Update slapp.py

slapp.py

@@ -1,45 +1,5 @@
-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:
-        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()
-
-# Define the FastAPI endpoint
-@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)
-
-        result = "Fraudulent transaction" if prediction[0] == 1 else "Acceptable transaction"
-        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")
@@ -91,10 +51,8 @@ if st.button("Predict"):
         if response.status_code == 200:
             result = response.json().get("prediction")
             if result == "Acceptable transaction":
-                print(result)
                 st.success("✅ " + result)
             else:
-                print(result)
                 st.error("🚨 " + result)
         else:
             st.error("Error: " + response.text)