@@ -0,0 +1,6 @@
+metric,precision,recall,f1-score,support
+not_fraud,1.00,1.00,1.00,1906322
+fraud,0.65,0.89,0.75,2464
+accuracy,,,1.00,1908786
+macro avg,0.83,0.95,0.88,1908786
+weighted avg,1.00,1.00,1.00,1908786