Update src/streamlit_app.py

src/streamlit_app.py

@@ -1,40 +1,53 @@
-import altair as alt
-import numpy as np
+import streamlit as st
 import pandas as pd
+import numpy as np
+import joblib
+
+model = joblib.load('Fraud_txn_detection_xgboost.pkl')
+
+st.title('Fraud Transaction detector ')
+st.markdown("Please fill in the detail and press predict")
+
+st.divider()
+
 import streamlit as st
+import numpy as np
+import pandas as pd
+
+st.title("Fraud Detection Input Form")
+
+type_map = {"TRANSFER": 0, "CASH_OUT": 1}
+type_choice = st.selectbox("Transaction Type", options=list(type_map.keys()))
+type_val = type_map[type_choice]
+
+
+amount = st.number_input("Transaction Amount", min_value=0.0, value=1000.0)
+
+oldbalanceOrg = st.number_input("Old Balance (Origin)", min_value=0.0, value=5000.0)
+newbalanceOrig = st.number_input("New Balance (Origin)", min_value=0.0, value=4000.0)
+
+oldbalanceDest = st.number_input("Old Balance (Destination)", min_value=0.0, value=0.0)
+newbalanceDest = st.number_input("New Balance (Destination)", min_value=0.0, value=1000.0)
+
+errordiffbalanceOrg = newbalanceOrig + amount - oldbalanceOrg
+errordiffbalanceDest = oldbalanceDest + amount - newbalanceDest
+
+if st.button("Predict"):
+    input_data = pd.DataFrame([{
+        'type': type_val,
+        'amount': amount,
+        'oldbalanceOrg': oldbalanceOrg,
+        'newbalanceOrig': newbalanceOrig,
+        'oldbalanceDest': oldbalanceDest,
+        'newbalanceDest': newbalanceDest,
+        'errordiffbalanceOrg': errordiffbalanceOrg,
+        'errordiffbalanceDest': errordiffbalanceDest
+    }])
+
+    prediction = model.predict(input_data)[0]
+    st.subheader(f"Prediction : {prediction}")
 
-"""
-# Welcome to Streamlit!
-
-Edit `/streamlit_app.py` to customize this app to your heart's desire :heart:.
-If you have any questions, checkout our [documentation](https://docs.streamlit.io) and [community
-forums](https://discuss.streamlit.io).
-
-In the meantime, below is an example of what you can do with just a few lines of code:
-"""
-
-num_points = st.slider("Number of points in spiral", 1, 10000, 1100)
-num_turns = st.slider("Number of turns in spiral", 1, 300, 31)
-
-indices = np.linspace(0, 1, num_points)
-theta = 2 * np.pi * num_turns * indices
-radius = indices
-
-x = radius * np.cos(theta)
-y = radius * np.sin(theta)
-
-df = pd.DataFrame({
-    "x": x,
-    "y": y,
-    "idx": indices,
-    "rand": np.random.randn(num_points),
-})
-
-st.altair_chart(alt.Chart(df, height=700, width=700)
-    .mark_point(filled=True)
-    .encode(
-        x=alt.X("x", axis=None),
-        y=alt.Y("y", axis=None),
-        color=alt.Color("idx", legend=None, scale=alt.Scale()),
-        size=alt.Size("rand", legend=None, scale=alt.Scale(range=[1, 150])),
-    ))
+    if prediction ==1:
+        st.error("This Transaction is fraud")
+    else:
+        st.success("Transaction is not fraud")