Update app.py

app.py

@@ -1,8 +1,8 @@
+# app.py
 import streamlit as st
 import pandas as pd
 import joblib
 
-# Load the XGBoost model
 @st.cache_resource
 def load_model():
     return joblib.load("fraud_xgb_model.pkl")
@@ -10,16 +10,15 @@ def load_model():
 model = load_model()
 
 st.title("💳 Fraud Detection App")
-st.write("Upload a CSV file to check for fraudulent transactions.")
 
-uploaded_file = st.file_uploader("Upload CSV", type="csv")
+uploaded_file = st.file_uploader("Upload a CSV file for prediction", type=["csv"])
 
-if uploaded_file is not None:
-    data = pd.read_csv(uploaded_file)
-    st.write("✅ Uploaded Data Preview:", data.head())
+if uploaded_file:
+    input_data = pd.read_csv(uploaded_file)
+    st.write("📄 Uploaded Data", input_data.head())
 
-    if st.button("🔍 Predict Fraud"):
-        predictions = model.predict(data)
-        data["Fraud Prediction"] = predictions
-        st.write("🔔 Prediction Results:", data)
-        st.download_button("⬇️ Download Results", data.to_csv(index=False), file_name="fraud_predictions.csv")
+    if st.button("Predict Fraud"):
+        predictions = model.predict(input_data)
+        input_data["Prediction"] = predictions
+        st.write("🧾 Results", input_data.head())
+        st.download_button("Download Results", input_data.to_csv(index=False), file_name="predictions.csv")