Update app.py

app.py

@@ -4,52 +4,59 @@ import pickle
 import streamlit.components.v1 as components
 from sklearn.preprocessing import LabelEncoder
 
-@st.cache_resource
+# Load the pickled model
 def load_model():
-    with open('online_payment_fraud_detection_randomforest.pkl', 'rb') as f:
-        return pickle.load(f)
+    return pickle.load(open('online_payment_fraud_detection_randomforest.pkl', 'rb'))
 
-@st.cache_resource
+# Load the LabelEncoder
 def load_label_encoder():
     with open('label_encoder.pkl', 'rb') as f:
         return pickle.load(f)
 
+# Function for model prediction
 def model_prediction(model, features):
-    features = np.array(features)
-    return str(model.predict(features)[0])
+    predicted = str(model.predict(features)[0])
+    return predicted
 
 def transform(le, text):
-    return le.transform(text)[0]
+    text = le.transform(text)
+    return text[0]
 
 def app_design(le):
     st.subheader("Enter the following values:")
 
-    step = st.number_input("Step: represents a unit of time where 1 step equals 1 hour", value=0)
+    step = st.number_input("Step: represents a unit of time where 1 step equals 1 hour")
+    
     typeup = st.selectbox('Type of online transaction', ('PAYMENT', 'TRANSFER', 'CASH_OUT', 'DEBIT', 'CASH_IN'))
     typeup = transform(le, [typeup])
-    amount = st.number_input("The amount of the transaction", value=0.0)
-    
-    nameOrig = st.text_input("Transaction ID")
-    oldbalanceOrg = st.number_input("Balance before the transaction", value=0.0)
-    newbalanceOrig = st.number_input("Balance after the transaction", value=0.0)
-    
-    nameDest = st.text_input("Recipient ID")
-    oldbalanceDest = st.number_input("Initial balance of recipient before the transaction", value=0.0)
-    newbalanceDest = st.number_input("The new balance of recipient after the transaction", value=0.0)
-    
+
+    amount = st.number_input("The amount of the transaction")
+
+    nameOrig = st.text_input("Transaction ID")  # Don't transform
+    oldbalanceOrg = st.number_input("Balance before the transaction")
+    newbalanceOrig = st.number_input("Balance after the transaction")
+
+    nameDest = st.text_input("Recipient ID")  # Don't transform
+    oldbalanceDest = st.number_input("Initial balance of recipient before the transaction")
+    newbalanceDest = st.number_input("The new balance of recipient after the transaction")
+
     isFlaggedFraud = st.selectbox('IsFlaggedFraud', ('Yes', 'No'))
     isFlaggedFraud = transform(le, [isFlaggedFraud])
 
+    # Create a feature list from the user inputs
+    # ➔ set nameOrig and nameDest as 0
     features = [[step, typeup, amount, 0, oldbalanceOrg, newbalanceOrig, 0, oldbalanceDest, newbalanceDest, isFlaggedFraud]]
-    
+
+    # Load the model
     model = load_model()
-    
+
+    # Make a prediction when the user clicks the "Predict" button
     if st.button('Predict Online Payment Fraud'):
         predicted_value = model_prediction(model, features)
         if predicted_value == '1':
-            st.success("🚨 Online payment fraud detected!")
+            st.success("⚠️ Online payment fraud detected")
         else:
-            st.success("✅ No online payment fraud detected!")
+            st.success("✅ No online payment fraud detected")
 
 def about_RamDevs():
     components.html("""