File size: 4,670 Bytes
ca7e86a
3700024
ca7e86a
 
3700024
ca7e86a
 
 
88c3f93
ca7e86a
d6a5aca
 
 
b989f44
d6a5aca
ca7e86a
 
 
 
 
d6a5aca
 
ca7e86a
 
d6a5aca
ca7e86a
50ce453
 
ca7e86a
 
 
 
5ec6deb
d6a5aca
ca7e86a
 
d6a5aca
ca7e86a
 
 
d6a5aca
ca7e86a
 
5ec6deb
d6a5aca
 
ca7e86a
d6a5aca
ca7e86a
 
 
 
 
 
 
d6a5aca
20ed5a3
ca7e86a
20ed5a3
ca7e86a
d6a5aca
 
 
19395ad
 
 
 
 
 
 
 
 
 
 
 
 
 
 
d6a5aca
 
ca7e86a
 
d6a5aca
 
ca7e86a
d6a5aca
 
 
 
ca7e86a
 
b989f44
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
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()