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Online-Payment-Fraud-Detection
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Online-Payment-Fraud-Detection
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import streamlit as st
import numpy as np
import pickle
import streamlit.components.v1 as components
from sklearn.preprocessing import LabelEncoder
le = LabelEncoder()

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

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

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


def app_design():
    # Add input fields for High, Open, and Low values
    image = '26.png'
    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([typeup])
    amount = st.number_input("The amount of the transaction")
    nameOrig = st.text_input("Transaction ID")
    nameOrig = transform([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([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([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_hidevs():

        components.html("""
        <div>
        <h4>🚀 Unlock Your Dream Job with HiDevs Community!</h4>
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        <a class="link" href="https://www.youtube.com/@HidevsCommunity1307/" target="__blank">YouTube</a>
        <a class="link" href="https://www.instagram.com/hidevs_community/" target="__blank">Instagram</a>
        <a class="link" href="https://medium.com/@hidevscommunity" target="__blank">Medium</a>
        <a class="link" href="https://www.linkedin.com/company/hidevs-community/" target="__blank">LinkedIn</a>
        <a class="link" href="https://github.com/hidevscommunity" target="__blank">GitHub</a></p>
      </div>
        """,
                    height=600)

def main():

        # Set the app title and add your website name and logo
        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!")
    
        app_design()    
        st.header("About HiDevs Community")
        about_hidevs()

if __name__ == '__main__':
        main()