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import streamlit as st
import numpy as np
import tensorflow as tf
import pandas as pd
import requests
from streamlit_lottie import st_lottie
st.title("Automated Fraud Detection System Web app")
st.write("""
This app will helps us to track what type of transactions lead to fraud. I collected a dataset from [Kaggle repositry](https://www.kaggle.com/datasets/jainilcoder/online-payment-fraud-detection)
,which contains historical information about fraudulent transactions which can be used to detect fraud in online payments.
""")
def load_lottieurl(url: str):
r = requests.get(url)
if r.status_code != 200:
return None
return r.json()
lottie_url = "https://assets8.lottiefiles.com/packages/lf20_yhTqG2.json"
lottie_hello = load_lottieurl(lottie_url)
with st.sidebar:
st_lottie(lottie_hello,quality='high')
st.sidebar.title('Users Features Explanation')
st.sidebar.markdown("**step**: represents a unit of time where 1 step equals 1 hour")
st.sidebar.markdown("**type**: type of online transaction")
st.sidebar.markdown('**amount**: the amount of the transaction')
st.sidebar.markdown('**oldbalanceOrg**: balance before the transaction')
st.sidebar.markdown('**newbalanceOrig**: balance after the transaction')
st.sidebar.markdown('**oldbalanceDest**: initial balance of recipient before the transaction')
st.sidebar.markdown('**newbalanceDest**: the new balance of recipient after the transaction')
st.header('User Input Features')
def user_input_features():
step = st.number_input('Step', 0, 3)
type = st.selectbox('Online Transaction Type', ("CASH IN", "CASH OUT", "DEBIT", "PAYMENT", "TRANSFER"))
amount = st.number_input("Amount of the transaction")
oldbalanceOrg = st.number_input("Old balance Origin")
newbalanceOrig = st.number_input("New balance Origin")
oldbalanceDest = st.number_input("Old Balance Destination")
newbalanceDest = st.number_input("New Balance Destination")
data = {'step': step,
'type': type,
'amount': amount,
'oldbalanceOrg': oldbalanceOrg,
'newbalanceOrig': newbalanceOrig,
'oldbalanceDest': oldbalanceDest,
'newbalanceDest': newbalanceDest}
features = pd.DataFrame(data, index=[0])
return features
input_df = user_input_features()
# Combines user input features with sample dataset
# This will be useful for the encoding phase
fraud_raw = pd.read_csv('samp_online.csv')
fraud = fraud_raw.drop(columns=['isFraud','nameOrig','nameDest','isFlaggedFraud'])
df = pd.concat([input_df,fraud],axis=0)
# Encoding of ordinal features
encode = ['type']
for col in encode:
dummy = pd.get_dummies(df[col], prefix=col)
df = pd.concat([df,dummy], axis=1)
del df[col]
df = df[:1] # Selects only the first row (the user input data)
# Reads in saved classification model
if st.button("Predict"):
load_clf = tf.keras.models.load_model('fraud.h5', compile=False)
load_clf.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])
# Apply model to make predictions
y_probs = load_clf.predict(df)
pred = tf.round(y_probs)
pred = tf.cast(pred, tf.int32)
st.markdown(
"""
<style>
[data-testid="stMetricValue"] {
font-size: 25px;
}
</style>
""",
unsafe_allow_html=True,
)
if pred == 0:
col1, col2 = st.columns(2)
col1.metric("Prediction", value="Transaction is not fraudulent ")
col2.metric("Confidence Level", value=f"{np.round(np.max(y_probs) * 100)}%")
else:
col1, col2 = st.columns(2)
col1.metric("prediction", value="Transaction is fraudulent")
col2.metric("Confidence Level", value=f"{np.round(np.max(y_probs) * 100)}%")
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