Update app.py

app.py

@@ -3,6 +3,7 @@ import joblib
 import gradio as gr
 from sklearn.preprocessing import OneHotEncoder
 from sklearn.impute import SimpleImputer
+from PIL import Image
 
 # Modeli, encoder'ı ve imputer'ı yükleme
 logreg_model = joblib.load('logreg_model.pkl')
@@ -58,9 +59,16 @@ def fraud_detection(accountAgeDays, numItems, localTime, paymentMethod, paymentM
     logreg_prediction = logreg_model.predict(input_data)[0]
     rf_prediction = rf_model.predict(input_data)[0]
 
-    # Sonuç görsellerini belirleme
-    logreg_image = "sahtekarlik_degil.jpg" if logreg_prediction == 0 else "sahtekarlik.jpg"
-    rf_image = "sahtekarlik_degil.jpg" if rf_prediction == 0 else "sahtekarlik.jpg"
+    # Sonuç görsellerini belirleme ve PIL Image objesi olarak yükleme
+    if logreg_prediction == 0:
+        logreg_image = Image.open("sahtekarlik_degil.png")
+    else:
+        logreg_image = Image.open("sahtekarlik.png")
+
+    if rf_prediction == 0:
+        rf_image = Image.open("sahtekarlik_degil.png")
+    else:
+        rf_image = Image.open("sahtekarlik.png")
 
     return logreg_image, rf_image
 
@@ -78,7 +86,7 @@ iface = gr.Interface(
         gr.Image(label="Logistic Regression Tahmini"),
         gr.Image(label="Random Forest Tahmini")
     ],
-    title="Ödeme Sahtekarlığı Tespit Sistemi suspicious.jpeg",
+    title="Ödeme Sahtekarlığı Tespit Sistemi",
     description="Gerekli bilgileri girerek işlemin sahte olup olmadığını tahmin edin.",
 )