Update slapp.py

slapp.py

@@ -1,110 +1,26 @@
-import streamlit as st
+from fastapi import FastAPI
 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
+        raise RuntimeError(f"Error loading model: {e}")
 
 model = load_model()
+app = FastAPI()
 
-# Title of the app
-st.title("Fraud Detection API")
-st.markdown("Welcome to the Fraud Detection API! Please enter the transaction details below:")
-
-# Tabs for input sections
-tab1, tab2, tab3 = st.tabs(["Basic Info", "Features (V1 - V14)", "Features (V15 - V28)"])
-
-# Horizontal layout for Basic Info
-with tab1:
-    st.header("Basic Information")
-    col1, col2 = st.columns(2)
-    with col1:
-        time = st.number_input("Time", min_value=0.0, step=0.1)
-    with col2:
-        amount = st.number_input("Amount", min_value=0.0, step=0.1)
-
-# Horizontal layout for Features V1 - V14
-with tab2:
-    st.header("Features (V1 - V14)")
-    cols = st.columns(7)
-    with cols[0]:
-        v1 = st.number_input("V1", step=0.01)
-        v2 = st.number_input("V2", step=0.01)
-    with cols[1]:
-        v3 = st.number_input("V3", step=0.01)
-        v4 = st.number_input("V4", step=0.01)
-    with cols[2]:
-        v5 = st.number_input("V5", step=0.01)
-        v6 = st.number_input("V6", step=0.01)
-    with cols[3]:
-        v7 = st.number_input("V7", step=0.01)
-        v8 = st.number_input("V8", step=0.01)
-    with cols[4]:
-        v9 = st.number_input("V9", step=0.01)
-        v10 = st.number_input("V10", step=0.01)
-    with cols[5]:
-        v11 = st.number_input("V11", step=0.01)
-        v12 = st.number_input("V12", step=0.01)
-    with cols[6]:
-        v13 = st.number_input("V13", step=0.01)
-        v14 = st.number_input("V14", step=0.01)
-
-# Horizontal layout for Features V15 - V28
-with tab3:
-    st.header("Features (V15 - V28)")
-    cols = st.columns(7)
-    with cols[0]:
-        v15 = st.number_input("V15", step=0.01)
-        v16 = st.number_input("V16", step=0.01)
-    with cols[1]:
-        v17 = st.number_input("V17", step=0.01)
-        v18 = st.number_input("V18", step=0.01)
-    with cols[2]:
-        v19 = st.number_input("V19", step=0.01)
-        v20 = st.number_input("V20", step=0.01)
-    with cols[3]:
-        v21 = st.number_input("V21", step=0.01)
-        v22 = st.number_input("V22", step=0.01)
-    with cols[4]:
-        v23 = st.number_input("V23", step=0.01)
-        v24 = st.number_input("V24", step=0.01)
-    with cols[5]:
-        v25 = st.number_input("V25", step=0.01)
-        v26 = st.number_input("V26", step=0.01)
-    with cols[6]:
-        v27 = st.number_input("V27", step=0.01)
-        v28 = st.number_input("V28", step=0.01)
-
-# 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
+@app.post("/predict")
+async def predict_transaction(data: dict):
+    try:
+        # Convert the input data to a DataFrame
+        transaction_data = pd.DataFrame([data])
         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.")
-
-
-
+        result = "Fraudulent transaction" if prediction[0] == 1 else "Acceptable transaction"
+        return {"prediction": result}
+    except Exception as e:
+        return {"error": str(e)}