import streamlit as st
import numpy as np
import pickle
import streamlit.components.v1 as components
from sklearn.preprocessing import LabelEncoder

# Load the pickled model
def load_model():
    return pickle.load(open('online_payment_fraud_detection_randomforest.pkl', 'rb'))

# Load the LabelEncoder
def load_label_encoder():
    with open('label_encoder.pkl', 'rb') as f:
        return pickle.load(f)

# Function for model prediction
def model_prediction(model, features):
    predicted = str(model.predict(features)[0])
    return predicted

def transform(le, text):
    text = le.transform(text)
    return text[0]

def app_design(le):
    # Add input fields for High, Open, and Low values
    image = 'Ramdevs2'
    st.image(image, use_column_width=True)
    
    st.subheader("Enter the following values:")

    step = st.number_input("Step: represents a unit of time where 1 step equals 1 hour")
    typeup = st.selectbox('Type of online transaction', ('PAYMENT', 'TRANSFER', 'CASH_OUT', 'DEBIT', 'CASH_IN'))
    typeup = transform(le, [typeup])
    amount = st.number_input("The amount of the transaction")
    nameOrig = st.text_input("Transaction ID")
    nameOrig = transform(le, [nameOrig])
    oldbalanceOrg = st.number_input("Balance before the transaction")
    newbalanceOrig = st.number_input("Balance after the transaction")
    nameDest = st.text_input("Recipient ID")
    nameDest = transform(le, [nameDest])
    oldbalanceDest = st.number_input("Initial balance of recipient before the transaction")
    newbalanceDest = st.number_input("The new balance of recipient after the transaction")
    isFlaggedFraud = st.selectbox('IsFlaggedFraud', ('Yes', 'No'))
    isFlaggedFraud = transform(le, [isFlaggedFraud])
    
    # Create a feature list from the user inputs
    features = [[step, typeup, amount, nameOrig, oldbalanceOrg, newbalanceOrig, nameDest, oldbalanceDest, newbalanceDest, isFlaggedFraud]]
    
    # Load the model
    model = load_model()
    
    # Make a prediction when the user clicks the "Predict" button
    if st.button('Predict Online Payment Fraud'):
        predicted_value = model_prediction(model, features)
        if predicted_value == '1':
            st.success("Online payment fraud not happened")
        else:
            st.success("Online payment fraud happened")

def about_RamDevs():
    components.html("""
    <div>
       #bla bla bla
    </div>
    """, height=600)

def main():
    st.set_page_config(page_title="Online Payment Fraud Detection", page_icon=":chart_with_upwards_trend:")
    st.title("Welcome to our Online Payment Fraud Detection App!")

    le = load_label_encoder()
    app_design(le)
    st.header("About RamDevs Community")
    about_RamDevs()

if __name__ == '__main__':
    main()