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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
# 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>
<h4>🚀 Unlock Your Easy Safety with RamDevs Community!</h4>
<p class="subtitle">🔍 Seeking the perfect hassle-free safe online transactions? RamDevs Community is your gateway to easier and safer transactions. Explore free expert sessions, customer support, and password transformation tips.</p>
<p class="subtitle">💼 We offer an upskill program in <b>CyberSecurity, Password management, Legal Terms and Services</b>, and assist customers in <b>security and safer online transactions</b> at minimal development costs.</p>
<p class="subtitle">🆓 Best of all, everything we offer is <b>completely free</b>! We are dedicated to helping society.</p>
<p class="subtitle">Book free of cost 1:1 mentorship on any topic of your choice — <a class="link" href="https://topmate.io/deepakchawla1307">topmate</a></p>
<p class="subtitle">✨ We dedicate over 30 minutes to each applicant’s Password selection, Online profile, mock frauds, and upskill program. If you’d like our guidance, check out our services <a class="link" href="https://RamDevscommunity.wixsite.com/RamDevs">here</a></p>
<p class="subtitle">💡 Join us now, and turbocharge your CyberSecurity!</p>
<p class="subtitle">
<a class="link" href="https://RamDevscommunity.wixsite.com/RamDevs" target="__blank">Website</a>
<a class="link" href="https://www.youtube.com/@RamDevsCommunity1307/" target="__blank">YouTube</a>
<a class="link" href="https://www.instagram.com/RamDevs_community/" target="__blank">Instagram</a>
<a class="link" href="https://medium.com/@RamDevscommunity" target="__blank">Medium</a>
<a class="link" href="https://www.linkedin.com/company/RamDevs-community/" target="__blank">LinkedIn</a>
<a class="link" href="https://github.com/RamDevscommunity" target="__blank">GitHub</a>
</p>
</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()
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