import streamlit as st
import joblib
model = joblib.load("fraud_model.pkl")

pages = ["Test Case 1", "Test Case 2"]

st.sidebar.title("Navigation")
selected_page = st.sidebar.radio("Go to", pages)

non_fraudulent_data = {
    "Name": "Jamshidov Ozod",
    "Location": "Tashkent",
    "Time since last transaction": "3 seconds",
    "Amount spent": "100,000 UZS",
    "Device used": "Xiaomi Phone",
    "Transaction type": "ATM withdrawal",
    "Frequency of transactions today": "5",
    "Distance from home": "10 km",
    "Past avg transaction amount": "50,000 UZS",
    "Merchant category": "Retail store (Grocery)",
    "User's spending limit": "2,000,000 UZS",
    "User's current balance": "1,500,000 UZS",
    "User's age": "35 years old"
}

fraudulent_data = {
    "Name": "Rustam Karimov",
    "Location": "Samarkand",
    "Time since last transaction": "20 seconds",
    "Amount spent": "2,000,000 UZS",
    "Device used": "iPhone 13",
    "Transaction type": "Online purchase",
    "Frequency of transactions today": "15",
    "Distance from home": "200 km",
    "Past avg transaction amount": "100,000 UZS",
    "Merchant category": "Electronics store (Retail)",
    "User's spending limit": "5,000,000 UZS",
    "User's current balance": "100,000 UZS",
    "User's age": "45 years old",
    "Risk score": "High"
}

if selected_page == "Test Case 1":
    st.subheader("Test Case 1: Non-Fraudulent Transaction")
    
    st.write("### Transaction Details")
    

    col1, col2 = st.columns(2)
    
    
    with col1:
        for key, value in list(non_fraudulent_data.items())[:6]:  
            st.write(f"**{key}:** {value}")
    
    with col2:
        for key, value in list(non_fraudulent_data.items())[6:]: 
            st.write(f"**{key}:** {value}")
    
    if st.button("Submit Data"):
        mapped_pca_values = [
            -0.986639536, 0.054619774, 0.89218939, 1.238546301, 1.153949202,
            -0.443156858, 1.158792971, -0.234932879, -0.748284316, 0.19999764,
            -0.377070284, -0.086545898, 0.217167156, 0.314289834, 1.295545516,
            -0.818801496, 0.059642158, -0.68956288, 0.684420234, 0.744324727,
            -0.15608398, -0.590576405, 0.432953111, -0.337631423, -0.272390765,
            -0.551474596, 0.171669591, 0.012712389, 148.81, 0.3333
        ]
        prediction = model.predict([mapped_pca_values])
        st.success(f"Prediction for Jamshidov Ozod: {'Fraud' if prediction[0] == 1 else 'Non-fraud'}")
        
elif selected_page == "Test Case 2":
    st.subheader("Test Case 2: Fraudulent Transaction")
    
    st.write("### Transaction Details")
    
    
    col1, col2 = st.columns(2)
    
   
    with col1:
        for key, value in list(fraudulent_data.items())[:6]:  # First half of the data
            st.write(f"**{key}:** {value}")
    
    with col2:
        for key, value in list(fraudulent_data.items())[6:]:  # Second half of the data
            st.write(f"**{key}:** {value}")
    
    if st.button("Submit Data"):
        positive_case_values = [
            28726, -29.87636551, 16.43452455, -30.55869682, 6.505861787, 
            -21.6656543, -4.940356329, -20.08139107, 19.58777262, -3.591491047,
            -7.800598208, 3.947839717, -5.487911486, 1.369940349, -4.829552443,
            -0.134412095, -5.121162405, -11.11819045, -4.65995587, 0.608930081,
            1.724778838, 1.812953975, -2.232251588, -1.412803443, 0.17873107, 
            2.156041858, -0.209384624, 1.255648901, 0.364530453, 99.99
        ]
        prediction = model.predict([positive_case_values])
        st.success(f"Prediction for Rustam Karimov: {'Fraud' if prediction[0] == 1 else 'Non-fraud'}")