Update app.py

app.py

@@ -5,10 +5,13 @@ import streamlit.components.v1 as components
 from sklearn.preprocessing import LabelEncoder
 
 # Load the pickled model
+@st.cache_resource
 def load_model():
-    return pickle.load(open('online_payment_fraud_detection_randomforest.pkl', 'rb'))
+    with open('online_payment_fraud_detection_randomforest.pkl', 'rb') as f:
+        return pickle.load(f)
 
 # Load the LabelEncoder
+@st.cache_resource
 def load_label_encoder():
     with open('label_encoder.pkl', 'rb') as f:
         return pickle.load(f)
@@ -25,38 +28,40 @@ def transform(le, text):
 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")
-    
+    step = st.number_input("Step: represents a unit of time where 1 step equals 1 hour", min_value=0)
     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")
+    amount = st.number_input("The amount of the transaction", min_value=0.0)
 
-    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")
+    nameOrig = st.text_input("Transaction ID (any ID)").strip()
+    nameOrig_transformed = transform(le, ['No'])  # Dummy fallback for ID field
 
-    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")
+    oldbalanceOrg = st.number_input("Balance before the transaction", min_value=0.0)
+    newbalanceOrig = st.number_input("Balance after the transaction", min_value=0.0)
 
-    isFlaggedFraud = st.selectbox('IsFlaggedFraud', ('Yes', 'No'))
-    isFlaggedFraud = transform(le, [isFlaggedFraud])
+    nameDest = st.text_input("Recipient ID (any ID)").strip()
+    nameDest_transformed = transform(le, ['No'])  # Dummy fallback for ID field
+
+    oldbalanceDest = st.number_input("Initial balance of recipient before the transaction", min_value=0.0)
+    newbalanceDest = st.number_input("The new balance of recipient after the transaction", min_value=0.0)
 
+    isFlaggedFraud = st.selectbox('Is this transaction flagged as fraud?', ('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]]
+    features = np.array([[step, typeup, amount, nameOrig_transformed, oldbalanceOrg, newbalanceOrig, nameDest_transformed, 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("""
@@ -81,7 +86,7 @@ def about_RamDevs():
 
 def main():
     st.set_page_config(page_title="Online Payment Fraud Detection", page_icon=":chart_with_upwards_trend:")
-    st.title("Welcome to our Online Payment Fraud Detection App!")
+    st.title("Welcome to our Online Payment Fraud Detection App! 🚀")
 
     le = load_label_encoder()
     app_design(le)