Update slapp.py

slapp.py

@@ -1,96 +1,101 @@
-import streamlit as st
-import pickle
-import pandas as pd
-
-# Load the saved model
-try:
-    model = pickle.load(open('model.pkl', 'rb'))
-except Exception as e:
-    st.error(f"Error loading model: {e}")
-    model = None
-
-# Streamlit app
-st.title("Fraud Detection API")
-st.write("Enter the transaction details to check if it's acceptable or fraudulent.")
-
-# Create input fields for the features
-time = st.number_input('Time')
-v1 = st.number_input('V1')
-v2 = st.number_input('V2')
-v3 = st.number_input('V3')
-v4 = st.number_input('V4')
-v5 = st.number_input('V5')
-v6 = st.number_input('V6')
-v7 = st.number_input('V7')
-v8 = st.number_input('V8')
-v9 = st.number_input('V9')
-v10 = st.number_input('V10')
-v11 = st.number_input('V11')
-v12 = st.number_input('V12')
-v13 = st.number_input('V13')
-v14 = st.number_input('V14')
-v15 = st.number_input('V15')
-v16 = st.number_input('V16')
-v17 = st.number_input('V17')
-v18 = st.number_input('V18')
-v19 = st.number_input('V19')
-v20 = st.number_input('V20')
-v21 = st.number_input('V21')
-v22 = st.number_input('V22')
-v23 = st.number_input('V23')
-v24 = st.number_input('V24')
-v25 = st.number_input('V25')
-v26 = st.number_input('V26')
-v27 = st.number_input('V27')
-v28 = st.number_input('V28')
-amount = st.number_input('Amount')
-
-# Prepare a button for prediction
-if st.button('Predict'):
-    try:
-        # Create a DataFrame from the input data
-        transaction_data = pd.DataFrame({
-            'Time': [time],
-            'V1': [v1],
-            'V2': [v2],
-            'V3': [v3],
-            'V4': [v4],
-            'V5': [v5],
-            'V6': [v6],
-            'V7': [v7],
-            'V8': [v8],
-            'V9': [v9],
-            'V10': [v10],
-            'V11': [v11],
-            'V12': [v12],
-            'V13': [v13],
-            'V14': [v14],
-            'V15': [v15],
-            'V16': [v16],
-            'V17': [v17],
-            'V18': [v18],
-            'V19': [v19],
-            'V20': [v20],
-            'V21': [v21],
-            'V22': [v22],
-            'V23': [v23],
-            'V24': [v24],
-            'V25': [v25],
-            'V26': [v26],
-            'V27': [v27],
-            'V28': [v28],
-            'Amount': [amount]
-        })
-
-        # Perform prediction using the loaded model
-        prediction = model.predict(transaction_data)
-
-        # Prepare response
-        if prediction[0] == 0:
-            st.success('Prediction: Acceptable transaction')
-        else:
-            st.error('Prediction: Fraudulent transaction')
-
-    except Exception as e:
-        st.error(f'Error: {str(e)}')
-
+import streamlit as st
+import pickle
+import pandas as pd
+
+# Load the saved model
+@st.cache_resource  # Cache the model loading to avoid reloading on each run
+def load_model():
+    try:
+        model = pickle.load(open('model.pkl', 'rb'))
+        return model
+    except Exception as e:
+        st.error(f"Error loading model: {e}")
+        return None
+
+model = load_model()
+
+# Title of the app
+st.title("Fraud Detection API")
+st.markdown("Welcome to the Fraud Detection API! Please enter the transaction details below:")
+
+# Input fields for the transaction features
+time = st.number_input("Time", min_value=0.0, step=0.1)
+v1 = st.number_input("V1", step=0.01)
+v2 = st.number_input("V2", step=0.01)
+v3 = st.number_input("V3", step=0.01)
+v4 = st.number_input("V4", step=0.01)
+v5 = st.number_input("V5", step=0.01)
+v6 = st.number_input("V6", step=0.01)
+v7 = st.number_input("V7", step=0.01)
+v8 = st.number_input("V8", step=0.01)
+v9 = st.number_input("V9", step=0.01)
+v10 = st.number_input("V10", step=0.01)
+v11 = st.number_input("V11", step=0.01)
+v12 = st.number_input("V12", step=0.01)
+v13 = st.number_input("V13", step=0.01)
+v14 = st.number_input("V14", step=0.01)
+v15 = st.number_input("V15", step=0.01)
+v16 = st.number_input("V16", step=0.01)
+v17 = st.number_input("V17", step=0.01)
+v18 = st.number_input("V18", step=0.01)
+v19 = st.number_input("V19", step=0.01)
+v20 = st.number_input("V20", step=0.01)
+v21 = st.number_input("V21", step=0.01)
+v22 = st.number_input("V22", step=0.01)
+v23 = st.number_input("V23", step=0.01)
+v24 = st.number_input("V24", step=0.01)
+v25 = st.number_input("V25", step=0.01)
+v26 = st.number_input("V26", step=0.01)
+v27 = st.number_input("V27", step=0.01)
+v28 = st.number_input("V28", step=0.01)
+amount = st.number_input("Amount", min_value=0.0, step=0.1)
+
+# Button to make predictions
+if st.button("Predict"):
+    if model:
+        # Create a DataFrame from the input data
+        transaction_data = pd.DataFrame({
+            'Time': [time],
+            'V1': [v1],
+            'V2': [v2],
+            'V3': [v3],
+            'V4': [v4],
+            'V5': [v5],
+            'V6': [v6],
+            'V7': [v7],
+            'V8': [v8],
+            'V9': [v9],
+            'V10': [v10],
+            'V11': [v11],
+            'V12': [v12],
+            'V13': [v13],
+            'V14': [v14],
+            'V15': [v15],
+            'V16': [v16],
+            'V17': [v17],
+            'V18': [v18],
+            'V19': [v19],
+            'V20': [v20],
+            'V21': [v21],
+            'V22': [v22],
+            'V23': [v23],
+            'V24': [v24],
+            'V25': [v25],
+            'V26': [v26],
+            'V27': [v27],
+            'V28': [v28],
+            'Amount': [amount]
+        })
+
+        # Perform prediction
+        prediction = model.predict(transaction_data)
+
+        # Display results
+        if prediction[0] == 0:
+            st.success("✅ Acceptable transaction")
+        else:
+            st.error("🚨 Fraudulent transaction")
+    else:
+        st.error("Model not loaded.")
+
+