Update slapp.py

slapp.py

@@ -1,18 +1,26 @@
-from fastapi import FastAPI
-import pickle
+import threading
+import uvicorn
+import streamlit as st
+import requests
 import pandas as pd
+import pickle
+from fastapi import FastAPI
+
+# Initialize FastAPI app
+app = FastAPI()
 
 # Load the saved model
 def load_model():
     try:
-        model = pickle.load(open('model.pkl', 'rb'))
+        with open('model.pkl', 'rb') as file:
+            model = pickle.load(file)
         return model
     except Exception as e:
         raise RuntimeError(f"Error loading model: {e}")
 
 model = load_model()
-app = FastAPI()
 
+# Define the FastAPI endpoint
 @app.post("/predict")
 async def predict_transaction(data: dict):
     try:
@@ -24,3 +32,69 @@ async def predict_transaction(data: dict):
         return {"prediction": result}
     except Exception as e:
         return {"error": str(e)}
+
+# Function to run the FastAPI server
+def run_fastapi():
+    uvicorn.run(app, host="0.0.0.0", port=8000)
+
+# Start the FastAPI server in a separate thread
+thread = threading.Thread(target=run_fastapi, daemon=True)
+thread.start()
+
+# Streamlit app interface
+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)
+    v1, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12, v13, v14 = [
+        st.number_input(f"V{i+1}", step=0.01) for i in range(14)
+    ]
+
+# Horizontal layout for Features V15 - V28
+with tab3:
+    st.header("Features (V15 - V28)")
+    cols = st.columns(7)
+    v15, v16, v17, v18, v19, v20, v21, v22, v23, v24, v25, v26, v27, v28 = [
+        st.number_input(f"V{i+15}", step=0.01) for i in range(14)
+    ]
+
+# Button to make predictions
+if st.button("Predict"):
+    # Create a dictionary from the input data
+    transaction_data = {
+        '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
+    }
+
+    # Send a POST request to the FastAPI server
+    try:
+        response = requests.post("http://localhost:8000/predict", json=transaction_data)
+        if response.status_code == 200:
+            result = response.json().get("prediction")
+            if result == "Acceptable transaction":
+                st.success("✅ " + result)
+            else:
+                st.error("🚨 " + result)
+        else:
+            st.error("Error: " + response.text)
+    except Exception as e:
+        st.error(f"Error connecting to API: {e}")