Create app.py

app.py

@@ -0,0 +1,25 @@
+import streamlit as st
+from transformers import pipeline
+
+# Load pretrained fraud detection model from Hugging Face
+@st.cache_resource
+def load_model():
+    return pipeline("text-classification", model="juliensimon/xlm-roberta-base-finetuned-fraud-detection")
+
+model = load_model()
+
+# Streamlit UI
+st.title("💳 Fraud Detection System (Hugging Face Model)")
+
+# User input text
+user_input = st.text_area("Enter transaction description:", "Payment of $500 for electronics purchase")
+
+if st.button("Check for Fraud"):
+    prediction = model(user_input)
+    label = prediction[0]["label"]
+    score = prediction[0]["score"]
+
+    if label == "LABEL_1":  # Assuming LABEL_1 means fraud
+        st.error(f"🚨 Fraudulent Transaction Detected! (Confidence: {score:.2f})")
+    else:
+        st.success(f"✅ Legitimate Transaction (Confidence: {score:.2f})")